Aluminium Two Thousand World Congress & International Conference on Extrusion and Benchmark

Selected peer-reviewed extended articles based on abstracts presented at the Aluminium Two Thousand World Congress & International Conference on Extrusion and Benchmark (Aluminium 2000 & ICEB2023)

Edited by
Prof. Lorenzo Donati

Aluminium Two Thousand World Congress & International Conference on Extrusion and Benchmark

Abstract

Selected peer-reviewed extended articles based on abstracts presented at the Aluminium Two Thousand World Congress & International Conference on Extrusion and Benchmark (Aluminium 2000 & ICEB2023)

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Edited by
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Scientific Books Collection, Vol. 156, pp -5--4

© 2024 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Accepted:
2024-05-21

Online:
2024-11-11

Prof. Lorenzo Donati

Dr. Walter Dalla Barba

Conference Chairmans

Preface

We are pleased to present this edition of the 12th Aluminium Two Thousand World Congress & 7th International Conference on Extrusion and Benchmark (ICEB).

This publication features articles by keynote speakers and contributors from both academia and industry who shared the latest developments in extrusion technology and simulation. The presented papers encompass economic and technical aspects of extrusion, casting, forming, anodizing, and coating linked to metallurgical and chemical aspects.

Aluminium Two Thousand Congress is an international conference covering all the topics of the aluminium world. With 12 editions, it is a reference event for the operators working in the Aluminum industry. Highly qualified "Aluspecialists" from companies, universities, and associations come from all over the world to present to a specialized and international audience the most innovative technologies and applications in the aluminium field.

ICEB is a two-in-one event, merging a conference on the latest Advances in Extrusion Technology and Simulation with an industrial worldwide contest: the "FEM codes Benchmark". The aim of the conference is to join together technical and scientific experts, to spread their knowledge, and to form an international community for the discussion of state-of-the-art, as well as future developments, in the field of extrusion and in its most powerful tool, numerical simulation.

The "Extrusion Benchmark" challenged participants to simulate a specifically designed industrial extrusion process using provided input parameters. These simulations were assessed against experimental results, which were unveiled during the conference. For the 2023 benchmark, a porthole die with three openings was designed to extrude hollow profiles simultaneously under different porthole features. This setup provided a robust basis for comparing simulation outcomes over monitored data under repeated conditions. The benchmark is more than a competitive exercise; it is an opportunity to identify, investigate, and discuss various issues related to common simulation practices. Participants bring their unique perspectives, fostering the advancement of simulation skills, process optimization research, and future benchmark endeavors.

We extend our heartfelt thanks to all authors, co-authors, reviewers, and the conference committee for their contributions to the conference's success. Special gratitude goes to our coorganizers: Prof. Barbara Reggiani, Dr. Rosario Squatrito, Dr. Marco Negozio, Dr. Riccardo Pelaccia, Dr. Antonio Segatori and Eng. Sara Di Donato for their invaluable support.

Lastly, we thank our generous sponsors for their financial support, which made the conference and benchmark trials possible. We hope this edition provides valuable insights for researchers, engineers, and students in both scientific and industrial fields.

Committees

Scientific Committee:

Prof. N. Ben Khalifa, Leuphana University Lüneburg, DE

Dr. S. Bong Kang, Korea Institute of Materials Science (KIMS), KR

Prof. A. Brosius, IF, TU Dresden, DE

Eng. S. Di Donato, DIN, University of Bologna, IT

Prof. L. Donati, DIN, University of Bologna, IT

Prof. A. R. Eivani, Assistant Professor, Iran University of Science and Technology (IUST)

Prof. M. El Mehtedi, University of Cagliari, IT

Prof. F. Gagliardi, University of Calabria, IT

Prof. W. Misiolek, Lehigh University, US

Dr. S. Müller, ERC, TU Berlin, DE

Dr. M. Negozio, DISTI, University of Parma, IT

Dr. R. Pelaccia, DISMI, University of Modena and Reggio Emilia, IT

Prof. B. Reggiani, DISMI, University of Modena and Reggio Emilia, IT

Prof. C. Sommitsch, TUG, Graz University of Technology, AT

Dr. R. Squatrito, Foundry consultant, IT

Prof. A. E. Tekkaya, IUL, TU Dortmund University, DE

Prof. L. Tomesani, DIN, University of Bologna, IT

Prof. H. Valberg, NTNU, Norwegian University, NO

Prof. J. Zhou, LMP, Delft University, NL

Industrial Committee:

F. Bagagli, Alutitan, IT

H. Gers, Honsel AG, DE

V. Giacomelli, Compes S.p.A., IT

L. Gorlani, Metra S.p.a, IT

T. Kloppenborg, ISPT GmbH & Co. KG, Dortmund, DE

J. Maier, WEFA Inotec, DE

T. Pinter, Almax Mori, IT

M. Rompato, Pandolfo Alluminio, IT

T. Rosenbaum, Wilke Werkzeugbau, DE

A. Selvaggio, ISPT GmbH & Co. KG, Dortmund, DE

F. Vincenzi, Surtec Italia, IT

J. Zhou, China Automotive Engineering Research Institute, CN

Sponsors:

Almax

Metra

Altex

Alumex PLC

Atieuno

Dimasimma

EKSENAL

Interall

Italtecno

KAUTEC SOLUTIONS

MICAS Simulations Ltd

C.R.V.

SAT

Qform

TRASMETAL

ThermoCalc

Supporters:

AAC

AIFM

AIM

Aital

Al Circle

Aluminium guide - ukraine

Amafond

Assofond

IPCM

La Rivista del Colore + Anver

FACE

Table of Contents

Preface

Chapter 1: Consolidated Report

Extrusion Benchmark 2023: Effect of Die Design on Profile Speed, Seam Weld Quality and Microstructure of Hollow Tubes

Riccardo Pelaccia, Marco Negozio, Sara Di Donato, Barbara Reggiani, Lorenzo Donati

Chapter 2: Modeling and Simulation

Prediction of Damage Behavior of Casting Aluminum Components Considering Inhomogeneous Properties

Yu Ling Lang, Dong Zhi Sun, Zhi Hua Zhu, Xiang Ci, De Cai Kong, Hai Bo Qiao, Wen Bo Wang

Microstructure Evolution and FEM Prediction on AA6XXX Alloys

Marco Negozio, Riccardo Pelaccia, Lorenzo Donati, Barbara Reggiani, Sara Di Donato

Simulation of Quench Rate by Jominy End Quench Test for 6082 Aluminium Alloys

Aleyna Gümüşsoy, Onur Gülbaba, Işık Kaya, Emrah Fahri Özdoğru

Recent Trends in Nitrogen Cooling Modelling of Extrusion Dies

Riccardo Pelaccia, Marco Negozio, Sara Di Donato, Lorenzo Donati, Barbara Reggiani

Hot Torsion Tests of AA6082 Alloy

Sara Di Donato, Riccardo Pelaccia, Marco Negozio, Mohamad El Mehtedi, Barbara Reggiani, Lorenzo Donati

Study of Direct Metal Extrusion by the Upper-Bound and Finite Volume Methods

José Divo Bressan, Marcelo Matos Martins

A Modelling Framework for Rapid Evaluation of Speed Limitations During Extrusion of Aluminium Profiles

Mads Iddberg, Ole Runar Myhr, Anders Nesse, Trond Furu

Automated Optimum Extrusion Die Design and Profile Quality Control Based on Simulation

Ivan Kniazkin, Nikolay Biba, Ivan Kulakov, Alexey Duzhev, Sergei Stebunov

Chapter 3: Materials Properties and Processing Technologies Assessment

Feasibility Study of Solid-State Recycling through Direct Hot Rolling of AA5754 Aluminum Chips for Automotive Applications

Mohamad El Mehtedi, Pasquale Buonadonna, Rayane El Mohtadi, Gabriela Loi, Francesco Aymerich, Noomane Ben Khalifa, Mauro Carta

Optimization of Geometric Design of Extruded Products Incorporating Properties, Cost and CO2 Footprint

Anders Nesse, Mads Iddberg, Ole Runar Myhr, Trond Furu

AlZn-Matrix Cast Composites Reinforced with Ti Aluminides

Paweł K. Krajewski, Alan Lindsay Greer, Jiří Orava, Beata Gracz, Grzegorz Piwowarski, Magdalena Kawalec, Ewa Olejnik, Witold K. Krajewski

Influence of Different Aging Times and Temperatures on Microstructural and Mechanical Properties of EN AW-6XXX Aluminum Alloys

Chiara Soffritti, Cindy Morales, Annalisa Fortini, Mattia Merlin

Effect of Homogenization Cycle on Precipitation of Dispersoids and Abnormal Grain Growth in an Al-Mg-Si Alloy

Sumit Kumar Gahlyan, Pankaj Shivaji Wanjari, Shavi Agrawal, Butchi Bharadwaj, Manu Saxena, Vivek Srivastava

Antimicrobial Performance of Different Metals

Ramakumar Jayachandran

The Influence of Surface Segregation on the Anodizing of AlSi11Cu2(Fe) Diecastings

Giulia Scampone, Antonio Russo, Roberto Muzzani, Alessio Carminati, Giulio Timelli

Influence of Gas Oven Temperature on the Microstructure and Extrudability of AI-Mg-Si Aluminium Alloys

Andreas Schiffl, Serena Tourey, Patrick Riepler

Keywords Index

Authors Index

Chapter 1

Consolidated Report

The original version of this eBook is available on https://www.scientific.net/book/12th-aluminium-two-thousand-world-congress-7th-international-conference-on-extrusion-and-benchmark/978-3-0364-1380-8

Key Engineering Materials, ISSN: 1662-9795, Vol. 988, pp 47-62

doi: 10.4028/p-gNXRC5

© 2024 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2023-11-22

Revised:
2024-03-05

Accepted:
2024-04-08

Online:
2024-09-27

Extrusion Benchmark 2023: Effect of Die Design on Profile Speed, Seam Weld Quality and Microstructure of Hollow Tubes

The original version of this paper is available on https://www.scientific.net/KEM.988.47.pdf

Riccardo Pelaccia , Marco Negozio , Sara Di Donato , Barbara Reggiani , Lorenzo Donati

¹DISMI Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Via Amendola 2, 42122, Reggio Emilia, Italy

DIN Department of Industrial Engineering, University of Bologna, Viale Risorgimento 2, 40136, Bologna, Italy

INTERMECH, University of Modena and Reggio Emilia, Italy

a*riccardo.pelaccia@unimore.it, marco.negozio2@unibo.it, sara.didonato2@unibo.it, barbara.reggiani@unimore.it, I.donati@unibo.it

Keywords: Extrusion benchmark, AA6082, process monitoring, charge welds, seam welds, microstructure, simulation validation.

Abstract

The Extrusion Benchmark 2023 was focused on the evaluation of different die design strategies for the manufacturing of AA6082 hollow tubes ( 40 mm external diameter and 4 mm thickness) through a porthole die with 3 openings. The extrusion process was monitored in industrial environment in terms of press load, profiles' speed, profiles' exit temperature, and die temperatures under different processing conditions (air quenching, water quenching, nitrogen die cooling). Extruded profiles were then analyzed in terms of seam weld quality, charge weld extension and microstructure evolution for both air/water quench and the presence/absence of nitrogen cooling. The results of the study are aimed at validating FEM simulation outputs in the context of the International Conference on Extrusion and Benchmark (ICEB).

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.988.47.pdf

Over the years, the FEM numerical simulation has become a powerful tool for die makers and, more recently, for extruders in order to investigate the die design and the process parameters effects in the extrusion of aluminum profiles. In order to provide to Extrusion Industry a place to discuss numerical techniques and to train users in FEM simulation, the 'ICEB - International Conference on Extrusion and Benchmark conference series was settled [1]. The aim of the conference is to merge the typical conference format with a benchmark session where participants are asked to analyze a specific extrusion problem whose results are disclosed to participants only during conference days. The Extrusion Benchmark is a quadrennial event where experts in the field of aluminum extrusion and FEM analysis join together to verify the progress of the performance and accuracy of FEM simulations for process optimization and to share knowledge advancements in the field. Within the benchmark session, in order to provide an even point of comparison between experiments and simulations, it is extremely important that experimental trials provide clear indications of critical aspects of the process. For these reasons, a different die is designed at every benchmark: in 2007 the focus was on pocket design [2]; in 2009 it was on the influence of tongue deflection in U-shape profiles [3]; in 2011 different strategies for port-hole balancing were used for hollow profiles extrusion [4] [5]; in 2013 the experimental investigations were aimed at predicting the effects of mandrel deflection [6]; in 2015 the effect of bearing shape (straight, chocked or relieved) and length was investigated [7] [8] while in 2017 the die was designed in order to allow mandrels shift during the process to verify the effects on the profiles [9].

This paper summarizes information in terms of simulation settings and output results related to the Extrusion Benchmark 2023. The benchmark was focused on the extrusion of 3 hollow tubes ( 40 mm external diameter and 4 mm thickness) with EN AW-6082 alloy. The die was designed and
manufactured by Almax Mori (Rovereto, TN) with different features (i.e. cylindrical or conical ports, bearing lengths, and radii) for each of the 3 openings to attain different metal flows (and consequently different strains, strain rates and temperatures) in the tubes. The trials were performed at the Metra plant in Rodengo Saiano (Brescia, IT) on a 40MN press; thermal field was monitored by means of a contactless pyrometer on a profile (profile 1) and eleven thermo-sensors were inserted in the die, in the die ring and the backer. The backer was characterized by a specific channel for nitrogen flowing aimed at investigating cooling effects. Several extrusion data were continuously acquired by the control system of the press including the puller speed and force and the extrusion load. After the extrusion trials, the profiles were analyzed at the University of Bologna in order to determine the position and quality of the seam welds, the extent of the charge welds, and the microstructure in the profiles cross-section under air or water quench and with/without nitrogen cooling.

Simulation Settings

The original version of this paper is available on https://www.scientific.net/KEM.988.47.pdf

In this section, all the information provided to the benchmark participants for running the analysis of the Extrusion Benchmark 2023 is reported. The benchmark campaign (Fig. 1) involved the extrusion of fifteen AA6082 billets by varying the nitrogen flow rate ( or valve opening) and the quenching conditions (only air or water/air spray). The first 3 billets were used to warm up the system (billets 1 to 3 ) and to approximate a 'steady state' thermal condition. Then, 2 more billets (billets 4 and 5 ) were extruded in the same conditions thus representing the benchmark trials with repetitions. The subsequent ten billets were extruded to analyze the efficiency of the nitrogen cooling and the effect of water quenching on the microstructure evolution of the extrudates. A ram speed of was used for all the trials; a ram stroke of 650 mm was set over a billet length of 680 mm (thus meaning 30 mm of rest length) and a puller tensile force of was applied. In detail, Tab. 1 summarizes the process parameters necessary for the simulation setting.

Fig. 1. Selected Design of Experiment (DOE) for the Extrusion Benchmark 2023: Fifteen billets were extruded by varying the nitrogen cooling and the quenching parameters.

Fig. 1. Selected Design of Experiment (DOE) for the Extrusion Benchmark 2023: Fifteen billets were extruded by varying the nitrogen cooling and the quenching parameters.

Metallographic analysis for Billet Skin Defect:

The original version of this paper is available on https://www.scientific.net/KEM.988.47.pdf

-Transition between third and fourth extrusion
-Transition between eighth and ninth extrusion

Metallographic analysis for Charge Welds:
-Transition between third and fourth extrusion
-Transition between eighth and nineth extrusion

Metallographic analysis of profile microstructure:

The original version of this paper is available on https://www.scientific.net/KEM.988.47.pdf

-fifth extrusion (front, middle, back)
-Twelfth extrusion (middle, exit 3)
-Fourteenth extrusion (middle, exit 1)

As reported in Fig. 1, some profiles were selected for the 'front and back-end defects' evaluation and microstructure analysis:

Transition between the third and the fourth billets for the evaluation of both skin defect and charge weld extent.

Transition between the eighth and the ninth billets for the evaluation of both skin defect and charge welds extent.

Fifth billet (all profiles: middle positions with respect to the extrusion direction) for the metallographic analysis of the profile microstructure using only air quenching.

Twelfth billet (profile 3: middle position with respect to the extrusion direction) for the metallographic analysis of the profile microstructure using both water quenching and nitrogen cooling.

Fourteenth billet (profile 1: middle position with respect to the extrusion direction) for the metallographic analysis of the profile microstructure using water quenching without nitrogen cooling.

Table 1. Process Parameters.

Process Parameter | Value
Profile alloy | AA6082
Billet diameter | 279 [mm]
Container diameter | 286 [mm]
Billet length | 680 [mm]
Billet rest | 30 [mm]
Skin layer thickness | 0.3 [mm]
Ram speed -all tested conditions- | 4.4 [mm/s]
Max press load | 4000 [ton]/40 [MN]
Ram Temperature | 350 [°C]
Container Temperature | 390-425 [°C] (ram-die)
Pre-heating Billet Temperature | 440 [°C] (no taper)
Pre-heating Die Temperature | 480 [°C]
Nitrogen Inlet Temperature | -196 [°C]
Nitrogen Inlet Pressure | 3 [bars]
Nitrogen Inlet Tube Diameter (internal) | 5,5 [mm]

Geometries. A porthole die with 3 tube-shaped openings for hollow profiles extrusion was used for the benchmark trials. The die assembly (AISI H-13 steel, tempered ) was composed of five parts: the mandrel, the die, the die ring, the backer, and the bolster (Fig. 2 and Fig. 3). Geometries CAD files can be asked to benchmark organizers (iceb.din@unibo.it). The final (cold) profile had an external diameter of 40 mm and a thickness of 4 mm , so the area of the single profile surface was thus leading to a total profiles area of . The material flow within the tooling set was different for the 3 profiles (Fig. 4) with the aim to generate high velocity deviations at the exit, phenomena to be captured with the numerical simulation:

Exit 1: Conical ports and cylindrical bearings. Bearings on the die surface had an inlet fillet radius of 1 mm and 6 mm in length, while the bearings on the mandrel surface had the same length without the fillet radius.

Exit 2: Cylindrical ports and cylindrical bearings. Bearings on the die surface had an inlet fillet radius of 1 mm and 7 mm in length, while the bearings on the mandrel surface had the same length without the fillet radius.

Exit 3: Conical ports and tapered bearings. Bearings on the die surface had an inlet fillet radius of 1 mm , a choke (angle of , length of 4 mm ) and a final cylindrical part 1 mm length. The bearings on the mandrel surface were 5 mm length.

Fig. 2. Die assembly with die ring, backer and bolster.

Fig. 2. Die assembly with die ring, backer and bolster.

Fig. 3. Rendering of the billet and tools set-up (left) and Cooling Channel Design (right).

Fig. 3. Rendering of the billet and tools set-up (left) and Cooling Channel Design (right).

Fig. 4. Bearings main dimensions.

Fig. 4. Bearings main dimensions.

The experimental trials involved the use of nitrogen cooling for some final tested conditions: the cooling channel was manufactured in the backer surface in contact with the die. In detail, Fig. 3b shows the cooling path and the channel dimensions. One of the main aim of 2023 Benchmark was to investigate the evolution of the thermal field during the extrusion process in both uncooled and cooled conditions, making a deeper monitoring of the thermal behavior of tools by means of several thermocouples necessary.

In detail, eleven thermocouples were used for monitoring the thermal evolution of the die/tools system (Fig. 5): thermocouples from T1 to T6 were placed on the surface in contact with the backer to follow the thermal field evolution along the nitrogen cooling path, from T7 to T9 were positioned near the bearing region, T10 in the mandrel and T11 in contact with the die ring. In addition, a contactless pyrometer was used for the monitoring of the profile 1 temperature at a distance of 1500 mm from the bolster surface (Fig. 6). Fig. 6 also shows the position of the quenching zones: air quenching was always turned on during the trials while water quenching was used only in trials from 10 to 15 in order to analyze the effects in terms of microstructure evolution of the profiles. As quenching rates for air/water, it was suggested to participants to use their own data if available; otherwise, the following data were suggested: for air quenching, a decrease of profile temperature of , while for water quenching of .

Fig. 5. Thermocouple positions (T1 to T11).

Fig. 5. Thermocouple positions (T1 to T11).

Fig. 6. Position of profile pyrometer spot and of quenching zones.

Fig. 6. Position of profile pyrometer spot and of quenching zones.

Billet flow stress. The AA6082 alloy was used for this edition of the Benchmark trials. Microstructure analysis and mechanical characterization of the alloy were carried out at the Department of Industrial Engineering of the University of Bologna. The material flow stress characterization was performed by means of hot torsion tests on homogenized alloy samples. Tests were performed with equivalent strain rates of , and , in the temperature range from 440 to (from 713.15 to 823.15 K ) and the results presented in terms of equivalent stress ( ) vs equivalent strain . Details of the experiment can be found in the reference paper [10]. The material flow stress curves were furthermore regressed by the Hensel-Spittel and the Hyperbolic sine models. The Hensel-Spittel law [11] for the prediction of the material flow stress is reported in eq. 1:

Regression of the flow stress data can be performed with all the 9 regression coefficients (A and to ) or with only a part of them: below correlations with and 9 coefficients are proposed for temperature expressed in K (Tab. 2) and (Tab. 3).

Table 2. Hensel-Spittel coefficients with temperature expressed in K (Temperature range validity ).

Parameters | 5 parameters | 6 parameters | 7 parameters | 8 parameters | 9 parameters
A [MPa] | 182.562 | 176.285 | 178.685 | 182.161 | 87.963
m1 [K⁻¹] | -0.0022426 | -0.0023064 | -0.0023004 | -0.002215 | -0.0035864
m2 | -0.0405691 | -0.0610555 | -0.0542036 | 0.008047 | 0.0081011
m3 | 0.105639 | 0.1052751 | 0.1062478 | -0.02175 | -0.0225208
m4 | -0.0012775 | -0.0012881 | -0.0012l413 | -0.00029 | -0.0002923
m5 [K⁻¹] | 0 | 7.54071E-05 | 5.60459E-05 | -7.2E-05 | -7.215E-05
m7 | 0 | 0 | -0.0003077 | 0.000282 | 0.0002987
m8 [K⁻¹] | 0 | 0 | 0 | 0.000142 | 0.0001588
m9 | 0 | 0 | 0 | 0 | 0.2766873
R2 | 0.972 | 0.960 | 0.965 | 0.969 | 0.939

Table 3. Hensel-Spittel coefficients with temperature expressed in (Temperature range validity ).

Parameters | 5 parameters | 6 parameters | 7 parameters | 8 parameters | 9 parameters
A [MPa] | 98.851 | 96.259 | 107.028 | 99.774 | 99.722
m1 [K⁻¹] | -0.0022408 | -0.0023101 | -0.0022578 | -0.002120 | -0.00212
m2 | -0.0405691 | -0.0550745 | 0.0074409 | 0.007399 | 0.007401
m3 | 0.105639 | 0.105639 | 0.0880616 | 0.017222 | 0.017216
m4 | -0.0012775 | -0.00128502 | -0.0002925 | -0.00029 | -0.00029
m5 [K⁻¹] | 0 | 0.00009201 | -0.00010522 | -0.0001 | -0.0001
m7 | 0 | 0 | 6.43551E-05 | 2.35E-05 | 2.37E-05
m8 [K⁻¹] | 0 | 0 | 0 | 0.000142 | 0.000142
m9 | 0 | 0 | 0 | 0 | 9.33E-05
R2 | 0.972 | 0.965 | 0.970 | 0.968 | 0.968

The eq. 2 describes the Hyperbolic sine model proposed by Garofalo [12] where R is the universal gas constant ( ), Q is the apparent activation energy for plastic flow, A, and n are material constants. The material parameters for temperature expressed in Kelvin are reported in Tab. 4

Table 4. Sin-hyperbolic model parameters.

Q [J/mol] | A [s⁻¹] | α [MPa⁻¹] | n
1.036E+05 | 7.95 E+04 | 0.045 | 5.499

Microstructural analyses of the tested billet were carried out to measure the initial grain shape and dimension. Fig. 7 reports the longitudinal and transversal section of a specimen extracted from the AA6082-O logs used for the trials. It is possible to observe that grains are almost spherical (equi-
axials) with an average diameter of in the longitudinal direction and in the transverse direction. The measurements were carried out according to the intercept method ASTM E 112-13. The acquired billet skin thickness was equal to 0.3 mm as reported also in Tab. 1 .

Fig. 7. Microstructural analyses of AA-6082 specimen extracted from tested billets (after polishing, electrolytic etching with Barker reagent and analysis with polarized light). Left longitudinal section of the billet (extrusion direction left to right); right transversal section (extrusion direction out of image plane).

Fig. 7. Microstructural analyses of AA-6082 specimen extracted from tested billets (after polishing, electrolytic etching with Barker reagent and analysis with polarized light). Left longitudinal section of the billet (extrusion direction left to right); right transversal section (extrusion direction out of image plane).

Thermal field, Heat exchange and friction. The porthole die was pre-heated in oven at . Then, during the die loading into the press, the temperature in the monitored locations decreased for the heat exchange to the "cold parts" of the press. For benchmark participants, the temperatures in all locations, before the first extrusion and before the fourth one (steady state uncooled condition), were an input, thus allowing to select different simulation strategies: transient simulations from the first run to reach the steady state condition, or steady state analysis of the runs selected for the experimental-numerical comparison. All the data on thermocouples' temperature are reported in Tab. 5 and Tab. 7, while the results are deeply discussed in the "experimental results" section.

Table 5. Initial Temperatures.

Ram
350 [°C]

Container
390-425 [°C] (ram-die)

Billet
Billet pre-heating temperature
440 [°C] (no taper)

Die
Die pre-heating oven (mandrel, die, backer and die-ring)
480°C

At beginning of trials (before extruding billet 1).
Point T1: 445 [°C]
Point T2: 457 [°C]
Point T3: 457 [°C]
Point T4: 440 [°C]
Point T5: 458 [°C]
Point T6: 458 [°C]
Point T7: 473 [°C]
Point T8: 468 [°C]
Point T9: 471 [°C]
Point T10: 476 [°C]
Point T11: 442 [°C]

Before extruding billet 4 (benchmark reference test).
Point T1: 359 [°C]
Point T2: 400 [°C]
Point T3: 428 [°C]
Point T4: 429 [°C]
Point T5: 432 [°C]
Point T6: 436 [°C]
Point T7: 468 [°C]
Point T8: 441 [°C]
Point T9: 483 [°C]
Point T10: 526 [°C]
Point T11: 306 [°C]

Heat exchange models may significantly vary within different numerical codes. However, based on the state of art [13] [14] and on the authors' knowledge, data in terms of heat exchange and nitrogen properties (Tab. 6) were provided to the participants, if code's database parameters were not available.

Table 6. Materials Heat Exchanges.

Aluminum
Conductivity | 210 | [N/(s·°C)]
Heat capacity | 2,94 | [N/(mm²·°C)]
Steel (AISI H-13)
Conductivity | 24,33 | [N/(s·°C)]
Heat capacity | 4,63 | [N/(mm²·°C)]
Aluminum-Steel
Heat exchange | 11 | [N/(s mm·°C)]
Nitrogen (-196°C at room pressure)
Physical Properties | Liquid Nitrogen | Gas Nitrogen
Density [kg/m³] | 806.59 | 4.56
Dynamic Viscosity [Pa·s] | 1.61E-04 | 5.43E-06
Heat capacity at constant pressure [J/(Kg·K)] | 2041 | 1123
Thermal conductivity [W/(m·K)] | 0.14605 | 7E-03

Concerning the simulation of nitrogen cooling, the assumption that only liquid nitrogen flowed within the cooling path could lead to an overestimate of the cooling efficiency, while an underestimation could be obtained by selecting a fully gaseous model. The use of a homogenous fluid approach [15] was suggested to benchmark participants, considering the liquid-gas mixture as a single fluid with pseudo properties.

Friction of aluminum on steel at extrusion temperatures is usually computed as complete sticking, except in bearing zones where some sliding may occur [14]. Commonly, the following shear friction model is implemented:

with being the shear friction stress, the material shear stress, and m the friction factor that is equal to 1 in the fully sticking condition.

Experimental Results

The original version of this paper is available on https://www.scientific.net/KEM.988.47.pdf

Thermal field. Fig. 8 shows the thermal and load histories of the whole experimental campaign: the temperatures acquired by the thermocouples and by the pyrometer, as well as the extrusion load, were reported in the graph. The porthole die was pre-heated in oven at then, during the die loading, the temperature in the monitored locations decreased to as shown in Tab. 7. In detail, temperatures in the backer (from T1 to T6 along the cooling path, T11 in contact with the die ring) reached values of about as a result of the contact with the cold parts of the press. In proximity to the bearings (T7, T8, T9) and within the mandrel (T10), the decrease in temperature was less pronounced with the minimum value of recorded in T8. During the first extrusion, the ram speed was lower, and the ram was also stopped to allow the puller to engage the profiles; all thermocouples, except T10, continued to record a decrease in temperature for the heat exchange with the press. At the beginning of the second extrusion, the ram speed was set to and the heat generated by the aluminum under deformation increased: the thermocouples T4-T6 nearby the profiles' exit recorded a maximum value of about and T 2 nearby the inlet channel marked and respectively, T 9 (bearings exit 2 ) indicated the value of (bearings exit 3) only , while T7 (bearings exit 1) recorded an intermediate value of . As expected, the highest temperature of was observed in T 10 (mandrel) and the lowest one at about
in T11 (die ring). Indeed, as a consequence of manufacturing issues of the holes, T7-T9 locations were not close enough to capture the real bearings temperature that should be very similar to the profile temperature ( ). In addition, T8 temperature was unexpectedly low if compared to T7 since profiles 1 and 3 had the same velocity (Fig. 9). Therefore, during the die loading, T8 had probably moved from its seat.

Fig. 8. Thermal and load history of the whole experimental campaign. (15 billets).

Fig. 8. Thermal and load history of the whole experimental campaign. (15 billets).

Table 7. Thermocouples' recordings.

Thermocouples' Temperature [°C] | T1 | T2 | T3 | T4 | T5 | T6 | T7 | T8 | T9 | T10 | T11
Before Extrusion 1 | 445 | 457 | 457 | 440 | 458 | 458 | 473 | 468 | 471 | 476 | 442
Before Extrusion 4 | 359 | 400 | 428 | 429 | 432 | 436 | 468 | 441 | 483 | 526 | 306
Extrusion 5 | 361 | 406 | 437 | 437 | 441 | 444 | 473 | 445 | 490 | 535 | 295
Extrusion 12 | 234 | 323 | 320 | 320 | 330 | 360 | 463 | 417 | 471 | 520 | 286

During the following two runs, the temperatures generally rose, while the difference between the values of the fourth and fifth extrusion was negligible, thus evidencing the achievement of the steady state uncooled condition (Tab. 7). For the fifth extrusion, T10 (mandrel) recorded a value of ( more than the second extrusion), T7 (bearings exit 1), T8 (bearings exit 3) and T9 (bearings exit 2) acquired and respectively, against and of the second extrusion. Except for T11 (die ring), which showed a temperature of ( lower than the second extrusion), the temperature in the backer remained more stable, evidencing differences below with respect to the second run. The pyrometer registered a maximum temperature of for profile more than the second extrusion).

During the sixth extrusion, the nitrogen valve was fully opened until the end of the twelfth run. The nitrogen valve remained opened also during the billet change in order to achieve a liquid nitrogen flow in the channel instead of gas nitrogen or a mixture [15], and to promote the achievement of the steady state condition in a few runs. The acquisition system crushed for some seconds during the tenth extrusion thus generating the lack of data at around 2400 seconds.

Analyzing the temperature evolution of T1 located nearby the nitrogen inlet, it is worth noting that the steady state condition was reached during the last extrusion with the nitrogen cooling, where the T1 temperature, after a significant decrease during the previous runs, remained constant at the value of . In detail, the temperature at T1 exhibited a rapid decrease during the first two runs (6th and 7th), dropping from to . Subsequently, the thermal gradient decreased, reaching a
steady-state value of after additional five billets. The other thermocouples in the backer showed a comparable behavior; however, it is worth noting the limits of the selected channel design: T6 located nearby the exit 2 recorded temperatures ( during the run) about higher than T3 and T4 ( during the run) positioned at the center of the channel, evidencing the unbalanced distribution of the nitrogen flow. The channel design promoted the nitrogen flow towards the exit of the profile 3 (T5 with a minimum value of ) and made much less effective the cooling around the other exits. This trend was confirmed also by the bearings temperatures, where the higher cooling effect was captured by T8 (exit 3, from to ), while the lower one by T7 (exit 1, from to ). The decrease in temperature registered by T 10 (mandrel, from to ) was not caused by the nitrogen since the cooling channel was too far from the mandrel [15]. During the last three extrusions, the nitrogen cooling was turned off and the temperatures generally rose, thus achieving comparable values between the last run and the steady state uncooled condition.

From the results discussion it is clear that, in this case, the cooling efficiency on the bearings surface was limited with a sole of change on the profiles exit temperature (from to for profile 1). According to the state of art [13] [15], the experimental campaign showed the potentiality of the nitrogen cooling, but also the wide margins of improvement of the channels if designed with the support of advanced numerical tools.

Extrusion Load. In term of extrusion load, a peak value of about 36 MN , over the 40 MN available, was registered and, as expected [15], the increase caused by the localized nitrogen cooling was negligible.

Profiles speed. Fig. 9 shows the velocity evolution of the profiles during the fourth extrusion (run selected for the benchmark analysis). During the first extrusion, before the use of the puller, the velocity of the profile 2 was qualitatively higher than the ones of the other two profiles. Then, despite the application of a tensile force of 3500 N , the profile 2 continued to flow through the puller at a higher speed. In Fig. 9 the velocity of the profiles 1 and 3 was imposed by the puller (average speed of ), while the profile 2 (average speed of higher than the puller one) was calculated by analysing the difference in terms of extrudates length at the end of the extrusion. The significant variations in profile speed are related to the variations in the geometries of the ports and bearings for the three profiles (Fig. 4).

Fig. 9. Profiles speed (Billet 4).

Fig. 9. Profiles speed (Billet 4).

In the present experimental campaign, contrary to the normal industrial practice that foresees the design of symmetrical geometries for multiple profiles, significant geometrical differences were voluntarily induced in the design of the ports and bearings of the 3 profiles with the aim to test the forecasting performance of numerical tools. Among them, profile 2 with cylindrical ports and cylindrical bearings resulted the faster one, while profile 3 with conical ports and choke was the slowest one. The cylindrical ports (profile 2) always guaranteed a faster material flow with respect to the conical ones (profiles 1,3), underlining that the effect of port geometry was more significant than that of the bearings in terms of speed changes. On one side, this was proved by the experimental
evidence that the speed difference between profile 3 and profile 1 (both extruded with conical ports) was not so high without the puller and negligible with the puller, despite the differences in the bearings configuration (choke for profile higher bearings length for profile 1). On the other side, despite the 1 mm higher bearings length of profile 2 with respect to profile 1 , the former flowed faster due to the cylindrical ports. These results suggest the need to ponder the effects of all the involved geometric parameters and of their interactions for an optimized die and process design. Indeed, in this case, compensating for the speed differences generated by a conical port design does not appear to be an "easy task" using only the bearings optimization.

Charge welds and billet skin. Fig. 10a summarizes the terminology that will be used in this paragraph. It can be useful to remind that billet skin is usually found only when a short billet rest is used and it is related to the contamination of the profile by the outer layer of the billet (i.e. the billet skin), whose typical dimension is around of , and accumulate at the billet end as a consequence of high sticking friction of aluminum on container. Billet skin contamination, when present, may start (BSS in Fig. 10a) before or after the stop mark and end (BSE) after it. Charge weld, instead, is related to the interaction between the old and the new billet in a continuous extrusion and its start (CWS) and end (CWE) are always located after the stop mark. Since the charge welds and the billet skin are deeply contaminated by oxides and impurities (thus leading to poor mechanical proprieties [10]), such portions (from BSS to CWE) have to be discarded and consequently the possibility of extent minimization is of relevant industrial interest.

Fig. 10. Charge welds evolution for transitions between billets 3-4 and 8-9, Profiles 1-3.

Fig. 10. Charge welds evolution for transitions between billets 3-4 and 8-9, Profiles 1-3.

All the analyzed profiles showed no evidence of billet skin contamination. Fig. 10b reports the evolution of charge welds in the transition between billets and for profiles 1 and 3 . As evident from the graph depicted in the figure, the four patterns closely resemble each other, indicating an initial defect onset within the range of +750 to +800 mm from the stop mark, followed by a rapid expansion and extension of the defect in the range of +2100 to +2200 mm from the stop mark. In Fig. 11, an example of how the charge welds was calculated is reported. The process involved the etching of the profile, the acquisition and the analysis of the images.

The trend for the profile 2 was not reported in the present document due to the presence of the PCG (Peripheral Coarse Grain), which posed a significant challenge in precisely delineating the start and end points of the defect, as exemplified in Fig. 12, following the etching phase.

Fig. 11. Charge welds image analysis (Billet 3-4 transition, profile 3).

Fig. 11. Charge welds image analysis (Billet 3-4 transition, profile 3).

Fig. 12. Microstructure of the profile 2 in the billet transition.

Fig. 12. Microstructure of the profile 2 in the billet transition.

Microstructure. Specimens extracted from the middle of the extruded profiles were analyzed in terms of microstructure evolution. The image was acquired by using polarized light microscopy of the samples anodized ( ) with Barker's reagent ( ). In order to properly discuss the obtained micrographs, it is important to remind that the billets had an average grain size of on longitudinal direction and on transverse direction. In Fig. 13, the microstructures of the profiles extruded from billet 5 with air quenching only and without nitrogen cooling are reported. The microstructure of profile 1 exhibits a fibrous grain structure with a thin PCG layer on the internal and external surfaces. In contrast, the microstructure of profile 2 differs from that of profile 1 due to a higher PCG thickness, both internally and externally (caused by the higher exit speed of the profile). Finally, profile 3 shows a completely fibrous microstructure. Measurements of grain sizes and PCG layers thicknesses are detailed in Tab. 8.

Fig. 13. Microstructure of profiles extruded from billet 5, middle of the extruded profiles: a) profile profile profile 3.

Fig. 13. Microstructure of profiles extruded from billet 5, middle of the extruded profiles: a) profile profile profile 3.

Fig. 14. Microstructure of the profiles extruded from a) Billet 14 profile 1, b) Billet 12 profile 3.

Fig. 14. Microstructure of the profiles extruded from a) Billet 14 profile 1, b) Billet 12 profile 3.

Table 8. Results of the PCG layer and grain size evaluation.

| Internal PCG layer [μm] | External PCG Layer [μm] | Grain size range - Fibrous [μm] | Grain size range - PCG [μm]
Billet 5 Exit 1 | 201 | 523 | 5-30 | 131-832
Billet 5 Exit 2 | 485 | 1154 | 5-30 | 142-1885
Billet 5 Exit 3 | 0 | 0 | 5-30 | -
Billet 14 Exit 1 | 235 | 572 | 5-30 | 134-901
Billet 12 Exit 3 | 0 | 0 | 5-30 | -

In Fig. 14, the microstructures of the profiles extruded from billet 14 (no nitrogen, water quenching, profile 1) (Fig. 14a) and billet 12 (nitrogen cooling, water quenching, profile 3) (Fig. 14b) are depicted. Notably, the microstructure in Fig. 14a closely resembles that in Fig. 13a (billet 5, profile 1), while the microstructure in Fig. 14b is completely fibrous as to the one in Fig. 13c (billet 5, profile 3). Stating these results, it is possible to understand that, in this case, the water quenching with air quenching generated a negligible effect on microstructure evolution with respect to the air quenching only. To measure the recrystallization layer on both the internal and external surfaces, 24 measurements were acquired (a measurement every of the circumference on internal and external surfaces) and an average value was computed. Tab. 8 displays the values of recrystallized thickness in the analyzed cases, along with the corresponding range of grain sizes.

Seam weld quality. For the characterization of the seam weld quality, bulge tests were selected with respect to the conical expansion since they provide a higher accuracy and repeatability of the results [16]. Bulge tests were carried out on an Instron 250 kN press with a rubber plug of 29 mm in diameter that was compressed between two punches inside the tube with a constant ram speed of . Plug pressure on the inside wall of the profile generated a biaxial tensile stress leading the tube to fracture in proximity of the seam welds. The two punches were constrained in order to displace in the longitudinal direction only and the movement was assigned to the upper one (Fig. 15a).

Following [18], a specimen length of 145 mm was selected in order to avoid any influence on the results. A uniform deformation in the rubber plug and the absence of any buckling were ensured by selecting a useful length of about two times the diameter of the tube. Based on these requirements, the rubber plug initial length was set to 64 mm . The profiles extruded from billet 5 were tested, and, for each profile exit, three different repetitions were performed for a total amount of 9 tests. For each test repetition, the internal rubber plug was changed.

Fig. 15. a) Schematization of the bulge test, b) Initial circumference and circumference at fracture, c) Profiles after the bulge test, d) Force-Upper punch displacement graph.

Fig. 15. a) Schematization of the bulge test, b) Initial circumference and circumference at fracture, c) Profiles after the bulge test, d) Force-Upper punch displacement graph.

Fig. 15d shows the typical load-stroke history of a bulge test with a linear behavior between 0 to 2 mm stroke for the upsetting of the plug to internal tube diameter, then from 2 mm the tube expansion is realized up to tube fracture. In Fig. 16, the main outcomes of the analyses are presented for each profile. In Fig. 16a, the hoop strain at fracture is reported, which was estimated as the natural logarithmic ratio between the final and the initial tube circumferences (Fig. 15b). Profile 1, 2 and 3 presented average values of and 0.14 , respectively. In Fig. 16b, the peak load at fracture is shown with average values of and 57.6 kN , respectively. The result of the experimental investigation clearly showed that the profile 3 has the best deformability (both in term of hoop strain and peak load), followed by the profile 1, while the profile 2 presented the lowest values as a consequence of the occurrence of the PCG (coarser grain structure) discussed in the microstructure section. Profile fractures occurred always in the same seam weld (among the three seam welds) within the three repetitions, thus indicating clearly weak welds inside each profile. The identification of the weak seam was not possible since no tracking of the weld orientation was kept on the profiles.

Fig. 16. Results: a) Hoop strain at fracture, b) Peak load at fracture.

Fig. 16. Results: a) Hoop strain at fracture, b) Peak load at fracture.

Conclusions

The original version of this paper is available on https://www.scientific.net/KEM.988.47.pdf

In the Extrusion Benchmark 2023, the processing conditions for extruding three AA 6082 tubes were monitored and analyzed. Starting material was tested in terms of flow stress behavior through hot torsion tests, and several input data formats were provided (tabular data, Hansel-Spittel and sinhyperbolic models). Initial microstructure shape and dimensions were also collected, together with the billet skin thickness. The process was carried out at a constant ram speed of . To guarantee steady state conditions for the analysis of the trials, 15 billets were extruded and the billets 4 and 5 were used for benchmark comparison. During the extrusion, the following process data were acquired in order to provide a reliable comparison of FEM outputs: press load, profile speed, profile temperatures and die temperatures in eleven different locations. Profiles were then analyzed in terms of quality of the seam welds, the microstructure size and the distribution in the profile of charge welds and billet skin evolutions. The analyses were performed on air and water quenched conditions and with the use or absence of liquid nitrogen cooling.

Acknowledgments

The original version of this paper is available on https://www.scientific.net/KEM.988.47.pdf

Authors would like to strongly acknowledge all the Companies and persons that made possible to carry out the experiment. In particular, the authors acknowledge the operators and the plant director Eng. Luca Gorlani from Metra plant in Rodengo Saiano (BS, italy) for the supply of material and the press usage, and Almax Mori Group (TN, Italy), in particular Eng. Tommaso Pinter, for the die design and manufacturing. All these companies and involved persons are strongly acknowledged for their kind support, activity and problem solving approach.

Funding

The original version of this paper is available on https://www.scientific.net/KEM.988.47.pdf

National Recovery and Resilience Plan (NRRP), Mission 04 Component 2 Investment 1.5NextGenerationEU, Call for tender n. 3277 dated 30/12/2021 (Award Number: 0001052 dated 23/06/2022).

References

The original version of this paper is available on https://www.scientific.net/KEM.988.47.pdf

B. Reggiani, L. Donati, L. Tomesani, ICEB - International conference on extrusion and benchmark, Light Metal Age 75 (5) (2017) 52-60.

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D. Pietzka, N. Ben Khalifa, L. Donati, L. Tomesani, A. E. Tekkaya, Extrusion Benchmark 2009 - Experimental analysis of deflection in extrusion dies. Key Eng. Mater. 424 (2010) 19-26.

A. Selvaggio, A. Segatori, A. Guzel, L. Donati, L. Tomesani, A. E. Tekkaya, Extrusion Benchmark 2011: Evaluation of different design strategies on process conditions, die deflection and seam weld quality in hollow profiles, Key Eng. Mater. 491 (2012) 1-10.

L. Donati. A. Segatori, et al., Effect of liquid nitrogen die cooling on extrusion process conditions Key Eng. Mater. 491 (2012) 215-222.

A. Selvaggio, T. Kloppenborg, M. Schwane, et al., Extrusion Benchmark 2013 - Experimental Analysis of Mandrel Deflection, Local Temperature and Pressure in Extrusion Dies, Key Eng. Mater. 585 (2013) 13-22.

A. Selvaggio, L. Donati, et al., Scientific Benchmark 2015: Effect of choking and bearing length on metal flow balancing in extrusion dies, Mater. Today: Proc 2 (2015) 4704-4713.

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L. Donati L., A. Segatori et al., Extrusion Benchmark 2017: Effect of Die Design on Profile Quality and Distortions of Thin C-Shaped Hollow Profiles, Mater. Today: Proc 10 (2019) 171184.

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L. Donati, B. Reggiani, R. Pelaccia, M. Negozio, S. Di Donato, Advancements in extrusion and drawing: a review of the contributes by the ESAFORM community, Int. J. Mater. Form. 15 (2022) 41.

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Chapter 2

Modeling and Simulation

The original version of this eBook is available on https://www.scientific.net/book/12th-aluminium-two-thousand-world-congress-7th-international-conference-on-extrusion-and-benchmark/978-3-0364-1380-8

Key Engineering Materials, ISSN: 1662-9795, Vol. 988, pp 3-10

doi: 10.4028/p-QY3Qy5

© 2024 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2023-11-20

Revised:
2024-03-07

Accepted:
2024-03-20

Online:
2024-09-27

Prediction of Damage Behavior of Casting Aluminum Components Considering Inhomogeneous Properties

The original version of this paper is available on https://www.scientific.net/KEM.988.3.pdf

Yuling Lang , Dongzhi Sun , Zhihua Zhu , Xiang Ci , Decai Kong , Haibo Qiao , Wenbo Wang

CITIC Dicastal Co., Ltd, No 185, Longhai Road, Economic and Technological Development Zone, Qinhuangdao, Hebei Province, 066011, China

sunCAE Consulting, Ginsterweg 3, 79194 Gundelfingen, Germany

cixiang@dicastal.com, kongdecai@dicastal.com, qiaohaibo@dicastal.com, wangwenbo1994@dicastal.com

Keywords: Casting aluminum alloy, Characterization, Damage model, Fatigue, Inhomogeneity, Simulation, Wheel, Knuckle.

Abstract

The purpose of the work is to quantify and predict the influence of inhomogeneity of local properties on the overall behavior of the selected casting aluminum wheel and knuckle in different loading cases. The investigated material is A356-T6. Smooth and notched tensile specimens and torsion specimens are extracted from different positions in the wheel and knuckle and tested. The dependences of the flow stress, the fracture strain, and the S-N curve on position for specimen extraction are evaluated. Metallographic investigations are performed to reveal the relations between microstructure/microdefects and the mentioned properties. A damage model based on a triaxiality-dependent fracture strain is calibrated and used to simulate the specimens and component tests. The simulations of static wheel tests and knuckle fatigue tests are performed with position-dependent material parameters. The prediction of the component tests is compared with the experimental results.

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.988.3.pdf

The inhomogeneity of local mechanical properties across a casting aluminum component can significantly affect its mechanical behavior in real loading situations. A promising approach for a reliable prediction of the deformation and fracture behavior of cast components is a coupling between process simulations and structural simulations [1,2] based on a quantitative description of the relations between microstructure and local properties. A relatively practicable approach is based on a direct characterization of mechanical properties for different positions and a subsequent component simulation with the data from the specimen tests [3,4]. Since the damage behavior strongly depends on stress state, it is complicated to quantify both effects of stress state and inhomogeneity on component fracture behavior. Until now there are only a few published results about a systematic characterization of the effects [1]. An observable inhomogeneity of fatigue properties in casting aluminum components is depicted in [5, 6]. However, it is still a great challenge to find a reliable method to predict the fatigue behavior of the corresponding components taking the inhomogeneity into account.

In this work, a systematic characterization, component tests and numerical simulations on a casting aluminum alloy are performed to develop and to verify an evaluation method concerning component safety under different loadings. The specimens extracted from different positions of the wheel and the knuckle are tested to quantify the inhomogeneity of local properties of the wheel and knuckle. The mechanical properties of A356-T6 alloy are characterized using smooth tensile, notched tensile, and torsion tests. The fatigue tests are conducted under cyclic loading. Metallographic investigation of different parts is conducted to verify and to analyze the inhomogeneity of microstructure across the wheel and the knuckle. The position-dependent damage model and S-N curves used for components simulations are evaluated based on the speciment tests. The results of component simulations (wheel crack test, knuckle fatigue test) with position-dependent material parameters are verified by the comparison with the experimental results.

Characterization of Mechanical Properties of A356-T6 Alloy

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Static tests on smooth tensile, notched tensile and torsion specimens. The samples were taken from wheels ( 18 inches, 16.8 kg ) with low-pressure die-casting A356 alloy subjected to T6 heat treatment. The main chemical composition of this alloy is shown in Table 1. Due to the complex geometry and inhomogeneous solidification rate in the wheel, seven positions were chosen for sampling different type specimens, as shown in Fig. 1. Smooth tensile specimens were tested according to DIN EN ISO 6892-1 on an Instron-8861 test machine at a strain rate of and room temperature. The engineering stress-strain curves of smooth tensile specimens sampling from all seven positions are displayed in Fig. 1. It is visible that the fracture strain considerably scatters even within a series for the same specimen position. A trend for a position-dependence of the flow stress and fracture strain can be recognized despite the scatters. Through analyzing the uniaxial tensile mechanical properties of all seven positions, the notched tensile specimens and torsion specimens were finally sampled in only three positions (positions 1: spoke, 3: rim, and 5: hub). The average mechanical properties at three positions of the wheel are displayed in Table 2, including the average yield stress ( ), the average ultimate tensile strength ( Rm ), and the average elongation (Et). The experimental results of notched tensile tests are shown in Fig. 6 together with numerical results. The results of torsion tests are given in [1].

Table 1. Chemical compositions of A356-T6 alloy used in this work.

Elements | Si | Mg | Ti | Fe | Cu | Mn | Zn | Al
wt.% | 6.8 | 0.25~0.45 | 0.13 | 0.10 | <0.03 | <0.03 | <0.03 | Bal.

Fig. 1. Sampling positions in the wheel and the engineering stress-strain curves of smooth tensile specimens from 7 positions.

Fig. 1. Sampling positions in the wheel and the engineering stress-strain curves of smooth tensile specimens from 7 positions.

Fig. 2. (a) S-N curves at three different positions of one knuckle; (b) specimen geometry; (c) schematic diagram of sampling location.

Fig. 2. (a) S-N curves at three different positions of one knuckle; (b) specimen geometry; (c) schematic diagram of sampling location.

Table 2. The average mechanical properties at different positions of the wheel and the knuckle.

Component | Position | Rp0.2 [MPa] | Rm [MPa] | Et [%]
Wheel | 1 | 215.8 ± 4.8 | 268.2 ± 3.8 | 5.13 ± 1.72
3 | 208.7 ± 5.5 | 281.9 ± 2.9 | 13.47 ± 1.16
5 | 201.8 ± 3.8 | 244.3 ± 16.8 | 3.12 ± 1.52
Knuckle | 1 | 267.5 ± 6.6 | 332.8 ± 5.6 | 8.26 ± 1.33
2 | 268.7 ± 7.2 | 339.6 ± 6.8 | 9.97 ± 1.41
3 | 271.3 ± 7.7 | 343.4 ± 9.3 | 11.69 ± 1.45

Fatigue tests on smooth specimens. The samples for the fatigue tests were taken from A356 knuckles ( 3.5 kg ) with counter-pressure die-casting and T6 heat treatment. The main chemical composition of this alloy is shown in Table 1. The sampling locations and fatigue specimen geometry (according to ASTM-E466 standard) are shown in Fig. 2(b, c). The average mechanical properties at three different positions of the knuckle are displayed in Table 2. The positions were selected based on the distance to the riser. It can be observed that the mechanical properties of the knuckle are better than the wheel. High-cycle fatigue tests were conducted on an electro-hydraulic servo fatigue testing machine (INSTRON8801). All specimens were tested at room temperature at the load ratio , and the frequency was 40 Hz . Fig. 2(a) shows the S-N curves at three different positions of one steering knuckle. Obviously, the highest S-N curve is given by position 1 and the lowest S-N curve is from position 3 . The k slope of position is , respectively.
Metallographic investigation. The microstructure of the wheel in the three positions is shown in Fig. 3. It is observed that a typical hypoeutectic alloy is formed by primary dendrites and eutectic Si particles. In addition, the cast pores (mostly shrinkage pores) could be obviously observed in position 1 and 5 . This indicates that the high porosities in position 1 and 5 are the main reason for the lower strength and elongation of both positions in comparison with the values of position 3 where no big pores are visible. Fig. 4 shows the microstructure of different positions in one knuckle with different SDAS, where the measured SDAS of position 1 is and position 3 is . Obviously, a smaller SDAS in the casting alloy results in a higher S-N curve.

Fig. 3. The microstructure of (a) position 1 (spoke), (b) position 3 (rim), (c) position 5 (hub), (d) eutectic Si.

Fig. 3. The microstructure of (a) position 1 (spoke), (b) position 3 (rim), (c) position 5 (hub), (d) eutectic Si.

Fig. 4. Metallographic structure of different positions in one steering knuckle with different secondary dendrite arm spacings: (a) position 1, (b) position 3.

Fig. 4. Metallographic structure of different positions in one steering knuckle with different secondary dendrite arm spacings: (a) position 1, (b) position 3.

Modelling of Damage Behavior of Different Specimens

The original version of this paper is available on https://www.scientific.net/KEM.988.3.pdf

Damage model and its parameter determination. A damage model based on a triaxiality-dependent fracture strain [7] is applied to simulate the static tests. The fracture strain is defined as a function of the stress triaxiality , which is the ratio of the mean stress to the von Mises effective stress . The values of triaxiality for torsion, uniaxial tension, and biaxial tension are zero, one-third, and two-thirds, respectively. The fracture occurs when the cumulative damage parameter D defined by Eq. 1 reaches the critical value of 1. Equation 1 shows the formula for the damage evolution, which can be used for a general non-linear damage accumulation:

denotes the equivalent plastic strain, and the exponent n is a parameter controlling the damage evolution. For proportional loading, the integration of Eq. 1 leads to

The dependence of fracture stain on triaxiality is determined by simulating the selected smooth and notched tensile tests and torsion tests and fitting the measured displacements at rupture. The fracture strain for each specimen type was taken from the equivalent plastic strain in the critical finite element showing the maximal damage variable D at the measured displacement at fracture. Since the fracture displacements of different specimens in a test series for the same specimen geometry usually lie in a scatter band, the averaged displacement at fracture was used for the fitting process. The corresponding triaxiality for the determined fracture was also taken from the critical finite element.

Fig. 5 shows the determined fracture strains as a function of the corresponding triaxiality for the three different sampling positions. The developments of the local equivalent plastic strain in the critical elements in selected specimens are also given in Fig. 5 with solid lines for the whole loading period. The symbols represent the fracture strains determined from the individual tests. It is obvious that the fracture strain was not only influenced by triaxiality but also by sampling position. The study in [1] indicates that the microstructure and microdefect depend strongly on the position in the wheel. Position 1 (spoke) and position 5 (hub) show a much larger porosity than positions 3 (rim). The
fracture strain under both loading cases of tension and torsion decreases with increasing porosity [1]. The subsequent simulations of specimen and component tests are performed with the fracture strain vs. triaxiality curves in form of tables.

Fig. 5. Fracture strains as function of triaxiality for three positions in a wheel of A356-T6.

Fig. 5. Fracture strains as function of triaxiality for three positions in a wheel of A356-T6.

Simulation of specimen tests. The quasi-static tests on the smooth tensile specimens, the notched tensile specimens with a notch radius of 1 mm and 4 mm and the torsion specimens were simulated with Abaqus/Explicit, the specimens of which were extracted from three different positions . The hexahedral elements were used in the four FE models and the element edge length was about 0.2 mm . The specimen tests for each position were simulated with the true stress vs. true strain curve and the damage curve determined for the position.

The measured and calculated engineering stress vs. strain curves for smooth tensile and notched tensile (R1, R4) tests for three positions are shown in Fig. 6. The solid line represents the experimental curve, and the dashed line represents the simulation results. It can be observed that the wheel rim (position 3) shows the best material properties, with the highest strength and largest elongation. The worst part is the hub (position 5). The calculated stress vs. strain curves at different positions are consistent with the experimental results. The simulated engineering fracture strain in the torsion test shows high consistency with the experimental results as well [ 1 ].

Fig. 6. Test and simulation results of (a) smooth tensile and (b) notched tensile specimens (R1, R4) for positions 1,3, and 5 in the wheel.

Fig. 6. Test and simulation results of (a) smooth tensile and (b) notched tensile specimens (R1, R4) for positions 1,3, and 5 in the wheel.

Prediction of Damage Behavior of a Casting Aluminum Wheel and a Knuckle

The original version of this paper is available on https://www.scientific.net/KEM.988.3.pdf

The damage behavior of a wheel under static loading and the fatigue behavior of a knuckle under cyclic loading are simulated based on the material parameters discussed above. The effects of the inhomogeneity of local properties are taken into account.

Modeling of the damage behavior of a wheel under static loading. The wheel compression tests were performed at the Fraunhofer Institute for Mechanics of Materials IWM. As displayed in Fig. 7(a), one side of the wheel is in contact with a rigid wall. The other side is loaded slowly with a rigid device until the fracture of wheel. During the wheel test, the force vs. displacement curve was recorded and the deformation pattern was documented by a video film.

The simulation model for the wheel compression test was established to check the influence of position-dependent properties on the fracture behavior of the wheel. The software used for simulation is Abaqus/Explicit. As shown in the schematic figure (Fig. 7), one rigid body is fixed at one side. Another rigid body, set on the other side, moves towards the right at a speed of until the entire wheel is completely crushed.

The settings of the FE model are as follows. The material of the rigid body is steel. The wheel material is aluminum alloy A356 T6, with a mesh size of 5 mm and the element type of C3D10M. The wheel is divided into three parts, named Pos. 1 (orange area, spoke), Pos. 3 (blue area, rim), and Pos. 5 (green area, hub), as shown in Fig. 7(b). The damage parameters are given in Fig. 5.

Fig. 7. (a) FE model of wheel compression test; (b) position-dependent material setting.

Fig. 7. (a) FE model of wheel compression test; (b) position-dependent material setting.

Fig. 8. Load vs. displacement curves and damage initiation (a) test (b) simulation.

Fig. 8. Load vs. displacement curves and damage initiation (a) test (b) simulation.

The fracture behavior of the wheel in the compression test is compared between simulation and test in Fig. 8. The damage patterns from experiment and simulation are displayed in Fig. 8(a) and (b), respectively. It can be observed that the simulated and the experimental results of the wheel compression test are highly consistent, considering the crack location.

The force vs. displacement curves derived from the simulation and the test are also displayed in Fig. 8. The green line represents the curve recorded during the test, while the red line stands for the simulation result. The peak value represents the fracture force of the wheel, which characterizes the initiation of the fracture. The fracture force of the simulation and the test is 395 kN and 421 kN , respectively. Both the fracture force and the course of the curve from the simulation are in good agreement with the experimental results.

Modeling of the fatigue tests on a knuckle. The setup of the knuckle fatigue test is shown in Fig. 9. The knuckle is fixed at the wheel center. The cyclic load, which is 15 kN , is applied to the steering arm at X-Y plane with the direction of to the clamping surface. The load frequency is 10 Hz . The crack location and fatigue cycles are recorded during the experiment. Totally three repeated tests were conducted.

Fig. 9. FE-Model of knuckle fatigue test.

Fig. 9. FE-Model of knuckle fatigue test.

Fig. 10. Calculated and experimental results of knuckle fatigue tests.

Fig. 10. Calculated and experimental results of knuckle fatigue tests.

To verify the applicability of the position-dependent S-N curves, the FE model was set up in accordance with the test. The damage was calculated with Abaqus/Standard and FEMFAT. The inhomogeneity of local mechanical properties was considered by dividing the knuckle into three parts, as shown in Fig. 9. The position-dependent material card is based on the results given in Fig. 2. The tetrahedral elements were used in the FE-model with the element size of 2 mm . The load cycles defined in the FEMFAT are 100000.

The simulated and experimental results are displayed in Fig. 10, including the position with the maximum damage from simulation and the crack locations from 3 different knuckle tests. It can be observed that the simulation results are highly consistent with the experimental results in predicting the crack location.

The simulation result is further validated based on the comparision of fatigue life. The calculated maximum damage is 0.382 . Life is the reciprocal of damage, which is 2.58 . According to the input cycles in FEMFAT, the fatigue cycles are calculated to be 258000 . The experimental cycles at crack initiation and the logarithmic error are displayed in Table 3. The logarithmic error of the fatigue test is , and , respectively, indicating a good agreement between the simulation and the test.

Table 3. Fatigue test cycles.

No. | Test cycles | Calculated cycles | Log error [%]
1 | 197106 |  | -2.16
2 | 307290 | 258000 | 1.40
3 | 333263 |  | 2.05

Conclusions

The original version of this paper is available on https://www.scientific.net/KEM.988.3.pdf

The characterization of different positions in the investigated casting aluminum components shows a strong inhomogeneity of mechanical properties. The ultimate tensile strength averaged from three positions in the casting wheel of A356 T6 changes from 244.2 MPa to 281.9 MPa . The average elongation changes from to . The metallographic investigation delivers the indication that the microstructure (e.g., SDAS) and microdefects (e.g., porosity) considerably affect the strength, fracture strain and fatigue life.

A damage model based on a triaxiality-dependent fracture strain was calibrated by simulating different tests on smooth and notched tensile specimens and torsion specimens extracted from three positions (spoke, rim, hub) in a casting wheel. The FE model for the wheel consisted of three material zones with the corresponding stress vs. strain curves and damage parameters. Both the measured force vs. displacement curves of the static wheel tests and the damage positions in the tested wheel were well predicted by the simulation with the applied position-dependent material parameters.

Fatigue tests on a casting aluminum knuckle were simulated and the damage was calculated with Abaqus/Standard and FEMFAT. The inhomogeneity of local mechanical properties was considered by dividing the FE model for the knuckle into three material parts. The corresponding S-N curve was used for each part. The calculated fatigue cycles and damage positions agree with the experimental results of the knuckle tests very well.

Acknowledgment

The original version of this paper is available on https://www.scientific.net/KEM.988.3.pdf

We are very grateful to the experts of the Institute of Metal Research, Chinese Academy of Sciences, Shenyang, and the Fraunhofer Institute for Mechanics of Materials IWM, Freiburg, Germany who supported this work.

References

The original version of this paper is available on https://www.scientific.net/KEM.988.3.pdf

D. Kong, D.-Z. Sun, B. Yang, H. Qiao, C. Wei, Y. Lang, H. Song, J. Gao, Engineering Failure Analysis 145 (2023) 107018.

F. Andrieux, D.-Z. Sun, A. Burblies, ALUMINIUM TWO THOUSAND, 10th International Congress & ICEB, 6th International Conference on Extrusion and Benchmark, Verona - Italy, 20-24 June 2017.

Y. Leost, A. Sonntag, T. Haase, Modeling of a cast aluminum wheel for crash application, 11th European LS-DYNA Conference 2017, Salzburg Austria.

G. Previati, F. Ballo, M. Gobbi, G. Mastinu, Radial impact test of aluminium wheels numerical simulation and experimental validation, Int. J. of Impact Engineering, 126, 117-134, 2019.

B. Yang, S. Chen, H. Song, S. Zhang, H. Chang, S. Xu, Z. Zhu, C. Li, Mater. Sci. Eng., A 857 (2022) 144106.

R. Hidalgo, J.A. Esnaola, I. Llavori, M. Larrañaga, I. Hurtado, N. Herrero-Dorca, Int. J. of Fatigue 125 (2019) 468-478.

D.-Z. Sun, A. Ockewitz, F. Andrieux, H. Klamser, Proc. of the 12th Int. Conference on Aluminium Alloys, September 5-9, 2010, Yokohama, Japan, ©2010 The Japan Institute of Light Metals.

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The original version of this eBook is available on https://www.scientific.net/book/12th-aluminium-two-thousand-world-congress-7th-international-conference-on-extrusion-and-benchmark/978-3-0364-1380-8

Key Engineering Materials, ISSN: 1662-9795, Vol. 987, pp 3-10

doi: 10.4028/p-qO8WAd

© 2024 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2023-11-18

Revised:
2024-03-02

Accepted:
2024-04-08

Online:
2024-09-23

Microstructure Evolution and FEM Prediction on AA6XXX Alloys

The original version of this paper is available on https://www.scientific.net/KEM.987.3.pdf

Marco Negozio , Riccardo Pelaccia , Lorenzo Donati , Barbara Reggiani , Sara Di Donato

University of Bologna, DIN Department of Industrial Engineering, Viale Risorgimento 2, 40136, Bologna, Italy

University of Modena and Reggio Emilia, DISMI Department of Sciences and Methods for Engineering, Via Amendola 2, 42122, Reggio Emilia, Italy

marco.negozio2@unibo.it, riccardo.pelaccia@unimore.it, I.donati@unibo.it, barbara.reggiani@unimore.it, sara.didonato2@unibo.it

Keywords: recrystallization, aluminum alloy, FEM, extrusion, microstructure.

Abstract

The microstructure evolution during the extrusion process of AA6XXX aluminum alloys is getting a significant interest from extruders and researchers because of the effect of the grain structure on the extruded component properties. Several process and material parameters such as chemical composition, homogenization, temperature evolution, extrusion speed, geometries and quenching have a direct impact on the final grain size of extruded profiles. Because there are so many affecting elements, it is extremely challenging to forecast the microstructure evolution and, as a result, research activities are still required to understand and control the aluminum alloy recrystallization behaviour. In this work, a methodology for the microstructural characterization of AA6XXX aluminum alloys is proposed. The methodology involves the experimental investigation of the profile grain evolution during the extrusion process, the development of a AA6XXX recrystallization model optimized to describe the AA6063 recrystallization behaviour and the simulation by means of finite element method of the final microstructure of the extruded profile.

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.987.3.pdf

Aluminum alloy extruded profiles have gained widespread prominence in contemporary engineering applications due to their combination of lightweight and mechanical strength . The microstructure of these profiles, defined as the arrangement of crystals, grains, and phases at the microscopic level, plays a lead role in determining their mechanical properties, formability, corrosion resistance, and overall quality [5-7]. As the demand for high-performance materials continues to grow, understanding and optimizing the microstructure of aluminum alloys becomes mandatory to meet stringent engineering requirements.

In recent years, analytical recrystallization models have been developed as valuable tools for predicting dynamic and static recrystallization kinetics and, consequently, for estimating grain size evolution during and after the extrusion process. This study explores the feasibility of coupling recrystallization model with finite element simulations of the extrusion process. The primary focus is on characterizing the microstructural evolution of a AA6XXX aluminum alloys during extrusion, specifically in the case of a AA6063 hollow profile. A systematic methodology is proposed, which involves the collection of experimental data of grain size of a AA6063 extruded profile. This data is used to determine the material constants of the selected AA6XXX recrystallization model [8] and to optimize it for the specific alloy under investigation. Finally, this optimized AA6063 recrystallization model is implemented into the Qform Extrusion finite element code to simulate the final microstructure of the extruded profile.

Methodology

The original version of this paper is available on https://www.scientific.net/KEM.987.3.pdf

An optimized recrystallization model for the 6XXX aluminum alloy under consideration will be developed. The base recrystallization model for AA6XXX aluminum alloys, which enabled the prediction of statically and dynamically recrystallized grain size and recrystallization thickness
during hot forming procedures, was built on the work of Negozio et al. [8]. The relevant material constants are computed by compiling a group of numerical and experimental data gathered through relevant simulations and experiments. In detail, profiles are extruded using a range of process variables (such as varying ram speeds, billet pre-heating temperatures, etc.), and all the microstructural data is collected. In addition, the temperature, strain rate, and strain data must be extracted from the FEM simulations of the extrusion campaign. These data are merged with experimental one in order to calibrate the recrystallization model using a regression method. To forecast the microstructure following hot forming procedures, the investigated alloy recrystallization model is implemented in a post-processing subroutine to FEM simulations using Qform Extrusion software.

Experimental Investigation

The original version of this paper is available on https://www.scientific.net/KEM.987.3.pdf

The grain size data used for the calibration of the AA6063 recrystallization model was taken from the extrusion of a hollow profile analysed in Gamberoni et al. [9]. In Fig. 1, the microstructure of the investigated profile is shown. In order to acquire the image, the sample was cut in 6 parts: each one was grinded, polished, anodized ( ) with Barker's reagent ( 15 mL HBF 4 , 750 mL H 2 O ) and then analysed using the optical microscopy Zeiss AXIO with polarized light. The profile was extruded using a 30 MN press at Sapa plant in Ornago (MI) and the sample was taken from the fifth billet of the extrusion cycle at the middle length of the extrusion profile.

The figure clearly depicts a fully recrystallized microstructure, with no evidence of fibrous grains and an average grain size ranging from to . A total of 100 points were randomly selected on the cross-section of the profile and analysed according to the ASTM E112 regulation. Half of these measurements were utilized to optimize the recrystallization model and determine the material constants for AA6063. This calibration process employed an optimization algorithm implemented in Matlab, as referenced in [8]. Subsequently, with the constants determined, the model was integrated into the post-processing subroutine of the FEM simulation. The remaining half of the measurements were then used to validate the model accuracy by comparing numerical predictions with the experimental grain size values.

All the process parameters and the geometries of the billet and tools are reported in Table 1.

Fig. 1. Microstructure of the extruded AA6063 profile.

Fig. 1. Microstructure of the extruded AA6063 profile.

Table 1. Process parameters and workpiece/tools geometries.

Process parameters and geometry tolerances | Profile
Aluminum alloy | AA6063
Extrusion ratio | 9.6
Ram speed [mm/s] | 8.5
Container temperature [°C] | 420
Billet temperature [°C] | 470
Die temperature [°C] | 450
Ram acceleration time [s] | 5
Billet length [mm] | 815
Ram stroke [mm] | 765
Billet diameter [mm] | 247
Container diameter [mm] | 257
Billet Rest length [mm] | 55
Billet grain size [μm] | 135
Puller tensile force [N] | 200

Hot torsion tests were performed in order to characterize the flow stress of the AA6063 billet material used for the extrusion of the profile under investigation [9]. A total of 12 conditions were tested, with temperatures of and strain rates of . The data acquired were used to calibrate the Hensel-Spittel law [5] applied in the simulation of the extrusion process (Eq. 1).

Table 2. Hensel-Spittel coefficients for the AA6063 aluminum alloy [9].

Parameters | AA6063
A | 1014.7 [MPa]
m1 | -0.00438 [K-1]
m2 | 0.2425
m3 | -0.0965
m4 | -0.000438
m5 | -0.000766 [K-1]
m7 | 0.0939
m8 | 0.000291 [K-1]
m9 | 0

Modeling and Simulation

The original version of this paper is available on https://www.scientific.net/KEM.987.3.pdf

The recrystallization model used in the simulation was taken by the work of Negozio et al. [8]. The average grain diameter of the static recrystallized grain was calculated as follows:

where is the nucleation density and is the fraction of recrystallized material. Since the obtained images demonstrate a totally recrystallized microstructure, the value was taken to be equal to 1 . In order to determine , different nucleation contributions must be considered [8]:

The is the nucleation from deformation zones around large particles, which involves the development of nuclei in high deformation zones with random orientations, is frequently the primary nucleation process in commercial alloys that contain large undeformable particles. Old grain boundaries, or , are the nucleation site for a random recrystallization texture. Around these deformation zones, randomly oriented subgrains can emerge. is the nucleation from retained cube grains from the original material that endured the deformation [8].

These three nucleation contributions depend on four different material parameters: , . In order optimize the 6XXX recrystallization model to describe the behaviour of the investigated alloy, these constants are calculated by comparing the results of the experimental grain size analysis and the output of the numerical simulation.

The simulation was performed using Qform Extrusion FEM code, which is an Arbitrarian Lagrangian Eulerian software specifically optimized for the extrusion process. Optimized friction conditions between workpiece and tools are set within the code, and the parameters of the simulated materials can also be retrieved from a database (Table 3).

Table 3. Friction conditions [5].

Surface | Friction condition
Billet-Container | Sticking condition
Billet-Ram | Sticking condition
Billet-Die | Sticking condition
Bearings | Levanov model (m = 0.3, n = 1.25)

Table 4. AA6063 material parameters [5].

Material Properties | AA6063
Density [Kg/m3] | 2690
Specific heat [J/kg K] | 900
Thermal conductivity [W/m K] | 200
Thermal expansivity [m/K] | 2.34*10-5
Young's modulus [GPa] | 68.9
Poisson's ratio | 0.33

Fig. 2. Numerical simulation of a) temperature, b) extrusion load, c) maximum value of strain rate reached during the deformation, d) strain.

Fig. 2. Numerical simulation of a) temperature, b) extrusion load, c) maximum value of strain rate reached during the deformation, d) strain.

The accuracy of the simulation was verified by comparing the extrusion peak load and exit temperature acquired from the experiments and predicted with the finite element simulation. After the validation stage of the extrusion simulation, the values of strain, strain rate and temperature, taken in the 100 points selected for the grain size measurements (see "Experimental Investigation" chapter), were stored and 50 of these were used to calibrate the recrystallization model, therefore calculating the AA6063 model material constants. After the model completed and calibrated for the investigated alloy, it was implemented using a post-processing subroutine in Qform Extrusion. This integration allowed for efficient and accurate simulations of the microstructural evolution during the extrusion process, taking into account the material behavior at different stages of deformation and temperature conditions. Consequently, the FEM code performed a post-processing analysis to predict the grain size after the extrusion process, providing insights into the material microstructure evolution during the manufacturing process (Fig. 3).

Fig. 3. Schematization of the methodology for the calibration of a AA6XXX recrystallization model for the prediction of the microstructure in the extrusion of a AA6063 aluminum alloy.

Fig. 3. Schematization of the methodology for the calibration of a AA6XXX recrystallization model for the prediction of the microstructure in the extrusion of a AA6063 aluminum alloy.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/KEM.987.3.pdf

The recrystallization model's material constants were determined through the utilization of the Levenberg-Marquardt non-linear regression algorithm [5], implemented in Matlab . To obtain these constants, the calibration set of points was utilized, where experimental grain size measurements were available. For each specific point in the calibration set, the Qform software was used for simulation, enabling the calculation of temperature, strain, and maximum strain rate values. Furthermore, a separate set of points known as the validation set was considered to assess and validate the accuracy of the numerical microstructure prediction results obtained through the calibration process. This approach ensured the robustness and reliability of the recrystallization model constants and the subsequent microstructure predictions. The calculated material constants are .

In Fig. 4, the numerical simulation results depicting the grain size distribution after the complete static recrystallization (SRX) process are visually presented. The profile is color-coded, with red and blue regions denoting areas with relatively larger and smaller grain dimensions, respectively. The data revealed a range of grain size dimensions spanning from to .

Fig. 5 presents a comparative analysis between the experimentally measured grain sizes and their corresponding numerical predictions for the selected validation set of points. The x-axis represents the experimentally determined grain diameters, while the y -axis represents the dimensions predicted through numerical simulations. The alignment between the experimental and numerical data points along the red line would indicate a high level of agreement between the two datasets, affirming the accuracy and reliability of the numerical model in predicting grain size evolution during the SRX process. To enhance the comprehension of prediction accuracy, two additional green lines were included, representing a error range. Given the substantial influence of numerous process and metallurgical factors on the final grain size, as well as the complexity of the extruded geometries and the limitations associated with the chosen measurement methodology [10] for experimental grain dimension analysis, the error range, which was similarly adopted by Donati L. et al. in [10], is regarded as indicative of good prediction accuracy.

Fig. 4. Numerical prediction of the grain size after the SRX.

Fig. 4. Numerical prediction of the grain size after the SRX.

Fig. 5. Comparison between experimental and numerical grain size after SRX.

Fig. 5. Comparison between experimental and numerical grain size after SRX.

Conclusions

The original version of this paper is available on https://www.scientific.net/KEM.987.3.pdf

In this study, the 6XXX recrystallization model proposed by [8] was optimized for the AA6063 aluminum alloy, using the data of the finite element simulation performed with Qform Extrusion and the experimental grain size values collected from a AA6063 extruded hollow profile. Notably, the prediction error for the average grain size was found to remain below for over of the analyzed points. This good level of agreement between experimental and numerical results demonstrates the reliability of the proposed model. The promising experimental-numerical concurrence must be further investigated by comparing the predictions with the experimental results of additional experimental campaign in order to assess the applicability for practical extrusion scenarios.

References

The original version of this paper is available on https://www.scientific.net/KEM.987.3.pdf

L. Donati, B. Reggiani, R. Pelaccia, M. Negozio, S. Di Donato, Advancements in extrusion and drawing: a review of the contributes by the ESAFORM community, International Journal of Material Forming 15(6) (2022).

R. Pelaccia, B. Reggiani, M. Negozio, L. Donati, Liquid nitrogen in the industrial practice of hot aluminium extrusion: experimental and numerical investigation, Interantional Journal of Advanced Manufacturing Technology 119(4) (2022) 1-15.

R. Pelaccia, M. Negozio, L. Donati, B. Reggiani, L. Tomesani, Extrusion of Light and Ultralight Alloys with Liquid Nitrogen Conformal Cooled Dies: Process Analysis and Simulation, Journal of Materials Engineering and Performance 31(4) (2022) 1-11.

M. Negozio, R. Pelaccia, L. Donati, B. Reggiani, T. Pinter, L. Tomesani, Finite Element Model Prediction of Charge Weld Behaviour in AA6082 and AA6063 Extruded Profiles, Journal of Materials Engineering and Performance 30 (2021) 4691-4699.

M. Negozio, R. Pelaccia, L. Donati, B. Reggiani, Simulation of the microstructure evolution during the extrusion of two industrial-scale AA6063 profiles, Journal of Manufacturing Processes 99 (2023) 501-512.

I. Kniazkin, R. Pelaccia, M. Negozio, S. Di Donato, L. Donati, B. Reggiani, N. Biba, R. Rezvykh, I. Kulakov, Investigation of the Skin Contamination Predictability by means of QForm UK Extrusion code, Materials Research Proceedings 28 (2023) 543-552.

M. Negozio, R. Pelaccia, L. Donati, B. Reggiani, L. Tomesani, FEM validation of front-end and back-end defects evolution in a AA6063 and AA6082 aluminum alloys profiles, Procedia Manufacturing 47 (2020) 202-208.

M. Negozio, R. Pelaccia, L. Donati, B. Reggiani, S. Di Donato, Experimental Analysis and Modeling of the Recrystallization Behaviour of a AA6060 Extruded Profile, Materials Research Proceedings 28 (2023) 477-486.

A. Gamberoni, L. Donati, B. Reggiani, M. Haase, L. Tomesani, A. Tekkaya, Industrial Benchmark 2015: Process Monitoring and Analysis of Hollow EN AW-6063 Extruded Profile, Materials Today: Proceedings 2 (10) (2015) 4714-4725.

L. Donati, A. Segatori, M. El Mehtedi, L. Tomesani, Grain evolution analysis and experimental validation in the extrusion of 6XXX alloys by use of a lagrangian FE code, International Journal of Plasticity 46 (2013) 70-81.

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The original version of this eBook is available on https://www.scientific.net/book/12th-aluminium-two-thousand-world-congress-7th-international-conference-on-extrusion-and-benchmark/978-3-0364-1380-8

Key Engineering Materials, ISSN: 1662-9795, Vol. 988, pp 11-20

doi: 10.4028/p-sEDG1G

© 2024 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2023-11-21

Revised:
2024-03-11

Accepted:
2024-03-12

Online:
2024-09-27

Simulation of Quench Rate by Jominy End Quench Test for 6082 Aluminium Alloys

The original version of this paper is available on https://www.scientific.net/KEM.988.11.pdf

Aleyna Gümüşsoy , Onur Gülbaba , Işık Kaya , Emrah Fahri Özdoğru

TRI Metalurji A.Ş., Istanbul, Turkey

aleyna.gumussoy@trimetalurji.com, onur.gulbaba@trimetalurji.com, isik.kaya@trimetalurji.com,

Keywords: EN AW 6082, Jominy End Quench Test, Quench Rate, Quench Sensitivity, Hardness.

Abstract

In the present study, it is aimed to investigate the influence of quench rate on the hardness and electrical conductivity that obtained after artificial aging by using Jominy End Quench test method. The Jominy End Quench test bars were solution heat treated at for 3 hours. After solution heat treatment, water, spray, and air quenching media were used in order to obtain different quench rates. After the quench, quench rate determination, hardness and electrical conductivity measurements were performed for three different quenching medias. Then, artificial aging heat treatment were applied to all samples at for 8 hours to understand the effect of quench rate on aging process. The relationship between quench rate, hardness and electrical conductivity have presented.

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.988.11.pdf

The 6XXX series aluminium alloys are an important subgroup of wrought aluminium alloys and have significant potential in many industrial applications. The most important feature of these alloys is their high strength and good workability properties obtained by the addition of elements such as magnesium and silicon [1]. The mechanical properties of this alloy series can be improved by heat treatment processes known as precipitation hardening. The EN AW 6082 aluminium alloys are particularly popular in structural applications, they are used in the crash boxes for automotive industry and battery trays for EV industry [2].

The heat treatment stages of 6XXX series aluminium alloys basically consist of three stages, solution heat treatment, quenching, and aging. The transformation during these processes is considered as follows, which is generally accepted in the literatures, is: SSSS → atomic clusters → GP zones (stable) where SSSS is the super saturated solid solution [3]. Quenching is an important stage in order to achieve the desired mechanical properties after aging. Therefore, it is crucial to determine the appropriate cooling conditions of the alloys in the heat treatment processes of the products in industrial applications.

The Jominy End Quench test is designed to measure the quench sensitivity of an alloy. The test was originally designed to measure the hardenability of steel; on the other hand, it is showed by other researchers that it could be an effect tool for other types of alloys including aluminium alloys. The test accomplishes this task by machining a Jominy bar out of the desired material and quenching the bar from one end giving a range of different cooling rates down the length of the bar. The hardness is then taken along the length of the bar in the axial direction and each measurement is corresponded to a cooling rate from each different section of the bar [4].

In this study, it is aimed to investigate the relationship between quench rate, hardness, and electrical conductivity test results of commercial EN AW 6082 alloy by using the Jominy End Quench test method. The Jominy End Quench test offers a method for studying different quenching conditions with EN AW 6082 samples. The present work identified the effect of quench rate on the hardness and electrical conductivity of EN AW 6082 alloys in water, spray and air quenching medias. The potential for developing a new understanding of aluminium alloys for processing conditions, especially cooling rate, and artificial aging, have presented.

Materials and Methods

The original version of this paper is available on https://www.scientific.net/KEM.988.11.pdf

Melting and alloying experiments were performed in electric resistance furnace at , after that the melt was subsequently poured into a steel mould to form Jominy test bar. Chemical composition of the studied EN AW 6082 alloy was measured by HITACHI Foundry-Master Smart optical emission spectrometer and given in Table 1.

Table 1. Chemical composition of the studied EN AW 6082 alloy (wt%)

Elements | Si | Fe | Cu | Mn | Mg | Ti
EN 573-3 | 0,7 – 1,3 | 0,50 | 0,10 | 0,40 – 1,0 | 0,6 – 1,2 | 0,10
Studied Alloy | 0,95 - 1,0 | 0,20 - 0,25 | 0,05-0,10 | 0,50 – 0,55 | 0,60 - 0,65 | 0,015 - 0,02

The Jominy End Quench specimens were prepared in accordance with ASTM A 255. Fig. 1 shows the Jominy end quench test specimen. 5 mm diameter holes were drilled at 10 and 90 mm for K type thermocouples for the temperature record during quenching. The specimens were placed in furnace and solution heat treated for 3 hours at .

Fig. 1 Jominy End Quench test specimen

Fig. 1. Jominy End Quench test specimen

The specimen was then removed from the furnace and placed into the Jominy End Quench test rig. Typical transfer time between the furnace door opening and the start of the quench was approximately 10 seconds. Water, spray and air quenching medias were used in order to obtain different quench rates. The nozzle used for quenching is conical shape and spray cone angle is . Tap water is used for water and spray quenching experiments. The spray water pressure initially fixed at 1,5 bar. The specimens remained in the Jominy quench rig for all three water, spray and air quenching experiments for 10 minutes to allow sufficient time to cool to the room temperature. Experimental flow sheet is given in Fig. 2.

Fig. 2 Experimental flow sheet

Fig. 2. Experimental flow sheet

After the specimens has been fully quenched, parallel flats were cut equally on the specimen and hardness and electrical conductivity measurements were performed at the center of the bars. Brinell hardness measurements were applied with DURASCAN 50G5 EMCOTEST and electrical conductivity measurements were performed with Fischer SIGMASCOPE SMP350 device with Fischer FS40 probe for 6 regions as described in Fig. 1.

After the Jominy end quench test, the samples taken from the and regions ( region: fast cooling area, region: slow cooling area) were grinded, polished, and etched with HF ( ) for metallographic investigation. Microstructural characterization was performed using NIKON LV150N Optical Microscope. Samples were aged at for 8 hours. After aging heat treatment, hardness and electrical conductivity measurements and microstructure investigations were performed for and regions samples.

Results and Discussion

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Cooling Curves

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The cooling rates are important to calculate because there is a direct correlation between cooling rate and the mechanical properties after the T6 heat treatment. After the heating and cooling data from fast and slow cooling area were collected, the filtered data was plotted as is to generate a timetemperature curve or cooling curve. The important transformations in these alloys take place while they are being cooled down from to through quenching [5]. The average cooling rates were calculated within this temperature range for water quenching (WQ), spray quenching (SQ) and air quenching (AQ) experiments. The cooling curves that obtained from water, spray and air quenched samples were given in Fig. 3.
Water Quenching

Fig. 3 Cooling curves (a) WQ, (b) SQ, (c) AQ

Fig. 3. Cooling curves (a) WQ, (b) SQ, (c) AQ

For the fast cooling area, the data shown in Fig. 3 shows extremely fast cooling of the sample for the WQ case (cooling rate of ) in contrast the SQ sample had an average cooling rate of . The AQ sample was cooled slowest at an average cooling rate of . Using these graphs, the cooling rate at any position along the Jominy bar can be obtained and a measured hardness can be correlated to a cooling rate. As shown in Fig. 3, the cooling rates are highest close to the quenched end and rapidly decrease along the length of the bar.

Hardness and Electrical Conductivity Measurements

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It is well known that hardness and electrical conductivity can be correlated with changing cooling rates. As mentioned in the previous sections, quenching with water was designed to provide the fastest cooling rate. The second quenching procedure was to cool sample with spray water and the third procedure was air quenching. Fig. 4 shows the results of the hardness and electrical conductivity plotted against the three different average cooling rates.

Fig. 4 Hardness and electrical conductivity test results after Jominy End Quench Test (a) WQ, (b) SQ, (c) AQ

Fig. 4. Hardness and electrical conductivity test results after Jominy End Quench Test (a) WQ, (b) SQ, (c) AQ

From Fig. 4, for all three conditions, it can be clearly seen that that the hardness decreased with decreasing cooling rates while the electrical conductivity increases. This is probably because of the precipitation of non hardening precipitates during slow cooling. Birol (2016) explained that the fractions precipitated during cooling are estimated from electrical conductivity measurements [6]. This is evidenced by the electrical conductivity values that increase with decreasing cooling rates.

The cooling rate can play a major role in the overall mechanical properties of the alloy [7]. To see the effect of quench rate on mechanical properties after aging, the samples were artificially aged at for 8 hours. The results are given in Fig. 5.

Fig. 5 Hardness and electrical conductivity test results after aging at for 8 hours (a) WQ, (b) SQ, (c) AQ

Fig. 5. Hardness and electrical conductivity test results after aging at for 8 hours (a) WQ, (b) SQ, (c) AQ

After the T6 heat treatment, WQ-fast cooled sample reached the highest hardness value as 120 HBW due to the highest cooling rate (Fig. 5a) while the AQ-slow cooled sample reached the lowest hardness value as 93 HBW (Fig. 5c). This is because when the cooling rate is too low, due to the growth of the coarse phase, the degree of supersaturation of solid solution is reduced and resulting in the reduction of the mechanical properties after aging [7]. Comparative hardness and electrical conductivity results of as-quenched and aged samples are given in Fig. 6.

Fig. 6 Hardness and electrical conductivity results of all experiments (a) hardness (b) electrical conductivity

Fig. 6. Hardness and electrical conductivity results of all experiments (a) hardness (b) electrical conductivity

From Fig. 6a, it can be observed that the alloy appear to be very quench sensitive between Region 1 and Region 6 due to the decreasing cooling rate for WQ and SQ experiments. On the other hand, in the AQ experiments, the cooling rate variation between Region 1 and Region 6 is quite low as to . For this reason, no significant difference was observed in the hardness values of the AQ
sample. From Fig. 6b, as expected, AQ samples reached the highest electrical conductivity results due to the slow cooling rates.

Microstructural Analysis

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After aging at for 8 hours, samples of interest were prepared using the metallographic sample preparation procedure. Six of the water, spray and air quenched samples which are represents the fast and slow cooling area were prepared for the metallographic examination. Fig. 7 shows the microstructures of fast and slow cooling areas after aging.

Fig. 7 Microstructures of the samples after aging (a) WQ-fast cooling area, (b) WQ-slow cooling area, (c) SQ-fast cooling area, (d) SQ-slow cooling area, (e) AQ-fast cooling area, (f) AQ-slow cooling area

Fig. 7. Microstructures of the samples after aging (a) WQ-fast cooling area, (b) WQ-slow cooling area, (c) SQ-fast cooling area, (d) SQ-slow cooling area, (e) AQ-fast cooling area, (f) AQ-slow cooling area

Fig. 8 SEM images of the samples after aging (a) WQ-fast cooling area ( ), (b) AQ-slow cooling area ( )

Fig. 8. SEM images of the samples after aging (a) WQ-fast cooling area ( ), (b) AQ-slow cooling area ( )

Depending on optical microscopy and SEM examinations, it can be observed that as the cooling rate decreases, the number of coarse precipitates increase (Fig. 7 and Fig. 8). As discussed in the previous section, when the cooling rate is too low the hardness decreases. The main reason is slow cooling tends to produce coarse phases (Fig. 8b).

And with decreasing cooling rate, precipitate free zones are more intensely. Slow cooling results in a higher number of grain boundary precipitates and a wider precipitate free zone (PFZ) [9]. In Figure 7b, for the AQ-slow cooled sample, precipitate free zones are more intense and larger in comparison with WQ-fast cooled sample (Fig. 7a).

Conclusion

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On the basis of investigation results presented above, the following conclusions can be drawn:
The cooling rate decreases with distance from the quenching end. Water quenching provides the highest quench rate.

With decreasing cooling rate, the hardness values of the alloy decrease while the electrical conductivity increases. After T6 heat treatment, WQ-fast cooled sample reached the highest hardness value due to the highest cooling rate ( 120 HBW ).

The reduction in the mechanical properties was attributed to the loss of solute atoms which precipitates as coarse precipitates during slow cooling. This is because at slower quench rates, the dispersoid phases are able to act as nucleation sites for coarse precipitates.

With decreasing cooling rates, precipitate free zones (PFZs) can be observed more intensely.

References

The original version of this paper is available on https://www.scientific.net/KEM.988.11.pdf

W.S. Miller, L Zhuang, J Bottema, A.J Wittebrood, P De Smet, A Haszler, A Vieregge, Recent development in aluminium alloys for the automotive industry, Materials Science and Engineering: A, 280(1) (2000), 37-49.

A. Güzel, A. Jäger, N. Ben Khalifa, A. E. Tekkaya, Simulation of the Quench Sensitivity of the Aluminum Alloy 6082, 424 (2010), 37-49

C. D. Marioara, S. J. Andersen, J. Jansen, H. W. Zandbergen, ). Atomic model for GP-zones in a system, Acta Materialia, 49(2) (2001), 321-328.

G.P. Dolan, R.J. Flynn, D.A. Tanner, J.S. Robinson, Quench factor analysis of aluminium alloys using the Jominy end quench technique. Materials Science and Technology, 21(6) (2005), 687-692.

A. Assaad, Quench Sensitivity of 6xxx Aluminum Alloys, Master's Thesis, University of Waterloo (2016).

Y. Birol, Effect of cooling rate on precipitation during homogenization cooling in an excess silicon AlMgSi alloy. Materials characterization, 73 (2012), 37-42.

H.Y. Li, C.T. Zeng, M.S. Han, J.J. Liu, X.C. Lu, Time-temperature-property curves for quench sensitivity of 6063 aluminum alloy. Transactions of Nonferrous Metals Society of China, 23(1) (2013), 38-45.

T. Saito, C.D. Marioara, J. Royset, K. Marthinsen, R. Holmestad, The effects of quench rate and pre-deformation on precipitation hardening in alloys with different Cu amounts. Materials Science and Engineering: A, 609 (2014), 72-79.

This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/12th-aluminium-two-thousand-world-congress-7th-international-conference-on-extrusion-and-benchmark/978-3-0364-1380-8

Key Engineering Materials, ISSN: 1662-9795, Vol. 987, pp 11-22

doi: 10.4028/p-Q0NDkB

© 2024 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2023-11-30

Revised:
2024-03-05

Accepted:
2024-03-20

Online:
2024-09-23

Recent Trends in Nitrogen Cooling Modelling of Extrusion Dies

The original version of this paper is available on https://www.scientific.net/KEM.987.11.pdf

Riccardo Pelaccia , Marco Negozio , Sara Di Donato , Lorenzo Donati , Barbara Reggiani

¹DISMI Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Via Amendola 2, 42122, Reggio Emilia, Italy

DIN Department of Industrial Engineering, University of Bologna, Viale Risorgimento 2, 40136, Bologna, Italy

INTERMECH, University of Modena and Reggio Emilia, Italy

a*riccardo.pelaccia@unimore.it, marco.negozio2@unibo.it, sara.didonato2@unibo.it, I.donati@unibo.it, ebarbara.reggiani@unimore.it

Keywords: Liquid Nitrogen, Cooling Channels; Extrusion Dies, Topological Optimization.

Abstract

Nitrogen cooling has been identified as a powerful industrial solution for the hot extrusion process to remove heat in the die and in the profile. The complexity involved in the design of cooling channels depends on many factors, including the cooling path, its position with respect to the hottest zones as well as the nitrogen phase-change that strongly affects the heat removal capacity. However, the industrial approach is still stuck in the empirical and based-experience practices that too often strongly limit the possibilities of obtaining a performing cooling solution. In this context, this work intends to summarize and discuss the advanced recent trends in the design of cooling channels for extrusion dies proposed by the authors based on the numerical approaches, with the final aim to propose possible solutions to fill the current gaps of the suboptimal industrial approaches.

Introduction

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In the industrial framework of the hot extrusion process, nitrogen plants are increasingly present with the aim to improve the extrudate's quality and the production rate. Indeed, the proper use of liquid nitrogen allows to reduce both the die and the profile temperature [1-3], to increase the ram speed [4] and to improve of the surface quality of the final products acting against the hot oxidation [1,5]. In detail, the tooling set is provided with a cooling channel usually made on the backer surface in touch with the die, in an effort to surround the exit of the profile and to localize the cooling nearby the hottest bearings' regions. From the nitrogen plant, the liquid nitrogen is transported to the cooling system under pressure in the range of 3-6 bars at a temperature of , thus trying to have the nitrogen evaporation only at the outlets of the channels, where the gas inert atmosphere strongly reduces the profile oxidation [3,5]. On the contrary, the phase change within the cooling path can lead to plug the channel (volume expansion of 177 times), drastically reducing the nitrogen flow rate and consequently the efficiency of the heat exchange [6]. For these reasons, the design of cooling channel involves many aspects (path design, channel geometry, distance from bearings, nitrogen parameters...) forcing the use of valid and robust criteria for the detection of the best efficient solution. On the contrary, the industrial approach is still stuck in empirical and experience-based practices that do not guarantee the achievement of optimized and efficient channel designs. Indeed, the efficiency of the cooling system is frequently evaluated only by analyzing the surface quality of the extrudate, erroneously merely correlating the higher "brilliant effect" with the cooling efficiency. However, as previously mentioned, the saving of the profile oxidation depends on the inert atmosphere generated by the gas nitrogen that invests the exit profile, and it is not a proper indication of the decrease in temperature within the die and in the profile. As a further limitation, the experimental evaluation of the cooling efficiency within the production line can be performed only by means of a monitoring temperature system using several thermocouples within the tooling set, an approach not always feasible for saving time and cost. Therefore, in this context,
the main purpose of the work is to present the recent trends in the design of cooling channels for extrusion die, showing in detail the potentiality of approaches based on the numerical simulation. Indeed, the numerical prediction of the cooling efficiency, taking into account all thermo-fluid dynamics aspects of the extrusion process, allows to overcome the current limits of industrial design practices by following robust methodologies. In literature, several works concern the simulation of the extrusion process [3] with the aim to capture all the aspects involved in the process optimization: loads and temperatures prediction [7-8], scrap assessment [9-11], evolution of the microstructure [12-13], optimization of the die design [7, 14] ...

To the best of the authors' knowledge, the first attempts to simulate the efficiency of the cooling in the extrusion process were proposed in the work of Holker and Tekkaya [15], where the use of a conformal cooling channel manufactured with additive manufacturing technologies was deeply analyzed. Both numerical and experimental results showed the potentiality of the cooling to reduce the die and the profile temperatures. However, in these first studies, only compressed air was tested as coolant, furthermore, simulating the heat exchange in a simplified way by imposing a constant temperature on the channel walls. Inspired by Finite Element (FE) modeling of cooling channels for plastic injection molding [16], die casting applications [17], and cutting tools [18], an innovative approach was proposed in the works of Pelaccia et al. [4, 6], where the 1D modelling of nitrogen cooling was integrated into 3D model of extrusion process. The reliability of the model was demonstrated by comparing the numerical results with different experimental case studies, showing average errors below in terms of temperature predictions. Starting from these works, Pelaccia and Santangelo [19] proposed the use of Homogenous Fluid Model (HFM) to capture the effect of the nitrogen phase change considering the liquid-gas mixture as a single fluid with pseudo properties. In addition, the potentiality of 1D approach, especially thanks to the low required computational time, was explored to implement an innovative generative digital twin based on genetic algorithms to design, in an automatic and iterative way, the best cooling channel in terms of drop of temperatures, balancing of the cooling around the bearings as well as saving of nitrogen consumption. Both size optimization and topological optimization approaches were proposed and discussed in the works of Pelaccia et al. [20-21].

From this basis, the aim of this work is to summarize and discuss the possible methodologies to model the extrusion process with nitrogen cooling proposed by the authors in the last years. The key achievements reached in the state of art were collected and summarized in the effort to fill the current gaps of the suboptimal industrial approaches.

Strategies for Nitrogen Die Cooling Modelling

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The simulation of nitrogen cooling during the extrusion process can be modelled following different strategies (for both steady state and transient analysis) (Fig.1):

by using an equivalent heat transfer coefficient (HTC) on the channel walls without considering the real nitrogen flow within the channel (1)

by modelling the nitrogen flow within the tooling set (1D or 3D modelling approach) and replacing the heat generated during the extrusion process with equivalent thermal boundary conditions (2)

by solving the thermo-fluid-dynamics problem that involves the nitrogen flow (1D 3) or 3D (4) modelling approach) coupled with the simulation of the extrusion process.

In more detail, the first approach (1) is the faster one because it is only necessary to add a thermal boundary condition without increasing the complexity of the model of the extrusion process. However, the experimental HTC is not constant along the cooling path and depends on the fluid-dynamic and the geometry of the cooling channel. Therefore, the use of this method is recommended only for fast preliminary analyses, but it cannot be used to predict the real cooling efficiency of the selected design, especially if the objective function is aimed at reducing the bearings' temperature uniformly.

Fig. 1. Possible strategies for modelling the nitrogen cooling.

Fig. 1. Possible strategies for modelling the nitrogen cooling.

The coupled modelling of the extrusion process with the nitrogen cooling (3) and (4)) is clearly the best approach to capture all the phenomena involved in the process and to accurately evaluate the efficiency of the selected channel design. In this case, the 3D model of the extrusion process can be combined with the 3D model of the channel or with the mono-dimensional (1D) one (Fig.2).

Fig. 2. Possible approaches to model the extrusion process coupled with nitrogen cooling (left 1D, right 3D model of the cooling channel).

Fig. 2. Possible approaches to model the extrusion process coupled with nitrogen cooling (left 1D, right 3D model of the cooling channel).

The 3D model of the channel allows for analyzing all the fluid-dynamic aspects of the nitrogen flow along the cooling path such as turbulences, pressure drops, presence of stagnation points, and so on. Nevertheless, the computational effort is very high even without considering the nitrogen phase change (even more days of calculation for complex case studies), making the 3D model of the extrusion combined with the 3D model of the channel not suitable for the standard industrial design time. On the other hand, the use of the 1D model of cooling channel drastically reduces the computational time, because the fluid-dynamic problem is reduced to the midline path of the channel, thus neglecting the turbulent effects and the velocity gradient along the 3D channel section. Despite this assumption, knowing the channel cross-section geometry - with potentially non-constant shape and dimensions through the cooling path - enables to calculate the variables involved (e.g., hydraulic diameter, Reynolds number, pressure drops...) with good accuracy [4, 6, 19-21].

The use of a 3D model of the cooling channel (approach (2)) can be taken into account when the extrusion process is replaced with thermal boundary conditions where the material flow is in contact with the tooling set (feeder, portholes, bearings...). In such a way, the study is isolated to the die-set with a significant computational time saving. The accuracy of this approach depends on the applied thermal boundary conditions that should be obtained by a preliminary simulation of the extrusion process without cooling. Indeed, the cooling path must be designed to further reduce the temperature in the hottest surfaces and, to a lesser extent in the other areas, so that the thermal gradient on the die walls caused by the material flow must be well modelled.

This approach of studying only the heat transfer within the die can be also used with the 1D approach with the aim to bring the computational needed time to a few minutes or less. It becomes then reasonable that the integration of the model within optimization and generative tools able to test automatically and iteratively many different designs, pursuing specified constraints and objective functions. Indeed, the 1D approach permits to easily change the cooling path and/or the channel section geometry without re-design (at each iteration) the die with the new 3D cooling channels.

In summary, the simulation of the nitrogen cooling system using the 1D model guarantees good results despite the simplifications, gaining on the other hand a drastic reduction of the computational time and the possibility to fast re-design the cooling channel. However, the 3D model of cooling channel can be simulated with acceptable computational time if the extrusion process is replaced with thermal boundary conditions, making this approach competitive to analyze accurately all fluid-dynamics phenomena involved during the nitrogen cooling. For example, the 1D model can be used to fast select some good designs that may be tested with the 3D approach for a robust and reliable final check.

1D vs 3D Approach: an Industrial Case Study

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A selected experimental case study was deeply analyzed in the work of Donati et al. [2] where an AA6060 industrial hollow profile (Fig. 3) was extruded in different conditions, varying the nitrogen flow rate ( ).

Fig. 3. Experimental case study analysed in the work of Donati et al. [2]: Rendering of the tools set up while forming the profile (left), Thermal history of the extrusion process (right).

Fig. 3. Experimental case study analysed in the work of Donati et al. [2]: Rendering of the tools set up while forming the profile (left), Thermal history of the extrusion process (right).

The cooling channel was manufactured in the backer surface in contact with the die, while eleven outlets were made in the die to convey the nitrogen at the exit from the bearings: the 3D CAD and the dimensions of the channel are reported in Fig. 4. Remarkable results are the maximum decrease in temperature of in the bearings (acquired by the thermocouple T5), of in the profile, and no significant temperature changes in the mandrel (thermocouples T2 and T4), where the cooling could backfire limiting the aluminum deformability. However, the cooling around the bearings was not homogenous since a maximum difference of was obtained between T5 and T 1 .

Fig. 4. The cooling channel designed by the die maker [2]: 3D rendering of the cooling path (left), Channel dimensions (right).

Fig. 4. The cooling channel designed by the die maker [2]: 3D rendering of the cooling path (left), Channel dimensions (right).

The case study was simulated testing both the 1D and 3D model of the cooling channel: the 3D model of the extrusion process was coupled with the 1D model of cooling channel in the first case (approach (3)), while the 3D model of the die-set was coupled with the 3D model of the cooling channel in the second case (approach (2)), replacing the aluminum flow with equivalent thermal boundary conditions. The latter was taken by the steady-state 3D simulation of the extrusion process without nitrogen cooling (billet 6 in Fig. 3).

The 1D nitrogen flow modeling was reported in detail in some works of Pelaccia et al. [4,6], while for the 3D approach, the Spalart-Allmaras turbulence model [22] was tested. The Multifrontal Massively Parallel Solver (MUMPS) was used for both cases. In Tab. 1 are reported all the process parameters set for the steady state simulations of the billet, experimental extruded with a of nitrogen valve opening (Fig. 3).

Table 1. Process Parameters for the steady simulations: 1D model vs 3D model (Billet 8).

Process Parameters
1D model
3D model

Initial Billet Temperature
460°C
Not simulated

Initial Die Temperature
500 °C
500 °C

Container Boundary Condition
Convection 427°C 11000 W m⁻² K⁻¹
Not simulated

Ram Boundary Condition
Convection 413°C 11000 W m⁻² K⁻¹
Not simulated

Ram Speed
2.71 mm s⁻¹
Not simulated

Ports/aluminium interface
Real conduction with aluminium flow
Convection 510°C 11000 W m⁻² K⁻¹

Welding chamber/aluminium interface
Real conduction with aluminium flow
Convection 540°C 11000 W m⁻² K⁻¹

Bearings/billet interface
Real conduction with aluminium flow
Convection 565°C 25000 W m⁻² K⁻¹

Inlet Nitrogen Pressure or Inlet nitrogen flow rate
2 bar (100%)
2 bar (100%)

Inlet Nitrogen Temperature
-196 °C
-196 °C

Liquid Nitrogen Density
(-196 °C, room pressure)
806.59 kg m⁻³
806.59 kg m⁻³

Liquid Dynamic Viscosity
(-196 °C, room pressure)
1.6137E-4 Pa s
1.6137E-4 Pa s

Liquid Nitrogen heat capacity at constant pressure
(-196 °C, room pressure)
2.041 J g⁻¹ K⁻¹
2.041 J g⁻¹ K⁻¹

Liquid Nitrogen thermal conductivity
(-196 °C, room pressure)
0.1465 W m⁻¹ K⁻¹
0.1465 W m⁻¹ K⁻¹

Figure 5 shows the thermal field around the bearings obtained with both numerical approaches: either way, the effect of nitrogen cooling was well captured as well as the inhomogeneity of the temperatures around the bearings caused by the selected channel design.

Fig. 5. The thermal fields obtained around the bearing zones: 1D (left) vs 3D (right) approach.

Fig. 5. The thermal fields obtained around the bearing zones: 1D (left) vs 3D (right) approach.

Table 2 reports the comparisons between the experimental and the numerical results: in detail, the simulation performed using the 3D channel showed a lower difference between T3 and T5 with respect to the experimental data, while the mono-dimensional approach ensured more reliable results. However, both approaches achieved good results with numerical errors below in terms of temperature prediction.

The fully 3D approach gained little less accurate results because a constant thermal boundary condition was adopted both on the ports, on the welding chamber, and on the bearing surface, while the 1D approach allowed simulating the 3D aluminum flow coupled with the nitrogen cooling, offering a more detailed modeling of the process [3]. For example, around the bearings, a difference in temperature of about was observed between T3 (the hottest) and T5 in uncooled conditions; the difference increased to when the nitrogen valve was fully opened (Fig. 3). Therefore, the thermal field generated by the aluminum flow around the bearings was not homogeneous and, in this case, the approximation with constant boundary conditions resulted in higher numerical errors, overestimating the temperatures around T5 and consequently underestimating the cooling efficiency in this region.

Table 2. Thermocouple temperatures in the die: Experimental vs Numerical results (Billet 8).

| Thermocouples Temperature [°C] | Computational time
| T1 | T3 | T5 |
Exp. | 490 | 460 | 430 |
1D Num. | 509 | 450 | 430 | 15 min
Err% | 3.9 | -2.2 | 0 |
3D Num. | 498 | 447 | 453 | 4 h
Err% | 1.6 | -2.8 | -5.3 |

Even if the numerical results obtained with the two presented approaches are comparable, the main difference emerged in terms of required computational time, which evidences the potentiality of the mono-dimensional model: 15 minutes using the 1D model coupled with the 3D model of the extrusion process against about 4 hours using the fully 3D model without the simulation of the aluminum flow (Intel® core processor i7-7700HQ and Ram 16 GB ). The computational time depends on many factors (mesh, solver, computational power of the computer…); however, it is clear how the 1D approach allows to drastically reduce the computational effort without losing in accuracy.

Automatic Design of Cooling Channel Using the 1D Approach: Size and Topological Optimization

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The decrease of the computational time ensured by the mono-dimensional approach can be exploited within the framework of the multi-objective optimization, where the best channel design is automatically searched and selected. Basically, these methods use algorithms (adaptive, evolutionary, heuristic...) to find the solution that better fits with the objective functions and the constraints, iteratively changing the input variables, that, in this context, are correlated to the geometry of the cooling channel. Therefore, it becomes clear how the low computational effort is fundamental for testing a large number of designs in a reasonable time.

In this paragraph, the most remarkable results of the works of Pelaccia et al. [20-21] are summarized, showing two different approaches for the optimization of the cooling channel design for the extrusion process.

The first work [20] involved the size optimization of the cooling channel (designed by the die maker without the use of numerical tools) tested in the work of Donati et al. [2], where, as previously shown, the nitrogen cooling was effective but not homogeneous around the bearings. The size optimization consists of changing the cross-channel section along the cooling path without modifying the latter. In this case, the 1D cooling path was divided into ten parts (Fig. 6), for each of which the hydraulic diameter was set as the input variable of the optimization. In addition, the nitrogen inlet pressure was selected as a further input variable in order to control the nitrogen flow rate with both the channel design and the valve opening.

The objective functions were the minimization of the temperature difference between the three thermocouples around the bearings (imposing an absolute target value of ), and the minimization of the nitrogen consumption. The algorithm used to solve the optimization problem was the Non-dominated Sorting Genetic Algorithm (NSGA-II). A total of 100 designs ( initial population*number of generations) were evaluated, starting from an initial population of 10 configurations generated using a Pseudo-Random Sobol DOE strategy: the FE model was launched by the optimizer, the results stored in a table, and, at each subsequent run, the input variables were changed by the algorithm with the aim to satisfy the objective functions. The FE model was simplified to reduce the computational time: the aluminum flow was replaced by the equivalent thermal boundary condition, thus reducing the simulation time from 15 minutes (see Tab. 2) to 5 minutes (Intel® core processor i and Ram 16 GB ). Therefore, a total computational time of about 9 hours was spent to evaluate 100 designs, perfectly in line with the demands of today's industrial framework.

Figure 7 shows the optimal design proposed by the optimizer: the thermal field around the bearings is more homogenous, obtaining a temperature of and for , and T5 respectively, also reducing the nitrogen consumption of about .

INHOMOGENEOUS COOLING

Fig. 6. Selection of the input variable for the optimization problem: Channel geometry parametrization.

Fig. 7. The optimal channel design: Channel dimensions (left) and thermal field around the bearings (right).

Fig. 7. The optimal channel design: Channel dimensions (left) and thermal field around the bearings (right).

The second proposed approach is the topological optimization, in which different cooling paths were created and tested by the optimizer, thus trying to satisfy the objective functions and the constraints. The cooling channel tested in this work [21], always designed by the die maker without the use of numerical tools, presented great limits in terms of cooling efficiency (Fig. 8 [6]). Indeed, the drops in temperature were negligible when the nitrogen valve was opened at , and, also after nine billets extruded with the nitrogen valve fully opened, the cooling around the bearings was not effective.

Fig. 8. Experimental case study analysed in the work of Pelaccia et al. [6]: Thermal history of the extrusion process (top), rendering of the tools set up while forming the profile and numerical thermal field along the cooling path (bottom).

Fig. 8. Experimental case study analysed in the work of Pelaccia et al. [6]: Thermal history of the extrusion process (top), rendering of the tools set up while forming the profile and numerical thermal field along the cooling path (bottom).

In this case, the excessive pressure drops, caused by too many ramifications and outlets, resulted in a great gas formation within the cooling path that strongly limited the heat exchange. Therefore, a simple size optimization could be not enough to obtain a good channel design. The 2D model of the backer without the channels was selected for the topological optimization: a convective heat flux perpendicular to the backer plane was defined as a boundary condition to replicate, in a simplified way, the heat exchange between the backer and the hot die; the minimization of the temperature
differences between eleven control points (Fig. 9) was chosen as an objective function; one inlet, 10 outlets, and a maximum available volume ( ) to create the cooling path was defined as constraints to avoid excessive voids in the backer (not feasible from a technological point of view). The nitrogen flow was modelled using the Darcy flow model, thus considering the backer as a porous media with porosity and the other physical properties controlled with penalization functions. Following this approach, the backer was "virtually milled" to create the cooling path: the elements of the mesh with a porosity equal to zero were considered solid with the physical properties of the steel, while the elements with porosity equal to one were treated as liquid nitrogen [21].

Figure 9 shows the nitrogen velocity field resulting from the topological optimization and the final design of the cooling channel inspired by the obtained results. In detail, the topological optimization suggested splitting the channel into three different paths instead of just one with a lot of ramifications as in the original design. The improved cooling efficiency of the new cooling path is highlighted in Fig. 10, where the thermal field in the backer generated by the original design was compared with the new one.

Fig. 9. The topological optimization: from the problem setting (left) to the design of the new cooling channel (right).

Fig. 9. The topological optimization: from the problem setting (left) to the design of the new cooling channel (right).

Fig. 10. The thermal field in the backer: Original design (left) vs New optimized design (right).

Fig. 10. The thermal field in the backer: Original design (left) vs New optimized design (right).

Homogenous Fluid Model (HFM) as a Simplified Approach to Capture the Effect of the Phase Change within the Cooling Path

The original version of this paper is available on https://www.scientific.net/KEM.987.11.pdf

One of the main drawbacks of a poorly designed cooling channel can be the formation of a high quote of gaseous nitrogen along the cooling path that strongly reduces the heat exchange with the tooling set. In this context, the use of a model (1D or 3D) with a fully liquid nitrogen can lead to a great overestimation of the cooling effect, while the fully gaseous model can cause the opposite effect. Therefore, it is required a numerical model able to capture the gas formation with the final aim to better understand the phenomena that affect the cooling efficiency. However, the computational effort to simulate the nitrogen phase change during the heat exchange with the tooling set can be extremely high, also without the coupling with the extrusion process.

In the work of Pelaccia and Santangelo [19] the 1D homogenous fluid model (HFM) was proposed as a simplified approach to take into account the gas formation without the real simulation of the phase change. The HFM approach considers the liquid-gas mixture as a single fluid with pseudo properties. Therefore, the physical properties of the nitrogen (dynamic viscosity, density, heat transfer coefficient etc.) were replaced with expressions that depend on the nitrogen vapor title , not set constant but as a function of the heat exchange within the channel [19].

An example of application of the HFM approach can be presented analyzing the experimental case study discussed in the work about the topological optimization [21], where, as previously mentioned, the cooling efficiency was very low (Fig. 8). Two steady-state simulations of the last extrusion ( billet) were performed: the numerical prediction with the fully liquid model was compared with the one obtained with the HFM approach. Table 3 reports the process parameters set in both simulations.

Table 3. Process Parameters for the steady simulations: fully liquid model vs HFM model (Billet 17 in Fig. 8).

Process Parameters
Fully Liquid
HFM model

Billet Temperature
480 °C
480 °C

Die Temperature
520 °C
520 °C

Container boundary condition
Convection 430 °C
11000 W m⁻² K⁻¹
Convection 430 °C
11000 W m⁻² K⁻¹

Ram boundary condition
Convection 440 °C
11000 W m⁻² K⁻¹
Convection 440 °C
11000 W m⁻² K⁻¹

Backer surfaces in contact with press
Convection 280 °C
3000 W m⁻² K⁻¹
Convection 280 °C
3000 W m⁻² K⁻¹

Ram Speed
12 mm/s
12 mm/s

Inlet Nitrogen Pressure
4 bar (100%)
4 bar (100%)

Inlet Nitrogen Temperature
-196 °C
-196 °C

Nitrogen Density
806.59 kg m⁻³
ρ(ωg) [19]

Dynamic Viscosity
1.6137E-4 Pa s
μ (ωg) [19]

Nitrogen heat capacity at constant pressure
2.041 J g⁻¹ K⁻¹
cp (ωg) [19]

Nitrogen thermal conductivity
0.1465 W m⁻¹ K⁻¹
k(ωg) [19]

Fig. 11. The thermal field in the backer: fully liquid model (left) vs HFM approach (right).

Fig. 11. The thermal field in the backer: fully liquid model (left) vs HFM approach (right).

Figure 11 shows the thermal field in the backer plane where the thermocouples were positioned: it is immediately clear how the fully liquid model overestimated the drop of temperature along the cooling path, while the HFM one well captured the drastic reduction of the heat exchange caused by the gas formation, that restricted the cooling only nearby the nitrogen inlet. In detail, Tab 4 reports the comparison of the two numerical models with the experimental data: the average errors in terms of temperature prediction were about the and for the HFM and the fully liquid model, respectively.

Table 4. Thermocouple temperatures in the backer: Experimental vs Numerical results (Billet 17).

17th extrusion | Thermocouples Temperature [°C]
| P1 | P2 | P3 | P4 | P5 | P6
Experimental | 196 | 219 | 296 | 326 | 340 | 350
Fully Liquid | 155 | 160 | 170 | 120 | 145 | 285
%Err Fully Liquid | -20.9% | -26.9% | -42.6% | -63.2% | -57.4% | -18.6%
HFM numerical | 205 | 210 | 298 | 260 | 355 | 335
%Err HFM | +4.6% | -4.1% | -1% | -20.4% | +4.4% | -4.3%

Conclusions

The original version of this paper is available on https://www.scientific.net/KEM.987.11.pdf

This work summarized the state of art about the recent trends in the design of cooling channels for extrusion dies proposed by the authors and based on the numerical simulation. The discussed experimental case studies showed the possible limits (low cooling efficiency, inhomogeneous cooling) of the cooling channels designed based on the current industrial practices. The analyzed works evidenced the potentiality of the numerical modelling for the prediction of the cooling efficiency: the 3D model of the cooling channel should be used only with appropriate simplifications to reduce the high computational effort, while the mono-dimensional approach demonstrated a good accuracy (average errors below 10%) despite the simplifications, thus achieving a saving CPU time of about . The advantage in terms of computational time allowed to integrate the 1D approach within an optimizer to automatically design/re-design the cooling path with the final aim of achieving the best cooling performances: the size optimization can be used to improve a quite good cooling path only changing the channel dimensions, while the topological optimization allow to create a new cooling path pursuing the selected objective functions. Among the most remarkable results, recently, a new 1D nitrogen model based on the homogenous fluid (HFM) approach was proposed and validated. The HFM makes it possible to capture the thermal effects of the nitrogen phase change, simulating a single fluid with pseudo properties that depend on the nitrogen vapor title which, in turn, is a function of the heat exchange within the channel.

Acknowledgment

The original version of this paper is available on https://www.scientific.net/KEM.987.11.pdf

Project funded under the National Recovery and Resilience Plan (NRRP), Mission 04 Component 2 Investment 1.5 - NextGenerationEU, Call for tender n. 3277 dated 30/12/2021. Award Number: 0001052 dated 23/06/2022.

References

The original version of this paper is available on https://www.scientific.net/KEM.987.11.pdf

P. Stratton, Raising productivity of aluminium extrusion with nitrogen, Int. Heat Treat. Surf. Eng. 2(3-4) (2008) 105-108.

L. Donati, et al, Effect of liquid nitrogen die cooling on extrusion process conditions, Key Eng. Mater. 491 (2012) 215-222.

L. Donati, B. Reggiani, R. Pelaccia, M. Negozio, S. Di Donato, Advancements in extrusion and drawing: a review of the contributes by the ESAFORM community, Int. J. Mater. Form. 15 (2022) 41.

R. Pelaccia, M. Negozio, L. Donati, B. Reggiani, L. Tomesani, Extrusion of Light and Ultralight Alloys with Liquid Nitrogen Conformal Cooled Dies: Process Analysis and Simulation, J. Mater. Eng. Perform. 31 (2021) 1991-2001.

A.F. Ciuffini, et al., Surface Quality Improvement of AA6060 Aluminum Extruded Components through Liquid Nitrogen Mold Cooling, Metals 8(6) (2018) 409.

R. Pelaccia, B. Reggiani, et al., Liquid nitrogen in the industrial practice of hot aluminium extrusion: experimental and numerical investigation, Int J Adv Manuf Technol. 119 (2022) 3141-3155.

D. Pazeto, J.L.J Pereira, G.F Gomes, Numerical simulation and multiobjective optimization of fluid-structure interaction in aluminum extrusion, Int J Adv Manuf Technol 124 (2023) 545-566.

M.M. Marín, A.M. Camacho, J.A. Pérez, Influence of the temperature on AA6061 aluminum alloy in a hot extrusion process, Procedia Manuf. 13 (2017) 327-334.

I. Kniazkin, R. Pelaccia, et al., Investigation of the skin contamination predictability by means of QForm UK extrusion code, Mater. Res. Proc. 28 (2023) 543-552.

J. Tang, L. Chen, Z. Li, et al., Evolution mechanisms of charge weld during porthole die extrusion of ZK60 Mg profile, J. Mater. Process. Technol. 300 (2022) 117401.

C. Zhang, Y. Dong, et al., Experimental and numerical investigations on transverse weld of hollow aluminum profile during porthole extrusion process, Procedia Eng. 207 (2017) 16531658.

L. Donati, A. Segatori, M. El Mehtedi, L. Tomesani, Grain evolution analysis and experimental validation in the extrusion of 6XXX alloys by use of a lagrangian FE code, Int. J. Plast. 46 (2013) 70-81.

M. Negozio, R. Pelaccia, L. Donati, B. Reggiani, Simulation of the microstructure evolution during the extrusion of two industrial-scale AA6063 profiles, J. Manuf. Process 99 (2023) 501-512.

E. Giarmas, D. Tzetzis, Optimization of die design for extrusion of 6xxx series aluminum alloys through finite element analysis: a critical review. Int. J. Adv. Manuf. Technol. 119 (2022) 5529-5551.

R. Hölker, A.E. Tekkaya, Advancements in the manufacturing of dies for hot aluminum extrusion with conformal cooling channels, Int. J. Adv. Manuf. Technol. 83 (2016) 12091220.

M. Mazur, P. Brincat, et al., Numerical and experimental evaluation of a conformally cooled H13 steel injection mould manufactured with selective laser melting, Int. J Adv. Manuf. Technol. 93 (2017) 881-900.

A. Armillotta, R. Baraggi, S. Fasoli, SLM tooling for die casting with conformal cooling channels, Int. J Adv. Manuf. Technol. 71(2014) 573-583.

C. Tahri, P. Lequien, et al., CFD simulation and optimize of LN2 flow inside channels used for cryogenic machining: Application to milling of titanium alloy Ti-6Al-4, Procedia CIRP 58 (2017) 584-589.

R. Pelaccia, P. E. Santangelo, A Homogeneous Flow Model for nitrogen cooling in the aluminum-alloy extrusion process, Int. J. Heat Mass Transf. 195 (2022) 123202.

R. Pelaccia, M. Negozio, et al., Assessment of the Optimization Strategy for Nitrogen Cooling Channel Design in Extrusion Dies, Key Eng Mater. 926 (2022) 460-470.

R. Pelaccia, B. Reggiani, M. Negozio, S. Di Donato, L. Donati, Investigation on the topological optimization of cooling channels for extrusion dies, Mater. Res. Proc. 28 (2023) 533-542.

M. Pizzarelli, Effectiveness of Spalart-Allmaras Turbulence Model in Analysis of Curved Cooling Channels, AIAA Journal 51(9) (2013) 2158-2167.

This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/12th-aluminium-two-thousand-world-congress-7th-international-conference-on-extrusion-and-benchmark/978-3-0364-1380-8

Key Engineering Materials, ISSN: 1662-9795, Vol. 988, pp 21-29

doi: 10.4028/p-5c0LIi

© 2024 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2023-11-19

Revised:
2024-03-05

Accepted:
2024-03-08

Online:
2024-09-27

Hot Torsion Tests of AA6082 Alloy

The original version of this paper is available on https://www.scientific.net/KEM.988.21.pdf

Sara Di Donato , Riccardo Pelaccia , Marco Negozio , Mohamad El Mehtedi , Barbara Reggiani , Lorenzo Donati

University of Bologna, DIN Department of Industrial Engineering, Viale Risorgimento 2, 40136 Bologna, Italy

University of Modena and Reggio Emilia, Via Amendola 2, 42122 Reggio Emilia, Italy

University of Cagliari, Via Marengo 2, 09123 Cagliari, Italy

sara.didonato2@unibo.it, riccardo.pelaccia@unimore.it, marco.negozio2@unibo.it, m.elmehtedi@unica.it, barbara.reggiani@unimore.it, I.donati@unibo.it

Keywords: Hot torsion test, Aluminum, Flow stress, Material characterization, AA6082.

Abstract

Materials characterization and the knowledge of their elastic-plastic behavior are of fundamental importance for the design of industrial manufacturing processes. Nowadays, FEM simulation is the main tool used to optimize product quality and minimize scraps, and the numerical codes have evolved over the years to obtain accurate solutions with reduced computational times. Nevertheless, in order to perform reliable simulations, it is necessary to include accurate modeling of the material flow stress.

Hot torsion is a powerful method for the characterization of the material flow stress because, tests can be carried out at constant speeds and temperatures, reaching large strain values, and thus getting over the limits of compression and tensile tests.

In this paper the hot torsion characterization applied to AA6082 alloy is presented: tests were performed with equivalent strain rates of , and , in the temperature range from 440 to (from 713.15 to 823.15 K ). The results are presented in terms of equivalent stress vs equivalent strain.

Finally, the material flow stress curve was predicted by the Hyperbolic sine model and HenselSpittel law, and the material parameters A , are provided for the temperature expressed in and K.

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.988.21.pdf

Finite Element Method is nowadays the most powerful tool for the design and optimization of the bulk-forming process. FEM codes have evolved over the years to gain accurate solving capabilities at reduced computational times, and this has led to a widespread diffusion of the numerical simulation tool in the manufacturing industry to improve product quality and reduce scraps. [1]

Especially in such sectors as aluminum extrusion, where on-plant experiments are complex and expensive for the complexity of the process, numerical simulation is of fundamental importance.

In the last few years, several research works have investigated different aspects of extrusion processes by means of FEM codes, from process and tool optimization to microstructure prediction, proposing different approaches and improving the accuracy of the simulation.

In order to perform reliable and accurate simulations, it is necessary to provide the FEM code with accurate modeling of the material flow stress. In this sense, many equations over the years have been defined and included in the codes. Among these, in the past years, the Hyperbolic sine model proposed by Garafolo [9] was one of the most widespread and used. In fact, the Hyperbolic sine law, associated with the Zener-Hollomon parameter, allows the modelling of the material flow stress for a wide range of deformations conditions. [10, 11, 12, 13]

Nevertheless, the Hyperbolic sine law is a strain-independent model and could lead to large values of error in the simulation of plastic deformation processes where high strain values are reached.

A more accurate model for predicting the flow stress behavior of materials is the law proposed by Hensel-Spittel [14], which, including 9 material parameters, accounts for the dependence of flow stress on temperature, strain rate and strain. However, the limitation of the HS law is that the constitutive parameters are difficult to regress, and the related literature is lacking.

Furthermore, appropriate characterization tests of the material are necessary, which allow to reach large strain values, maintaining constant temperature and strain rate. In this context, the most effective test is the hot torsion test. A solid-bar or tubular specimen cylinder is twisted by a torque acting around its axis with constant speed and this allows it to reach extended strain values overcoming the limits of the tensile test, in which necking occurs characterizing the specimen up to strain about 0.5 0.8, and of the compression test, where the strain is limited to around 1 due to friction and barreling.

The aim of this work is to present the characterization of AA6082 aluminum alloy performed through hot torsion tests with the testing machine of the University of Bologna - Department of Industrial Engineering. Tests were carried out with equivalent strain rates of , and , in the temperature range from 440 to (from 713.15 to 823.15 K ) and the results are presented in terms of equivalent stress vs equivalent strain.

Finally, the material flow stress curve was predicted by the Hyperbolic sine model and HenselSpittel law, and the material parameters A, are provided for the temperature expressed in and K.

Methodology and Experimental Procedures

The original version of this paper is available on https://www.scientific.net/KEM.988.21.pdf

Hot torsion test. The hot torsion tests were performed on AA6082 alloy homogenized solid-bar specimens. Although the torsion test has been used for many years, any standard doesn't exist for specimen design, and researchers all over the years tried to use different geometries for the specimens with a gauge-length-to-radius ratio approximately from to .

In general, the design of the sample, including the shoulder and grip ends, is determined largely by the method of heating and the type of torsion machine to be used. In this work, a specimen with a gauge length and a diameter of 10 mm was used, as shown in Fig. 1 a.

During the test, the temperature, measured by a thermocouple inserted inside the specimen, is kept constant by a PID controller. The samples were heated at approximately by means of a copper induction coil (Fig. 1 b) and maintained for 5 minutes at the testing temperature before torsion, in order to allow a homogeneous temperature distribution from the external diameter to the center.

Fig. 1. a) Geometry of the AA6082 alloy solid bar specimen with dimensions in mm ; b) Picture of the sample during the test performed with the Torsion testing machine of the University of

Fig. 1. a) Geometry of the AA6082 alloy solid bar specimen with dimensions in mm ; b) Picture of the sample during the test performed with the Torsion testing machine of the University of

Bologna - Department of Industrial Engineering.

The torsion test provides the relationship between the angular deformation applied to the sample and the torque required to obtain it. The outputs of the test machine are the torque and the angle of twist . The Fields-Backofen solution was applied to calculate the shear strain and the corresponding shear stress [17]. Applying this method, the shear strain in the relation and the shear strain rate are considered to be those corresponding to the outside diameter of the solid bar sample (Eq. 1, Eq. 2).

where L is the gauge length of the specimen and R is the radius . Fig. 2 The shear stress is calculated from Eq. 3:

where the strain hardening coefficient is the instantaneous slope of versus and the strain rate sensitivity coefficient is the slope of versus at fixed values of . Several studies demonstrated that the effects of and coefficients can be neglected for aluminum alloys to simplify the stress calculation, as they don't significantly affect the material behavior. [11, 12, 13, 18, 19]

In the present work, the equivalent Von Mises stress and strain were calculated neglecting the effects of m and n coefficients according to Eq. 4 and Eq. 5.

Fig. 2. a) Picture of the Torsion testing machine of the University of Bologna - Department of Industrial Engineering; b) Scheme of the specimen; c) Scheme of the shear strain on the gauge length.

Fig. 2. a) Picture of the Torsion testing machine of the University of Bologna - Department of Industrial Engineering; b) Scheme of the specimen; c) Scheme of the shear strain on the gauge length.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/KEM.988.21.pdf

Experimental results. Fig. 3 shows the experimental results in the form of equivalent stress vs equivalent strain calculated by Eq 4 and Eq. 5. The tests were carried out with equivalent strain rates of , and , maintaining four temperature values, , and ; for each condition, at least two trials were performed to verify the repeatability of the test.

The curves have the typical shape of many aluminum alloys behavior, with a peak at low strains followed by a softening steady state. The tests were stopped after the specimens fractured which occurred over an equivalent strain equal to 5 for most conditions.

Fig. 3. Experimental curves. The tests were performed with four values of temperature: 440 , 480,520 and , and four values of strain rates: , and . For each condition two trials were carried out.

Fig. 3. Experimental curves. The tests were performed with four values of temperature: 440 , 480,520 and , and four values of strain rates: , and . For each condition two trials were carried out.

Hyperbolic sine law. The hyperbolic sine law is a model proposed by Garofalo [9] which, associated with the Zener-Hollomon parameter, allows the modelling of the material flow stress for a wide range of deformations conditions [10, 11, 12, 13]. The expression describes the flow stress dependence on temperature and strain rate, without considering the strain, and assumes the following form: Eq. 6

with stress expressed in MPa and temperature T expressed in is the universal gas constant is the apparent activation energy for plastic flow, , and n are material constants.

The values of the material parameters were found from the hot torsion tests' experimental results, considering the peak stress value for each condition and they are reported in Table 1.

Table 1. Sin-hyperbolic model parameters.

Q [J/mol] | A [s⁻¹] | α [MPa⁻¹] | n
103639.3118 | 7.95 E+04 | 0.045 | 5.49933

Fig. 4 shows the comparison between the experimental flow stress curves plotted with dashed lines and the predicted stress values, calculated by Eq. 6, drawn with solid lines. It's clear that the curves calculated with the Hyperbolic sine law appear constant as the strain varies and the correlation is good only for the peak stress values as shown in Fig. 5, except for the test conditions with the strain rate of and temperature of 793 and 823 K , where the predicted peak stress is lower than the experimental peak.

Fig. 4. Comparison between the experimental curves (dashed lines) and the stress values predicted by Sinh law (solid lines).

Fig. 4. Comparison between the experimental curves (dashed lines) and the stress values predicted by Sinh law (solid lines).

Fig. 5. Comparison between peak stress values predicted by Sinh law on the y -axis and the experimental peak stress values on the x -axis. The orange dashed line represents the alignment between the predicted and experimental data and indicates a theoretically perfect level of agreement between the values.

Fig. 5. Comparison between peak stress values predicted by Sinh law on the y -axis and the experimental peak stress values on the x -axis. The orange dashed line represents the alignment between the predicted and experimental data and indicates a theoretically perfect level of agreement between the values.

Hensel and Spittel law. The Hensel and Spittel law [14] is a model for predicting material flow stress behavior involving temperature, strain rate and strain dependence. The equation, described in Eq. 7, contains 9 material parameters: ,

Where is expressed in MPa and the coefficients assume different values depending on whether the temperature is expressed in or K . The coefficients and define the material's sensitivity to temperature, defines the coupling temperature and strain, the term couple temperature and strain rate, , and coefficients define the material's sensitivity to strain and depends on the material's sensitivity to strain rate. [20,21]

The values of the coefficients were found from the hot torsion tests' experimental results and are listed in Table 2 for the temperature expressed in K and in Table 3 for the temperature expressed in .

Table 2. Hensel-Spittel coefficients with temperature expressed in K (Temperature range validity ).

Parameters | 5 parameters | 6 parameters | 7 parameters | 8 parameters | 9 parameters
A [MPa] | 182.562 | 176.285 | 178.685 | 182.161 | 87.963
m1 | -0.0022426 | -0.0023064 | -0.0023004 | -0.002150 | -0.0035864
m2 | -0.0405691 | -0.0610555 | -0.0542036 | 0.008047 | 0.0081011
m3 | 0.105639 | 0.1052751 | 0.1062478 | -0.02175 | -0.0225208
m4 | -0.0012775 | -0.0012881 | -0.0012141 | -0.00029 | -0.00029223
m5 | 0 | 7.54071E-05 | 5.60459E-05 | -7.2E-05 | -7.215E-05
m7 | 0 | 0 | -0.0003077 | 0.000282 | 0.0002987
m8 | 0 | 0 | 0 | 0.000142 | 0.0001588
m9 | 0 | 0 | 0 | 0 | 0.2766873
R² | 0.972 | 0.960 | 0.965 | 0.969 | 0.939

Table 3. Hensel-Spittel coefficients with temperature expressed in (Temperature range validity ).

Parameters | 5 parameters | 6 parameters | 7 parameters | 8 parameters | 9 parameters
A [MPa] | 98.851 | 96.259 | 107.028 | 99.774 | 99.722
m1 | -0.0022408 | -0.0023101 | -0.0022578 | -0.00212 | -0.00212
m2 | -0.0405691 | -0.0550745 | 0.0074409 | 0.007399 | 0.007401
m3 | 0.105639 | 0.105639 | 0.0880616 | 0.017222 | 0.017216
m4 | -0.001275 | -0.0012852 | -0.0002925 | -0.00029 | -0.00029
m5 | 0 | 0.00009201 | -0.0001052 | -0.0001 | -0.0001
m7 | 0 | 0 | 6.43551E-05 | 2.35E-05 | 2.37E-05
m8 | 0 | 0 | 0 | 0.000142 | 0.000142
m9 | 0 | 0 | 0 | 0 | 9.33E-05
R² | 0.972 | 0.965 | 0.970 | 0.968 | 0.968

The H-S law can be used in a simplified form, using only 5 material coefficients, or getting all 9 of them. For the AA6082 alloy tested in the present work, it was found that the stresses predicted by using the Hensel-Spittel law with 5 coefficients present an excellent agreement with the experimental stresses, as shown in Fig. 6, where the comparison between the experimental flow stress curves plotted with dashed lines and the predicted stress values, expressed in K, drawn with solid lines, is shown. By increasing the number of coefficients, the agreement between the predicted flow stress and the experimental one gets worse, causing a decrease in the from a value of 0.972 for the H-S law with 5 coefficients to a value of 0.939 for the H-S law with 9 coefficients, as shown in Fig. 7 and Fig. 8. The same considerations can be made for the values of the Hensel-Spittel coefficients expressed in .

Fig. 6. Comparison between the experimental curves (dashed lines) and the stress values predicted by Hensel-Spittel law with 5 coefficients: A, (solid lines).

Fig. 6. Comparison between the experimental curves (dashed lines) and the stress values predicted by Hensel-Spittel law with 5 coefficients: A, (solid lines).

Fig. 7. Comparison between the experimental curves (dashed lines) and the stress values predicted by Hensel-Spittel law with 5 coefficients: A, (solid lines).

Fig. 7. Comparison between the experimental curves (dashed lines) and the stress values predicted by Hensel-Spittel law with 5 coefficients: A, (solid lines).

Fig. 8 Comparison between stress values predicted by Hensel-Spittel law on the y -axis and the experimental stress values on the x-axis (with 5 coefficients on the left and 9 coefficients on the right). The orange dashed line represents the alignment between the predicted and experimental data and indicates a theoretically perfect level of agreement between the values.

Fig. 8. Comparison between stress values predicted by Hensel-Spittel law on the y -axis and the experimental stress values on the x-axis (with 5 coefficients on the left and 9 coefficients on the right). The orange dashed line represents the alignment between the predicted and experimental data and indicates a theoretically perfect level of agreement between the values.

Conclusions

The original version of this paper is available on https://www.scientific.net/KEM.988.21.pdf

In this paper, the AA6082 alloy's plastic behaviour was characterised and analysed by means of the hot torsion test performed with the testing machine of the University of Bologna - Department of Industrial Engineering. The trials were carried out applying four strain rate values , and , and four temperatures , and and 823 K . At least two tests were performed for each condition showing good repeatability of the results.

Two constitutive flow stress models for the tested alloy were investigated: the Hyperbolic sine law and the Hensel-Spittel law. The models were regressed from the hot torsion tests' experimental results and the material coefficients were provided. The Hyperbolic sine law is a strain-independent model and it exhibited good accuracy in predicting peak stress values. The Hansel-Spittel law includes 9 parameters that account for the dependence of material flow stress on temperature, strain rate, and strain. In the present work, the Hansel-Spittel parameters were provided both for the temperatures expressed in K and in . It was found that the stresses predicted by using the model with 5 coefficients showed better agreement with the experimental data than the stresses calculated by H-S model with 6, 7,8, and 9 parameters respectively.

Acknowledgments

The original version of this paper is available on https://www.scientific.net/KEM.988.21.pdf

The authors would like to strongly acknowledge all the Companies and persons that made possible to carry out the experiment. In particular, the authors acknowledge the operators and the plant director Eng. Luca Gorlani from Metra plant in Rodengo Saiano (BS, Italy) for the supply of material and the press usage, and Almax Mori Group (TN, Italy), in particular Eng. Tommaso Pinter, for the die design and manufacturing. All these companies and involved persons are strongly acknowledged for their kind support, activity and problem-solving approach.

Funding

The original version of this paper is available on https://www.scientific.net/KEM.988.21.pdf

National Recovery and Resilience Plan (NRRP), Mission 04 Component 2 Investment 1.5NextGenerationEU, Call for tender n. 3277 dated 30/12/2021 (Award Number: 0001052 dated 23/06/2022).

References

The original version of this paper is available on https://www.scientific.net/KEM.988.21.pdf

L. Donati, B. Reggiani, R. Pelaccia, M. Negozio, S. Di Donato, Advancements in extrusion and drawing: a review of the contributes by the ESAFORM community, International Journal of Material 1541 (2022).

L. Donati, A. Segatori, M. El Mehtedi, L. Tomesani, Grain evolution analysis and experimental validation in the extrusion of 6XXX alloys by use of a lagrangian FE code, International Journal of Plasticity 46 (2013) 70-81.

R. Pelaccia, B. Reggiani, M. Negozio, L. Donati, Liquid nitrogen in the industrial practice of hot aluminium extrusion: experimental and numerical investigation, Int J Adv Manuf Technol 119 (2022) 3141-3155.

M. Negozio, R. Pelaccia, L. Donati, B. Reggiani, FEM Analysis of the Skin Contamination Behavior in the Extrusion of a AA6082 Profile, Key Engineering Materials Trans Tech Publications Ltd. (July 22, 2022).

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I. Kniazkin, R. Pelaccia, M. Negozio, S. Di Donato, L. Donati, B. Reggiani, N. Biba, R. Rezvykh, I. Kulakov, Investigation of the skin contamination predictability by means of QForm UK extrusion code, Materials Research Proceedings 28 (2023) 543-552.

M. Negozio, L. Donati, R. Pelaccia, B. Reggiani, S. Di Donato, Experimental analysis and modeling of the recrystallization behaviour of a AA6060 extruded profile, Materials Research Proceedings 28 (2023) 477-486.

M. Negozio, R. Pelaccia, L. Donati, B. Reggiani, Simulation of the microstructure evolution during the extrusion of two industrial-scale AA6063 profiles, Journal of Manufacturing Processes 99 (2023) 501-512.

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M. El Mehtedi, S. Spigarelli, F. Gabrielli, L. Donati, Comparison Study of Constitutive Models in Predicting the Hot Deformation Behavior of AA6060 and AA6063 Aluminium Alloys, Materials Today: Proceedings 210 Part A (2015) 4732-4739.

A. Hensel, T. Spittel, Kraft- und Arbeitsbedarf bildsamer Formgebungsverfahren, Deutscher Verlag für Grundstoffindustrie: Leipzig, Germany (1978) 336-360.

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Key Engineering Materials, ISSN: 1662-9795, Vol. 987, pp 23-30

doi: 10.4028/p-Ub1VHl

© 2024 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2023-11-23

Revised:
2024-03-06

Accepted:
2024-03-12

Online:
2024-09-23

Study of Direct Metal Extrusion by the Upper-Bound and Finite Volume Methods

The original version of this paper is available on https://www.scientific.net/KEM.987.23.pdf

JOSÉ DIVO Bressan , MARCELO Mattos Martins

University of Santa Catarina State - UDESC, Department of Mechanical Engineering, CEP 89219710, Joinville, SC, Brazil

Centre University Catholic of Santa Catarina, CEP 89203-005 Joinville, SC, Brazil

ªjosedivo.bressan@udesc.br, bdemaram@gmail.com

Keywords: Extrusion, Analytical modelling, Upper-Bound method, Finite-volume method.

Abstract

The velocity fields of axisymmetric direct extrusion of metals was analysed by the upperbound method and compared with the results from the finite-volume method, FVM. The upper-bound technique proposed by Avitzur and by Zhao et al. together with the streamline functions were employed to calculate the analytical velocity fields, which consider the friction at die wall. Moreover, the components of strain-rate are also presented. Additionally, the axisymmetric extrusion process was modelled by the FVM method to calculate the velocity fields and compared with the Avitzur's and by Zhao's solutions. The FVM velocity fields were calculated by using the Eulerian approach of fixed grid, the governing equations of metal plastic flow and conservation laws discretized by the FVM and the Explicit MacCormack method in structured and collocated mesh were also employed. Friction at die wall was modelled by the friction factor model, using the tangential shear stress boundary conditions. The examined material experimental parameters were obtained from the Al 6351 aluminium alloy in the direct extrusion process at . Velocity fields of the longitudinal and radial velocity distributions by the upper-bound and FVM methods are presented and compared. Good agreement is shown between the radial velocity component from the Avitzur's and FVM results, but poor for the longitudinal velocity . From the analysis of velocity fields, the most severe condition of wear on the inner wall of the die and material surface damage occurs in the area near the exit corner of the die. However, the predicted location of the severe wear region in the die wall by the FVM method is located prior to the point predicted by the Avitzur model.

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.987.23.pdf

The extrusion process of metals is a thermo-mechanical metalworking operation largely applied in the manufacturing of steel and aluminium tubes, bars and aluminium profiles. Thus, it is an important metalworking and industrial process for manufacturing structural parts. Extrusion process of metals is carried out at warm and hot temperatures, consequently, process modelling has been investigated by two independent approaches: the mechanics of metal flow and the metallurgical evolution of microstructural features such as grains and porosities.

Historically, the Upper-bound, the Slip-line fields and the Finite Element Methods have been applied with relative success for decades to calculate loads, stress, strain, strain-rate and velocity fields in the mechanics analysis of metal extrusion [1,2,3]. Experimental visioplasticity techniques have been also employed to physically simulate and visualize the details of metal flow with model materials such as plasticine, lead, aluminium and the gridded split billet and the strip-pattern techniques. Nevertheless, recently in the academy, Bressan et al. [4] have also investigated the use of the Finite Volume Method, FVM, to analyse metal flow in extrusion: literature suggests that metal flow in extrusion can be analysed as a viscous fluid, therefore, modelled by the plastic flow formulation [4, 5]. Applications of FVM to solid mechanics was also recently reviewed by Cardiff and Demirdžić [6]. Thus, plastic flow of metal in extrusion and drawing can be modelled as the flow of an incompressible non-linear viscous fluid. This hypothesis can be assumed because metal extrusion is a metal flows without volume change.

The MacCormack Numerical Method is traditionally used to solve the governing differential equations in fluid dynamics applications, particularly in the complex aerodynamics situations. This method has the advantage to smooth the pressure pick discontinuities produced by the pressure shock waves. Additionally, it is a numerical method of second order accuracy in time and space. Thus, the MacCormack method is commonly used to model compressible and incompressible fluid flow, using the FVM to solve the governing differential equations [7].

Present work compares the solutions of FVM numerical scheme proposed by Bressan et al. [4] with the upper-bound method (model proposed by Avitzur [8] and the model proposed by Zhao et al. [9]) for calculating the velocity fields of metal flow in the extrusion process, in steady state conditions. In the Finite Volume Method, the governing equations were discretized using the Explicit MacCormack Method in structured and collocated mesh.

Axisymmetric Extrusion Analysis by the Upper-Bound Method and the Streamlines

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The Upper-bound technique is an analytical method to calculate metal forming operation loads and power. The method assumes a kinematically admissible velocity fields and the existence of surfaces of velocity discontinuities. The kinematics of plastic deformation or kinematically admissible velocity field of the metal forming process should be obtained from the die geometry and velocity components which satisfy the boundary conditions and volume constancy. Flow streamlines, on the other hand, are defined as the lines that describe the path followed by a particle during plastic flow. Thus, the velocity field components are derived from the streamfunction which is assumed to describing the flow and generally no friction is assumed at the die.

According to the Avitzur's extrusion model [8] in the ( )-axis for a spherical velocity field, the velocity components, the surfaces of velocity discontinuities and streamlines in the deforming region II of the axisymmetric direct extrusion process, as seen in Fig.1, are calculated by the equations,

where and are the velocity components, is the die entry velocity, is the entry radius of the die, is the current radius in the deforming region II, is the streamline angle in the region II and is the die semi-angle. Avitzur's streamlines drawing is depicted in Fig.2a.

Fig. 1. Avitzur's model: schematic drawing of the geometry of axisymmetric extrusion process with the entrance velocity , velocity components and streamlines.

Fig. 1. Avitzur's model: schematic drawing of the geometry of axisymmetric extrusion process with the entrance velocity , velocity components and streamlines.

Fig. 2. Extrusion streamlines, die semi-angle : a) Avitzur's model and b) Zhao's model.

Fig. 2. Extrusion streamlines, die semi-angle : a) Avitzur's model and b) Zhao's model.

An alternative streamfunction and the correspondent velocities components, assuming no friction at die, for the axisymmetric direct extrusion process was proposed by Zhao et al. [9].

In cylindrical coordinates system ( ), the governing equation for an incompressible flow or the plastic flow continuity governing equation is given by,

where and are the velocity components. For an axisymmetric extrusion process without rotation, the velocity component , therefore, the governing Eq. 2 is reduced to,

Assuming the definition of Stokes' streamfunction , which satisfy Eq.3, the velocity components and the resultant velocity V are calculated by,

After the integration of Eq.4, defining at the axisymmetric extrusion axis and , constant at the die boundary AB , the Zhao's streamline function is obtained as,

And the Zhao's velocities components in the region II for the axisymmetric extrusion are [9],

where the volume constant, . Zhao's streamlines drawing is shown in Fig.2b.
The strain-rate components are calculated from the following equations, ,

Axisymmetric Extrusion Modelling by the Finite Volume Method

The original version of this paper is available on https://www.scientific.net/KEM.987.23.pdf

The FVM method or control volume method is a numerical method to calculate all metal forming process variables. The deforming material domain of the analysed process is divided into small volume units, named control volumes, which constitute the grid or mesh.

The differential equations of the governing conservation laws (of mass, momentum and energy) in cylindrical coordinates system ( ) for the axisymmetric extrusion case, for , can be merged into a compact matrix structure in the form of [4]:

where is time, and and are flow vectors which assume the following format in the Euler approach of fixed mesh in space:

where is material density, T temperature, the rate of heat transfer, the stress components, and the equivalent strain-rate. The field variables to be determined are in the flow vector . the equivalent stress

Integrating Eq. 8 over the control volume, employing Gauss' theorem, the following differential equation is obtained:

where is the outward vector of the surface and is the control volume area. Therefore, the conservation laws are applied to each control volume.

Eq. 10 was solved numerically, employing the MacCormack numerical strategy [7], which comprises the predictor and corrector steps in the numerical convergence process. Thus, the MacCormack converged current step is calculated by the average between the predictor and corrector steps, is the current time and is a virtual time step. Therefore, the numerical MacCormack method is a pseudo-transient calculation process, where is a virtual time increment to obtain the final converged solution. The required time step to attain numerical stability in the numerical convergence process was .

Present metal plasticity constitutive equation for the axisymmetric plastic flow in extrusion considered the material to be incompressible, isotropic and rigid-viscous-plastic. The relation between the stress component and strain rate tensor was written in the following form , where is hydrostatic pressure calculated by . Metal plastic flow equivalent viscosity was calculated from the associated plastic flow potential , thus, . The equivalent strain rate was defined as and the equivalent stress was , where is the deviatoric stress component.

The boundary conditions applied in the present direct extrusion analysis [4], at the solid wall interface between material and die, considered friction factor, , where is the tangential friction stress, k the material yield shear stress and the friction factor. Assuming isotropic material, von Mises yield criteria and the shear strain rate , the tangential friction shear stress is: .

Material and Experimental Procedure

The original version of this paper is available on https://www.scientific.net/KEM.987.23.pdf

Comparison of results of direct extrusion of Al 6351 aluminium alloy by the analytical solutions and the FVM method developed by Bressan et al. [4] are presented and discussed in the next session. The Al 6351 billets were extruded in controlled conditions of and the extrusion speed of (details are reported in [10]) and were simulated by FVM method.

The experimental average parameters of Al 6351 alloy, the extrusion process and simulation parameters used in the FVM simulations are seen in Table 1. The tools were modeled as rigid solid. The viscoplastic friction model for sliding without lubrication and no-stick conditions was adopted with the friction factor m equal to 0.5 . Flow stress of Al 6351 alloy were obtained from tensile tests at and strain rate of 0.001 and . The curves were fairly linear with almost constant yield stress which can be represented by a rigid-perfectly-plastic material. Thus, the material flow stress behavior was modeled as rigid-strain rate sensitive material with the hardening law: .

Table 1. Parameters used in the analysis of Al 6351 alloy direct axisymmetric extrusion.

Parameters | value | Parameters | value
Density (ρ) | 2710 [kg.m⁻³] | Quantity of control volumes | 1110
Yield stress (σY = 2k) | 255 [MPa] | Time step (Δt) | 10⁻¹⁸ [s]
Die entry radius (RO) | 30 [mm] | Die semi-angle (α) | 32.3°
Area reduction (Ar = 9) | 89 % | Friction factor parameter (m) | 0.5
Die exit radius (Rex) | 10 [mm] | Material extrusion temperature (T) | 450 [°C]
Inlet extrusion velocity (Vo) | 20 [mm/s] | Material strain hardening law: rigid perfect-plastic, strain rate sensitivity | K=255 [MPa] M = 0.10

Results and Discussions

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The geometry of the fixed mesh with 1110 volumes employed in the present FVM numerical simulations and the Avitzur's model of the axisymmetric plastic flow of direct hot extrusion of Al 6351 aluminium alloy is shown in Fig.3. The required time step to attain numerical stability in the numerical convergence process was , and the number of iterations loops was approximately 30.000 loops.

Fig. 3. Geometry of fixed mesh in space used in the present FVM numerical simulations of the axisymmetric direct extrusion of Al 6351 aluminium billets, using 1110 control volumes.

Fig. 3. Geometry of fixed mesh in space used in the present FVM numerical simulations of the axisymmetric direct extrusion of Al 6351 aluminium billets, using 1110 control volumes.

Fig. 4. Comparison of the velocity field results by the FVM numerical simulations and the Avitzur's model: (a) axial component Vz and (b) radial component Vr by the FVM simulations. (c) axial component Vz and (d) radial component Vr by the Avitzur's model. Friction factor .

Fig. 4. Comparison of the velocity field results by the FVM numerical simulations and the Avitzur's model: (a) axial component Vz and (b) radial component Vr by the FVM simulations. (c) axial component Vz and (d) radial component Vr by the Avitzur's model. Friction factor .

The comparisons of the velocity field results by the FVM numerical simulations and the Avitzur's analytical model are depicted in Fig.4. In the FVM approach, the friction factor at the die wall, , was assumed equal to . However, in the Avitzur's analytical velocity field model seen
in Eq.1a and Eq.1b no friction was considered. Therefore, the Avitzur's field of the axial velocity component Vz at the die deformation region is quite different from the FVM model. Nevertheless, the Avitzur's field of the radial velocity component Vr is very similar to the FVM model.

In Fig.5, assuming friction factor in the FVM numerical simulations, the FVM field of the axial velocity component Vz has no change, except near the die wall. Thus, the difference of the fields, distribution of velocity bands of the axial velocity Vz is quite large compared with Avitzur.

Comparisons of the velocity component Vr field results by the FVM simulations and the Zhao's model is shown in Fig.6. The maximum radial velocity attained in extrusion at die exit corner was: (FVM approach) and (Zhao's approach).

Fig. 5. Comparison of the velocity Vz field results by the (a) FVM numerical simulations and the (b) Avitzur's model. Assuming friction factor m at die wall equal to zero, .

Fig. 5. Comparison of the velocity Vz field results by the (a) FVM numerical simulations and the (b) Avitzur's model. Assuming friction factor m at die wall equal to zero, .

Fig. 6. Comparison of the velocity Vr field results by the (a) FVM numerical simulations and the (b) Zhao's model. Assuming friction factor m at die wall equal to zero, .

Fig. 6. Comparison of the velocity Vr field results by the (a) FVM numerical simulations and the (b) Zhao's model. Assuming friction factor m at die wall equal to zero, .

Conclusions

The original version of this paper is available on https://www.scientific.net/KEM.987.23.pdf

Based on the analysis of present analytical models of axisymmetric extrusion proposed by Avitzur and by Zhao, and the FVM numerical simulation results for the velocity fields of direct hot extrusion of Al 6351 aluminium alloy billet, the following conclusions can be summarized,
a) In the analytical models proposed by Avitzur and the Zhao for the field of velocity components, no friction between the die wall and the extruded material is present in the equations.
b) The distribution of the radial component of velocity Vr from the Avitzur's and the FVM's models are quite similar.
c) However, the difference of the fields, distribution of velocity bands, for the longitudinal component of velocity Vz from the Avitzur's and the FVM's models is quite large.
d) Assuming friction zero, , in the FVM numerical simulation, the difference of the longitudinal component of velocity Vz with the Avitzur' solution is still great.
e) The distribution and values of the longitudinal velocity Vz of FVM simulations are similar to Zhao's approach, but had poor correlation with the radial velocity Vr.
f) The maximum radial velocity attained in extrusion at die exit corner was: (FVM approach), (Avitzur's approach) and (Zhao's approach).
g) Therefore, the region of most severe condition of wear on the inner wall of the die and material surface damage occurs in the area near the exit corner of the die. However, the predicted location of the severe wear region in the die wall by the FVM method is located prior to the point predicted by the Avitzur model.

References

The original version of this paper is available on https://www.scientific.net/KEM.987.23.pdf

W. Johnson, R. Sowerby and R.D. Venter, Plane Strain Slip-Line Fields for Metal Deformation Processes. Pergamon Press, Oxford, 1982.

H.S. Valberg, Applied Metal Forming, Including FEM Analysis. Cambridge Univ. Press, Cambridge, 2010.

L. Donati and L. Tomesani, Extrusion Benchmark 2017. International Conference on Extrusion and Benchmark (ICEB 2017) edition and edition of Aluminium Two Thousand World Congress. Materials Today: Proceedings, January (2019), 10 (Part 2).

J.D. Bressan, M.M. Martins and S.T. Button, Analysis of metal extrusion by the Finite Volume Method. Procedia Engineering Vol. 207 (2017) 425-430.

O.C. Zienkiewicz, P.C. Jain and E. Onate, Flow of solids during forming and extrusion: some aspects of numerical solutions. Int. J. Solids Struct., 14 (1978) 15-38.

P. Cardiff, I. Demirdžić, Thirty Years of the Finite Volume Method for Solid Mechanics. Arch Computat Methods Eng 28, 3721-3780 (2021).

J.C. Tannehill, D.A. Anderson and R.H. Pletcher, Computational Fluid Mechanics and Heat Transfer. Taylor & Francis (1997).

B. Avitzur, Analysis of Metal Extrusion. J. Eng. Ind., Vol. 87(1) (1965) 57-69.

D. Zhao, Z. Zhao, Q. Zhang, Integral as a function of the upper limit to solve axial symmetrical rod drawing and extrusion. J. Met. Sci. Technol., Vol. 6 (1990) 282-288.

J.D. Bressan, M.M. Martins and S.T. Button, Analysis of aluminium hot extrusion by finite volume method, in: Aluminium Two Thousand World Congress and International Conference on Extrusion and Benchmark ICEB 2015. Materials Today: Proceedings 2 (2015) 4740-4747.

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Key Engineering Materials, ISSN: 1662-9795, Vol. 988, pp 31-46

doi: 10.4028/p-G6Pyxv

© 2024 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2023-08-08

Revised:
2024-03-04

Accepted:
2024-03-06

Online:
2024-09-27

A Modelling Framework for Rapid Evaluation of Speed Limitations During Extrusion of Aluminium Profiles

The original version of this paper is available on https://www.scientific.net/KEM.988.31.pdf

Mads Iddberg , Ole Runar Myhr , Anders Nesse and Trond Furu

SINTEF Manufacturing, Raufoss, Norway

Hydro Aluminium R&D, Sunndalsøra, Norway

NTNU, Department of Structural Engineering, Trondheim, Norway

Hydro Extrusions, Oslo, Norway

Norsk Hydro, Corporate Technology Office, Oslo, Norway

NTNU, Department of Material Science and Engineering, Trondheim, Norway

mads.iddberg@sintef.no, ole.runar.myhr@hydro.com, anders.nesse@hydro.com, dtrond.furu@hydro.com

Keywords: Extrusion simulations, Temperature models, Extrusion speed, 6xxx alloys.

Abstract

Hot tearing is a well-known limitation when trying to maximize the throughput rate in aluminium extrusion. In the present work an analytical modelling framework is presented which can be used to predict the maximum extrusion speed that can be applied in production without formation of this type of surface defect. The modelling framework allows almost instantaneous estimates on the resulting productivity in terms of maximum extrusion speed. This is obtained by developing an analytical model for the maximum temperature at the die exit which incorporate the effect of alloy composition and billet processing. The results are consolidated into extrusion limit diagrams, mapping the maximum allowable extrusion speed as a function of billet pre-heat temperature, alloy composition, and homogenisation heat treatment. The calculated temperatures and extrusion limit diagrams obtained from the analytical model are compared with measured temperatures and critical extrusion speeds from extrusion tests of various 6xxx series alloys for a simple rod-shaped geometry. The comparisons indicate that the presented modelling approach gives sufficiently accurate predictions for future application in optimisation of alloy composition and process parameters in extrusion of profiles.

Introduction

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In the production of aluminium extrusions, it is crucial to closely control the surface temperature of the profile at the die exit in order to prevent surface defects [1-3]. This quality requirement must be met while also striving for high extrusion speeds to minimize production costs. Today, this delicate balance between profile quality and productivity is increasingly guided by computer simulations utilizing commercially available FEM codes. Even though these numerical tools are highly sophisticated and can account for detailed behaviour in extrusion equipment and material flow, the relatively high computational time makes them unsuited for rapid evaluation of the combined effect of changes in multiple process parameters. From a productivity point of view, the question is what selection of parameters yield the highest profile throughput. This question can not be answered by a single FEM simulation, which only provides results from one combination of process parameters. In contrast, analytical models allow almost instantaneous estimates of the parameters evaluated, making them well suited to map the connection between the process parameters yielding maximum productivity.

A main objective of the present work is to create a model capable of predicting the effect of different alloy compositions and heat treatment schedules on the resulting maximum temperature of the profile at the die outlet. The model is tested against experimental data as well as compared to predictions made with the commercially available software HyperXtrude [4]. To establish the extrusion limitations, it requires that the hot-deformation resistance of the alloy can be estimated and
included in the constitutive equations of the models. The hot-deformation resistance, defined as the effective flow stress in compression, differs significantly at different positions of the billet and profile as it depends on the instantaneous values of strain rate and temperature, as well as the instantaneous state of the microstructure. For 6xxx series alloys containing dispersoids, it has been shown that these particles increase the deformation resistance significantly [5,6]. Hence, two billets with identical alloy composition may give different hot-deformation resistance if they have been homogenised at different temperatures. This is because the resulting dispersoid size distribution depends on the homogenisation temperature, and because the deformation resistance increases with increasing number density of dispersoids. The relationship between homogenisation cycle, dispersoid density, and resulting hotdeformation resistance has important practical implications, since an alloy with a high hotdeformation resistance cannot be extruded as fast as an alloy with low hot-deformation resistance when surface defects are to be avoided. In the present work, the microstructure model Alstruc [7,8], which will be briefly described below, has been used to predict the resulting dispersoid levels after casting and homogenisation for the various alloys considered.

While the first part of the article deals with predictions of temperatures, the second part utilizes the calculated temperatures to establish extrusion limit diagrams. Different types of surface defects may form during extrusion, like die lines, pick-up and hot tearing [1-3]. In the present work, hot tearing is assumed to be the critical surface defect which determines the maximum allowable extrusion speed. Hot tearing is usually attributed to two different types of mechanisms [2,3,9], i.e. tearing that occurs when the aluminium matrix can no longer withstand the frictional forces of the die, or tearing caused by incipient melting of particles close to the surface. Both tearing mechanisms are associated with critical temperatures that are alloy specific and can be estimated from phasediagrams [2,3,9].

The calculated temperatures and the calculated extrusion limit diagrams are validated by comparing predictions with a comprehensive experimental database containing measurements of the die exit temperatures and corresponding data for the transition from acceptable surface appearance to surface tearing. These comparisons indicate that the presented modelling approach gives sufficiently accurate predictions for future application in optimisation of process parameters in extrusion of aluminium profiles.

Theory

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The different models used in the present work are outlined below. The microstructure model and the FEM-model are both just briefly described since details of the underlying theory and assumptions are given in the references. The theoretical background for the analytical model is described in more details since this model has not been published previously.
Microstructure Model. The microstructure-based simulation model Alstruc [7,8] is used to predict important microstructure parameters for the alloys. The program predicts the formation of dispersoids that may precipitate from solid solution during the homogenisation cycle if the alloy contains elements like Mn or Cr , and the resulting mean concentration of elements in solid solution. The required inputs to this model include chemical composition, the local solidification rate during casting, and the complete homogenisation cycle. Other outputs from the model, which are not used in the present work, include the volume fraction and size distributions of coarse primary particles, and the local variations of alloying elements (i.e. microsegregation) across a representative dendrite arm.
FEM Model. The extrusion process is simulated using HyperXtrude , which is a commercially available FEM software. In this work, the software is used to calculate the temperature at the surface of the profile at the die exit, assuming steady state conditions and rigid tool surfaces. The simulation requires a description of the flow stress in the material, which in the present work is modelled by a modified Sellars-Tegart relationship, Eq. 3, described in more detail in [6,10]. This relationship predicts the flow stress in a material point as a function of strain rate, temperature, and the initial state of the microstructure in the billet. The friction at the bearing region is described with a viscoplastic relation [4], applying a friction coefficient of 0.3 . All other contact between the solid boundary and
the flow domain is modelled with the Dirichlet boundary condition. The FEM predicted exit temperatures are compared to the experimentally obtained data both as a verification of the implemented material model and as a reference for the results obtained with the analytical model.
Analytical Model. The analytical model is based on the simplified assumption of independent temperature contributions that are added linearly similar to the approach used in several previous developed models [11-14]. The profile surface temperature at the die exit at steady state conditions can then be expressed as follows:

Here, is the initial billet temperature, and is the specific temperature increase from mechanism i. Eq. 1 assumes that the individual contributions to the temperature increase are mutually independent, which is obviously a simplification. The use of Eq. 1 as a basis for temperature predictions is justified by comparing temperatures obtained from this equation with temperature measurements and more accurate predictions obtained by finite element simulations. In the following, the different contributions in Eq. 1 are explained in more details.
Adiabatic Heating. The first contribution, i.e. , is the temperature increase caused by the work of deformation that is almost entirely converted into heat (i.e. adiabatic heating). This contribution can be expressed as follows [15]:

Here, represents an efficiency in terms of the fraction of the deformation work that is converted to heat. is the effective flow stress, R is the reduction ratio, and and c are the density and the specific heat capacity of the alloy, respectively. This idealised heating does not account for "redundant work", which according to Dieter [15] is "work involved in internal shearing processes due to non-uniform deformation that does not contribute to the change in the shape of the body". Neither does account for heat generation due to friction.

It follows from Eq. 2 that is proportional to the mean equivalent stress . As shown in Refs. [6,10], the following version of the Sellars-Tegart relationship has been demonstrated to give good agreement with hot-deformation tests for a range of different 6xxx series alloys:

Here, and are material constants which describe the deformation resistance of the alloy. C and n are material constants with values set to and , respectively [6,10]. ZH is the Zener-Hollomon parameter defined as:

Here, , and are the strain rate, the apparent activation energy, the universal gas constant, and the deformation temperature, respectively. For , the common value of has been applied in the simulations. in Eq. 5 is estimated as the effective strain rate defined by the time for material to transverse through a truncated conical volume of deformation zone defined by the container diameter , the extrusion diameter , and the semi-die angle . For a ram speed , we get [16]:

In Eq. 3, and are material constants which can be attributed to the deformation resistance from elements in solid solution and from non-shearable particles, respectively. depends on the solid solution concentrations of the alloy and is expressed by an overall concentration parameter as follows :

Here, is a constant equal to . in Eq. 3 can be estimated from the number density and size of the dispersoids that have formed during homogenisation according to the following relationship [6,10]:

In this equation, is a constant equal to , and and are the number density and average radius of the dispersoids, respectively.

The temperature increase can now be calculated by combining Eqs. 3-7. When the deformation temperature is approximated by the initial billet temperature , we get:

From Eq. 8, the effective flow stress can be calculated as a function of geometric dimensions (i.e. , and ), extrusion process parameters as given by the initial billet pre-heat temperature and the ram speed , as well as the material parameters , and . These material parameters are obtained from the Alstruc-model and represent the link between the casting and homogenisation processes, and the extrusion process in the present temperature model.
Heat Generation Due to Friction Between Profile and Die. Friction between the deforming profile and the bearing surface in the die channel leads to heat generation. In parts of the bearing surface, sticking friction may be prominent while sliding friction may occur in other parts of the bearing surface [17]. In the present model, this temperature increase has been calculated as follows:

Here, , is the extrusion speed equal to , where R is the reduction ratio. is the bearing length of the die and is the thermal diffusivity of the material equal to . Eq. is derived by assuming that the heat generation due to friction between the die and the profile gives a temperature increase in a surface shell with a thickness corresponding to an estimated diffusion length , where the time can be approximated by [11]. When the diffusion length exceeds the die bearing channel thickness, i.e. the profile thickness d, the generated heat is assumed to contribute to an increase in the temperature in the whole thickness, as expressed by Eq. 9b. Eq. 9a and Eq. 9b assume heat generation along the whole bearing length . If this is not the case, must be replaced by an efficient bearing length which corresponds to the estimated length where the friction is strong enough to cause significant heating.
Heat Generation Due to Friction Between Container and Billet. There will be a temperature increase at the billet surface caused by friction between the container wall and the billet surface, which can be estimated as follows [11]:

Here, is the billet length. The equation is derived by assuming that the heat developed by friction is divided equally and uniformly between the billet and the container to a depth that is estimated as where is approximated as [11].
Heat Conduction in Container and Billet. Usually, there is an initial temperature difference between the billet and the container. Since the heat transfer coefficient between the billet surface and the container wall is deemed to be high when the extrusion starts, it may be reasonable to assume that there is a common interface temperature between billet and container. It can be shown that does not vary significantly during an initial time period [18], which allows the temperature in the billet at a distance from the billet surface at time to be calculated as follows [18]:

Here, and are the temperature at the interface between the aluminium billet and the steel container, and the initial billet temperature, respectively, and erf is the error function. Similarly, as for the prediction of , the conduction in the billet is assumed to be one dimensional, which is a reasonable approximation for large cylinder diameters. The interface temperature can be estimated as follows [18]:

Here, is the initial temperature of the container. , and c , are thermal conductivity, density, and specific heat capacity, respectively, and index c refers to the container. The thermal diffusion distance can be approximated as follows:

Here, is a dimensionless constant, which depends on the temperature considered. In the present work, a best fit value of 0.55 was used in the model. By combining Eq. 11 and Eq. 13, we get the following expression for the billet temperature at a distance from the billet surface at time :

Finally, we define as the difference between the actual billet temperature and the initial billet temperature as follows:

By combining Eq. 14 and Eq. 15, we get:

Here, is given in Eq. 12. Note that is negative when the initial container temperature is lower than the initial billet temperature, which is usually the case in practice.
Heating due to redundant work. The equations above describing the different contributions to the temperature increase, are highly idealised. In addition, the so-called redundant work has not been accounted for. According to Dieter [15], the redundant work is the work involved in internal shearing processes due to non-uniform deformation that does not contribute to the change in the shape of the body. Redundant work includes the heat generation due to friction between the flowing metal and the stationary metal in the dead-metal zone. According to Saha [19], this heat generation scales with the effective flow stress and the local material speed at the given position. Hence, it is reasonable to
assume that the contribution to temperature increases due to redundant work, i.e. , also scales with the same parameters. If we approximate the local material speed to the ram speed, we get:

In this expression, the constant is expected to depend on several geometrical parameters including the reduction ratio R . In the present work, was calibrated, and a best fit value of was used for all temperature calculations.

Experimental Data

The original version of this paper is available on https://www.scientific.net/KEM.988.31.pdf

The experimental data used for validation of the predictive power of the model is a dataset containing results for extrusion of a solid rod, extruded with eight different 6xxx series alloys, reported in [5, 20]. Included in this work is experimental results of a total of 207 unique combinations of alloy composition, homogenisation cycle, billet preheat temperatures and ram speeds. For each experiment the temperature was measured at the die exit and the surface quality was categorised in one of the three categories: no tearing, incipient tearing or tearing. For each combination of alloy and homogenisation cycle, the experiments were conducted by increasing the ram speed for relatively constant values of billet preheat temperature until tearing occurred. The approximate levels of billet preheat temperature tested was and . The minimum ram speed for all billet preheat temperatures was , but the maximum ram speed tested varied with the ability of the material to withstand tearing.
Alloy Composition and Homogenisation Cycles. Cylindrical ingots with 95 mm diameter were direct chill cast to approximately 1.8 m lengths and cut into 200 mm billet lengths for extrusion tests. The alloy composition is given in Table 1. Two different homogenisation cycles were used for each alloy composition. The first homogenisation cycle consisted of six hours heating time, followed by soaking at for two hours before cooling at a rate of /hour. The second homogenisation cycle consisted of four hours heating time, followed by soaking at for two hours before cooling at a rate of /hour. [5, 20]. The former homogenisation cycle was given index "1", and the latter " 2 ". In the article, the combination of alloy composition and homogenisation cycle is referred to as for instance A1, which means alloy composition A, and homogenisation cycle 1. All experimental data, with the exception of C1, is used in comparison with both the analytical model and FEM simulations. The exception is due to the lack of tabulated data for this alloy-treatment group. Extrusion Tests. The extrusion tests were performed on an 800-ton hydraulic laboratory extrusion press as reported in [5,20]. The billets were induction heated at the press, with a heating rate of about per minute to different pre-heating temperatures without any taper. The container diameter was 100 mm and the container temperature was kept at for all experiments. In order to measure the critical temperature when surface defects appear during extrusion, thermocouples were inserted into the bearing surface of the extrusion die protruding 0.1 mm into the surface of the extruded rod [5,20]. The profile was a solid rod with diameter of 9 mm and the die consisted of a 3 mm and 1degree chocked region, and a 1 mm parallel bearing surface. The profiles were water quenched about three meters from the die.

Table 1. Chemical composition ( ) of the alloys used in the present investigation [5, 20].

Alloy | Mg | Si | Fe | Mn | Cr | Cu
A | 0.58 | 0.91 | 0.20 | - | - | -
C | 0.60 | 0.92 | 0.22 | 0.52 | - | -
D | 0.58 | 0.93 | 0.21 | - | 0.15 | -
E | 0.61 | 0.94 | 0.20 | 0.53 | 0.15 | -
F | 0.60 | 0.95 | 0.21 | 0.53 | 0.14 | 0.10
G | 0.60 | 1.26 | 0.21 | 0.56 | - | -
H | 0.80 | 0.92 | 0.20 | 0.54 | - | -
I | 0.85 | 1.26 | 0.22 | 0.56 | - | -

Table 2. Input parameters used in analytical temperature model.

Parameter | Unit | Value | Comment
αd | degrees | 45 | Chosen value for the semi-die angle
β | - | 0.95 | Typical value for efficiency factor [1]
c | J (kg K)⁻¹ | 900 | Specific heat capacity of alloy in billet (estimated)
cc | J (kg K)⁻¹ | 490 | Specific heat capacity of container alloy (steel) [1]
C | s⁻¹ | 7.0 x 10⁸ | Material constant in Eq. 3, [6,10]
Dc | m | 0.100 | Container diameter
De | m | 0.009 | Extrusion diameter (rod shaped extrusion)
kss | MPa (wt%)-3/4 | 3.0 | Material constant in Eq. 6, [6,10]
kd | MPa (m⁻¹) | 4.8 | Material constant in Eq. 7, [6,10]
k₄ | - | 0.55 | Constant in Eq. 13
k₅ | Kms(N)⁻¹ | 8.0 x 10⁻⁸ | Constant in Eq. 17
λ | W (mK)⁻¹ | 150 | Thermal conductivity of alloy in billet (estimated)
λc | W (mK)⁻¹ | 32.7 | Thermal conductivity of container alloy (steel) [1]
Lb | m | 0.20 | Billet length
Ld | m | 0.001 | Die bearing length
n | - | 5.0 | Material constant in Eq. 3, [6,10]
Qapp | J (mol)⁻¹ | 156000 | Apparent activation energy [6,10]
ρ | kg (m⁻³) | 2700 | Density of alloy in billet (estimated)
ρc | kg (m⁻³) | 7860 | Density of container alloy (steel) [1]
Rg | J (K mol)⁻¹ | 8.314 | Universal gas constant
Tc⁰ | °C (or K) | 430 (703) | Initial container temperature

Results and Discussion

The original version of this paper is available on https://www.scientific.net/KEM.988.31.pdf

In the following section, results using the modelling framework described above are presented and compared with experimental data from the extrusion trials for the solid bar profile reported in [20].
Input Data. The analytical temperature model requires a wide spectrum of input data before a prediction can be made. These are summarised in Table 2. Both the FEM model as well as the analytical temperature model require input data for the deformation resistance as expressed by the parameters and . These parameters have been predicted by the microstructure-based model Alstruc [7,8] for the various alloys and homogenisation cycles in the present investigation, and the values are given in Table 3. The table also includes measured temperatures for incipient surface tearing for the different alloys from [20].

Table 3. Measured temperature for incipient surface tearing for the different alloys [20] and predicted hot-deformation parameters and from Alstruc [7,8]. (The critical temperature was not obtained for alloy C1).

Alloy | A1 | A2 | C1 | C2 | D1 | D2 | E1 | E2
Tc (°C) | 575 | 571 | - | 587 | 583 | 583 | 593 | 594
σss (MPa) | 16.81 | 16.57 | 17.17 | 16.91 | 17.50 | 17.50 | 17.20 | 16.94
σd (MPa) | 0.52 | 1.84 | 3.64 | 7.53 | 1.29 | 1.58 | 3.69 | 9.25
Alloy | F1 | F2 | G1 | G2 | H1 | H2 | I1 | I2
Tc (°C) | 591 | 591 | 560 | 565 | 588 | 588 | 579 | 584
σss (MPa) | 17.50 | 17.50 | 17.50 | 17.50 | 17.50 | 17.50 | 17.70 | 17.50
σd (MPa) | 3.51 | 9.28 | 4.51 | 7.56 | 3.77 | 7.13 | 4.27 | 7.36

Temperatures from FEM-simulations Figure 1 (b) shows a typical example of outputs from a finite element simulation, visualising contour plots of the temperature distribution in the billet and the rod during extrusion. Figure 1 (a) shows the flow domain as the material exits the die through the choked area before flowing across the parallel bearing surface.

Fig. 1. Example of outputs from the finite element simulations. (a) and (b) shows the contour plot of the temperature distribution during extrusion of alloy A1, applying a ram speed of and an initial billet temperature of . (b) bearing and choke region of the flow domain.

Fig. 1. Example of outputs from the finite element simulations. (a) and (b) shows the contour plot of the temperature distribution during extrusion of alloy A1, applying a ram speed of and an initial billet temperature of . (b) bearing and choke region of the flow domain.

Flow Patterns Figure 2 shows the corresponding flow pattern for the simulation presented in Figure 1. The dead metal zone is visible as a volume with little to no material flow in the extrusion direction, located near the end of the container. The semi-die angle which is used as input to the analytical model, is superimposed in Figure 2 (a). It is evident from this figure that is not clearly defined, as the flow patterns are curved. However, it seems reasonable to use values in the range degrees in the analytical solution, and a value of 45 degrees was selected in the present work.

Fig. 2. Simulation of material flow in the extrusion direction near the end of the container evaluated for alloy A1 applying a ram speed of and an initial billet temperature of . (a) 2D cross section with the superimposed semi-die angle used in the analytical model. (b) 3D contour plot of the material flow in the extrusion direction shown for the quarter model.

Fig. 2. Simulation of material flow in the extrusion direction near the end of the container evaluated for alloy A1 applying a ram speed of and an initial billet temperature of . (a) 2D cross section with the superimposed semi-die angle used in the analytical model. (b) 3D contour plot of the material flow in the extrusion direction shown for the quarter model.

Temperature Contributions in Analytical Model. In Figure 3, the temperature contributions , , and as predicted by the analytical model, are plotted as a function of the ram speed for alloy A1. The Figure show results for the experimental data grouped by three different
initial billet temperatures, i.e. 420,460 , and , respectively. Each figure also shows the sum of these contributions, and a comparison with corresponding measured temperature increase, i.e. the measured exit temperature minus the initial billet temperature. It is evident that the sum of the contributions agrees well with the measured temperature increase for all billet temperatures, although the model seems to underestimate the temperature increase for the initial billet temperatures of in Figure 3(a).

A closer inspection of Figure 3 reveals that adiabatic heating, , yields the largest temperature contribution for all combinations of billet temperature and ram speed. The figure also shows that the contribution from redundant work is the second largest positive contribution. This contribution also shows a stronger dependency on the ram speed than the other contributions in Figure 3. This is not surprising since is proportional to according to Eq. 17.

The friction-based contributions and show similar dependencies with the ram speed, and their magnitude are also in the same range. The expressions in Eq. 9a and Eq. 10, yield and proportional to and , respectively, when , as assumed in this work. Since , it follows that the difference between the two contributions can be expressed as when Eq. 9a applies, which is the case in the present examples. Hence, , as observed in Figure 3.

The contribution accounting for heat conduction in container and billet, i.e. can be positive or negative, depending on whether the container temperature is higher or lower than the initial billet temperature. In Figure 3(a), is small but positive which means a weak heating of the billet. This is because the initial billet temperature is which is slightly lower than the container temperature of . In Figure 3(b) and 3(c) is negative since the container temperature is lower than the billet temperature.

Fig. 3. The contributions to the profile surface temperature at the die outlet position, and the overall temperature increase compared with measurements for alloy A1. (a) billet temperature . (b) billet temperature . (c) billet temperature .

Fig. 3. The contributions to the profile surface temperature at the die outlet position, and the overall temperature increase compared with measurements for alloy A1. (a) billet temperature . (b) billet temperature . (c) billet temperature .

Overall Agreement Between Calculated and Measured Temperatures The described modelling framework was applied to calculate the exit temperatures for all tested combinations of alloy and homogenisation conditions, except C 1 . Figure 4 (a) shows the overall agreement between measured and calculated temperatures for FEM simulations, and the same comparison is done for the analytical model in Figure 4 (b). Measured and calculated temperatures are grouped by alloy and homogenisation condition. A closer inspection reveals that each alloy and homogenisation combination, e.g. A1 shows three groups of temperatures, corresponding to three different billet temperatures. In each group, the temperatures increase up to a maximum, and then drops suddenly. The temperature increase corresponds to increasing ram speed at constant billet temperature, while
the sudden drop, corresponds to transition from one billet temperature to another. The results in Figure 4 and the deviation between measured and predicted data are collected in scatter plots in Figure 5.

From these diagrams, it is evident both the numerical model and the analytical temperature model provide quite accurate predictions of the measured temperatures. Hence the maximum absolute value of the deviation between calculated and measured temperatures is for the FEM model, while the standard deviation is . The corresponding values for the analytical temperature model are , and , respectively. Hence, the analytical model gives slightly less accurate predictions than the FEM model for the present alloys, which is to be expected when considering the difference in complexity between the models.

Fig. 4. Comparison between the predicted and measured temperatures at the die outlet position for all alloy compositions and homogenisation treatment groups, except C1. (a) Predictions made by the FEM model compared to measured temperatures, grouped by alloy and homogenisation treatment. (b) Predictions made by the analytical model compared to measured temperatures, grouped by alloy and homogenisation treatment.

Fig. 4. Comparison between the predicted and measured temperatures at the die outlet position for all alloy compositions and homogenisation treatment groups, except C1. (a) Predictions made by the FEM model compared to measured temperatures, grouped by alloy and homogenisation treatment. (b) Predictions made by the analytical model compared to measured temperatures, grouped by alloy and homogenisation treatment.

Fig. 5. Comparison of the deviation between the predicted and measured temperatures at the die outlet position. (a) Scatter plot of FEM predictions vs. measured temperatures. (b) Scatter plot of analytical predictions vs. measured temperatures. In both (a) and (b) the fully drawn line indicates a one-to-one relation between experimentally obtained results and predicted temperatures. The dashed line indicates the one-to-one relation +/- two times the standard deviation calculated for the total deviation between measured and predicted exit temperatures.

Fig. 5. Comparison of the deviation between the predicted and measured temperatures at the die outlet position. (a) Scatter plot of FEM predictions vs. measured temperatures. (b) Scatter plot of analytical predictions vs. measured temperatures. In both (a) and (b) the fully drawn line indicates a one-to-one relation between experimentally obtained results and predicted temperatures. The dashed line indicates the one-to-one relation +/- two times the standard deviation calculated for the total deviation between measured and predicted exit temperatures.

Extrusion Limit Diagrams The predicted surface temperatures from the previous section were used to construct extrusion limit diagrams which are plots of the maximum allowable ram speed with respect to surface tearing as a function of billet preheat temperature. Such diagrams require that the critical temperature, above which hot tearing appears, is known. In the present work, the critical temperature for each alloy was obtained from measured temperatures at the profile surface combined with visual inspection of the surface at the die outlet during extrusion tests as described in [20]. In the tests, the profile surface was classified in three groups, i.e., no tearing, incipient tearing, or tearing. By analysing the data, critical temperatures for the transition from "no tearing" to "tearing" for each alloy, given in Table 2, were estimated. These temperatures, and the predicted deformation resistance parameters for each alloy in Table 2 were used as inputs to the analytical temperature model to calculate the resulting temperature at the die outlet as a function of billet temperature and ram speed. The resulting extrusion limit diagrams are shown as solid lines in Figure 6. The scattered data in Figure 6 show the experimental surface characterisation from the extrusion trials described in [20].

The calculated extrusion limit curves in Fig. 6 are generally in good agreement with the experimental results represented by the symbols. The model seems to capture the observed effect of alloy composition and homogenisation cycle on the resulting extrusion limits. Hence, alloys with low deformation resistance allow higher ram speeds than alloys with high deformation resistance. This can be seen by comparing for instance alloy A1 and F2 for a billet temperature of , where the critical ram speeds are and , respectively. The reason for higher ram speed for A1 compared with F2 is because the deformation resistance parameters and are lower for A1 than for F2, as can be seen from Table 2.

There is also an effect of critical temperatures for the transition from "no tearing" to "tearing" for each alloy, which is alloy dependent and therefore affects the diagrams in Fig. 6. The higher , the higher ram speed can be applied. This is the reason why for instance alloy E2, which has a high deformation resistance in terms of and values, still can be extruded with relatively high ram speeds according to Fig. 6. This is because for alloy E 2 is , which is the highest temperature in Table 2. Hence, it is the combination of , and that give the resulting extrusion limitation curves, and low and values in combination with high values are all positive in order to displace the extrusion limitation curves to higher values.

Since the predicted extrusion limit curves in Fig. 6 are significantly influenced by the estimated temperatures, it follows that the accuracy of the predicted temperatures at the die exit are also crucial for realistic results when applying the diagrams. Hence, relatively small inaccuracies in the prediction of the die exit temperature, may give serious inaccuracies in the predicted extrusion limitation curves. This is shown in Fig. 7, where the various curves represent different deviations from the predicted profile exit temperatures for alloy A1 from Fig. 6. It follows from this diagram that an inaccuracy of in the predicted exit temperature, yields a corresponding inaccuracy of about .

Fig. 6. Experimental distribution of surface tearing data for all alloy-treatment groups, except C1, compared to predicted extrusion limit. The experimental data are grouped by alloy composition and homogenisation treatment and redrawn from [20]. For each alloy-treatment group, open circles indicate a surface without hot tearing defects, partially filled circles indicate incipient hot tearing, and fully filled circles indicate hot tearing. Solid line shows the predicted extrusion limit diagrams applying the analytical exit temperature model.

Fig. 6. Experimental distribution of surface tearing data for all alloy-treatment groups, except C1, compared to predicted extrusion limit. The experimental data are grouped by alloy composition and homogenisation treatment and redrawn from [20]. For each alloy-treatment group, open circles indicate a surface without hot tearing defects, partially filled circles indicate incipient hot tearing, and fully filled circles indicate hot tearing. Solid line shows the predicted extrusion limit diagrams applying the analytical exit temperature model.

Fig. 7. Effect of deviation in surface exit temperature on resulting extrusion limitation curves, visualised for alloy composition and homogenisation group A1. Solid line indicates the original extrusion limit prediction, as shown for A1 in Figure 6. In increments of , the dashed lines show how temperature deviations in the analytically calculated exit temperature affects the prediction of the extrusion limit curve.

Fig. 7. Effect of deviation in surface exit temperature on resulting extrusion limitation curves, visualised for alloy composition and homogenisation group A1. Solid line indicates the original extrusion limit prediction, as shown for A1 in Figure 6. In increments of , the dashed lines show how temperature deviations in the analytically calculated exit temperature affects the prediction of the extrusion limit curve.

Conclusions

The original version of this paper is available on https://www.scientific.net/KEM.988.31.pdf

The main conclusions that can be drawn from this investigation are as follows:

A comparison between predicted and measured temperatures at the die exit shows that the presented analytical model is reasonably accurate compared with FEM simulations for the present profile geometry. Hence, standard deviations of and were obtained for the FEM model and the analytical temperature model, respectively, while the maximum absolute value of the deviation between calculated and measured temperatures was for the FEM model and for the analytical temperature model.

Both the analytical model and the FEM simulations capture the observed effect of alloy composition and homogenisation heat treatment on the resulting temperatures. This is because both models incorporate the effect of processing and alloy composition in the constitutive equations for the deformation resistance.

A comparison between calculated extrusion limit diagrams using the analytical temperature model, and results from real extrusion tests shows good correlations for the different alloys and homogenisation heat treatments applied.

A sensitivity analysis performed for one of the alloys in this investigation reveals that the predicted extrusion limit diagrams are sensitive to variations in the predicted exit temperature. Hence, an inaccuracy of in the predicted exit temperature, yields a corresponding inaccuracy of in the diagrams for the investigated alloy.

Based on the results in this investigation, it is concluded that the presented modelling approach gives sufficiently accurate predictions for future application in optimisation of process parameters in extrusion of profiles in the 6xxx series aluminium alloys.

Future Work

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Future work will be focused on extending the analytical modelling approach to more complex profile cross sections including hollow profiles. Work is also in progress to couple the critical temperature for incipient surface tearing to temperatures that can be estimated from multi-component phase diagrams using various types of microstructure-based models like the Alstruc-model that has been briefly described in the present article.

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The original version of this paper is available on https://www.scientific.net/KEM.988.31.pdf

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The original version of this eBook is available on https://www.scientific.net/book/12th-aluminium-two-thousand-world-congress-7th-international-conference-on-extrusion-and-benchmark/978-3-0364-1380-8

Key Engineering Materials, ISSN: 1662-9795, Vol. 987, pp 31-40

doi: 10.4028/p-8ZRuLD

© 2024 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2023-12-14

Revised:
2024-03-11

Accepted:
2024-03-12

Online:
2024-09-23

Automated Optimum Extrusion Die Design and Profile Quality Control Based on Simulation

The original version of this paper is available on https://www.scientific.net/KEM.987.31.pdf

Ivan Kniazkin , Nikolay Biba , Ivan Kulakov , Alexey Duzhev , Sergey Stebunov

QForm Group FZ LLC, Fujairah-Creative City, P. O. Box 4422 Fujairah, UAE

Micas Simulation Limited, Temple Court, 107 Oxford Road, Oxford, OX4 2ER, UK

ivanknjazkin@gmail.com, nick@qform3d.com, kulakov@qform3d.com, serg@gform3d.com

Keywords: Extrusion, Die Design, Optimisation, Simulation

Abstract

The paper presents the experience of development and implementation of an integrated approach of extrusion simulation with the automated design of the dies as a new way to speed up the technology development and its optimisation based on the QForm UK Extrusion simulation program and QForm Extrusion Die Designer (QExDD) design system. Bearing and prechamber optimisation types are considered for the porthole design. Welding quality and possible streaking lines in the profile are analysed for the tool construction with optimised prechamber contour.

Introduction

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Rapid electrification of automotive transport has significantly increased demand for high-strength aluminium alloy profiles. Competition among producers demands that the aluminum industry produce extrusions quickly with the highest shape accuracy. Therefore, the rapid development of effective extrusion technology becomes vital which defines the need for a modern approach to process analysis i.e., numerical simulation.

The article discusses the importance of considering various parameters when designing a new die set, apart from just the profile flow. To illustrate this point, the analysis of other parameters relevant to the final product requirements is provided. The most challenging defects resulting from die design are identified as welding quality of longitudinal seams [1], underfilling of the profile [2], and streaking lines [3].

Numerous techniques and approaches exist for extrusion tool design. Noteworthy publications [4][5] offer general guidance on tool design, ranging from drawing the hot profile and determining its position on the die to constructing the welding chamber of the tooling set. Additionally, new methods for extrusion tool design have emerged, encompassing various specialized techniques. One such example is presented in work [6], which introduces a novel design quality estimation method. This method utilizes the difference in bearing length within a local coordinate system, as opposed to commonly employed global coordinates.

The applicability of machine learning and artificial intelligence (AI) in porthole die design is explored in the works of authors [7]-[8]. These studies highlight the potential of using AI and machine learning techniques in the context of porthole die design. Furthermore, the design of porthole extrusion dies is extensively covered in the article [9]. The paper focuses on an optimisation process based on a Pareto-based genetic algorithm, aiming to demonstrate an integrated solution that combines a CAD-design system for extrusion dies and finite element method (FEM) simulation with an automated optimisation procedure.

Overall, the article emphasizes the need to consider multiple parameters and introduces various techniques and methodologies for die design, showcasing advancements in the field and the potential of integrating CAD-design systems, FEM simulations, and optimisation procedures.

Initial data for numerical simulation is a 3D model of the tooling set for which all forming surfaces, basic contours, and dimensions have to be as close as possible to the real tool to ensure the practical value of simulation results. A typical extrusion die set consists of a mandrel, die plate, and backer
(Fig. 1). Despite the great variety of the forming surfaces defining different conditions of flow there are three basic elements of the tooling set the modification of which directly affects the exit distribution of velocities in the profile: ports, prechamber, and bearings.

Fig. 1. Simplified representation of porthole die set for aluminium profile extrusion

Fig. 1. Simplified representation of porthole die set for aluminium profile extrusion

Automated optimisation of ports is a complicated task that affects not only the flow balance of the profile but other output parameters of the extrusion process and the tool structure itself which defines deflection and tool life respectively. Whereas bearings and prechamber modification don't lead to a significant change in extrusion parameters except for velocity distribtuion, although in some cases may slightly affect charge weld propagation and surface quality of the profile.

Bearings and prechamber have limitations in influencing the profile flow depending on the type of the profile, its thickness, and other geometrical specifics of the process. Thus, in the case of a poorly designed tool, it's impossible to balance the flow by varying bearing heights or prechamber shapes. In general, bearings should not be too short to ensure the required tool life but there is also no sense to make them too long since deformation during the extrusion process leads to inclinations of bearing surfaces since friction is only applied on some effective length with the bearing in excess of this length causing no impact on local velocity of the profile. A similar limitation applies to the prechamber: its contour is limited by the welding chamber contour from one side and by the profile contour from the other side. However, since it's practically the easiest way to adjust the tool and improve the flow without significant structural change, bearings and prechamber play a significant role in the tool correction and designing.

CAD-CAE Combination

The original version of this paper is available on https://www.scientific.net/KEM.987.31.pdf

The proposed approach to optimal technology design includes several steps (Fig. 2). The first step is a preliminary design of the extrusion tooling set using a profile drawing. Using built-in algorithms and specialised tools, the user creates the general shape of the tool set including support components. At the same time, the most labor-consuming parts of the work are automated by specialised bearing, prechamber, relief and other contour editors. This method significantly speeds up the work and stores the created shapes in the parametric form that is the most important for subsequent optimisation. Then the other process parameters (alloy grade, temperature, extrusion velocity, etc.) are added and the process data are transferred to the simulation. As soon as the simulation is completed, its results are returned to the design program that uses different optimisation algorithms to vary the geometry of the prechamber or bearings to reduce the velocity non-uniformity until convergence is reached.

Fig. 2. Graphical representation of the optimisation integration of CAD and CAE

Fig. 2. Graphical representation of the optimisation integration of CAD and CAE

Optimisation Process

The original version of this paper is available on https://www.scientific.net/KEM.987.31.pdf

Traditionally the design verification iterations were carried out manually, which increased the requirements for the designer's skills: based on the analysis of the simulation results they had to improve the model according to possible correction strategies [10]-[11]. Meanwhile, automated optimisation eliminates routine steps of simulation setup and further geometry re-preparation. Furthermore, optimisation allows the user to skip the correction step since CAD-CAE chain automatically improves the model using optimisation algorithms according to the simulation results.

As it was mentioned before, the optimisation of ports is an extremely challenging task since it affects many aspects of the extrusion process. This is why the main automatization attempts in case of port modifications are focused on the development of the methodology of how to make an initial design of the ports good enough for further bearings/prechamber adjustment in case of unbalanced flow or how to re-design ports based on simulation results [12] but not how to correct them.

The main goal of bearings [13] or prechamber [14] optimisation is to balance the flow of the profile. Using finite elements method (FEM) language it means that absolute velocity value ( ) in each node ( ) has to be as close as possible to average profile velocity ( ) that is calculated as follows:

where is the total amount of nodes in the profile.
From a mathematical point of view the optimisation of profile flow can be formulated as a minimisation of the variance ( ) of velocity distribution in the profile:

Eventually, for an ideally balanced flow, variance is equal to zero.
In QForm UK Extrusion simulation there is another distribution used for quantitative analysis of the flow imbalance - velocity deviation (VD) that is calculated as follows:

The utilization of this distribution enables the assessment of flow imbalance as a percentage of the average profile velocity. A positive sign indicates a higher profile velocity at a specific location compared to the average, while a negative sign signifies the opposite. Hence, the objective is to minimise the deviation in velocity along the entire profile contour, striving for a value as close to zero
as achievable. Essentially, this parameter corresponds to the relative velocity ( ) of the profile, employed in QExDD interface, and its calculation can be expressed as follows:

For the parameter, values exceeding 1 indicate regions of the profile with velocities higher than the average, while values below 1 indicate the opposite. The optimisation objective in this scenario is to achieve a relative velocity of 1 . Consequently, the ultimate goal is to get the VD (velocity deviation) and (relative velocity) obtained from the FEM simulation as close as possible to the respective aiming values.

The optimisation scheme depicted in Figure 3, encompasses the definition of initial bearings and prechamber, as well as the addition and removal of tool material in the respective components through the optimisation procedure.

Fig. 3. On the explanation of optimisation procedure

Fig. 3. On the explanation of optimisation procedure

After conducting the simulations, the objective of optimisation is to determine the optimal coefficient ( ) that governs the influence of relative velocity ( ) on the modification of prechamber offset or bearing length thereby ensuring a uniform profile flow. This coefficient is incorporated into the correction formula, which controls the adjustment of the prechamber offset ( ) during prechamber optimisation, or the bearing length during bearing optimisation:

where and initial values of the prechamber offset and bearing length respectively.
There are three ways of optimisation available in QExDD: subsequent optimisation, batch optimisation and combined approach. In the case of prechamber, the variable parameter is the distance between the bearing contour and prechamber wall, whereas for the bearings it is height.

Subsequent optimisation. This way of optimisation allows guaranteed step-by-step improvement of the profile flow. In this case, the same correction parameter is used in optimisation formula for each optimisation iteration until the required convergence is reached. It allows the user subsequently get closer to the desired result by automated modification of the tool geometry.

Batch optimisation. This way of optimisation allows running simulations in parallel with different correction parameters of optimisation formula. Thus, this method allows getting the most appropriate correction parameter for the design ensuring the minimum of velocity variance.

Combined approach. This way of optimisation combines the advantages of previously described types. Since subsequent optimisation doesn't ensure fast convergence using the default value of the correction parameter but batch optimisation finds the optimal one, then the combination of these two approaches allows faster minimisation of velocity variance. Thus, first of all, batch optimisation is used in order to find an optimised value of the correction parameter, and then this parameter is used for subsequent optimisation.

Application of the Optimisation Methodology

The original version of this paper is available on https://www.scientific.net/KEM.987.31.pdf

In this work, a porthole die set for the extrusion of a hollow profile with (1.6-2.2) mm thickness was designed in order to investigate the capabilities of the optimisation engine of QExDD based on the simulation results obtained in QForm UK Extrusion.

This work aims to present a case study for industrial tool design. The design of the ports was deemed acceptable and sufficiently accurate, allowing for further flow improvement by adjusting other components. Consequently, optimisation was carried out by automating the variation of bearing length and prechamber offset.

Process parameters and geometrical details of the process are listed in Table 1. For this project, bearings and prechamber optimisations were used separately in order to check the sensitivity of the optimisation process to different optimisation variables and to discuss the limitations of these variables.

Table 1. Technological and geometrical parameters of the project

Parameter | Value
Alloy | AA 6063
Extrusion ratio | 74.2
Container temperature [°C] | 430
Billet temperature [°C] | 480
Tool temperature [°C] | 450
Extrusion velocity [mm/s] | 5
Billet length [mm] | 1000
Billet diameter [mm] | 203
Container diameter [mm] | 210

Bearing optimisation. The original design of this project contained certain prechamber contour and flat bearings with 4 mm height without transitions. For this design, the simulation showed a significantly unbalanced flow. However, after the first iteration of optimisation process, the simulation showed a much more uniform flow and predicted the ideal flow for the second iteration (Fig. 4).

Fig. 4. Velocity deviation (3) distribution for different bearing designs: initial, intermediate. c-optimised

Fig. 4. Velocity deviation (3) distribution for different bearing designs: initial, intermediate. c-optimised

Bearing maps generated during the optimisation process are presented in Figure 5. As can be seen, the difference in bearing heights for the optimised construction was about 6 mm which is quite a lot for this kind of profile. Additionally, the minimal height of the bearing was set to be equal to 1.3 mm which is in general too short for a profile of a designed thickness. This basically means that the quality of the initial design of the ports isn't sufficient to allow profile flow adjustment by bearings modification alone.

Fig. 5. Bearing maps for different designs: initial, intermediate, optimised

Fig. 5. Bearing maps for different designs: initial, intermediate, optimised

Prechamber optimisation. In this case, the prechamber contour was varied for the design with 4 mm height flat bearings. In a similar way to the bearings optimisation process, the goal of optimisation was achieved for the second iteration (Fig. 6). Unlike the bearings, the variable contour of the prechamber has a geometrical limitation as the profile contour from one side and the welding chamber from another, therefore the algorithm follows these rules by default while optimising.

Fig. 6. Velocity deviation (3) distribution for different prechamber designs: initial, intermediate. c - optimised

Fig. 6. Velocity deviation (3) distribution for different prechamber designs: initial, intermediate. c - optimised

Initial and generated contours of prechamber as well as the distribution of relative velocity for the initial design are presented in Figure 7. It's clearly seen that for the places where the profile flowed faster, the prechamber contour got closer to the profile which allowed the reduction of the material stream through the considered place, whereas for the slow parts the contour got wider.

For reference, respective values of velocity variance for different bearing designs and prechamber contours are presented in Table 2.

Fig. 7. Distribution of initial relative velocity ( 4 ) with prechamber contours: blue - initial, green - intermediate, red - optimised

Fig. 7. Distribution of initial relative velocity ( 4 ) with prechamber contours: blue - initial, green - intermediate, red - optimised

Essentially, although velocity variance is relatively less for the bearings optimisation, the prechamber optimisation is preferable for this project since it ensures reliable tool design within geometrical limitations common for the extrusion tools.

Profile Quality Control

The original version of this paper is available on https://www.scientific.net/KEM.987.31.pdf

In general, achieving uniform material flow in profile extrusion doesn't guarantee the complete optimisation of the design. Therefore, it is essential to analyse other parameters such as tool stress, deformation, and profile quality indexes using simulation results. By varying appropriate technological or geometrical parameters, these aspects can be improved [15]. This implies that the optimisation process should consider not only flow as the objective but also factors like tool life, welding quality, charge weld length [16], back-end defects [17]-[18], microstructural defects [19][21] and more. Consequently, the optimisation process becomes more time-consuming and complex.

Additionally, the requirements for profiles are directly linked to their practical applications. For automotive profiles, welding quality [22]-[23] is a crucial parameter in quality control, while decorative profiles prioritize the quality of visible parts on the surface of the final product. One wellknown defect that significantly affects profile surface quality is streaking, which appears as shaded lines darker or lighter than the core material [24]. Therefore, the absence of streaking lines and high welding quality are vital for ensuring the profile meets high standards, regardless of its intended application.

This means that the optimisation function should offer flexibility to include or exclude specific objectives based on the specific requirements. This conclusion sets the direction for the further development of automated optimisation in the extrusion industry.

Figure 8 presents the results of calculations using QForm UK's built-in streaking lines criterion and welding quality index. It is evident that all streaking lines are either located on the corners or on the inner surfaces of the profile. Moreover, the welding quality index is close to or exceeds 1 for the welding seams, indicating the formation of strong structural bonds between metal streams.

Fig. 8. Streaking lines (left) and welding quality (right) distributions calculated for the omtiimised design

Fig. 8. Streaking lines (left) and welding quality (right) distributions calculated for the omtiimised design

Conclusions

The original version of this paper is available on https://www.scientific.net/KEM.987.31.pdf

As a summary, the presented work allows to draw the following key conclusions:

Flow optimiser of QExDD together with simulation in QForm UK Extrusion allows reliable automated bearing and prechamber optimisation

Both bearings and prechamber have limited possibility to influence the flow. Ports design defines the possibility to improve the flow by bearings adjustment or prechamber modification

Automated optimisation is possible only when the modification process doesn't negatively affect the structural specifics of the tooling set. Therefore, the optimisation of the prechamber and bearings is practically safe and reasonable

After the straight and uniform material flow is reached, the other parameters of the profile quality can be analysed using the results of simulation depending on the application of the final product

The proposed integrated approach has shown its great practical applicability and it follows the general trends of Industry 4.0

References

The original version of this paper is available on https://www.scientific.net/KEM.987.31.pdf

L. Donati, L. Tomesani, G. Minak, Characterization of seam weld quality in AA6082 extruded profiles, Journal of Materials Processing Technology, 191 (2007) 127-131.

I. Kniazkin. Prediction of Underfilling Defect in Aluminium Profile Extrusion Based on ALE Simulation. Key Engineering Materials, 926 (2022) 537-544.

Y. Pio Lim, H. Kam Lim, Quality Assurance of Aluminium Extrusion for 6xxx Series, Recent Advancements in Aluminum Alloys, IntechOpen, 2024.

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P.K. Saha, Aluminum Extrusion Technology, ASM International Materials Park, 2000, pp 92-104.

C. Lin, R. Ransing, An innovative extrusion die layout design approach for single-hole dies, Journal of materials processing technology, 209 (2009) 3416-3425.

J. Llorca-Schenk, J. Rico-Juan, M. Sanchez-Lozano. Designing porthole aluminium extrusion dies on the basis of eXplainable Artificial Intelligence, Expert Systems with Applications, 222 (2023) 119808.

G. Zhao, H. Chen, C. Zhang, Y. Guan, Multiobjective optimization design of porthole extrusion die using Pareto-based genetic algorithm, The International Journal of Advanced Manufacturing Technology, 69 (2013) 1547-1556.

C. Lucignano, R. Montanari, V. Tagliaferri, N. Ucciardello, Artificial neural networks to optimize the extrusion of an aluminium alloy, Journal of Intelligent Manufacturing, 21 (2010) 569-574.

N. Biba, S. Stebunov, A. Vlasov, Application of QForm Program for Improvement of the Die Design and Profile Extrusion Technology, Proceedings of the Ninth International Aluminum Extrusion Technology Seminar & Exposition, 2008.

A. Selvaggio, A. Segatori, A. Guzel, L. Donati, L. Tomesani, E. Tekkaya, Extrusion Benchmark 2011: Evaluation of different design strategies on process conditions, die deflection and seam weld quality in hollow profiles, Key Engineering Materials, 491 (2012) 1-10.

J. Llorca-Schenk, I. Sentana-Gadea, M. Sanchez-Lozano, Design of porthole aluminium extrusion dies through mathematical formulation, Materials Today Communications, 27 (2021) 102301.

V. Viswanath Ammu, P. Mahendiran, A. Agnihotri, S. Ambade, P.R. Dungore, A simplified approach for generation of bearing curve by velocity distribution and press validation for aluminum extruded profile, The International Journal of Advanced Manufacturing Technology, 98 (2018) 1733-1744.

C. Zhang, S. Yang, Q. Zhang, G. Zhao, P. Lu, W. Sun, Automatic optimization design of a feeder extrusion die with response surface methodology and mesh deformation technique. The International Journal of Advanced Manufacturing Technology 91 (2017) 3181-3193.

N. Biba, I. Kniazkin, Digital Certification of Extrusion Dies Based on Simulation, Light Metal Age, April, (2022) 26-28.

E.C. Sariyarlioglu, M. Negozio, T. Welo, J. Ma, Charge weld evolution in hollow aluminum extrusion: Experiments and modeling, CIRP Journal of Manufacturing Science and Technology, 49 (2024) 14-27.

M. Negozio, R. Pelaccia, L. Donati, B. Reggiani, FEM Analysis of the Skin Contamination Behavior in the Extrusion of a AA6082 Profile, Key Engineering Materials, 926 (2022) 452459.

I. Kniazkin, R. Pelaccia, S. Di Donato, L. Donati, B. Reggiani, N. Biba, R. Rezvykh, I. Kulakov, Investigation of the skin contamination predictability by means of QForm UK extrusion code, Materials Research Proceedings, 28 (2023) 543-552.

M. Negozio, A. Segatori, R. Pelaccia, B. Reggiani, L. Donati, Experimental investigation and numerical prediction of the peripheral coarse grain (PCG) evolution during the extrusion of different AA6082 aluminum alloy profiles, Materials Characterization, (2024) 113723.

M. Negozio, R. Pelaccia, L. Donati, B. Reggiani, Numerical investigation of the surface recrystallization during the extrusion of a AA6082 aluminum alloy under different process conditions, The International Journal of Advanced Manufacturing Technology, 129 (2023) 1585-1599.

M. Negozio, R. Pelaccia, L. Donati, B. Reggiani, Simulation of the microstructure evolution during the extrusion of two industrial-scale AA6063 profiles, Journal of Manufacturing Processes, 99 (2023) 501-512.

I. Kniazkin, A. Vlasov. Quality prediction of longitudinal seam welds in aluminium profile extrusion based on simulation, Procedia Manufacturing, 50 (2020) 433-438.

J. Yu, G. Zhao, W. Cui, L. Chen, X. Chen, Evaluating the welding quality of longitudinal welds in a hollow profile manufactured by porthole die extrusion: Experiments and simulation, Journal of Manufacturing Processes, 38 (2019) 502-515.

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Chapter 3

Materials Properties and Processing Technologies Assessment

The original version of this eBook is available on https://www.scientific.net/book/12th-aluminium-two-thousand-world-congress-7th-international-conference-on-extrusion-and-benchmark/978-3-0364-1380-8

Materials Science Forum, ISSN: 1662-9752, Vol. 1130, pp 3-12

doi: 10.4028/p-J0bRTh

© 2024 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2023-11-18

Revised:
2024-02-12

Accepted:
2024-03-05

Online:
2024-10-31

Feasibility Study of Solid-State Recycling through Direct Hot Rolling of AA5754 Aluminum Chips for Automotive Applications

The original version of this paper is available on https://www.scientific.net/MSF.1130.3.pdf

Mohamad El Mehtedi , Pasquale Buonadonna , Rayane El Mohtadi , Gabriela Loi , Francesco Aymerich , Noomane Ben Khalifa , Mauro Carta

Department of Mechanical, Chemical and Materials Engineering, University of Cagliari, Italy

Institute for Production Technology and Systems, Leuphana Universität Lüneburg, Germany

Institute of Material and Process Design, Helmholtz-Zentrum Hereon, Germany

m.elmehtedi@unica.it, pasquale.buonadonna@unica.it, rayane.elmohtadi@unica.it, gabriela.loi@unica.it, francesco.aymerich@unica.it, noomane.ben_khalifa@hereon.de,

Keywords: Solid-state recycling, AA5754 chips, Direct Hot Rolling, microstructure, automotive.

Abstract

Recently, researchers have done a lot of efforts to develop new solid-state recycling processes, both experimentally and developing numerical models. This kind of process is energysaving and environmentally friendly compared to the conventional aluminum recycling process because it avoids the melting step. The purpose of this study is to assess the feasibility of an innovative solid-state recycling process using direct hot rolling in a non-heat-treatable aluminum alloy for automotive applications. The chips made of AA5754 have been produced by turning a bar without the usage of lubricants and compacted with a 150 kN load; the compacted billets were treated at and directly hot rolled in several successive passes. Rolled samples are then analyzed in terms of Vickers microhardness and microstructure in both as-rolled and heat treatment conditions, this last was performed at simulating the process of paint-bake. The produced sheets exhibit an excellent consolidation and bonding between chips.

Introduction

The original version of this paper is available on https://www.scientific.net/MSF.1130.3.pdf

The automotive industry is globally recognized for its technological advancement and innovation. Modified or newly designed aluminum alloys are being proposed as specific solutions, due to increasing economic and political pressure to reduce fuel consumption and emissions, there has been a significant increase in efforts towards lightweight construction in automobiles [1]. Specifically, sheet applications for new lightweight structural parts and body-in-white construction are those of main interest. Major producers of semi-finished aluminum alloy products are making significant efforts to meet the primary requirements, which include sufficient strength, good formability, joining, high corrosion resistance, recyclability, and low material and fabricating costs [2]. The demand for aluminum in finished products has risen dramatically by 30 times since 1950, currently reaching 45 million tons per year. Experts forecast that this trend will persist, with demand expected to increase by times the current levels by the year 2050. However, the production of aluminum accounts for of the world's electricity usage and contributes to of global emissions. Meeting the target of reducing emissions by by 2050 while accommodating the growing demand for aluminum will require a significant reduction of at least in emissions per ton of aluminum produced, which poses a daunting challenge [3]. The primary industry of aluminum presents a significant environmental challenge due to the high level of energy consumption and emissions associated with the Bayer and Hall-Héroult processes, mining exploitation and waste release, in particular red mud [4-6]. Secondary aluminum metallurgy is a well-established and widely used methodology that can significantly reduce the environmental impact of primary aluminum production, requiring only of the total energy and reducing emissions by [7]. Recent estimates suggest that the quantity of recyclable aluminum will increase by more than double by 2050 [8]. Accordingly, in these years it has been studied how to
recycle aluminum scrap, including both post-consumer and new scrap from working processes. To achieve further energy savings and optimize the recycling process, solid-state recycling (SSR) techniques have been proposed such as friction stir extrusion (FSE), friction stir consolidation (FSC), equal channel angular pressing (ECAP), and other severe plastic deformation processes (SPD) [9-11]. FSE is still in its early development stage due to a lack of literature on the subject and industrial competitiveness issues. Tang et al. [12] used FSE to create defect-free wires of aluminum AA2050 and AA2195 from machining chips, and they observed that the wires displayed a favorable combination of microhardness and bend ductility. El Mehtedi et al. [13,14] studied the feasibility of FSE process to produce defect-free wires of pure AA1090 and AA1099 aluminum chips. Other researchers studied FSE process of pure magnesium and AZ31 chips [15,16]. Baffari et al. [17] designed a prototype of a continuous FSE machine to make the process continuous. Other studies focus their attention on energy efficiency characterization to recycle aluminum alloy scraps by FSE [18,19]. FSC is a process similar to FSE with the exception of the die that doesn't have an extrusion channel. According to previous research papers concerned with solid-state recycling process, it allows obtaining energy saving with respect to the conventional remelting-based route [20] and mechanical properties comparable to the base material [21]. SPD is a commonly utilized method for creating materials with a fine-grained structure that enhances strength and fracture toughness [22]. ECAP is one of the common processes commonly used for SSR: there are only a few research papers that examine the combination of extrusion and ECAP process. Ying et al. [23] proved that the ECAP process led to a refinement in crystal grain size in the recycled specimens, and to an improvement in bonding thanks to the combination with hot extrusion. Also, Krolo et al. studied the effect of direct hot extrusion and ECAP at different temperatures to investigate the microhardness, electrical conductivity, and mechanical properties of recycled samples [24,25]. Hasse et al. [26,27] integrated an ECAP die into a conventional hot extrusion process to produce superior SSR samples.

Miscellaneous methods that include rolling have been investigated by few authors. Suzuki et al. [28] employed hot rolling as the second step in the SSR process after extruding chips, while Chiba et al. [29] investigated the possibility of recycling chips through cold extrusion followed by cold rolling.

In another study, Allwood et al. [30] investigated the potential of cold bonding through forging, rolling, or extrusion processes, performed independently. Their results showed that bonding could occur with sufficient strain, but some voids may remain.

To the authors' best knowledge, this should be the first paper to propose a new technique that involves only direct hot rolling of compacted chips of AA5754, without any preceding processes. Rolling was chosen as a promising method since it represents one of the main working processes and it is used in the production of many automotive components. This paper aims to achieve two main objectives. The first objective is to investigate the microstructure of the recycled samples, and on the other hand, to analyze the microhardness of the AA5754 alloy before and after a heat treatment that simulates the paint-bake process, which is commonly used in the automotive industry.

Experimental Procedures

The original version of this paper is available on https://www.scientific.net/MSF.1130.3.pdf

The chips were produced by turning a bar of AA5754 aluminum alloy without the use of lubricants. The nominal chemical composition of the studied alloy is reported in Table 1.

Table 1. Chemical composition of the alloy.

Elements
(wt.%)
Si
Fe
Cu
Mn
Mg
Zn
Ti
Cr
Al

<0.40
<0.40
<0.10
<0.50
2,90
<0.20
<0.15
<0.30
bal.

Afterward, the chips were cold compacted in a circular mold with a force of 150 kN and then underwent a two-hour heat treatment at in a muffle furnace. The resulting discs have a diameter of 40 mm and a thickness of 9 mm . Subsequently, the compacted chips were hot
rolled (HR) at for 6 passes, without the use of lubricants. The rolling machine is a two-highrolling mill "BW200", manufactured by Carl Wezel, characterized by a 130 mm roll diameter and a constant rotational speed of 52 rpm . A schematic overview of the process is reported in Fig.1. The specimen returns to the furnace for 10 minutes after each pass to restore the initial temperature. The final thickness is achieved through a last cold rolling (CR) pass. A detailed breakdown of all the rolling passes is provided in Table 2.

Fig. 1. Schematic overview of the process.

Fig. 1. Schematic overview of the process.

Afterwards, the jagged borders of the samples were cut, and some samples underwent heat treatment (HT) at for 20 minutes to simulate the paint-baking process applied in the automotive industry. The temperature was measured on the surface of the samples using a K thermocouple-type and a Portable Digital Thermometer, TASI-8620.

Table 2. Detailed rolling schedules.

| HR passes | CR
Pass number | 1 | 2 | 3 | 4 | 5 | 6 | 7
Thickness (mm) | 9 | 5 | 4 | 2.5 | 1.75 | 1.25 | 0.8
True strain |  | 0.6 | 0.2 | 0.5 | 0.4 | 0.3 | 0.4

Vickers Microhardness was assessed under two conditions: as rolled and after heat treatment (HT), using a Shimadzu Microhardness Tester, type M, with a load of 200 gf . A minimum of six measurements were taken for each condition after grinding the surface of every sample with 2500-grit paper. Hardness was measured in both the cross-section and the rolling direction section of all samples, as well as on the surface of each sample. Micrographs were carried out for both conditions, as rolled and after HT, under polarized light using a light microscope (Olympus BX53M) equipped with an Olympus UC90 camera. To reveal the grain microstructure, the samples were electropolished with at and then etched with Barker's reagent at 15 volts for 30s.

Results and Discussions

The original version of this paper is available on https://www.scientific.net/MSF.1130.3.pdf

Fig. 2 shows the produced samples starting from the compacted chips, after hot rolling, and after cold rolling. Vickers microhardness has been measured in the as-rolled sample, and on the same sample after the heat treatment at that simulates the paint bake. The measurements were performed on the same sample for each condition to reduce the variability of the measurements, as the process itself is highly variable depending on the original recycled material. Nevertheless, for each condition, there is a lot of variability in the values of the microhardness as is possible to see in Table 3. The mean value on the cross-section is around 80 HV while it's higher in the rolling direction (RD) with 88 HV . On the surface, the hardness is considerably high reaching . The increase in hardness is affected by the oxide present on the surface and by the strengthening effect due to the roll friction effects, which lead to a greater hardness near the surface rather than in the middle of the sheet's thickness. After heat treatment, the hardness decreases in all zones except for the cross-section where the mean value stays constant as shown in Fig. 3.

Fig. 2. a) Compacted chips before recycling, b) sample after 4 passes hot rolling, c) sheet after cold rolling.

Fig. 2. a) Compacted chips before recycling, b) sample after 4 passes hot rolling, c) sheet after cold rolling.

In Figs. 4 and 5, it is possible to observe the microstructures of the recycled sheet samples. The length of the markers in the figures is represented by the entire length of the white box. They appear like a very layered microstructure due to the characteristic of the original material and oriented along the rolling direction. The recrystallization of the grains could be stacked by the presence of oxide around the original chips. It is characterized by higher elongated grains in the RD with respect to the cross-section. The heat treatment appears to not affect the shape of the grains. Each layer is separated and adorned by the oxide that is present on the surface of the original chips. It is shown by several authors that bonding occurs between chips if the oxide layer is broken during plastic deformation. For example, Suzuki et al. [28] have increased the bonding between chips after extrusion, with the usage of differential speed rolling, increasing also the mechanical properties and the corrosion resistance.

Table 3. measured on different zones of the sample, and in different conditions.

| HV₂₀₀ of recycled-chips | HV₂₀₀ of recycled-chips after HT
| CROSS | RD | SURF | CROSS | RD | SURF
Mean | 79.2 | 88.2 | 104.2 | 79.2 | 85.0 | 97.6
St. dev. | 5.6 | 6.5 | 4.8 | 3.6 | 7.7 | 5.1

Fig. 3. Vickers microhardness ( ) measured on samples.

Fig. 4. Optical micrograph of the samples, a) Recycled chips in the RD, b) recycled chips in the RD after HT; c) Recycled chips in the cross section; d) Recycled chips in the cross section after HT.

Fig. 4. Optical micrograph of the samples, a) Recycled chips in the RD, b) recycled chips in the RD after HT; c) Recycled chips in the cross section; d) Recycled chips in the cross section after HT.

According to Zhang et al. [31], the oxide layers inhibit the recrystallization of the grains during the recycling process in AA6061 alloy, resulting in a different microstructure in the recycled samples. Consequently, lower mechanical properties are obtained in the recycled samples, which are also attributed to the lack of Mg from the original composition of the alloy caused by the oxidation reaction.

The mechanical properties of the recycled sheets, in terms of microhardness, are comparable to those obtained by the combination of heat treatment, friction stir welded, and cold rolled sheets [32]. Thus, this indicates the excellent bonding of the chips after the direct hot-rolling process demonstrated in this study.

In Fig. 6, it is possible to observe the oxide layer that separates each layer. It is broken into numerous parts, meaning that bonding occurs. Moreover, with higher magnification, it is not possible to see any difference among the as-rolled condition and after the heat treatment.

Chino et al. [33] used a 1600:1 extrusion ratio to achieve a uniform dispersion of oxide layers around the chips, which ideally translates to a real strain of 7.4. In rolling processes, high deformation is not typically undergone; the maximum deformation experienced in this case, according to the rolling schedule in Table 2, has been 0.6 during the initial rolling pass, leading to a total real strain of 2.4 from the initial thickness to the final pass. However, it is noticeable that the oxide layer around the chips has broken at several points, revealing some continuity in the matrix of the aluminum alloy.

Fig. 5. Optical micrograph of the samples, a) Recycled chips in the RD, b) recycled chips in the RD after HT; c) Recycled chips in the cross section; d) Recycled chips in the cross section after HT.

Fig. 5. Optical micrograph of the samples, a) Recycled chips in the RD, b) recycled chips in the RD after HT; c) Recycled chips in the cross section; d) Recycled chips in the cross section after HT.

Fig. 6. Optical micrograph of the samples, a) Recycled chips in the RD, b) recycled chips in the RD after HT; c) Recycled chips in the cross section; d) Recycled chips in the cross section after HT.

Fig. 6. Optical micrograph of the samples, a) Recycled chips in the RD, b) recycled chips in the RD after HT; c) Recycled chips in the cross section; d) Recycled chips in the cross section after HT.

Conclusions

The original version of this paper is available on https://www.scientific.net/MSF.1130.3.pdf

Solid-state recycling processes have been receiving considerable attention from researchers due to their energy-saving and environmentally friendly attributes. This study aimed to evaluate the feasibility of a new solid-state recycling process involving direct hot rolling of AA5754 chips, a commonly used alloy in automotive applications. The results demonstrate the successful transformation of chips into rolled sheets.

To summarize, the results presented in this paper indicate that:

recycling methods employing direct hot rolling produce jagged edges that require refiling, resulting in the generation of scraps that need further recycling; hence, additional research is necessary to optimize the process and minimize waste;

microstructures show different textures compared to typical 5xxx aluminum sheets in the asrolled condition; chips-recycled samples display a layered microstructure, with oxide seeming to separate each layer. At higher magnification, the oxide layer appears broken at various points, indicating excellent bonding between chips.

microhardness measurements conducted across different zones of the sample, both in the asrolled condition and after simulating the paint bake process via heat treatment, demonstrate
slight variability along the sheets' sections. The Surface and rolling direction exhibit higher hardness, while the cross-section displays lower values;

microhardness decreases after heat treatment on the surface and in the rolling direction of the sample but remains constant in the cross-section.
In conclusion, this innovative recycling process shows promising potential, though this is just a first study. Subsequent studies will involve SEM analysis.

Funding

The original version of this paper is available on https://www.scientific.net/MSF.1130.3.pdf

This research received co-financing of the European Union - FESR o FSE, PON Research & Innovation 2014-2020.

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The original version of this paper is available on https://www.scientific.net/MSF.1130.3.pdf

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The original version of this eBook is available on https://www.scientific.net/book/12th-aluminium-two-thousand-world-congress-7th-international-conference-on-extrusion-and-benchmark/978-3-0364-1380-8

Materials Science Forum, ISSN: 1662-9752, Vol. 1130, pp 13-23

doi: 10.4028/p-Sx9Jhd

© 2024 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2023-11-21

Revised:
2024-03-13

Accepted:
2024-03-18

Online:
2024-10-31

Optimization of Geometric Design of Extruded Products Incorporating Properties, Cost and CO2 Footprint

The original version of this paper is available on https://www.scientific.net/MSF.1130.13.pdf

Anders Nesse , Mads Iddberg , Ole Runar Myhr , Trond Furu

Hydro Extrusions, Drammensveien 264, Oslo, Norway, anders.nesse@hydro.com

SINTEF Manufacturing, Grøndalsvegen 2, Raufoss, Norway, mads.iddberg@sintef.no

Hydro Aluminium R&D, Romsdalsvegen 1, Sunndalsøra, Norway, ole.runar.myhr@hydro.com

Norwegian University of Science and Technology, Department of Structural Engineering, Richard Birkelands vei 1A, Trondheim, Norway

Norsk Hydro, Corporate Technology Office, Drammensveien 264, Oslo, Norway, trond.furu@hydro.com

Norwegian University of Science and Technology, Department of Material Science and Engineering, Alfred Getz vei 2, Trondheim, Norway

anders.nesse@hydro.com, mads.iddberg@sintef.no, ole.runar.myhr@hyodr.com, trond.furu@hydro.com

Abstract

In this paper, a numerical simulation methodology has been applied to optimize the design of extruded aluminium products. The methodology, TM, incorporates product properties, production- and material costs as well as footprint in an optimisation procedure. This allows for multi-objective optimisation and avoids sub-optimisation of for instance properties on the expense of production costs or emissions. The outcome that follows from this multi-objective optimisation procedure, is that the resulting profile cross section will be different when the optimisation is based solely on property considerations, than when costs, and emissions are introduced in the optimisation procedure.

The present methodology requires that the main processes and operations along the aluminium process chain are represented by physics based, predictive models of various types, including material- and mechanical models, in addition to cost-, and sustainability models. A standard multiobjective optimization algorithm is used to combine the models and for automatic running throughprocess simulations in iterations.

In this article, the methodology has been applied for optimisation of the profile cross section in case-studies with various user requirements. It has been demonstrated that the resulting cross section geometry depends on the specified relative importance of conflicting requirements like the desire for high productivity on the one hand, and the desire for low material costs and low emissions on the other.

Introduction

The original version of this paper is available on https://www.scientific.net/MSF.1130.13.pdf

To compete in a wide range of market segments, the aluminium industry must meet the customer requirements in terms of product quality, consistency, and price. In addition, sustainability and low carbon products are now becoming increasingly important for consumers, producers, and legislators. The complexity of balancing these opposing product requirements is proposed solvable with a holistic modelling tool, . With respect to the -emission, each process and operation along the aluminium production chain, from bauxite and alumina to the finished extruded profile, must be taken into consideration. The use of end-of-life aluminium products becomes more and more attractive as a metal source as there is a reduction of energy consumed compared to primary based metal [4]. A metal charge consisting of significant amounts of post-consumer scrap (PCS) may have a wider chemical composition window than primary based metal depending on the scrap type
and the available sorting methodologies. Consequently, high levels of PCS may affect both the thermomechanical treatment along the value chain and the properties of the product.

The cost of an aluminium product depends on the weight of the extruded profile. The present article demonstrates that the optimum profile design for a given product can be influenced by the amount of PCS used, as the mechanical properties may vary due to the larger chemical composition window. This article does not consider the design of the profile itself, nor does it employ the use of meta-models, such as other articles have [5-11]. It instead relies solely on physics-based and finite element modelling. While previously, has been used to optimize processing parameters, similarly to other authors [12], the current article focuses on the design of the profile.

A supplier and its customers of aluminium products are always searching for the best possible compromise in relevant properties, weight, processability and the -footprint of the product. The -methodology has the potential to propose the optimal compromise.

The work presented in this article takes the established TM [1-3] concept to a new level. In addition to a holistic consideration of the process chain and the use of an optimization engine, the methodology is used for design of the product and analyse its performance in a digital environment like a digital twin. In sum, the tool can offer a strong compromise between processability, performance, and environmental impact.

Modelling Methodology

The original version of this paper is available on https://www.scientific.net/MSF.1130.13.pdf

PRO . The present work is based on the TM concept - Profit generation through Product and Process Optimization - a digital twin of the product and its manufacturing process throughout the value chain - from casting to final properties. The backbone of consists of predictive, physics-based microstructure models on different length scales from nanometers to micrometers. combined with macro models (e.g., Finite Element Models) describing the industrial process of interest. The sophistication and complexity of each model varies from basic to cutting edge. Python has been used as an orchestration tool, responsible for facilitating the simulations, transferring data between models, and allowing the optimization software to control the process- and design parameters.

With reference to Fig. 1, each simulation starts with a selection of inputs chosen by the optimization tool. For this instance, the algorithm chooses a profile cross section design within predefined boundaries, and a recycled scrap content from 0 to . The profile is constructed using a finite element tool, and the design and material are evaluated in a defined case study. If the design passes product requirements, the extrudability is evaluated as well as the and costs of manufacture. The optimization algorithm is based on pymoo [13], an open-source multi-objective optimization tool.

Fig. 1. TM schematic diagram [1-3]. TM contains a variety of different models, each describing a process or phenomenon along the aluminium process chain from casting to finished product. The simulations are orchestrated by a Python framework, and the in- and outputs are governed by an optimization tool based on the open-source optimization package pymoo.

Fig. 1. TM schematic diagram [1-3]. TM contains a variety of different models, each describing a process or phenomenon along the aluminium process chain from casting to finished product. The simulations are orchestrated by a Python framework, and the in- and outputs are governed by an optimization tool based on the open-source optimization package pymoo.

Models

The original version of this paper is available on https://www.scientific.net/MSF.1130.13.pdf

Each of the applied models are explained in short in the following section. For a detailed description of each model, the reader is referred to dedicated articles.
FEM model. IMPETUS Afea Solver® [14] is a commercially available finite element code specialized in nonlinear explicit analysis. Here, IMPETUS is used as an evaluation tool for the crushing of a profile. It is suited due to its implementation of higher order elements, making it less prone to inaccuracies due to slightly distorted element geometries. The iteration loop in this study relies on autogenerating a new mesh for each new set of geometry parameters and the auto generation algorithms found in most FEM software is prone to produce badly shaped element for complex cross section unless manually controlled.
NaMo. NaMo is a combined precipitation, yield strength and work hardening model for 6xxx alloys. The model predicts the evolution of the precipitate structure during a non-isothermal heat treatment based on alloy composition. The relevant parameters from the precipitate module of NaMo are used to calculate the yield stress and the work hardening rate through dislocation mechanics, and a complete stress-strain curve at room temperature can be predicted. The model has been described in several publications, such as [15-17].
AlEx. This model is an analytical approach to extrusion simulation, suited for rapid evaluation of the maximum ram speed resulting in surface tearing [18]. The model takes alloy composition, homogenisation cycle, and profile geometry as input and estimates the maximum allowable ram speed before the onset of surface tearing for billet preheat temperatures between 400 and . To enable the analytical solution of the resulting exit temperature, the model relies on the assumption that the surface exit temperature can be estimated as a sum of individual temperature contributions, as given in Eq. ( 1 ):

Here, is the initial billet temperature and are the individual heat contributions due to adiabatic heating, friction with the die and the container, heat conduction between the billet and the container, and additional redundant work. The contributions are described in detail in separate paper to be presented at this conference [18].
Costs and Carbon Footprint. Concerning the production- and material costs and expenditure, a simplified expression calculates these based on the recycled content and processing parameters. The total costs of an aluminium profile can be divided into three constituents as shown below:

The contributions represent the cost of material ( ), production ( ), and logistics ( ). For simplicity's sake, logistics is neglected in this article. is calculated from the costs of primary metal and scrap. The production term covers all aspects of manufacturing and can be further divided into two terms, one for fixed costs, and one for variable costs. The fixed costs are ignored in this case since they do not affect the optimisation. While not always the case, productivity of extruded products is often limited by the ram speed during extrusion. When compared to a reference case, what remains is a simplified expression for relative cost [2]:

Here, represents the cost of operating an extrusion press. This term will vary between different sites depending on factors such as the fixed costs and costs of labour and energy. and are the extrusion ram speed for the comparative case and for a reference case, respectively. If extrusion ram speed is measured as millimetres per second, the term is measured in cost per second of operation.

From bauxite to anodizing - each process step for manufacturing of aluminium extrusions can be tied to some emission of . Here, we estimate the total amount of , in kg per kg of finished aluminium product as follows [2]:

Here, the indices denote E - electrolysis, C - casting, R - rest. As with cost, the rest term is considered constant and therefore neglected such that only a relative emission is considered. The remaining terms, and , depend on the fraction of electrolysis metal ( ), the fraction of process scrap ( ) and the fraction of post-consumed scrap ( ).

Through the electrolysis process, is directly emitted when the carbon anode is consumed. Indirectly, is also emitted in the consumption of energy - mainly electricity - which varies widely depending on the source. Hydroelectric being one of the least emissive, while coal power is among the most emissive. Furthermore, the remainder of the process chain will emit either directly or indirectly through the consumption of energy. Simplifying this system into only electrolysis and recycled (post-consumed scrap) metal, the -index M can be expressed as follows [2]:

Here, a represents carbon emission of the electrolysis process, and is the carbon emission of energy production. and are the energy expenditure of electrolysis and remelting, respectively.
is the carbon emission associated with other processing steps than electrolysis and casting. is the fraction of post-consumer scrap. For the optimisation simulations presented in this paper, M is treated as a function of , where the latter is chosen at the will of the optimisation algorithm with a value from 0 to 1 inclusive. Typical values for the parameters in Eq. ( 5 ) are listed in Table 1 [2].

Table 1. Parameters for calculations [2].

a
[kg CO₂/ kg Al]
b
[kg CO₂/ kWh]
Qe
[kWh/kg Al]
Qr
[kWh/kg Al]
Mrest
[kg CO₂/ kg Al]

Value
1.7
0.005*
1.0**
13.0
0.65
3.6

Hydroelectric [19].
** Coal [19].
Optimization Tool. The optimization algorithm used in these simulations is NSGA-II [20] - Nondominated Sorting Genetic Algorithm - implemented in the open-source Python package pymoo. The algorithm is genetic with modified mating and survival selection. In simpler terms, the algorithm treats every set of inputs as an individual with the possibility of survival and mating based on the optimization criterion. The most fit inputs merge pairwise, while the least fit set of inputs are discarded.

Simulation, Results, and Discussion

The original version of this paper is available on https://www.scientific.net/MSF.1130.13.pdf

This section details cases where has been applied to handle real and relevant challenges from the industry. After outlining the simulation sequence, the next important step is to set the upper and lower bounds of the input variables. A wide range increases the likelihood that the optimal solution is found, but also increases the convergence time of the optimization algorithm.
Alloy. In an effort to restrain the complexity of the present cases and to demonstrate the effect of alloy composition on product design, the candidate alloys have been narrowed down to one 6082type of alloy where the iron content is varied between , and as shown in Table 2. Such a high Fe-content could result from a poorly sorted scrap pile with high levels of iron. This fictive recycled alloy - although unrealistic with the current standards of recycled aluminium represents a more difficult type of scrap that is detrimental to the properties and processability that may become more common in the future. In the simulations, the algorithm can mix the primary and recycled material in any balance from 0 to .

Table 2. Alloy composition for a primary 6082-alloy with very low Fe-content, and a fictive recycled 6082 from a poorly sorted scrap pile with unusually high levels of iron.

Si
[wt%]
Fe
[wt%]
Cu
[wt%]
Mn
[wt%]
Mg
[wt%]
Cr
[wt%]
Al
[wt%]

0.98
0.05
0.025
0.48
0.64
0.015
bal.

0.98
1.0
0.025
0.48
0.64
0.015
bal.

It must be emphasised that remelt plants usually hold several possible sorted and separated materials to choose from when producing extrusion billets, in addition to primary aluminium from the electrolysis process, and different types of master alloys.

The processing conditions assumed in the simulations corresponds to a typical thermo-mechanical process route for 6082 alloys used in the industry. In this instance, each alloy has been homogenised for 2.25 hours at and extrudability was evaluated by comparing productivity on a rod shape for each given material. To estimate the strength, each alloy was subjected to a single step artificial ageing heat treatment at . Simulation results from NaMo are shown in Fig. 2 for the 6082
variants in Table 2 at four different Fe-concentrations. Fig. 2 (a) shows the yield strength evolution during artificial ageing, while Fig. 2 (b) shows the resulting stress-strain curves at maximum (T6) strength.

Fig. 2. Results from NaMo simulations for a 6082-type of alloy with different Fe-content. (a) Yield stress curves during artificial ageing at . (b) Stress-strain curves at peak strength.

Fig. 2. Results from NaMo simulations for a 6082-type of alloy with different Fe-content. (a) Yield stress curves during artificial ageing at . (b) Stress-strain curves at peak strength.

As demonstrated in Fig. 2, iron has an increasingly detrimental effect on the strength of the material, especially above . The main effect of iron on the strength is that silicon is tied up in the Fe-rich phases that form during casting and homogenisation, reducing the available solid solution concentration of Si to form hardening Mg-Si type of particles during artificial ageing.

Load Case. A rear underrun protection device (RUPD), commonly made in steel or aluminium, is a safety measure meant to protect smaller vehicles in the event of a rear-end collision with a large vehicle. The load case presented is based on this automotive device.

Fig. 3 (a) shows a schematic illustration of the placement and function of a RUPD. In this work, the design considerations needed to fully design a functional RUPD profile will be simplified considerably, e.g., the fastening system is entirely neglected. The load scenario will be divided into two separate load cases, i.e., crush load and bend load. Both loads are considered quasi-static and are inspired by UN Regulations [21]. A schematic RUPD and the loads applied to the structure is shown in Fig. 3 (b).

Crush load, F1: The RUPD is to withstand a crush load of 180 kN applied perpendicular to the profile length direction. The crush scenario is modelled with a finite element model as a short profile segment crushed between two rigid blocks. The profile is only allowed minor plastic deformation.

Bend load, F2: The RUPD is assumed as an idealised cantilever beam in bending, subjected to the moment caused by a point force of 100 kN with a 500 mm lever arm. The load causing onset of plastic deformation in the cross section is assumed as the dimensional criteria.

Fig. 3. Illustration of the function and load case for a rear under protection device (RUPD). (a) Schematic illustration of a RUPD meant to protect smaller vehicles in the event of a rear-end collision with a large vehicle. (b) Schematic illustration of RUPD and the loads applied to the structure.

Fig. 3. Illustration of the function and load case for a rear under protection device (RUPD). (a) Schematic illustration of a RUPD meant to protect smaller vehicles in the event of a rear-end collision with a large vehicle. (b) Schematic illustration of RUPD and the loads applied to the structure.

Parameterised Geometry. In this work it was chosen to investigate a simple two-chamber profile, neglecting any details needed for assembly of the profile to the vehicle. A schematic of the profile is given in Fig. 4 (a), showing the orientation of the cross section of the profile relative to the load direction. The profile was parameterized assuming two planes of symmetry, allowing the wall thicknesses, the outer and inner radii, and a thickness transition area at the mid wall to vary. The outer contour of the profile was assumed to be square and fixed with side lengths of 140 mm . The double symmetric representation of the profile is given in Fig. 4 (b).

Fig. 4. (a) The full profile, illustrated in relation to the load direction. (b) Double symmetric representation of the RUPD profile, indication what parameters was allowed to vary.

Fig. 4. (a) The full profile, illustrated in relation to the load direction. (b) Double symmetric representation of the RUPD profile, indication what parameters was allowed to vary.

In addition to the constraints imposed by the fixed outer geometry and the symmetry definition, each geometry parameter was given a range interwall. The allowable variance of each parameter is given in Table 3. The profile and parameterisation chosen is relatively simple to ease the effort of autogenerating a mesh for the profile. It is worth noting that the type of geometry representation chosen in this work considerably constrains the solution space explored by the optimisation algorithm. As the solution space only covers square double symmetric two-chamber profiles with a fixed outer geometry, it must be stated that the universally optimal profile for this type of application may not be found. Representation of geometry in optimization applications is a separate field of study and is beyond the scope of this work. The main motivation behind the chosen parameterisation was
to give the modelling framework a representative geometry representation to analyse and at the same time ensure that the geometry was easily meshed and handled by the other computational modules.

Table 3. Value range for variables in the parameterised geometry illustrated in Fig. 4 (b).

T1
[mm]
T2
[mm]
T3
[mm]
T4
[mm]
R
[mm]

Parameter
bounds
2 – 8
2 – 8
1 – 4
1 – 10
2.5 – 20

Case Studies

The original version of this paper is available on https://www.scientific.net/MSF.1130.13.pdf

Case is the reference case, where only the geometry of the profile is optimized to both demonstrate a working framework and optimization algorithm, as well as establish a reference for comparison in the latter case.

The restrictions of the profile design are based on Fig. 4 and Table 3. Looking at the convergence plot in Fig. 5, it is evident that both optimisation runs converge towards a favourable light weight solution that satisfies the constraint equations defined by the minimum allowable bending and crush load. On average, the primary based alloy has a lower profile weight compared to the PCS-based alloy. The difference reflects the reduction in yield and flow stress as shown in Fig. 2 (b) when introducing a high iron content.

Fig. 5. Key results from Case 0 optimisation simulation. (a) shows how the simulations converge towards a lighter design for both primary and recycled material, while (b) shows the pareto fronts for bend load vs profile area.

Fig. 5. Key results from Case 0 optimisation simulation. (a) shows how the simulations converge towards a lighter design for both primary and recycled material, while (b) shows the pareto fronts for bend load vs profile area.

Based on the simulation runs visualised in Fig. 5, one can identify the two most lightweight solutions that satisfies the load-based constraint equations. The two designs are shown in Fig. 6, where Fig. 6 (a) shows the lightest design with primary aluminium and Fig. 6 (b) shows the lightest design with recycled material. As one would expect the optimisation framework pushes the mass of the profiles as far away from the neutral axis of the profile as possible to maximize the second area moment of the profiles to satisfy the demand on bending load. Given the parameterisation chosen in this demonstration case, the algorithm must add additional mass in the corner section of the mid-wall to satisfy the load constraint for the simulation run with recycled aluminium due to the lower strength of the material.

Fig. 6. Resulting profile designs comparing the two edge cases of primary and recycled material. (a) primary material, simulation #583-1. (b) 100% recycled material, simulation #596-2.

Fig. 6. Resulting profile designs comparing the two edge cases of primary and recycled material. (a) primary material, simulation #583-1. (b) 100% recycled material, simulation #596-2.

Case 1 builds upon Case 0 and introduces the importance of production costs, material costs and footprint. When designing profiles, it may be tempting to create the most topologically optimized unit. However, if this design is difficult to extrude, the savings from lightweighting can be cancelled out by lower productivity, or vice versa. In this case, the profile simulated in Case 0, #583-1 from Fig. 6 (a) is used as a reference design for comparison.

In the setup for the simulations, the following parameters were minimized by the algorithm: -, material-, and production costs - while also maintaining the mechanical load requirements. Similar to Fig. 5 from Case 0, Fig. 7 shows the key results from Case 1. The two figures are very comparable, but an additional layer of complexity is added by having alloys with a continuous spectrum of primary and recycled content.

Fig. 7. Key results from TM Case 1 optimization simulation. (a) shows how the simulations converge towards a lighter design for different fractions of recycled metal, while (b) shows the pareto fronts for bend load vs profile area.

Fig. 7. Key results from TM Case 1 optimization simulation. (a) shows how the simulations converge towards a lighter design for different fractions of recycled metal, while (b) shows the pareto fronts for bend load vs profile area.

This extra dimension allows customers and manufacturers to make a smarter selection when encountering strict demands. E.g., for the automotive industry, this analysis would allow for a better compromise between unit weight and carbon footprint. Perhaps choosing a design that is lighter than #596-2, but more environmentally friendly than #583-1, both from Case 0.

The optimisation parameters are illustrated in Fig. 8, where manufacturing- material-, carbon and total monetary cost is illustrated.

From Fig. 8 (a), it is shown that the profile exit speed is comparable for all balances between primary and recycled metal. On one hand, for a given extrusion ram speed, the smaller cross section profile will extrude faster than a larger cross section profile, taking into account of the difference in reduction ratios. On the other hand, the models prefer to extrude high iron content alloy due to the lower deformation resistance, as evidenced by Fig. 2. The two effects seem to cancel each other out.

From Fig. 8 (b), it is shown that it is possible to be competitive on cost while also delivering a lightweight and low carbon product, indicated by the leftmost markers. While the recycled material is in this instance cheaper to purchase, the corresponding profiles are bigger, leading to more material spent.

Fig. 8. Illustration of the main optimization parameters from the TM Case 1 optimisation simulation. Designs that did not meet the load requirements has been omitted. (a) shows the profile extrusion speed vs profile area. (b) shows the relative cost (compared to the best design from Case 0) vs profile area.

Fig. 8. Illustration of the main optimization parameters from the TM Case 1 optimisation simulation. Designs that did not meet the load requirements has been omitted. (a) shows the profile extrusion speed vs profile area. (b) shows the relative cost (compared to the best design from Case 0) vs profile area.

Concluding Remarks

The original version of this paper is available on https://www.scientific.net/MSF.1130.13.pdf

This paper describes a holistic approach to optimisation of product properties, production costs and footprint by using a simulation methodology called TM . The case studies in the present article are related to production of an automotive component based on 6xxx series aluminium extrusions with given functional requirements on crush- and bend loads. At the same time, footprint and extrusion costs were attempted minimised. These examples illustrate how optimisation can be used by a product developer to estimate the effects of changes of the profile geometry profile before they are made.

The software is relevant for all levels in the organization. From the operator responsible for setting the extrusion recipe, to the salesperson giving quotes on products, and the strategic planners who decide on which alloys a plant should carry. Decision makers could decide on the right compromise between robustness of the mechanical performance of the product versus the emissions.

References

The original version of this paper is available on https://www.scientific.net/MSF.1130.13.pdf

T. Furu and O. R. Myhr, "Method for the Optimization of Product Properties and Production Cost of Industrial Process," (2014).

O. R. Myhr, R. Østhus, J. Søreide and T. Furu, "A Novel Methodology for Optimization of Properties, Cost and Sustainability of Aluminium Extrusions," in ET2016, Orlando, (2016).

T. Furu, R. Østhus and O. R. Myhr, "A Digital Twin for Production of Aluminium Extrusions," in ET2020, Orlando, (2022).

Hydro, "Aluminium Recycling," [Online]. Available: .

B. Reggiani, L. Donati and L. Tomesani, "Multi-goal optimization of industrial extrusion dies by means of meta-models," The International Journal of Advanced Manufacturing Technology, vol. 88, 2017.

R. Pelaccia, M. Negozio and B. D. L. Reggiani, "Assessment of the Optimization Strategy for Nitrogen Cooling Channel Design in Extrusion Dies," Key Engineering Materials, vol. 926, 2022.

G. Zhao, H. Chen, C. Zhang and Y. Guan, "Multiobjective optimization design of porthole extrusion die using Pareto-based genetic," The International Journal of Advanced Manufacturing Technology, vol. 69, 2013.

N. Lebaal, F. Schmidt and S. Puissant, "Design and optimization of three-dimensional extrusion dies, using constraint optimization algorithm," Finite Element in Analysis and Design, vol. 45, no. 5, pp. 333-340, 2009.

Z. Lin, X. Juchen, W. Xinyun and H. Guoan, "Optimization of die profile for improving die life in the hot extrusion process," Journal of Materials Processing Technology, vol. 142, pp. 659-664, 2003.

S. T. Wang, R. S. Lee, H. Y. Li and C. H. Chen, "Optimal Die Design for Three-Dimensional Porthole Extrusion Using the Taguchi Method," IMechE, vol. 220, pp. 1005-1009, 2006.

F. Gagliardi, G. Ambrogio, C. Ciancio and L. Filice, "Metamodeling technique for designing reengineered processes by historical data," Journal of Manufacturing Systems, vol. 45, 2017.

S. Jajimoggala, N. Murali Krishna and K. Syed, "Selection of optimal hot extrusion process parameters for AA6061using hybrid MCDM technique," Materials Today: Proceedings, vol. 18, pp. 278-290, 2019.

J. Blank and K. Deb, "pymoo: Multi-Objective Optimization in Python," doi: 10.1109/ACCESS.2020.2990567, pp. 89497-89509, (2020).

IMPETUS Afea Solver®, IMPETUS Afea AS.

O. R. Myhr, Ø. Grong and C. Schäfer, Metall. Mater. Trans A, vol. 46A, p. 6018, (2015).

O. R. Myhr, Ø. Grong and K. O. Pedersen, Metall. Mater. Trans A, vol. 41A, p. 2276, (2010).

O. R. Myhr, O. S. Hoppestad and T. Børvik, Metall. Mater. Trans A, vol. 49A, p. 3592, (2018).

M. Iddberg, O. R. Myhr, A. Nesse and T. Furu, A Modelling Framework for Rapid Evaluation of Speed Limiations of Aluminium Profiles, Aluminium 2000, (2023).

Environmental Metrics Report, International Aluminium Institute, (2014).

K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, (2002).

Addendum 57: UN Regulation No. 58.

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Materials Science Forum, ISSN: 1662-9752, Vol. 1129, pp 19-26

doi: 10.4028/p-UrQZs9

© 2024 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2024-03-04

Accepted:
2024-03-06

Online:
2024-10-30

AlZn-Matrix Cast Composites Reinforced with Ti Aluminides

The original version of this paper is available on https://www.scientific.net/MSF.1129.19.pdf

Paweł K. Krajewski , A. Lindsay Greer , Jiri Orava , Beata Gracz , Grzegorz Piwowarski , Magdalena Kawalec , Ewa Olejnik , Witold K. Krajewski

AGH University of Krakow - Faculty of Foundry Engineering, 23 Reymonta Street, Krakow 30-059, Poland

University of Cambridge - Department of Materials Science and Metallurgy, 27 Charles Babbage Road, Cambridge CB3 0FS, UK

Jan Evangelista Purkyne University, Pasteurova 3632/15, Usti nad Labem 400 96, Czech Republic

krajpaw@agh.edu.pl, alg13@cam.ac.uk, Jiri.Orava@ujep.cz, gracz@agh.edu.pl,

Keywords: AIZn in situ composites, Ti aluminides, Grain refinement, Ductility, Strength and Wear properties.

Abstract

Doping high-zinc aluminium alloys with Ti builds in-situ composites reinforced with ternary aluminides with a significantly grain-refined matrix. In a series of studies, Ti was introduced with (AlTi6) and (ZnTi4) master alloys in amount to contribute about in the examined alloys. The alloy microstructure has been studied using light microscopy, SEM / EDS and XRD measurements. The observed significant refinement of the alloys matrix should lead to improvement in ductility, while the in-situ reinforcement should improve tensile strength and wear properties.

Introduction

The original version of this paper is available on https://www.scientific.net/MSF.1129.19.pdf

Along with the increase in the global production of castings from Al-based alloys, a steady increase in the importance of AlZn -based alloys is observed [1]. The group of AlZn -based alloys with Zn content increased to has a special combination of properties that allows them to be used for shaped castings with good damping and tribological properties [2-7]. However, their wider application in practice requires the improvement of rather poor mechanical properties, e.g. ductility, strength and abrasion resistance. Increased ductility of the coarse dendritic matrix, Fig. 1(a), can be achieved by inoculation of the liquid prior to casting into the mould, while strength and abrasion resistance can be improved by the addition of reinforcing phase particles.

Fig. 1. Examples of microstructures of sand-cast based alloy: (a) initial alloy with coarse dendrites of solid solution of Zn in Al ; (b) refined structure of the alloy inoculated with small addition of the Al-12Ti MA; (c) Al-46 at% alloy with visible ternary reinforcing particles.

Fig. 1. Examples of microstructures of sand-cast based alloy: (a) initial alloy with coarse dendrites of solid solution of Zn in Al ; (b) refined structure of the alloy inoculated with small addition of the Al-12Ti MA; (c) Al-46 at% alloy with visible ternary reinforcing particles.

The main objective of the present research was to strengthen the structure by building a composite based on these alloys. In the field of composite design, various additives of the
reinforcing phase are used, while the crystalline structure of these particles usually differs from the structure of the crystalline matrix. Such differences often cause internal stresses, which is relevant for the soundness of the structure as a whole and the properties of the material. In addition, the quality of the surface of the reinforcing-phase particles introduced from the outside, for example, various types of impurities that prevent close bonding with the matrix, may have a large impact.

The concept of the current research assumes the formation of the reinforcing phase directly from the liquid during its cooling and solidification, i.e. the implementation of the concept of building composites in-situ. In a series of tests of double alloys, an addition of was used with (Al-(6-12) Ti MA ) and MA) master alloys. A small addition of the master alloys causes significant fragmentation of the dendritic matrix, Fig. 1(b), while a larger addition leads to the in-situ formation of a reinforcing phase in the matrix, corresponding to the composition of ternary aluminides with the general stoichiometry of , Fig. 1 (c). This article is a continuation of the series published in Materials Today - Proceedings [4, 8] and presents the results of research conducted at the AGH University of Science and Technology and the University of Cambridge - Department of Materials Science and Metallurgy.

The publication presents the results of the increased Ti addition to the structure of high-zinc Al alloys and is addressed to foundry industry engineers as well as to researchers in order to transfer knowledge in the area of theory and practice of cast in-situ aluminum-zinc matrix composites.

Experimental

The original version of this paper is available on https://www.scientific.net/MSF.1129.19.pdf

Melting. The alloys and the master alloy were melted from elements of minimum purity (all compositions in wt. %). Melting was performed in an electric furnace with an argon protective atmosphere, in a clay-graphite crucible of 2 litre capacity (the details of melting and casting are given in Refs [11-13]). The melted charge of the Al-Zn alloys superheated to about was flushed with pure argon for 10 min . Then, 10 min . after finishing the flushing the dross was removed from the melt surface.
Melt doping with Ti and casting. During these experiments an amount of the ZnTi-based and AlTi-based master alloys were added to the melt to introduce about of Ti . The melt was held for about 10 minutes to ensure complete dissolution of the added master alloy. Next the melt was stirred for about 2 minutes with a quartz-glass tube, and finally the alloy was cast into copper moulds to obtain plate samples and conical samples for structural tests, Fig. . The composition of the examined alloys was as follows:

Al47-Zn50-Ti3 (Al67.8-Zn29.8-Ti2.4 at%). Ti introduced with commercial Al-Ti6 master alloy, KBM Affilips B.V,

Al25-Zn72-Ti3 (Al44-Zn53-Ti3 at%). Ti introduced with the Zn-Ti4 master alloy.

Structural examination. Optical light metallography (LM) was performed using Leica DM IRM and Zeiss Axio Imager A1m light microscopes. SEM investigations were performed using lowvacuum JEOL 5500 WL microscope equipped with energy-dispersive spectrometry (IXRF EDS) system, at accelerating voltage of 20 kV . The LM and SEM samples were occluded using Struers Citopress-5 device and the microsections were ground on abrasive paper (grit 200-1000) and then were polished using sub-microscopic aluminium oxide in water-alcohol suspension using Struers LaboPol-5 device. The samples for SEM examination were unetched, while the samples used in LM examinations were etched chemically with Keller's reagent or Nital, or electrochemically with Barker's reagent. X-ray diffraction (XRD) studies were carried out in the angle range using Kristalloflex 4H and Philips PW1710 diffractometers, using Co Kα radiation filtered with graphite in the plane. Step measurements were carried out with the following parameters: step/time . X-ray diffraction patterns were analysed using XRayan and Philips PC-APD computer programs. Samples were also measured by diffraction of high-energy synchrotron radiation ( 87.1 keV ) in transmission geometry using the HZG materials science beam-line P07B at DESY in Hamburg, Germany.

Fig. 2. (a) Copper moulds ; (b) The samples for structural examination ; (c) Bright fields of the samples.

Fig. 2. (a) Copper moulds ; (b) The samples for structural examination ; (c) Bright fields of the samples.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/MSF.1129.19.pdf

Structural refinement. Figure 3 shows the structure of the examined alloys with visible titanium aluminide phases and highly refined matrix. The titanium aluminides in the alloy doped with the AlTi6 MA have a needle-like form, similar to the pure phase. The titanium alumindes in the alloy doped with ZnTi 4 have a more compact form, similar to the pure phase. The matrix is slightly more refined in the latter case. The visible refinement of the matrix is affected by: the rate of solidification in the Cu moulds, heterogeneous nucleation of the phase on the substrates of the titanium aluminides and the effect of titanium in solution blocking the growth of crystallites of the matrix (a solid solution of zinc in aluminium). In conclusion, the described fineness of the matrix should promote the improvement of examined alloys plastic properties.

Fig. 3. Macrostructures of the examined alloys (left) and (right).

Fig. 3. Macrostructures of the examined alloys (left) and (right).

Composition and crystal structure of the reinforcing particles. The master alloys of titanium with aluminium, e.g. AlTi6, are composed of phase of tetragonal structure of - tI8 type and space group I4/mmm and lattice parameters and [9]. As regards AlZn -based alloys: phase has in atomic plane identical arrangement of atoms as phase (solid solution of Zn in Al ) in atomic plane . In the most densely occupied atomic plane {111} of phase , the smallest distance between atoms dmin , while in atomic plane of phase this distance is dmin . The difference between both these distances is only , which confirms that master alloys containing the phase are effective substrates for heterogeneous nucleation of phase . Similarly, in the system there is the regular phase, with superstructure , with lattice parameter . This phase in molten AlZn -based alloys transforms into a ternary phase with stoichiometry and lattice parameter . The smallest interatomic distances in the plane of both phases are (for phase) and , for phase with lattice parameter . The difference between the shortest interatomic distances in the
atomic plane of both phases is only about . The same type of crystalline structure and a small difference in interatomic distances in the most densely packed atomic plane of both analysed phases cause titanium aluminide in ( )-Ti master alloy to be an effective substrate for heterogeneous nucleation of dendrites of . The similarities mentioned above cause the observed intensive matrix refinement of the examined alloys, visible in Fig. 3.

The characteristics given above include rather smaller particles of titanium aluminides, with an average diameter of up to about 5 micrometres, the presence of which was revealed in the centres of fragmented dendrites of a solid solution of zinc in aluminium, as shown, for example, in Fig. 4.

Larger particles of titanium phases build reinforcement in the tested alloys, Figs 5-8. An example of reinforcing phases from the master alloy (MA) are shown in Figs 5 and 6, while their chemical compositions are collected in Table 1. As can be seen from the data presented in Table 1 and in Fig. 6, the binary phase of introduced with the ZnTi 4 MA is transformed into a ternary phase with stoichiometry , where the amount of Al substituting Zn atoms is greater at the periphery compared to the central parts of these phases. This observation is consistent with the prediction that the binary-to-ternary phase transformation is more advanced in the binary phase external parts in contact with the liquid alloy before pouring into the mould.

Fig. 4. Exemplary particle revealed in centre of the refined dendrite in the alloy ZnAl25 doped with Zn4Ti MA [12].

Fig. 4. Exemplary particle revealed in centre of the refined dendrite in the alloy ZnAl25 doped with Zn4Ti MA [12].

A similar analysis was carried out for the alloy doped with the AlTi6 MA, and the results are presented in Figures 7 and 8, and in Table 2. The data in Table 2 show that also in this case the binary phase is transformed into the ternary phase . At the same time, the presence of Ti was found in the alloy matrix (Fig. 7, points 2 and 3 of the EDS analysis). The presence of Ti in the high-aluminium solid solution (dark fields) makes it possible to limit the growth of dendrites of this phase, because titanium is known as one of the most effective elements inhibiting the growth of the dendrites, i.e. with a high GRF (growth restriction factor) parameter [13]. The process of binary-to-ternary transformation of Ti aluminides has also been confirmed in the XRD studies presented below. Figure 9 shows the X-ray diffraction pattern of a powder sample composed of titanium aluminides extracted from the matrix of ZnAl 25 alloy doped with ZnTi 4 MA . Fig. 10 shows X-ray diffraction patterns obtained from solid samples of the examined alloys. Comparison of both series of X-ray diffraction patterns from Figures 9 and 10 shows that in the examined alloys doped with AlTi6 and ZnTi4 master alloy, introducing about , there are reflections originating from the ternary aluminides , which were assumed to be the reinforcing phase of the composites based on these alloys.
Phases Ti-Al

Fig. 5. Exemplary Ti-phases present in the alloy Al25-Zn72-Ti3.

Fig. 5. Exemplary Ti-phases present in the alloy Al25-Zn72-Ti3.

Table 1. EDS analysis of the phases from Fig. 5.
Analysis (1)

Elt.
Line
Intensity
[c/s]
Error
2-sig
Gauss
Fit
Atomic
[%]
Weight
[%]

Al
Ka
11.65
1.246
0.95
12.710
6.106

Ti
Ka
56.54
2.745
1.37
24.809
21.145

Zn
Ka
55.56
2.721
1.83
62.481
72.749

Analysis (2)

Al
Ka
19.85
1.838
1.92
20.197
10.214

Ti
Ka
57.09
2.759
1.43
24.428
21.916

Zn
Ka
50.58
2.822
1.53
55.374
67.869

Analysis (3)

Al
Ka
30.99
2.033
1.88
28.059
15.040

Ti
Ka
60.52
2.840
1.67
24.406
23.208

Zn
Ka
46.13
2.480
2.06
47.536
61.752

Analysis (2)

Al | Ka | 19.85 | 1.838 | 1.92 | 20.197 | 10.214
Ti | Ka | 57.09 | 2.759 | 1.43 | 24.428 | 21.916
Zn | Ka | 50.58 | 2.822 | 1.53 | 55.374 | 67.869

Analysis (3)

Al | Ka | 30.99 | 2.033 | 1.88 | 28.059 | 15.040
Ti | Ka | 60.52 | 2.840 | 1.67 | 24.406 | 23.208
Zn | Ka | 46.13 | 2.480 | 2.06 | 47.536 | 61.752

Fig. 6. Line-scan of the Ti-phase present in the alloy Al25-Zn72-Ti3.

Fig. 6. Line-scan of the Ti-phase present in the alloy Al25-Zn72-Ti3.

PhasesTi-Zn

The original version of this paper is available on https://www.scientific.net/MSF.1129.19.pdf

Fig. 7. Exemplary structure of the alloy Al47-Zn50-Ti3.

Fig. 7. Exemplary structure of the alloy Al47-Zn50-Ti3.

Table 2. EDS analysis of the phases from Fig. 7.
Analysis (1)

Elt.
Line
Intensity
[c/s]
Error
2-sig
Gauss
Fit
Atomic
[%]
Weight
[%]

Al
Ka
308.85
6.417
6.52
75.148
62.750

Ti
Ka
102.94
3.705
2.18
24.052
35.631

Zn
Ka
1.35
0.424
0.30
0.800
1.619

Analysis (2)

Table 2. EDS analysis of the phases from Fig. 7.
Analysis (1)

Al | Ka | 39.93 | 2.307 | 2.36 | 37.047 | 19.551
Ti | Ka | 0.55 | 0.271 | 0.26 | 0.203 | 0.190
Zn | Ka | 66.31 | 2.973 | 1.82 | 62.751 | 80.259

Analysis (3)

Al | Ka | 255.43 | 5.836 | 5.94 | 84.683 | 69.555
Ti | Ka | 0.33 | 0.211 | 0.21 | 0.082 | 0.120
Zn | Ka | 25.09 | 1.829 | 1.23 | 15.234 | 30.325

Fig. 8. Line-scan of the Ti-phase present in the alloy Al47-Zn50-Ti3.

Fig. 8. Line-scan of the Ti-phase present in the alloy Al47-Zn50-Ti3.

Fig. 9. XRD pattern of a powder sample of the phase obtained by dissolving the Zn 25 Al alloy matrix doped with ZnTi 4 MA. Co Kα radiation.

Fig. 9. XRD pattern of a powder sample of the phase obtained by dissolving the Zn 25 Al alloy matrix doped with ZnTi 4 MA. Co Kα radiation.

Fig. 10. XRD patterns of solid samples of the examined alloys: (a) Al47-Zn50-Ti3, Ti introduced with commercial AlTi6 MA; (b) Al25-Zn72-Ti3, Ti introduced with the Zn-Ti4 MA.

Fig. 10. XRD patterns of solid samples of the examined alloys: (a) Al47-Zn50-Ti3, Ti introduced with commercial AlTi6 MA; (b) Al25-Zn72-Ti3, Ti introduced with the Zn-Ti4 MA.

4. Conclusions

The original version of this paper is available on https://www.scientific.net/MSF.1129.19.pdf

On the basis of the presented results it can be concluded that the research allows simultaneous achievement of three important goals in one step, i.e.:

grain refinement of the composites' Al-Zn-X matrix through heterogeneous nucleation on fine particles of Ti-base aluminides. This refinement should increase ductility of the matrix,

additional matrix refinement due to the presence of Ti in solid solution, retarding dendrite growth,

strengthening the structure by larger particles of Ti-aluminides playing the role of reinforcement with crystal structure very similar to that of the matrix, which should improve mechanical properties and mitigate the generation of internal stresses.

The results presented in this paper should make a contribution to the field of metallurgy / foundry engineering of the AlZn - matrix cast composites reinforced with in-situ Ti-aluminides.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/MSF.1129.19.pdf

The research was financially supported by grant No. 2017/25/B/ST8/00150, National Science Centre Poland.

References

The original version of this paper is available on https://www.scientific.net/MSF.1129.19.pdf

51st Census of World Casting Production, Mod. Cast. 107 (2017) 24-28.

Y. Alemdag, T. Savaskan, Mechanical and tribological properties of alloys, Tribol. Int. 42 (2009) 176-182.

W.K. Krajewski, A.L. Greer, P.K. Krajewski, Trends in developments of high-aluminium zinc alloys of stable structure and properties, Arch. Metall. Mater. 58 (2013) 845-847.

W.K. Krajewski, J. Buraś, P.K. Krajewski, A.L. Greer, K. Faerber, P. Schumacher, New developments of Al-Zn cast alloys, Aluminium Two Thousand World Congress and ICEB 2015, Mater. Today-Proc. 2 ( 2015 ) 4978-4983.

W.K. Krajewski, A.L. Greer, EBSD Study of ZnAl 25 alloy inoculated with ZnTi 4 master alloy, Mater. Sci. Forum 508 (2006) 281-285.

K. Haberl, W.K. Krajewski, P. Schumacher, Microstructural features of the grain-refined sand cast AlZn20 alloy, Arch. Metall. Mater. 55 (2010) 837-841.

W.K. Krajewski, J. Buras, P.K. Krajewski, A.L. Greer, P. Schumacher, K. Haberl, New developments on optimizing properties of high-Zn aluminium cast alloys, IOP Conf. Ser. Mat. Sci. 143 (2016), paper No. 012029. DOI:10.1088/1757-899X/143/1/012029.

W.K. Krajewski, J. Buras, P.K. Krajewski, G. Piwowarski, A.L. Greer, New developments of high-zinc Al-Zn-Cu-Mn cast alloys, Aluminium Two Thousand World Congress and ICEB 2017, Mater. Today-Proc. 10 (2019) 306-311.

T.B. Massalski, Binary Alloy Phase Diagrams, ASM International, Materials Park, OH, 1987, p. 226.

J.L. Murray, Ti-Zn (Titanium-Zinc), in: Binary Alloy Phase Diagrams, ed. T. Massalski, ASM International, Materials Park, OH, 1987, pp. 3500-3501.

P. Villars, R.D. Calvert, Pearson's Handbook of Crystallographic Data for Intermetallic Phases, second ed., ASM International, Materials Park, OH, 1992.

P.K. Krajewski, A.L. Greer, and W.K. Krajewski, Main directions of recent works on Al-Znbased alloys for foundry engineering, J. Mater. Eng. Perform. 28 (2019) 3986-3993.

M. Bunn, P. Schumacher, M. Kearns, C. Boothroyd, A. L. Greer, Grain refinement by Al-Ti-B alloys in aluminium melts: a study of the mechanisms of poisoning by zirconium, Mater. Sci. Tech. 15 (1999) 1115-1122.

This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/12th-aluminium-two-thousand-world-congress-7th-international-conference-on-extrusion-and-benchmark/978-3-0364-1380-8

Materials Science Forum, ISSN: 1662-9752, Vol. 1130, pp 25-34

doi: 10.4028/p-rNtYY7

© 2024 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2023-11-23

Revised:
2024-03-08

Accepted:
2024-03-20

Online:
2024-10-31

Influence of Different Aging Times and Temperatures on Microstructural and Mechanical Properties of EN AW-6XXX Aluminum Alloys

The original version of this paper is available on https://www.scientific.net/MSF.1130.25.pdf

Chiara Soffritti , Cindy Morales , Annalisa Fortini , Mattia Merlin

Department of Engineering (DE), University of Ferrara, Via Saragat 1, 44122 Ferrara, Italy

*Corresponding author: chiara.soffritti@unife.it, mrlcdy@unife.it, annalisa.fortini@unife.it, mattia.merlin@unife.it

Keywords: Wrought aluminum alloys; Heat treatment; Microstructure; Mechanical properties.

Abstract

The heat-treatable EN AW-6005 and EN AW-6060 alloys are by far the most used alloys for manufacturing extruded parts for automotive, aerospace, food and healthcare industries. The strength of these alloys is usually achieved by T6 heat treatment, whose efficacy depends on the chemical composition and solubilization/aging times and temperatures. This study aims to evaluate the influence of aging parameters on the microstructural and mechanical properties of two extruded components made of the above-mentioned alloys and supplied by a healthcare company. Samples were drawn by both parts in as-received condition. Part of them was directly artificially aged at different aging times and temperatures, while the rest was subjected to tailored T6-type heat treatments. The microstructure was first analyzed by optical and scanning electron microscopy. The mechanical properties were then investigated by Brinell hardness and Vickers microhardness measurements. At last, all hardness data were compared to those obtained on specimens drawn from two other components made of the same alloys, which were heat-treated according to the parameters commonly used by the company. Based on the results, the microstructure of both components exhibited coarse grains plus lamellar and rounded intermetallic particles. Irrespective of the heat treatment route, no changes were detected in the microstructure. For each alloy, the heat treatment conditions that guarantee the best mechanical resistance were established.

Introduction

The original version of this paper is available on https://www.scientific.net/MSF.1130.25.pdf

Aluminum has good extrudability, and the heat-treatable aluminum alloys (6XXX series) are nowadays the most used alloys for manufacturing extruded parts for automotive, aerospace, food and healthcare industries [1]. Among the 6XXX series, alloys such as EN AW-6005 and EN AW-6060 cover a great share of the total extruded volume thanks to a favorable combination of properties such as high strength to weight ratio, excellent formability, good corrosion resistance and outstanding response to surface finishing procedures [2-4]. The strength of these alloys is usually achieved by precipitation hardening (or T6 heat treatment), i.e., a heat treatment that involves three main stages: (i) a first solubilization step at a high temperature; (ii) a rapid quenching to room temperature to generate a supersaturated solid solution (SSSS) of alloying elements; (iii) a final artificial aging (AA) consisting of the controlled decomposition of the SSSS to create a dispersion of sub-micrometric particles into the metal matrix [5]. During this process, the alloying elements exert, to various degrees, solution and dispersion hardening effects, with Mg and Si being the major solid-solution strengtheners [6]. In the literature, the generally accepted sequence for the formation of nano-sized particles during precipitation hardening is as follows: SSSS → clusters/GP zones → pre- , where "GP zones" stands for Guinier-Preston zones. The needle-like phase is regarded as the most effective hardening phase for aluminum alloys [7-11]. Nevertheless, it was also found that the addition of Cu to the alloys of 6XXX group modifies the above-mentioned sequence as: SSSS → clusters/GP zones . Hence, the precipitation of more metastable phases accompanying is claimed to strongly influence the strength of EN AW-6005 and EN AW-6060 aluminum alloys [12].
In general, the efficacy of precipitation hardening in improving the performance of aluminum alloys depends on several factors such as the chemical composition of the alloy and
solubilization/aging time and temperature [6,13-17]. G. Mrówka-Nowotnik et al. [13] evaluated the influence of different solubilization temperatures (from to ) and cooling conditions (quench in oil, water, air and slow furnace cooling) on the microstructure and mechanical properties of commercial 6005 and 6082 wrought alloys. They found that the hardness of the 6082 alloy increased with rising quench severity and, to a minor extent, solubilization temperature; conversely, the hardness and aging kinetics of the 6005 alloy were not affected by the solubilization parameters. In [14], an experimental investigation on artificial aging of 6061-T6 alloy was conducted by varying the aging temperature between and , and the aging time from 30 min to 10 hours. The authors demonstrated that the optimum aging could be obtained at temperatures from to for times from 2 to 6 hours; especially, the maximum strength was referred to the samples aged at for 6 hours. Similar results were reported by Polat et al. in [6]. For the same aluminum alloy, it was suggested that the best compromise between hardness, yield and tensile strength, and elongation could be attained with artificial aging at for times between 5 and 40 hours. Ding et al. [15] highlighted that the amount of sub-micrometric precipitates in the microstructure of aged 6005A extrusions was maximum at . In addition, aging times from 4 to 8 hours enabled to reach yield and tensile strength in longitudinal direction of more than 270 and 300 MPa , respectively. Different artificial aging at under-, peak- and over-aged temperatures were performed by Siddiqui et al. [16] with the aim of evaluating their influence on the mechanical properties of 6063 alloy. Based on the results, aging at for times from 8 and 10 hours was able to guarantee the greatest mechanical resistance, but the best fatigue fracture behavior was registered for samples aged at for 6 hours. In under- and peak-aged conditions (i.e., for aging temperatures and times of at least 8 hours), the strengthening effect was a result of two main mechanisms: (a) the impedance of dislocation motion and (b) the high mobility of vacancies playing a pivotal role in the generation of GP zones. In over-aged conditions (i.e., for aging temperatures and times longer than 8 hours), the coalescence of nano-sized particles into large precipitates occurred with a consequent decrease in their capability to hinder the movement of dislocations. This phenomenon led, in turn, to a worsening of the mechanical properties of the alloy. Concerning the 6022 alloy, peak-aged conditions were achieved at aging temperatures higher than and for aging times less than 2 hours [17].
In industrial practice, extruded components are unavoidably subjected to short- or long-term storage before artificial aging. During this time, natural pre-aging (NA) at room temperature would occur, producing clusters or co-clusters of dissolved alloying elements (mostly Mg and Si ) [18]. In the literature, the effects of natural pre-aging on the hardening response of the material during the subsequent artificial aging can be stated as either positive or negative ones in relation to the alloy chemistry: when the total amount of is positive, the opposite when [19,20]. In the first case, it was reported that the AA of natural pre-aged lean alloys slowly dissolved the vacancy-free co-clusters favoring the hardness and strength increase at the aging temperatures [18,21-23]. In the second case, some studies assigned the adverse influence of NA on AA to the depletion of supersaturated solutes in metal matrix caused by the formation of durable NA clusters, being unfavorable nucleation sites for precipitation of phase during artificial aging [24,25]. Other research further demonstrated that the same clusters could annihilate or trap the quenched-in vacancies, thus retarding the precipitation process by slowing down solution diffusion [26]. Nevertheless, Martinsen et al. [27] assessed that in 6005 and 6060 alloys the negative NA effect may be reversed upon long-term storage (higher than the typical industrial storage time of 4 hours), but also at very early stages of natural pre-aging ( 30 min and below).
In this framework, this study aims to evaluate the influence of aging parameters on the microstructural and mechanical properties of two industrial components supplied by a healthcare company and made of EN AW-6005 and EN AW-6060 aluminum alloys. A number of samples were drawn by both parts in as-received condition. Part of the specimens was directly artificially aged at different combinations of aging time and temperature, while the rest was subjected to tailored T6-type heat treatments. The microstructure of as-received and heat-treated samples was first investigated by optical microscopy and scanning electron microscopy equipped with energy
dispersive spectroscopy. The mechanical properties were then determined by hardness measurements. Finally, all hardness data were compared to those obtained on other two components made of the same aluminum alloys, which were heat-treated according to the parameters commonly used by the company.

Materials and Methods

The original version of this paper is available on https://www.scientific.net/MSF.1130.25.pdf

Two extruded components (labeled as C 1 and C 2 ) made of EN AW-6005 and EN AW-6060 aluminum alloys, respectively, were provided by FERNO S.r.l., a commercial company operating in the healthcare industry. Details of the nominal chemical composition of the alloys are reported in Table 1.

Table 1 Nominal chemical composition of the alloys employed in this study.

Si | Mg | Mn | Cu | Cr | Zn | Ti | Fe | Al
EN AW-6005 | 0.60-0.90 | 0.40-0.60 | ≤ 0.10 | ≤ 0.10 | ≤ 0.10 | ≤ 0.10 | ≤ 0.10 | ≤ 0.35 | Balance
EN AW-6060 | 0.30-0.60 | 0.35-0.50 | 0.10 | ≤ 0.10 | ≤ 0.05 | ≤ 0.15 | ≤ 0.10 | 0.10-0.30 | Balance

In as-received condition, both parts were in T4 state, i.e., solution heat treated and naturally aged at room temperature until reaching a substantially stable condition. From each component, samples were drawn in longitudinal direction (parallel to the metal surface) and cross-direction (perpendicular to the metal surface) by a TR80 EVOLUTION (Remet, Bologna, Italy) cutting machine. Three specimens per section were mounted in resin, polished and analyzed by a Leica DMi8 A optical microscope (Leica, Wetzlar, Germany) under polarized light exposure. The microstructural investigations were carried out after electrolytic etching in Barker's reagent ( distilled water) by applying a direct current of 15 V for 1 min through a Polectrol (Struers, Copenaghen, Denmark) power supply. For each aluminum alloy, nine composite micrographs were acquired in cross-section to calculate the average grain size. Each composite micrograph consisted of nine images taken at a magnification of 500 x , which guaranteed the investigation of an area sufficiently representative of the entire sample. In this area, a minimum of 300 grains were processed following the intercept method described in the UNI EN ISO 643:2020 standard. An in-depth analysis of the microstructure was also conducted by a Zeiss EVO MA 15 (Zeiss, Oberkochen, Germany) scanning electron microscope equipped with an Oxford X-Max 50 (Oxford Instruments, Abingdon-on-Thames, UK) microprobe for energy-dispersive spectroscopy (SEM/EDS). The SEM micrographs were recorded in secondary electron imaging (SEI-SEM) and back-scattered electron (BSE-SEM) mode.
For C1 component, the mean Brinell hardness was evaluated in agreement with the UNI EN ISO 6506-1:2015 standard by a ERNST AT-130D (CISAM-ERNST S.r.l., Induno Olona, Italy) tester, using a tungsten carbide ball 2.5 mm in diameter and a load for 15 s . Each hardness value was an average of five measurements. No Brinell hardness measurements were collected for the C2 due to geometric constraints. A Future-Tech FM-110 (Future-Tech Corp., Kawasaki, Japan) microindenter was also employed for determining the mean Vickers microhardness (HV0.1) of both components under a load for 15 s and according to the UNI EN ISO 6507-1:2018 standard. The mean value was calculated as an average of ten indentations.

Table 2 Details of direct aging parameters employed in this study.

C1 (EN AW-6005) | C2 (EN AW-6060)
170 °C / 4, 6, 8, 10 h | 170 °C / 4, 6, 8, 10 h
180 °C / 4, 6, 8, 10 h | 180 °C / 4, 6, 8, 10 h
185 °C / 4, 6, 8, 10 h | 185 °C / 4, 6, 8, 10, 15, 20 h
190 °C / 4, 6, 8, 10 h | 190 °C / 4, 6, 8, 10 h

Two different heat treatment routes were then adopted. Part of the samples was directly artificially aged (DA) at different temperatures (between and ) and for different aging times (from 4 to 20 h ) in a Lenton LTF 12 (Lenton Furnaces & Ovens, Hope, UK) tube furnace. The rest of the specimens was subjected to tailored T6-type heat treatments consisting of: (i) a solubilization step at for 1 h in a REMET mod. E-79N (Remet, Italy) muffle furnace; (ii) water quenching to room temperature; (iii) final artificial aging at for 4, 6,8 and 10 h in the same tube furnace. It should be pointed out that before AA the quenched samples were stored at to avoid natural pre-aging. For the aluminum alloys under investigation, all heat treatment routes were established in agreement with [6,13-17]. Details of direct aging parameters employed in this study are summarized in Table 2, while a scheme of T6-type heat treatments is shown in Fig. 1.

Fig. 1 Scheme of T6-type heat treatments.

Fig. 1. Scheme of T6-type heat treatments.

After heat treatment, the microstructure and mechanical properties of all samples were evaluated by the already described procedure. Finally, all hardness data were compared to those obtained on specimens drawn from two other components made of the same aluminum alloys, which were heattreated according to the parameters commonly used by the company and subsequently characterized by the above-mentioned instrumentations. These latter heat treatment parameters are confidential.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/MSF.1130.25.pdf

The representative optical micrographs in Fig. 2 depict the microstructure of C1 and C2 components observed in the longitudinal section and cross-section, respectively. Both micrographs were collected on samples in as-received condition and after electrolytic etching in Barker's reagent.

Fig. 2 Representative optical micrographs of the microstructure of C1 and C2 components observed in (a) longitudinal section and (b) cross-section. Both images were collected on samples in asreceived condition and after electrolytic etching in Barker's reagent.

Fig. 2. Representative optical micrographs of the microstructure of C1 and C2 components observed in (a) longitudinal section and (b) cross-section. Both images were collected on samples in asreceived condition and after electrolytic etching in Barker's reagent.

In the T4 state, the microstructure of the two extruded parts consisted of coarse equiaxed grains with a great number of intermetallic particles uniformly distributed in the metal matrix (Fig. 2a and b). In cross-section, the calculated average grain size of C1 component (corresponding to EN AW6005 alloy) was , whereas that of the C2 (corresponding to EN AW-6060 alloy) was . In the longitudinal section, grains and inclusions were arranged parallel to plastic deformation (Fig. 2a), thus generating a fibrous microstructure.
Based on the SEM/EDS observations, the intermetallic particles (light contrast areas in the BSESEM micrograph of Fig. 3) displayed lamellar and rounded morphology. The semi-quantitative EDS analysis of lamellar inclusions indicated the presence of aluminum, silicon, iron, manganese and chromium (site 1 in Fig. 3b), with a ( )/Si ratio, expressed as atomic percentage , around 1.0. As a result, these particles could be ascribed to the phase [13]. Concerning rounded particles, the same elements were detected (site 2 in Fig. 3b), but with a ratio of about 1.5; hence, these intermetallics could be determined as the phase [28]. All EDS results are consistent with previously reported data for extruded alloys [29,30].
Irrespective of the heat treatment route, the microstructure and average grain size of heat-treated samples were similar to those of as-received specimens. As already detailed in the Introduction section, the main outcome of aging process is the strengthening of alloys due to the precipitation of sub-micrometric particles in the metal matrix [5]. The density of these precipitates is strongly affected by the heat treatment parameters, with the aging kinetic being controlled by the diffusion of the solute atoms [31]. With reference to the aging sequence reported in the same section, the GP-I zones with a size in the range of are the earliest particles able to produce a distinct contrast for identification by the more recent transmission electron microscopy techniques [32]. It should be highlighted that the needle-like phase has been considered as the most developed GP-I zones [21]. Accordingly, no microstructural changes could be recognized in heat-treated samples due to the limited resolution of the microscopy instrumentations employed in this study.

Fig. 3 BSE-SEM micrograph of the microstructure of C1 component observed in cross-section and semi-quantitative EDS results (expressed as atomic percentage [at%]) of lamellar and rounded particles in the same microstructure (sites 1 and 2, respectively).

Fig. 3. BSE-SEM micrograph of the microstructure of C1 component observed in cross-section and semi-quantitative EDS results (expressed as atomic percentage [at%]) of lamellar and rounded particles in the same microstructure (sites 1 and 2, respectively).

Concerning the mechanical properties of the two components in cross-section and in as-received condition, the C1 showed a mean HB10 of and a mean HV0.1 of , whereas the C2 exhibited a mean Vickers microhardness of .
The mean values ± standard deviations of Brinell hardness of C1 component measured in crosssection and after the two different heat treatment routes are shown in Fig. 4. First of all, it should be noted that the mean Brinell hardness of most samples subjected to T6-type heat treatments (Fig. 4b) was higher than that of directly aged specimens (Fig. 4a). Since the extruded part was in T4 state before heat treatments, this could suggest the occurrence of a negative natural pre-aging effect, which was partially withdrawn by the subsequent precipitation hardening. Considering the nominal chemical composition of EN AW-6005 alloy collected in Table 1, this is in agreement with the literature data for natural pre-aged 6xxx alloys containing a total amount of [19,20]. After direct aging, the mean HB10 increased with increasing aging time and temperature (Fig. 4a), with the highest mean values being obtained for aging temperatures between and and for aging times of 8-10 h (blue dotted lines in the same figure). When the same alloy was solubilized at for 1 h , water quenched to room temperature and artificial aged at for 10 h , the mean Brinell hardness was maximum and equal to (Fig. 4b). A comparison between the mean HB10 registered under these latter heat treatment conditions and the mean Brinell hardness of the alloy heat-treated according to the company parameters (purple bar in Fig. 4b) enable to estimate an increase in mechanical resistance of about 12 %.

Fig. 4 Mean values ± standard deviations of Brinell hardness of C1 component measured in crosssection and after: (a) direct aging at the different combinations of aging time and temperature; (b) T6-type heat treatments. The blue dotted lines in (a) enclose the direct aging conditions displaying the highest mean Brinell hardness. In (b), the HB10 of samples directly aged at for and 10 h and that of the same component heat-treated by the company were also included. (HT heat treatment; HTC= heat treatment performed by the company)

Fig. 4. Mean values ± standard deviations of Brinell hardness of C1 component measured in crosssection and after: (a) direct aging at the different combinations of aging time and temperature; (b) T6-type heat treatments. The blue dotted lines in (a) enclose the direct aging conditions displaying the highest mean Brinell hardness. In (b), the HB10 of samples directly aged at for and 10 h and that of the same component heat-treated by the company were also included. (HT heat treatment; HTC= heat treatment performed by the company)

Figure 5 depicts the mean values standard deviations of Vickers microhardness of C2 component measured in cross-section and after the two different heat treatment routes. As for the C 1 extruded part, the mean Vickers microhardness of several samples subjected to T6-type heat treatments (Fig. 5b) was slightly higher than that of directly aged specimens (Fig. 5a). Accordingly, it was supposed that also the EN AW-6060 alloy experienced a negative natural pre-aging effect, albeit to a minor extent. In this case, the results are partially in agreement with the literature data for natural pre-aged 6xxx alloys containing a total amount of [19,20]. Concerning direct aging, once the aging time is fixed, no specific trends of HV0.1 can be defined for the different aging temperatures (Fig. 5a). Besides, the highest mean values of Vickers microhardness were obtained with DA at and for 8 h , and at for 10 h (blue dotted lines in the same figure). Direct aging at for 15 and 20 h led to mean HV0.1 of and , respectively. These values are similar to the highest ones; nevertheless, the achieved mechanical properties do not justify the adoption of such time and energy-consuming heat treatments. When the EN AW-6060 alloy underwent a T6-type heat treatment comprising artificial aging at for 10 h , the mean HV0.1 was maximum and equal to (Fig. 5b). A comparison between the mean Vickers microhardness measured under these latter heat treatment conditions and the mean HV0.1 of the alloy heat-treated according to the company parameters (purple bar in Fig. 5b) enable to assess, once again, an increase in the mechanical resistance of about .

Fig. 5 Mean values ± standard deviations of Vickers microhardness of C2 component measured in cross-section and after: (a) direct aging at the different combinations of aging time and temperature; (b) T6-type heat treatments. The blue dotted lines in (a) enclose the direct aging conditions displaying the highest mean Vickers microhardness. In (b), the HV0.1 of samples directly aged at for 4, 6, 8 and 10 h and that of the same component heat-treated by the company were also included. (HT = heat treatment; HTC=heat treatment performed by the company)

Fig. 5. Mean values ± standard deviations of Vickers microhardness of C2 component measured in cross-section and after: (a) direct aging at the different combinations of aging time and temperature; (b) T6-type heat treatments. The blue dotted lines in (a) enclose the direct aging conditions displaying the highest mean Vickers microhardness. In (b), the HV0.1 of samples directly aged at for 4, 6, 8 and 10 h and that of the same component heat-treated by the company were also included. (HT = heat treatment; HTC=heat treatment performed by the company)

Conclusions

The original version of this paper is available on https://www.scientific.net/MSF.1130.25.pdf

This study investigates the effects of different aging times and temperatures on the microstructure and mechanical properties of two extruded components for healthcare industry, which were made of EN AW-6005 and EN AW-6060 aluminum alloys. Based on the results, the following conclusions can be drawn:

The microstructure of both components exhibited coarse equiaxed grains (with an average grain size between 83 and in cross-section) plus lamellar and rounded intermetallic particles, which could be ascribed to the and phases.

Irrespective of the heat treatment route, no changes were detected in the microstructure and average grain size.

For the EN AW-6005 alloy, the highest mean Brinell hardness was obtained after direct aging between and for and after artificial aging at for 10 h during T6type heat treatment.

For the EN AW-6060 alloy, the highest mean Vickers microhardness was obtained after direct aging at and for 8 h , and at for 10 h . The mean HV0.1 was then maximum after T6-type heat treatment comprising AA at for 10 h .

Finally, all the above-mentioned heat treatments favored an increase in the mechanical resistance up to about with respect to that of the alloys heat-treated according to the company parameters.

Acknowledgments

The original version of this paper is available on https://www.scientific.net/MSF.1130.25.pdf

The authors wish to acknowledge Eng. Vittorio Ghelli for his valuable contribution to the experimental part of the present investigation, and FERNO S.r.l (Pieve di Cento, BO, Italy) for the financial support and the supply of the material examined.

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The original version of this paper is available on https://www.scientific.net/MSF.1130.25.pdf

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Materials Science Forum, ISSN: 1662-9752, Vol. 1130, pp 35-48

doi: 10.4028/p-kCuq7j

© 2024 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2023-12-04

Revised:
2024-03-09

Accepted:
2024-03-18

Online:
2024-10-31

Effect of Homogenization Cycle on Precipitation of Dispersoids and Abnormal Grain Growth in an Al-Mg-Si Alloy

The original version of this paper is available on https://www.scientific.net/MSF.1130.35.pdf

Sumit Kumar Gahlyan , Pankaj Shivaji Wanjari , Shavi Agrawal , Butchi Bharadwaj , Manu Saxena , Vivek Srivastava

Hindalco Innovation Centre-Semifab, Hindalco Industries Ltd, Plot no.2, Taloja MIDC, Navi Mumbai-410208, Maharashtra, India

Materials Engineering, Indian Institute of Science, CV Raman Road, Bangaluru, Karnataka, 560012, India

sumit.gahlyan@adityabirla.com, pankaj.wanjari@adityabirla.com, shaviagrawal@iisc.ac.in, manu.saxena@adityabirla.com, butchi.bharadwajc@adityabirla.com, srivastava.v@gmail.com

Keywords: Dispersoids, Homogenisation heating rate, Peripheral Coarse Grain (PCG).

Abstract

AA6xxx alloys are used for various automotive and architectural applications where microstructural characteristics are critical to have acceptable final properties. Abnormal Grain Growth (AGG) in these alloys, industrially termed as Peripheral Coarse Grain (PCG) is undesirable and based non-coherent dispersoids are used to control the extent of PCG. Homogenization soaking temperature and time along with heating rates determine the size, distribution with grains and volume fraction of these dispersoids. In this study the heating rates are varied in lab and industrial setting to assess the effect of aforementioned dispersoid features using SEM and digital microscopy. It is found that higher heating rates lead to coarser and lower area fraction of dispersoids which finally results in markedly large PCG in industrial extrusion. Observed dispersoid features were described based on basic kinetics and Thermo-CalcTM predicted trends of microsegregation, fraction of dispersoids and fraction of potential nucleating sites ( ) of dispersoids. A static recrystallization model was used to calculate the driving and retarding pressures based on substructural (EBSD analysis) and dispersoid features (SEM+ image analysis). The predicted recrystallisation response and PCG grain size was in close agreement with the observed values. This study highlights the significance of homogenization heating rates in addition to soaking time and temperature for PCG control.

Introduction

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AA6xxx or based aluminium alloys are commercially preferred for extrusion products which are used for various automobile and architectural applications. Each of the processing steps starting from casting till the finishing operation influence the final mechanical, corrosion, and cosmetic properties. However, in the commercial production, some of the processing steps maybe compromised to reduce costs without significantly affecting the final properties. Homogenisation, although a critical step in extrusion process is energy intensive, and it is always desirable to reduce the processing time here. Hence there is a strong motive to make the homogenisation heating rates faster. However, moving beyond a certain heating rate may negatively impact the final properties since the microstructural features might change significantly.

Dispersoids, type, are important microstructural features in AA6xxx alloys for grain size control which are sensitive to heating rates and the heating rate may affect their distribution within grains . It has been observed previously that the grain centres start to have lower number density of dispersoids with increased heating rates which has been related to presence of micro-segregation in the as cast microstructure. Nucleation of these non-coherent dispersoids is reported to occur heterogeneously either on or phases which is dependent on super-saturation of solutes and hence on micro-segregation. Dispersoids size and content significantly influence the deformation [9], dynamic recovery [10] and grain boundary mobility which are critical factors for control of recrystallisation and abnormal grain growth [11,12].

Corresponding to high strain, strain rates and high temperatures at extrudate surface, formation of Abnormal Grain Growth or as is commonly termed Peripheral Coarse Grain (PCG) is a known phenomenon and AA6xxx alloys are especially prone to these defects. In case of these alloys, static recrystallisation occurs as the extrudate exits the die, followed by Abnormal Grain Growth forming layer of coarse grains [13,14,15]. Variety of factors affect the formation and extent of PCG viz. extrusion ratio, container temperature, billet temperatures, die designs, cooling rate post extrusion and the microstructural features of billet . Among these features, the role of dispersoids is critical for PCG control as has been described in aforementioned studies.

The effect of heating rate on segregation and with respect to that the role of final homogenisation temperature and time on final size, distribution, and fraction of dispersoids is described inadequately in literature. More importantly, the impact of heating rates, with industrial homogenisation trials, on dispersoids size, fraction, and subsequent formation of PCG is not documented. This paper attempts to do that through lab and industrial homogenisation trials followed by industrial extrusion trials. The dispersoid characteristics are explained in terms of the non-uniform distribution of nucleating sites of dispersoids on account of micro-segregation which is impacted by soaking temperature and time as well. The impact of heating rates on size and area fraction of dispersoids and consequently on PCG post extrusion is shown perhaps for the first time for an industrial production case. This study brings forth criticality of heating rate in addition to the soaking time and temperature for coarse grain control in an industrial setting.

Materials and Methods

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A commercial AA6xxx series alloy was cast in a commercial Direct Chill (DC) caster in form of logs at Hindalco Industries Ltd, the nominal composition is tabulated in Table 1. To observe the effect of heating rate and soaking time on size, area fraction and distribution of dispersoids, samples from as cast condition were lab homogenised at two heating rates i.e., (LS) and (LF), and water quenched after soaking for a duration 3 and 9 hrs at . Homogenisation trials were also conducted in continuous type industrial homogenisation furnace with two heating rates i.e., PS and PF such that heating rates were in following order PS with soaking temperature of and soaking time of 9 hrs . The homogenised billets of PS and PF condition were extruded in commercial production with an extrusion ratio of around 35 with an exit temperature of , water quenched after exiting the die.

Table 1. Nominal composition ( ) of alloy used for present investigation.

Elements | Si | Fe | Mn | Mg | Cu | Ti | Cr | Al
Wt % | 0.72 | 0.17 | 0.39 | 0.87 | 0.35 | 0.017 | 0.017 | Balance

To obtain representative sample of the logs, 20 mm thick slices were cut in as cast and homogenised condition from the stable portion of the DC cast logs. For lab heat treatment simulations, samples were cut from the mid-radius of the log slices and homogenised in air circulating Carbolite furnace (HRF07045B). The samples for grain structure and EBSD were taken from the middle of extrudate in T5 condition. Routine metallographic preparation techniques were used to obtain mirror finish at sample surface for all the microscopic analyses. For grain structure imaging, polished samples were electro-etched in Barker's reagent in Struers Lectropol-5 at 25 V for 220s. EDS analysis was carried on polished samples with Hitachi, S3400N fitted with Bruker EDS system. To perform EBSD analysis, mechanically polished samples were electropolished in Struers A2 solution at 21 V for 10s in Struers Letcropol-5. Imaging of dispersoids and EBSD mapping was carried in Gemini-400, Zeiss FEG-SEM with Oxford EBSD equipped with TSL8. The size and area fraction of dispersoids was estimated using Fiji ImageJ [19] by the method of thresholding the images. Segregation analyses was performed through Thermo-Calc 2023a [20] with TCAL5 database with built-in Scheil solidification model.

Result and Discussion

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Micro-Segregation Analysis. As discussed in previous section, dispersoids formed during high heating rate homogenisation have inhomogeneous distribution from grain edges to centres. Elemental segregation in the as cast condition is a critical aspect that leads to dispersoid ±inhomogeneities and size differences within the grains. In the analysed as-cast sample, it is observed that there is significant amount of micro-segregation and absence of dispersoid (Fig.1) Colour contrast within grains and EDS mapping indicate local variations in elemental composition. In an industrial process, the cast logs are subsequently homogenised where one of the objectives is dispersoids precipitation. However, fast heating rate lead to low area fraction or absence of dispersoids towards grain centre and along the grain boundaries (Fig.2).

Fig. 1. As cast sample images in (a) Polarised light optical microscope; (b) Elemental SEM-EDS mapping showing Si segregation; (c) Digital microscope without any dispersoid precipitation.

Fig. 1. As cast sample images in (a) Polarised light optical microscope; (b) Elemental SEM-EDS mapping showing Si segregation; (c) Digital microscope without any dispersoid precipitation.

Fig. 2. Digital microscope image showing regions of lower dispersoid density (a) Grain centres (b) Along grain boundaries;(c) In a single grain (etched in ).

Fig. 2. Digital microscope image showing regions of lower dispersoid density (a) Grain centres (b) Along grain boundaries;(c) In a single grain (etched in ).

Micro-segregation is the result of industrial DC casting where rapid solidification does not provide enough time for solute re-distribution. Solidification regime encountered in DC casting is closer to Scheil-Gulliver solidification [21] where the diffusion in solid is assumed to be zero and diffusion in liquid is assumed to be infinite. This solidification regime was used from Scheil module of Thermo-Calc to estimate the segregation encountered in DC casting.

To estimate the elemental segregation from grain centres to the grain boundaries, the concentration of each element in FCC was calculated as the solidification progressed in Scheil Module. Since solidification starts from grain centre ( 0.0 R ) and terminates at grain boundary ( 1.0 R ), solute content in FCC/Aluminium matrix at liquid corresponds to the concentration at grain centre and solute content towards the end of solidification, liquid corresponds to the concentrations close to grain boundary.

Fig. 3. Variation in solute concentration from grain centre ( 0 R ) towards the grain boundary ( 1.0 R ) obtained by (a) Scheil solidification, Thermo-Calc ; (b) SEM-EDX line profile

Fig. 3. Variation in solute concentration from grain centre ( 0 R ) towards the grain boundary ( 1.0 R ) obtained by (a) Scheil solidification, Thermo-Calc ; (b) SEM-EDX line profile

Considering this procedure in Thermo-Calc, the variation in solute content from grain edge towards the grain centre was calculated. This was compared with representative SEM-EDS line profile of one of the grains in as cast samples. It can be observed that the predictions follow the essential trend of SEM-EDS analysis (Fig.3). All the major alloying elements have a concentration gradient which is most prominent for Mg followed by Si and Mn . Mn concentration near 0.95D is tending to zero, this is due to partitioning of Mn to type intermetallics which are formed during solidification at grain boundaries. It could be expected that if these local variations in elemental compositions are not removed during homogenisation, it would influence subsequent dispersoid precipitation and lead to inhomogeneities.

Segregation Effect on '- and Dispersoid Precipitation. As mentioned previously, the nucleation of dispersoids is a heterogenous process and it occurs on or u' phase. Nucleation of u' phase is also reported to occur on [8] and hence uniform precipitation of is critical for subsequent uniformity of dispersoids. Precipitation of occurs through homogenous nucleation dependent on extent of Mg and Si supersaturation in the matrix. The temperature for precipitation and dissolution of is dependent on Mg and Si content and the phases formed prior to that and the temperature window of could be considered as important [22]. The dissolution occurs close to whereas the dispersoids precipitation would initiate at [8]. Conisdering the mentioned precipitation and dissolution processes at overlapping temperatures, the heating rates need to be estimated and adjusted accordingly. For example, the diffusion distance for Mg and is for a duration of 1 hr at and it would be even lower for lower temperature, hence the heating rates need to be slower in the initial ramp up from 200 to for uniform precipitation of ’ phase [23].

However, during the industrial homogenisation processes, the heating rate may not be slow enough to remove segregation and have uniform precipitation. To demonstrate a case where the heating rate is such that the micro-segregation is not fully removed in the temperature range of ' precipitation, Thermo-Calc software is used for prediction of ' and dispersoids precipitation. Since and dispersoids precipitation is dependent on local elemental supersaturation, the equilibrium volume fraction of dispersoids within regions of a grain could be estimated based on the local chemical composition. The local composition at few points were selected from the segregation profile of the Thermo-Calc results (Fig.3,a) for calculation of equilibrium ' and dispersoids volume fraction. The calculated results are plotted at and for , and at for dispersoids precipitation (Fig.4)

Fig. 4. (a)Volume fraction of dispersoids and across the grain, calculated using ThermoCalc (b) SEM image showing size and number area fraction variation across a grain in LF condition.

Fig. 4. (a)Volume fraction of dispersoids and across the grain, calculated using ThermoCalc (b) SEM image showing size and number area fraction variation across a grain in LF condition.

From the predicted results (Fig.4), it can be observed that the volume fraction of '- varied significantly from centre to edge implying that the nucleating sites for dispersoids will have similar trend and this non-homogeneity would be translated to dispersoids fraction. It is to be noted that the volume fraction calculated here is based on equilibrium conditions and kinetic consideration may change the fractions while the trends are expected to be retained. It could be observed that volume fraction of is almost zero near the grain centres and the nucleation sites thus would be of similar order. It is also observed that the volume fraction of dispersoids is not affected as severely as those of , hence the number of nucleation sites or number density of dispersoids would be affected more prominently compared to the volume fraction across the grains. This consideration therefore implies that in regions with low number of nucleating sites, the few dispersoids may grow to large sizes to achieve the desired volume fraction. These predictions are in line with the observations of high heating rate samples where the grain centre has very few but coarse dispersoids (Fig.4). The other important prediction is regarding the absence of dispersoids near grain boundaries which is a consequence of Mn segregation profile predicted earlier. This is in agreement to the absence of dispersoids adjacent to the grain boundaries, (Fig.2,b). The comments above need to be considered as qualitative since for quantitative predictions, more rigorous prediction tools and procedures would be needed.

Dispersoids Characterisation, Lab Schedule (LS, LF). In lab homogenised samples i.e., and , the fraction and size of dispersoids at grain centres varied significantly from grain to grain within a single sample leading to significant scatter in image analysis. Considering this, image analysis for dispersoids characterisation was performed on images taken near the grain edges since the scatter within the samples was much less here. Based on the analysis (Fig.5,c), it could be observed that the median size of dispersoids in LS is higher than LF cycle for a soaking duration of 3 hrs whereas for a duration of 9 hrs the sizes were identical. The size of dispersoids increased in both the samples, however the change was more prominent in LF sample. The area fraction of dispersoids in LS is significantly lower than LF sample in case of 3 hrs soak. With increased soaking time of 9hrs, the area fraction remained stable for LS sample whereas in case of LF, it reduced significantly up to from 0.052 to 0.042 .

Fig. 5. SEM images of dispersoids for in region around (a) Grain edge, (b) Grain centre ; Plots of LS and LF for (c) Median dispersoid size (b) Area fraction (%) and ratio( ).

Fig. 5. SEM images of dispersoids for in region around (a) Grain edge, (b) Grain centre ; Plots of LS and LF for (c) Median dispersoid size (b) Area fraction (%) and ratio( ).

In the temperature range of ' precipitation, at higher heating rates, segregation removal would be low since the diffusion distances are less for solute elements to redistribute. The grain boundaries have the highest supersaturation for Mg and Si atoms which can translate into higher number and volume fraction of nucleation sites and consequently higher number density and higher fraction of dispersoids. Therefore, this results in higher area fraction for faster heating rate (LF) compared to LS at 3 hr soaking period.

At higher soaking time, the diffusion distances even for low diffusivity elements viz. Mn and Fe [23,24] becomes sufficient for long range diffusion. This would lead to significant reduction in local composition gradients, resulting in segregated zones attaining nominal composition across the grains. In this scenario the higher fraction of dispersoids precipitated out near the grain boundaries on account of higher supersaturations would also tend to move towards lowered equilibrium volume fractions as the soaking time increases. Hence, the volume fractions reduce near the grain boundaries and more prominently for the case where the initial segregations were high i.e., LF samples. Thus, for the fast-heating rate, LF there is a sharp drop in area fraction but the drop in LS is nominal indicating that at heating rate of LS, sufficient micro-segregation was removed. Similar area fractions for LS and LF at 9hrs soaking also indicate that the equilibrium area fraction for this alloy which would be was achieved by LS in 3 hrs soaking.

The coarsening of dispersoids on account of Gibbs-Thomson effect [21] is also observed for both rates and hence the dispersoid size is similar at 9 hrs soaking. The initial difference in size of dispersoids between LS and LF samples could be explained by presence of higher number of nucleating sites in case of LF samples due to greater Mg and Si supersaturation. The gradient in volume fraction of is much higher than that of dispersoids (Fig.4,a). Thus, between LS and LF, when LF would retain higher segregation, the difference between number of nucleating sites would be higher than that of volume fraction of dispersoids. This would hence translate in finer dispersoid size at faster heating rate (LF) due to presence of higher number of nucleating sites per unit fraction of dispersoids. However, with increased soaking time, the finer dispersoids would dissolve due to coarsening and reduction in local equilibrium dispersoid volume as described previously. Thus, at 9 hrs soaking time, the sizes in LS and LF become identical near grain boundaries as observed in these findings.

Dispersoids Characterisation, Plant Schedule (PS, PF). Subsequent to the lab homogenisation trials (LS, LF), to observe the effect of different heating rates on dispersoid evolution and consequently on coarse grain control, cast logs were homogenised in an industrial furnace ( ) with two heating rates as mentioned previously i.e., PF and PS. The logs were soaked for a duration of 9 hrs instead of 3 hrs , since ratio (fraction of dispersoids/radius), didn't change significantly for LS samples, and additionally it was similar for both LF and LS samples. Higher soaking times are also preferred for better extrudability since AlFeSi intermetallics transform from type to type for longer soaking durations [25].

Fig. 6. SEM image of dispersoids in extruded samples processed with heating rate(a)PF (b) PS; Plots of LS, PS and PF, LF for (c) Median dispersoid size (b) Area fraction (%) and ratio( ) for homogenization.

Fig. 6. SEM image of dispersoids in extruded samples processed with heating rate(a)PF (b) PS; Plots of LS, PS and PF, LF for (c) Median dispersoid size (b) Area fraction (%) and ratio( ) for homogenization.

The image analysis for these samples was done on cross section of extruded samples at locations 1 mm from surface. The distance between two original grain boundaries reduces due to heavy deformation during extrusion and hence the analysed images of PS and PF represent dispersoid content averaged out over the radius of grains. The median dispersoids size in LS and LF samples was whereas the dispersoids in plant cycle PF is coarser i.e., and marginally finer i.e., in PS. Higher size in PF could be explained from the fact that the in the analysis for PF sample, contribution of coarse dispersoids from grain centres is also accounted for as against the LS, LF samples where only regions near grain boundaries were examined. In case of PS sample, since the heating rates are slowest of all the examined cases, it would be expected that the coarsening at grain centres would be lower than any other case and it would shift the median size closer to LS median size.

However, the more critical difference is observed in case of area fractions wherein the area fraction of dispersoids in PF is almost lower than that of PS samples. The measured area fraction in case of extruded (PS, PF) samples have contribution each from grain centres and grain edges as against the analysis of lab homogenised samples (LS, LF) where the fractions were determined only from the edges. At soaking time of 9hrs, as observed previously in LF sample, local equilibrium area fraction of dispersoids would be lowered and the dispersoids near the grain boundaries would gradually dissolve for the PF samples and hence the area fraction would be lowered near grain boundaries. PF is the highest heating rate in this study and hence the retention of concentration gradient would be highest for this case, also implying higher fraction of regions (grain centre) with lower number density of dispersoids and a greater number of such
grains (Fig.2,a). Additionally, at grain centres, no new dispersoids would nucleate since newer nucleating sites( ) would not form at these temperatures. The effect of lowering of dispersoid fractions near the grain boundaries complemented with lower or no contribution of dispersoids towards grain centres would lead to significantly lower area fractions for PF samples. It follows that these effects would be accentuated with higher heating rates for a material with high microsegregation and hence a remarkably low fraction of dispersoids is observed in case of PF sample as against PS sample. In case of PS, the area fraction is closer to LS samples as the heating rate is slowest of all cases and hence the degree of segregation would be marginal during precipitation of and dispersoids. Thus, variation across grains would be minimal and increased soaking won't affect the area fraction of dispersoids.

PCG and Recrystallisation Model. The billets from each plant homogenisation schedule (PS, PF) were extruded with identical extrusion parameters followed by water quench at the press. Cross-section of the samples from the middle of the extrudate length were characterised for PCG using optical microscope. Faster heating rate (PF) led to formation of PCG which covered up to of the rod surface where the size of grains ranged from to (Fig.7,b,d). In sharp contrast to that slower heating rate samples (PS) although subjected to same homogenisation soaking time and temperature restricted PCG levels up to from surface.

Fig. 7. Grain structure at cross-section of extrudate for billets processed with (a), (c) Plant slower heating rates, PS; (b),(d) Plant faster heating rate, PF.

Fig. 7. Grain structure at cross-section of extrudate for billets processed with (a), (c) Plant slower heating rates, PS; (b),(d) Plant faster heating rate, PF.

For substructural examinations, EBSD mapping of the extrudate samples was performed on the cross section at from the surface. Because of heavy coarse graining in PF sample, only limited localised region with absence of PCG was observable and one of such regions was mapped at approx. 1 mm from the surface.

The volume fraction of important micro-texture components were calculated through ATEX [26] with misorientation tolerance angle of , it was observed that both samples had significant fraction of cube texture but none of the other important components were present, (Fig.8,a,b) in line with previous observation [27]. Grain boundary misorientation (MO) calculations showed that the fraction of High Angle Grain Boundaries (HAGB) i.e., boundaries with misorientation was whereas the Low Angle Grain Boundaries (LAGB), MO:1.5-5 was in PF as against 26% in PS sample(Fig.8,c). Due to higher fraction of LAGB, PF sample has greater potential of static recrystallisation.

Fig. 8. Comparison of PS and PF samples in terms of (a) Micro-texture components (Cube & others); Pole figure derived from EBSD maps(b)PS(c)PF; (d)Grain boundary misorientation distribution; (e) Boxplot of subgrain size.

Fig. 8. Comparison of PS and PF samples in terms of (a) Micro-texture components (Cube & others); Pole figure derived from EBSD maps(b)PS(c)PF; (d)Grain boundary misorientation distribution; (e) Boxplot of subgrain size.

Higher degree of localised strains near the surface, accompanied by dynamic recovery processes may lead to progressive accumulation of dislocations at subgrain boundaries and hence increasing the boundary misorientation towards HAGB. This process is termed as Continuous Dynamic Recrystallisation (CDRX) [28,29] which may be accompanied by Geometric Dynamic Recrystallisation (GDRX). GDRX occurs when the preexisting grains are deformed to sizes closer to subgrain size and the grain boundary serrations eventually join to form HAGB subgrains. Both CRDX and GDRX have been reported as the mechanism of dynamic recrystallisation in extrusion conditions similar to here [14]. Since in recrystallisation process grains with HAGB grow to form strain free grains replacing LAGBs, the current microstructure has a potential for static recrystallisation which has previously been described for PCG mechanism [13,14,30]. Additionally, cube component in aluminium alloys is favourable for higher growth rates [ ] which may aid in PCG.

The subgrain size was calculated considering the minimum grain size to be and grain tolerance angle to be for both PS and PF samples. The minimum size and tolerance were fixed through multiple iterations of comparing subgrains from band contrast images with the calculated output grain images. The subgrain size distribution is different in PS and PF samples where the median subgrain size is in PS samples as against in PF samples. Subgrains with larger size, highly mobile high angle grain boundary breakaway to form abnormally large grain during static recrystallisation where the cube grains growth is especially faster than other textures [33,34].

Due to dynamic recovery, it may be assumed that there would be no free dislocations inside the subgrains and the driving pressure for static recrystallisation would be provided by the dislocations from the subgrain boundaries. The driving pressure for this condition is described in Eq. 1 [35] where is subgrain boundary energy dependent on mean misorientation ( ) [36]. Here is calculate based on Eq. 2 where is the grain boundary energy associated with misorientation i.e., [37] and mean misorientation, between subgrains is calculated using EBSD. Subgrain size( ) is dependent on Zener-Holloman parameter for an alloy [38], in this study it is the median subgrain size of , assuming no considerable coarsening has occurred here due to large pinning pressure of dispersoids and rapid water quench. This value is closer to previously reported value for a similar system containing dispersoids [39], but slightly higher, probably due to lower extrusion ratio.

Counter acting to the driving pressure, non-coherent dispersoids provide retarding pressure through Zener pinning [40], Eq. 3 where is the area/volume fraction of dispersoids, and is median dispersoid radius [41]. Additionally, on account of Gibbs-Thomson effect, grain curvature will also provide a retarding pressure, Eq. 4 [30] where R and are the radius and surface energy of recrystallising grain. Considering that highly misoriented and larger size grains would be potential recrystallising grains, surface energy associated with grain boundary ( ) and quartile grain size (grain tolerance angle ) was selected for Eq.4.

Table 2. List of parameters/constants used for recrystallisation model.

Parameter/constant | PS | PF
Grain boundary energy,(HAGB), γSB | 0.324J/m² | 0.324J/m²
Subgrain size, δ | 3.0μm | 3.0μm
Mean misorientation, θ | 7.4° | 6.6°
Fraction of dispersoids, Fv | 0.041 | 0.026
Median dispersoid size, r | 0.16μm | 0.19μm
3rd Quartile recrystallising grain radius, R | 6.6μm | 16.5μm
Time for static recrystallisation, t |  | 15s
Mobility of random orientation boundary(Al-1Mg) [42] | Mo: 367m⁴J⁻¹s⁻¹; Q:170kJmol⁻¹

Table 3. Driving, retarding pressures for recrystallisation and resultant grain size for PS, PF cycles.

PD
(MPa)
PZ
(MPa)
PC
(MPa)
Pnet
(MPa)
Grain size
(μm)
Exit Temp:
450°C
Grain size
(μm)
Exit Temp:
490°C
Grain size
(μm)
Exit Temp:
510°C

PS
0.27
0.21
0.20
-0.13
NA
NA
NA

PF
0.26
0.10
0.08
0.08
225
990
2316

The net pressure for static recrystallisation would be determined by the difference of driving and retarding pressures for PS and PF cases, Eq.5. Using the values obtained from EBSD analysis (Table 2) and equations described above, the pressures were calculated as listed in Table 3. Due to markedly low fraction and higher size of dispersoids, Zener pinning pressure is almost halved in PF sample as compared to PS sample. Additionally, higher subgrain size distribution in PF sample provides lower retarding pressure from grain boundary curvature. Both these retarding contributions are higher in PF samples whereas the driving pressure was similar for both the cases. As a result, PS has a net negative pressure and hence no recrystallisation would be expected whereas in case of PF there is a net positive driving pressure suggesting potential for recrystallisation. To estimate the approximate size of recrystallised grain, the grain boundary velocity(v), is multiplied by quench delay time, t which was around 15s in this case. The velocity is calculated by taking the product of mobility of grain boundary and net driving pressure, Eq.6. Mobility of a random grain boundary is obtained here from the recrystallisation data of in [42] extrapolating to the typical exit temperature of .

The calculations predicted grain size around 1 mm which is consistent with the size of coarse grains observed in PCG zone. It is to be noted that in this estimation, it is assumed that there is no impingement or restriction to the moving grain boundary from other recrystallising grains which would change when number of recrystallisation nucleation sites are very high, and in that case the final grain size could become much smaller. The actual grain boundary energies may be different and the subgrain size maybe lower since some degree of coarsening may have happened. Nevertheless, these calculations provide a good estimation of the order of recrystallised grain size and what could be expected in the given conditions. This study brings forth the significance of heating rate which impacts the Zener pinning, consequently subgrain coarsening and subsequent static recrystallisation leading to abnormal grain growth.

Summary and Conclusions

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A range of homogenisation heating rates were studied in lab and plant conditions for evolution of dispersoids in alloy in terms of size, area fraction and the distribution within grains. Nonuniform precipitation of dispersoids was observed within grains where grain centres had coarser and fewer precipitates and frequency of such regions increased with higher heating rates. This inhomogeneity was explained in terms of micro-segregation that occurs during industrial DC solidification, resulting in inhomogeneous precipitation of dispersoid nucleation sites i.e., u' or . Micro-segregation profile and precipitation of dispersoids was predicted through Thermo-Calc which showed the precipitation trends observed in homogenised samples. Basis that, absence of dispersoids adjacent to the grain boundaries, absence or lower fraction towards grain centres, size evolution and changes in area fractions with changes in heating rates and soaking durations were explained.

To assess the effect of heating rates on final extrusion substructure and microstructure, two samples were subjected to varied heating rates in industrial homogenisation furnace (PS, PF) and extruded through industrial extrusion parameters. Although both PS and PF samples were homogenised at identical soaking temperature and time, the heating rate difference led to markedly large fraction of PCG in PF samples. The subgrain size and grain boundary misorientations analysed through EBSD were used in a static recrystallisation model. This model was used to demonstrate that reduction in Zener pinning pressure considering lower fraction and size of dispersoids and higher subgrain size in PF samples influenced the driving pressure for recrystallisation. Negative driving pressure in PS samples restricted the recrystallisation whereas positive driving pressure in PF aided by higher extrusion temperatures led to abnormal grain growth. The estimated grain size was close to observed size of coarse grains in PCG regions. The major takeaways could be listed down as below:

Micro-segregation in as cast material is retained at high heating rate homogenisation cycle leading to variation in dispersoids precipitation within the grains. It is observed that the number density of dispersoids reduce whereas the size of dispersoids increase from grain boundary to grain centre.

These variations could be explained by precipitation behaviour of dispersoid forming nucleating sites which are influenced by heating rates in the temperature range .

Compared to dispersoid size, the overall area fraction of dispersoids is affected more pronouncedly by heating rates.

The effect of heating rates is detrimental for PCG control in industrial settings even though the soaking time and temperature may be identical. The Zener pinning pressure is markedly reduced due to lower area fraction and higher dispersoid size at faster heating rates.

Based on microstructural features of extrudate, a static recrystallisation model could be used to estimate the final grain size in case of PCG formation.

The principles and modelling predictions used here for dispersoid feature evaluation may be used for prediction of dispersoids precipitation across grains in terms of size and number density with respect to various heating rates in future work. The recrystallisation model used here is basic and
could be used for initial estimation of coarse graining in AA6xxx alloys. To the best of our knowledge the combined impact of heating rates on dispersoid size, distribution and consequently on PCG has not been reported previously and this study brings forth this close and critical association which has great industrial significance.

Acknowledgements

The original version of this paper is available on https://www.scientific.net/MSF.1130.35.pdf

I would like to thank Mahan team (Mr. Sanjay Chaturvedi and Mr. Deepak Khandelwal) for homogenisation trials and Renukoot team (Mr. Akhilesh Gupta, Mr. Rajan Deo) for the extrusion trials. I thank Dr Gautam Wagle for the review of manuscript and Prof. Satyam Suwas for providing the lab facilities at IISc, Bangalore.

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The original version of this paper is available on https://www.scientific.net/MSF.1130.35.pdf

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Materials Science Forum, ISSN: 1662-9752, Vol. 1129, pp 43-52

doi: 10.4028/p-LW1j3p

© 2024 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2023-11-20

Revised:
2024-03-29

Accepted:
2024-04-08

Online:
2024-10-30

Antimicrobial Performance of Different Metals

The original version of this paper is available on https://www.scientific.net/MSF.1129.43.pdf

Ramakumar Jayachandran

Hydro- Extruded Solutions, Operational Excellence Drammensveien, 264 Oslo, Norway

Ramakumar.Jayachandran@hydro.com

Keywords: Antimicrobial performance, Testing, E coli, MRSA bacteria, Aluminium

Abstract

Health care associated infections or nosocomial infections (NI) is the fourth leading cause of disease and the most common complication affecting hospitalised patients in addition to a minimum of 175,000 deaths every year in industrialised countries. The Center for Disease Control and Prevention (CDC) states that influenza is transmitted from person to person primarily via large virus-laden droplets or through direct or indirect contact with respiratory secretions when touching surfaces contaminated with influenza virus and approximately of the infections are transmitted via touch surfaces. In the year 2020 the Coronavirus (Covid 19) spread has affected the global community and also caused a great concern for the people and health care workers with a global infected population of more than five million.

With the ongoing population rise in the cities growing drug resistant bacteria, increasing infection rate in hospitals and communities, ageing world population strongly indicates the need to minimise the spread of infections via touch surfaces. Metals (and products manufactured from them) such as copper and silver are known to exhibit antimicrobial properties. These metals, or composites containing them, can be used as additives and incorporated into other materials such as paints, plastics and powder coatings to imbue these materials with antimicrobial properties.

In this paper we present the inherent antimicrobial properties of a copper containing alloy, two alloys of hospital grade steel ( 304 and 316), extruded aluminium (606013), anodized aluminium (606013) and zinc clad aluminium (3003-7072). Additionally, these materials were coated in epoxy resin powder coating with and without silver based antimicrobial additive. The ability of these metal alloys to reduce the population of inoculated microorganism numbers was assessed via the international standard (ISO) 22196:2011 Measurement of antimicrobial activity on plastics and other non-porous surfaces.

Introduction

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Nosocomial infection or healthcare associated infections occur in a patient who is under the treatment of medical care in the hospital, but the infection was absent for the patient at the time of hospital admission. These infections affect the well-being of the patients, health care staff and may also occur after the patient is discharged from the hospital [1]. In the year 2020 the Coronavirus (COVID-19) spread has affected the global community and caused a great concern for the state and health care workers with a global infection of more than fifty million. The coronavirus spreads through the respiratory droplets and when these droplets are in contact with another person the infection spreads to the new persons. These droplets are airborne which potentially infect any person within a radius range of 1.5 m . Another mode of transmission is these droplets land on a surface of any inanimate objects and subsequently touched by other person. Hence the World Health Organisation [2] recommended the handwashing and disinfection of the surface are vital to minimize the spread of the infection. This recommendation is strengthened considering that people touch their face on an average of 23 times per hour, with of these occurrences involving the mucous membranes of the mouth and/or nose [3].

Health care associated infections or nosocomial infections (NI) is the fourth leading cause of disease and the most common complication affecting hospitalised patients in addition to a minimum of 175,000 deaths every year in industrialised countries [4]. The Center for Disease Control and Prevention (CDC) states that influenza is transmitted from person to person primarily via large virus-
laden droplets of respiratory secretions. These droplets infect animate (people to people transmission) or inanimate (people -surface-people) surface via contact.[5] A study showed that approximately of the infections are transmitted via touch surfaces [6]. A survey from OECD [7] countries from the impact of sick leave cost on economy shows of EU GDP. An European survey [8] showed the total number of NI in European acute care hospitals was 3,2 million in 2011-2012.

Background

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With growing global population thriving in the cities, growing drug resistant bacterias, Increasing infection rate in hospitals and communities and ageing world population, strongly indicates the need to minimise the spread of infections via touch surfaces. Despite the investments in health care and improved cleaning and hygiene process the Noscomical infection rates are increasing. To combat this problem, new antimicrobial metals or surfaces with antimicrobial coating are used in hospitals and public spaces. Traditionally in the hospitals the most common material used in door handles, bed rails, furnitures, medical cabinets, racks etc are made of stainless steel and are sterlised on a periodic basis. The main advantage of using the stainless steel is that this material can withstand all types of cleaning products. However, several studies have proven that the surfaces made by stainless steel become a breeding ground for the microbes [9].

Trends and drivers

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The COVID-19 virus origninated in Wuhan province in China in late 2019 and on January 2020, WHO declared the outbreak of the virus in China as a global pandemic. This outbreak forced several countries to shutdown non-essential economic activities which led to an economic downturn not even seen in 2008-09 financial crisis. The increased global travel levels also has raised the spread of COVID-19 to almost all the parts of the world. A recent study also suggest that there is no evidence in support or against the use of antiviral therapy or a specific antiviral agent with a majority of the COVID 19 infections are attributed due to the global travel [10]. The trends seen in NI and associated deaths, growing drug resistant bacteria in hospitals and communities are also increasing.

Certain metals and products manufactured from them are known to exhibit antimicrobial properties. These metals, or composites containing them, can be used as additives, and incorporated into other materials such as paints, plastics, and powder coatings to imbue these materials with antimicrobial properties as of the infections are spread through a touch surfaces [6]. Among the different materials copper, silver and Zinc oxides are known to exhibit antimicrobial properties. The copper technology road map [11] has cited that copper may be used in emerging application such as an antimicrobial surface. The products that copper may be expected to replace are door handles, knobs, touch surfaces in schools, hospitals, public buildings, transports etc. There has been several research conducted comparing the antimicrobial performance of different materials such as copper, stainless steel, zinc, lead, brass tin etc. but there is a limited literature availability on the performance of aluminium on the bacteria or viruses. This paper presents a comparison of extruded aluminium, anodized aluminium, powder coated aluminium, zinc clad aluminium, copper sheet, stainless steel 304 and 316 grades used in hospitals.

Method

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ISO 22196 (JIS Z2801- Japanese Test Method) is widely used to determine the antibacterial performance of the surfaces. The samples are prepared with dimension and thickness less than 10 mm . Figure 1 shows the test processs defined by the ISO 22196. In the first step (A) a known quantity of test organism is placed on to a treated and untreated sample. Normally three samples are taken to ensure the repeatability of the process. The loaded samples are covered with a microfilm ( and thickness of less than ) to ensure the humidity levels are maintained and incubated at for 24 hours (B). After incubation the bacteria are recovered from the samples by washing off into a bag and the surviving bacteria is transferred for growth in petri dish ( C ). Finally, the antimicrobial performance is determined based on the number of bacteria initially placed and finally recovered (D).

In bacteriology, the bacteria are classified into two groups gram positive and gram-negative bacteria with a gram staining procedure, which differentiates bacteria based on the cell wall structure. Upon using a crystal violet dye on the bacteria, gram positive bacteria retain the violet colour and gramnegative bacteria do not retain and show it as either red or purple. To understand the efficacy of the material, ISO 22196 requires the test shall be conducted on both gram positive and gram-negative bacteria. The two bacteria used in the test are Methicillin-resistant Staphylococcus aureus (MRSA) refers to a group of Gram-positive and Escherichia coli (E-Coli) refer to the Gram-negative bacteria.

Fig.1. Test Protocol

Fig.1. Test Protocol

Materials

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The material selected for the test are shown in table 1. The extruded aluminium profile was made from 606013 alloy and zinc clad aluminium sheet comprised of 7072 clad on the outside and 3005 in the core and were produced by Norsk Hydro.

Table 1. Test materials

Material
Alloy/ grade
Major Content

Extruded aluminium
606013
Aluminium > 98.8%

Stainless steel
304
Fe>70%

Stainless steel
316
Fe>60%

Copper sheet
CW004A
Cu>99.9%

Powder coated extruded aluminium
(with & without antimicrobial coating)
606013
Aluminium > 98.8%

Anodised extruded aluminium
(with & without antimicrobial coating)
606013
Aluminium > 98.8%

Zinc clad aluminium ( Zn 0,95%)
7072/3003
Aluminium > 97.8%

The extruded aluminium profile was 3 mm thick. The Zinc clad material was 0.4 mm in thickness and consisted of a 3005LL alloy with a clad layer of 7072 alloy (approximately 40 microns) on one side and 4343 silicon clad on the other side. The material was tested only on zinc clad side. The power coating was done on two different process one with the normal powder coating and the second with Interpon powder coat (Antimicrobial product particle size, active substance silver phosphate glass). The thickness of the stainless-steel sheet ( 304 and 316 grade) used was and the thickness of the copper sheet was . The extruded aluminium sample was also analysed by Optical Emission Spectroscopy (OES) to validate the chemical composition and the OES result is shown in table 2.

Table 2. OES analysis of extruded aluminium samples

Element % | Si | Fe | Cu | Mn | Mg | Cr | Zn | Ti | Al
Sample 1 | 0.5 | 0.2 | 0.01 | 0.05 | 0.37 | 0 | 0.01 | 0.02 | Remaining
Sample 2 | 0.5 | 0.2 | 0.01 | 0.05 | 0.38 | 0 | 0.01 | 0.02 | Remaining

Results- Test phase 1- Twenty-four hours exposure

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According to the ISO 22196, the antibacterial activity is calculated using the equation (1). In simple terms, the antibacterial activity of the test sample is deducted from the controlled sample (which exhibits no antibacterial activity).

Where,
R is the antibacterial activity.
Uo is the average of the common logarithm of the number of viable bacteria in cells , recovered from the untreated test specimens immediately after innoculation.

Ut is the average of common logarithm of the number of viable bacteria is cells , recovered from the untreated test specimens after 24 h

At is the average of common logarithm of the number of viable bacteria is cells , recovered from the treated test specimens after 24 h

Greater the R value, higher the antibacterial activity, although the standard do not prescribe any value the test lab conducted the test set an internal quality control percentage reduction requirement of reduction of test organism.

The test results are summarized in table 3 after different materials are evaluated for antibacterial efficacy after 24-hour period. As expected, the anodized and powder coated aluminium samples did not show any antibacterial activity. However, extruded aluminium and zinc clad aluminium performed even better than the copper. Stainless steel 316 grade showed antibacterial efficacy on E coli bacteria but failed on MRSA and stainless steel 304 grade failed both on Ecoli and MRSA bacteria. Using this self-imposed standard of efficacy (>95%) we can consider stainless steel and anodized aluminium to have 'failed' this test. Extruded aluminium, copper sheeting and zinc clad aluminium all exceeded the efficacy requirement.

Table 3. Antibacterial efficacy of different materials after 24 hours

Material
Ecoli
"R"
Ecoli
Result
MRSA
"R"
MRSA
Result

Extruded aluminium
≥99,99%
Pass
≥99,99%
Pass

Copper sheet
99,93
Pass
≥99,92%
Pass

Anodised aluminium
Growth
Fail
90,72
Fail

Powder coated aluminium
Growth
Fail
Growth
Fail

Zinc clad aluminium
≥99,99%
Pass
≥99,95%
Pass

Stainless steel 316
≥99,99%
Pass
91,79
Fail

Stainless steel 304
77,18
Fail
92,70
Fail

To validate the test results, the extruded aluminium, stainless steel 304 and copper were selected and tested in a different lab in parallel to the test done in the first lab. The test results are shown in figure 2 for Ecoli bacteria and figure 3 for MRSA, where 51883A represents extruded aluminium, 51883B represents 304 stainless steel and 51883 C represent copper sheet. In this test for control sample, untreated polyethylene was used, and the antibacterial efficacy values are summarized in Table 4 and table 5 for E coli and MRSA bacteria respectively

Fig. 2. Antibacterial efficacy of three different material against Ecoli bacteria

Fig. 2. Antibacterial efficacy of three different material against Ecoli bacteria

Fig 3. Antibacterial efficacy of three different material against MRSA bacteria

Fig 3. Antibacterial efficacy of three different material against MRSA bacteria

Table 4. Antibacterial efficacy of different materials against E coli after 24 hours

Material
Ecoli CFU
0hrs
Ecoli CFU
24hrs
E coli
Reduction
Antibacterial
efficacy test

Control
6300
880000
NA
Control

51883A- Aluminium
6300
1
>99.9999%
Pass

51883B- Steel-304
6300
260000
70.5% -growth
Fail

51883C- Copper
6300
1
>99.9999%
Pass

Table 5. Antibacterial efficacy of different materials against MRSA after 24 hours

Material
MRSA CFU
0hrs
MRSA CFU
24hrs
MRSA
Reduction
Antibacterial efficacy test

Control
6900
8100
NA
Control

51883A- Aluminium
6900
1
>99.988%
Pass

51883B- Steel-304
6900
810
90% reduction
Fail

51883C- Copper
6900
1
>99.988%
Pass

The validated test results show the extruded aluminium performs as equivalent to copper in antibacterial efficacy, while stainless steel 304 grade do not show any such effect.

Results- Test phase 2- Four hours exposure

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Having demonstrated potential antimicrobial effects for all metal surfaces examined, we started to investigate when these properties might manifest, initially with a four-hour exposure of organism to surface, followed by the standard 24-hour incubation of survivors. For metals, the results are shown below in Table 5. The extruded aluminium samples, zinc clad aluminium sample and copper sample exhibited antibacterial efficacy after four hours of exposure to Ecoli, whereas the test results were aborted for Copper sheet and stainless steel 316 grade for MRSA bacteria due to test issues.

Table 5. Antibacterial efficacy of different materials after 4 hours

Material
Ecoli
"R"
Ecoli
Result
MRSA
"R"
MRSA
Result

Extruded aluminium
95.53%
Pass
99.78%
Pass

Copper sheet
95.95%
Pass
*
-

Anodised aluminium
Growth
Fail
40.15%
Fail

Zinc clad aluminium
99.95%
Pass
>99.95%

Stainless steel 316
84%
Fail
*
-

Results- Test phase 3- Twenty-four hours exposure -Antimicrobial surface coatings

The original version of this paper is available on https://www.scientific.net/MSF.1129.43.pdf

In the phase three of the test, the aluminium samples were powdered coated and anodised with and without antimicrobial coating and tested after 24 hours of exposure to the bacteria. Powder coating of all samples was performed by Akzo Nobel, utilizing a variant the Interpon powder coat with and without silver based antimicrobial additive. In addition, a special anodized process that incorporates antimicrobial layer on the surface was also tested. Table 6 shows the results of the antibacterial efficacy of powdered coated and anodized aluminium samples after 24 hours. The antimicrobial coated samples exhibited a good antimicrobial efficacy while the normal powder coated samples exhibited growth and failed the efficacy test. Similarly, the special anodized process also displayed antimicrobial efficacy.

Table 6. Antibacterial efficacy of powdered coated and anodized aluminium samples after 24 hours

Material | Ecoli "R" | Ecoli Result | MRSA "R" | MRSA Result
Extruded aluminium-Powder coated | Growth | Fail | Growth | Fail
Extruded aluminium-With antimicrobial powder coat | 99.5% | Pass | 99.5% | Pass
Anodised extruded aluminium-Powder coated | Growth | Fail | Growth | Fail
Anodised extruded aluminium-With antimicrobial powder coat | 99.5% | Pass | 99.5% | Pass
Anodised extruded aluminium | Growth | Fail | 90.72% | Fail
Antimicrobial -Anodised extruded aluminium | 99.51% | Pass | 99.99% | Pass
Zinc clad aluminium-Powder coated | Growth | Fail | Growth | Fail
Zinc clad aluminium-With antimicrobial powder coat | 99.5% | Pass | 99.5% | Pass

Results- Test phase 4- Four hours exposure--Antimicrobial surface coatings

The original version of this paper is available on https://www.scientific.net/MSF.1129.43.pdf

In the phase four of the test, the aluminium samples were powdered coated and anodised with and without antimicrobial coating and tested after four hours of exposure to the bacteria. Powder coating of all samples was performed by Akzo Nobel, utilizing a variant the Interpon powder coat with and without silver based antimicrobial additive. In addition, a special anodized process that incorporates antimicrobial layer on the surface was also tested. Table 7 shows the results of the antibacterial efficacy of powdered coated and anodized aluminium samples after 4 hours. The antimicrobial coated samples exhibited a good antimicrobial efficacy while the normal powder coated samples exhibited growth and failed the efficacy test. Similarly, the special anodized process also displayed antimicrobial efficacy. As more than planned samples were used in earlier test, test with MRSA bacteria within four hours could not be completed except for anodized aluminium with and without antimicrobial surface. It should be noted that all these tests were done in 2013-14 before theCOVID19 outbreak.

Table 7. Antibacterial efficacy of powdered coated and anodized aluminium samples after 4 hours

Material
Ecoli
"R"
Ecoli
Result
MRSA
"R"
MRSA
Result

Extruded aluminium-Powder coated
Growth
Fail
NA
NA

Extruded aluminium-With antimicrobial powder coat
99.87%
Pass
NA
NA

Anodised extruded aluminium-Powder coated
Growth
Fail
NA
NA

Anodised extruded aluminium-With antimicrobial powder coat
99.87%
Pass
NA
NA

Anodised extruded aluminium
Growth
Fail
40.15%
Fail

Antimicrobial -Anodised extruded aluminium
> 99.99%
Pass
99.99%
Pass

Zinc clad aluminium-Powder coated
Growth
Fail
NA
NA

Zinc clad aluminium-With antimicrobial powder coat
99.87%
Pass
NA
NA

Discussion

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In the last few years there has been several publications promoting copper has an antimicrobial material; pure copper can eliminate bacteria in one hour but for copper with content requires more than two hours to achieve the similar antimicrobial activity [9]. Aluminium alloy do exhibit antimicrobial activity as seen in the study. It is not clear what causes the antimicrobial effect in aluminium alloy, is it the presence of copper and Zinc that promotes antimicrobial effect? Hypothetically, the presence of traces of copper and zinc in aluminium alloy will not have that significant effect on antimicrobial efficacy as we see a big difference in antimicrobial efficacy between copper and copper. In addition to the chemical composition influence, the physical characteristic of the surface also as an impact on the antimicrobial efficacy [12]. One such parameter is the wettability, which depends on the surface roughness. High contact angles ( >65) are recommended for antimicrobial application as these surface inhibit cell attachment as they are attributed to hydrophobic behavior and do not promote adhesion properties. The bacteria are laid on the surface and they are primarily in contact with the surface layer of metals such as aluminium, copper and zinc which are typically oxides. Studies have shown the oxide layer of copper has detrimental effect on cell wall of the bacteria [13]. The antimicrobial behaviour of other metal oxides is also of interest, but studies are very scarce. Most consider oxidation on particles/nanoparticles and therefore it is difficult to assess whether the antimicrobial performance is caused by the oxides or particle size [9].

In this study the antimicrobial performance of different metals was studied, and it can be seen extruded aluminium alloy 606013 showing a good antimicrobial efficacy. Further work should focus on understanding the effect of metallic oxides and metal ions on the antimicrobial efficacy. In this study only two bacteria families were looked into MRSA (gram positive) and E.Coli ( gram negative); other microbes should also be explored to have a broader understanding of the performance of aluminium alloys against these microbes. Touch surfaces are typically cleaned once every day, but many individuals touch the surface quite often, it is important to understand the antimicrobial efficacy of the different metals and coating on short intervals such as 10 minutes, 20 minutes etc and also simulating different environments such as temperatures, humidity and repeated touch exposure on
the surface. It should be noted that the test was carried out in laboratory environment, Further work can also focus on comparing the performance of different metallic touch surface in the real day to day environment.
Although the touch surface products are showing good antimicrobial efficacy, it is important to maintain daily hygienic practice such as day to day cleaning of the metallic surfaces. There is also a lack of information about the effect of corrosion of touch surface products with antimicrobial behaviour (there is neither information about the effect of direct contact between cleaning products and microbes nor the effect of cleaning products on the chemistry change of cleaned surfaces) [9]. It is also important to understand and provide recommendations on the appropriate cleaning products for different materials to ensure the antimicrobial performance are maintained.

Conclusion

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Eighty percent of the infections are transmitted via touch surface and with metallic touch surface being widely used in hospitals, transportation, public places etc. they seem to be a menace to control the infection spread. The study showed potential benefits of using extruded aluminium, powder coated aluminium (with antimicrobial coating) and special anodised aluminium (with antimicrobial layer) all having a good antimicrobial efficacy. It should be noted with the current LME pricing the weight for weight the aluminium products will cost only a third of touch surface made by copper.

2020 COVID 19 situation showed the need for minimizing the spread of infection via touch surfaces and the need for products that show antimicrobial efficacy in public places. Even if the infection rate is reduced by , the positive impact on lives saved and on nation's economy is high. With recycled aluminium such as Hydro CIRCAL, the touch surfaces produced from recycled aluminium promotes sustainability and as well as the well-being of the people.

Acknowledgment

The original version of this paper is available on https://www.scientific.net/MSF.1129.43.pdf

The author thanks Norsk Hydro for the funding the project to promote innovation.

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The original version of this paper is available on https://www.scientific.net/MSF.1129.43.pdf

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J. Noyce et al Inactivation of Influenza A Virus on Copper versus, Stainless Steel Surfaces, Applied and environmental microbiology, Apr. 2007, p. 2748-2750

Tierno, P. (2001): The Secret Life of Germs. Atria Books: New York, NY, USA.

Wenzel et al, EmergInfDis2001)

European Centre for Disease Prevention and Control. Point Prevalence Survey of Healthcare Associated Infections and Antimicrobial Use in European Acute Care Hospitals; ECDC: Stockholm, Sweden, 2013.

Villapún et al ., Antibacterial Metallic Touch Surfaces, Materials, 2016, V-736, I-9, p 1-23

Calvo et al., Recommendations on the clinical management of theCOVID-19 infection by the <new coronavirus>SARS-CoV2. Spanish Paediatric Associationworking group, Anales de Pediatría (English Edition) April ( 2020) V 92, I- 4, April 2020, Pages 241.e1-241.e11

Copper Technology Road map TechRoadmap-2017.pdf

Wojcieszak, et al, Influence of Cu-Ti thin film surface properties on antimicrobial activity and viability of living cells. Matr. Sci. Eng. C Mater. 2015, 56, 48-56.

Hans, et al, Role of Copper Oxides in Contact Killing of Bacteria. Langmuir 2013, 29, 16160-16166.

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The original version of this eBook is available on https://www.scientific.net/book/12th-aluminium-two-thousand-world-congress-7th-international-conference-on-extrusion-and-benchmark/978-3-0364-1380-8

Materials Science Forum, ISSN: 1662-9752, Vol. 1129, pp 77-82

doi: 10.4028/p-xhb8Qs

© 2024 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2023-11-21

Revised:
2024-03-20

Accepted:
2024-03-20

Online:
2024-10-30

The Influence of Surface Segregation on the Anodizing of AlSi11Cu2(Fe) Diecastings

The original version of this paper is available on https://www.scientific.net/MSF.1129.77.pdf

Giulia Scampone , Antonio Russo , Roberto Muzzani , Alessio Carminati and Giulio Timelli

University of Padova, Department of Management and Engineering, Stradella S. Nicola, 3, 36100 Vicenza, Italy.

Alfa Ossidazione - Gaser group, Via Dott. Raffaele De Troya, 65/67, 25010 Borgosatollo, Italy.

Gaser Ossido Duro- Gaser group, Via Po, 27, 20089 Rozzano, Italy.

giulia.scampone@phd.unipd.it, antonio.russo@gruppogaser.com,

roberto.muzzani@gruppogaser.com, alessio.carminati@gruppogaser.com,

Keywords: Anodizing; Al-Si alloy; High pressure diecasting; Microstructure; Surface mechanical properties.

Abstract

A positive segregation is usually formed on the casting surfaces produced by high-pressure die casting (HPDC). In diecast Al-Si-Cu alloy components, this segregation shows a higher content of Si compared to the nominal composition of the alloy and it drastically affects the anodizing response of the casting surface. In the present work, HPDC components were produced by AlSi11Cu2(Fe) alloy, grit-blasted, and then anodized in a sulfuric acid electrolyte at the temperature of . Before the anodizing process, some regions of the casting were also milled, in order to completely remove the surface segregation. Microstructural investigations were carried out on gritblasted and milled surfaces to characterize the initial substrates before anodizing, and to study their effect on the growth of the anodic layer. Scratch and wear tests were also performed to investigate the surface mechanical properties after anodizing. The results show that the surface segregation and the rough surface present on grit-blasted substrate leads to the formation of a thin and homogeneous anodic layer. On the contrary, a thicker and scalloped oxide film is formed on the milled surfaces. After anodizing, grit-blasted surfaces show lower wear and scratch resistance than milled substrates. The presence of surface segregation prevents the thickening of the anodic layer, negatively affecting the surface wear resistance due to the reduced oxide thickness.

Introduction

The original version of this paper is available on https://www.scientific.net/MSF.1129.77.pdf

High-pressure die-casting (HPDC) is a widely used casting method in the automotive sector due to its ability to produce net-to-shape components at a high production rate. This method enables the production of light and high-performance aluminum (Al) castings with complex geometries and thin walls [1]. However, one of the main limitations of HPDC components is the formation of a heterogeneous microstructure, which can negatively affect the final performance of the castings [2]. There are various types of microstructural heterogeneities, such as defect bands, externally solidified crystals, cold flakes and casting defects. However, one of the most significant heterogeneities concerning the surface properties of castings is the formation of a surface segregation. It consists of a solute-enriched surface layer, formed by inverse segregation and exudation phenomena during the casting process [3]. A greater amount of eutectic structure and intermetallic compounds converge on the casting surface compared to the underlying material. This microstructural heterogeneity may severely affect the final result of finishing processes, such as anodizing.

Anodizing is an electrochemical treatment that aims to form a hard and high-resistance oxide layer on the casting surface. The aluminum component is immersed in an electrolytic bath and exposed to a current flow, which promotes the formation of a surface anodic layer. This process aims to increase the hardness, corrosion and wear resistance of Al castings [4].

The integrity, thickness and performance of the formed oxide layer are deeply influenced by the anodizing parameters and the microstructure of the Al substrate. The greater is the heterogeneity of the substrate, the worse are the performance of the anodic layer [4-6].

The presence of surface segregation is a non-negligible factor in the anodizing response of Al castings. Indeed, the higher amount of solute may negatively influence the growth of a thick and homogeneous anodic layer. Therefore, this study aims to analyze the impact of surface macrosegregation on the growth and properties of the oxide layer formed on castings in alloy.

Experimental Procedure

The original version of this paper is available on https://www.scientific.net/MSF.1129.77.pdf

Due to the widespread use of AlSi11Cu2(Fe) alloy in HPDC, grit-blasted castings produced with this alloy were selected for the study. Some regions of the castings were milled to remove the surface segregation, and then the components were anodized at in a sulfuric acid electrolyte with a concentration of . The current density was first linearly increased from 0 to 0.88 in 20 minutes, and then kept constant for 25 minutes. Cold sealing in a solution was performed at the end of the anodizing process.

Microstructural and mechanical investigations were performed in two regions of the castings, corresponding to the grit-blasted and milled areas, respectively. The cross-sections of samples drawn from these two regions were polished with diamond paste and colloidal suspension. Firstly, the microstructure of the substrate was characterized in the two regions of the casting. The fraction of the eutectic structure was semi-quantified at the optical microscope according to ASTM E562-19e1 standard after a chemical etching in Murakami solution ( , and 10 g NaOH ). Moreover, secondary dendrite arms spacing (SDAS) measurements were performed according to the according to the line intercept method (EN ISO 1463:2004).

A field-emission gun scanning electron microscope (FEG-SEM) was used to characterize the thickness and morphology of the anodic layer. Due to the heterogenous morphology of the oxide layer, at least 50 measurements of its thickness were acquired in each investigated region to ensure statistically reliable results. Finally, the average surface roughness (Ra) of the anodized surfaces was measured with a stylus profilometer.

The scratch resistance of the anodized surfaces was quantified with an Erichsen Hardness Test Pencil according to EN ISO 22557:2020, by ranging the applied load from 0 to 20 N .

Ball-on-disc wear tests were performed to assess the wear resistance of the survey surfaces before and after the anodizing process. The counter body was an alumina sphere that slid for 400 m at a speed of on the sample surface. At least four repetitions for each condition were performed at room temperature, under dry conditions, and with an applied load of 2 N . The wear rate was calculated as the ratio of the wear volume ( V ) to the product of the sliding distance ( L ) and the applied load (N), as shown in Eq. 1. V was calculated as the average area of the wear track integrated over the track perimeter.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/MSF.1129.77.pdf

During the HPDC process, high cooling rates and intense shear stresses are developed inside the die cavity, resulting in the precipitations of less-branched and equiaxed dendrites surrounded by a fine Al-Si eutectic mixture (Fig. 1). Blocky-like intermetallics and compounds also precipitate in the interdendritic regions.

From the casting surface to the center of the component, a gradient in the colling rate occurs. The regions close to the die solidify faster, leading to the formation of a fine microstructure, while the
solidification time in the inner regions of the casting increases and the microstructure results coarser with higher SDAS values. The milling process exposes an inner region of the casting compared to the grit-blasted one, resulting in a coarser microstructure. Indeed, the secondary dendrite arms spacings in the grit-blasted and milled regions are and , respectively. The grit-blasted substrate shows an ultra-fine fibrous eutectic structure surrounding smaller cells of the primary -Al phase (Fig. 1a). In contrast, the milled substrate exhibits a coarser microstructure characterized by plate-like Si particles and larger cells and intermetallic compounds (Fig. 1b).

Gourlay et al. [3] demonstrated that the HPDC process leads to the formation of a surface segregation on Al-Si casting surface, consisting of a solute-enriched layer with a thickness of approximately [2]. This surface macrosegregation shows a higher area fraction of eutectic and intermetallic compounds, as also reported in other studies [7,8]. In the present work, the milling operation removed the surface segregation formed on the casting surface, as evidenced by the decrease in the eutectic area fraction from in the grit-blasted region to in the milled substrate.

Fig. 1. Microstructure of the substrate in the (a) grit-blasted and (b) milled regions.

Fig. 1. Microstructure of the substrate in the (a) grit-blasted and (b) milled regions.

Variations in the microstructure of the substrate strongly affect the anodizing response. Indeed, the greater is the electrochemical heterogeneity of the substrate, the more difficult it is to form a thick high-performance oxide layer. During anodizing, the oxidation front preferentially grows in the lessresistant regions of the substrate, which correspond to the microstructural phases with the lowest standard potentials on the galvanostatic scale [4]. Si particles show higher potentials compared to the -Al matrix, resulting in a slower oxidation rate during anodizing. When the oxidation front comes across a Si eutectic particle, it reacts and forms a thin layer of , but then it tends to grow in the surrounding aluminum matrix, circumnavigating and incorporating the un-anodized Si phase inside the andic layer [5].

Fig. 2a shows the oxide layer forms on the grit-blasted substrate. It is a thin and well-bounded film with a homogeneous and continuous morphology. The average thickness is (Fig. 3), due to the presence of the surface segregation which limits the growth of the anodic layer. Indeed, the greater eutectic fraction hinders the advancement of the oxidation front and prevents the thickening of the oxide layer.

Fig. 2. FEG-SEM micrographs of the oxide layer grown on the (a) grit-blasted and (b) milled substrates.

Fig. 2. FEG-SEM micrographs of the oxide layer grown on the (a) grit-blasted and (b) milled substrates.

The anodic layer formed on the milled surface is thicker and exhibits a scalloped oxide-metal interface, as shown in Fig. 2b. The morphology of the layer is not uniform and alternates between areas of limited thickness and areas where the oxide grew unrestrictedly. This is related to the distribution of and Si phases in the substrate prior to anodizing, which prevents the oxidation front from advancing evenly. Indeed, it grows freely within the -Al dendrites, while it is hindered in the regions with local segregation of eutectic structure.

Compared to grit-blasted surface, the milled substrate has a higher amount of aluminum matrix in the form of larger, less-branched dendrites, resulting in the formation of a thicker anodic layer, with an average value of approximately (Fig. 3). The higher standard deviation value (approximately ) is consistent with experimental observations and reflects the scalloped morphology of the anodic layer.

Fig. 3. Thickness of the anodic in the investigated substrates.

Fig. 3. Thickness of the anodic in the investigated substrates.

The limited thickness of the oxide layer formed on the grit-blasted substrate due to the presence of surface segregation negatively affects the surface mechanical properties, particularly the wear resistance. Fig. 4 shows the trend of the wear rate before and after anodizing. Both substrates exhibit a wear rate of approximately , which decreases after anodizing. Indeed, the electrochemical process leads to the formation of a hard surface layer of oxide, which decreases the wear rate, according to Archard's law (i.e. the wear volume is inversely proportional to the hardness
of the material). With fixed tribology test conditions, a thicker anodic layer results in a lower wear of the softer aluminum substrate and therefore a lower wear rate. The wear rate values for the milled and grit-blasted anodized surfaces are and , respectively. The milled surface shows greater wear resistance than the grit-blasted region due to the thicker anodic layer formed after the removal of the surface segregation.

Fig. 4. Trend of the wear rate before and after anodizing in the investigated regions.

Fig. 4. Trend of the wear rate before and after anodizing in the investigated regions.

After anodizing, the grit-blasted surface shows lower scratch resistance than the milled region. The critical load, i.e. the load necessary to scratch the anodic layer and exhibit the substrate, is N for the grit-blasted surface and for the milled region (Fig. 5).

The literature reports that smooth surfaces have higher scratch resistance than rougher ones. This is because the contact and residual stresses generated on a rough surface are significantly intensified, leading to increased plastic strain and reduced scratch resistance of the material [9]. In this study, the surface roughness of the milled anodized surface is , while for the grit-blasted region it values . As a result, the higher surface roughness of the grit-blasted surface reasonably leads to an increase in the local stresses during the scratch test, decreasing the critical load and the scratch resistance of the surface.

Fig. 5. Scratch resistance in the grit-blasted and milled surfaces after anodizing.

Fig. 5. Scratch resistance in the grit-blasted and milled surfaces after anodizing.

Conclusions

The original version of this paper is available on https://www.scientific.net/MSF.1129.77.pdf

This research analyzes the effect of the surface segregation on the anodizing response of castings in AlSi111Cu2(Fe) diecast alloy. A grit-blasted industrial component was selected for the study, and some regions were milled to completely remove the surface segregation.

It was found that the presence of the surface segregation creates an electrochemically heterogeneous surface, that negatively affects the growth of the anodic layer. In fact, the higher
fraction of Al-Si eutectic mixture hinders the propagation of the oxidation front during anodizing, leading to the formation of a thinner anodic layer. The limited thickness of the anodic layer results in reduced wear resistance, due to greater wear of the softer aluminum substrate. Moreover, the greater surface roughness of the grit-blasted surface compared to the milled one leads to lower scratch resistance. After anodizing, scratch and wear resistance are maximized on the anodized milled surface due to its lower surface roughness and the removal of the surface segregation.

Acknowledgments

The original version of this paper is available on https://www.scientific.net/MSF.1129.77.pdf

Financed by the European Union - NextGenerationEU (National Sustainable Mobility Center CN00000023, Italian Ministry of University and Research Decree n. 1033-17/06/2022, Spoke 11Innovative Materials & Lightweighting). The opinions expressed are those of the authors only and should not be considered as representative of the European Union or the European Commission's official position. Neither the European Union nor the European Commission can be held responsible for them.

References

The original version of this paper is available on https://www.scientific.net/MSF.1129.77.pdf

F. Casarotto, A.J. Franke, R. Franke, High-pressure die-cast (HPDC) aluminium alloys for automotive applications, in: J. Rowe (Ed.), Advanced Materials in Automotive Engineering, Woodhead Publishing Limited, Cambridge, UK, 2012, pp. 109-114.

G. Timelli, A. Fabrizi, The Effects of Microstructure Heterogeneities and Casting Defects on the Mechanical Properties of High-Pressure Die-Cast AlSi9Cu3(Fe) Alloys, Metall. Mater. Trans. A - Phys. Metall. Mater. Sci. 45 (2014) 5486-5498.

C.M. Gourlay, H.I. Laukli, A.K. Dahle, Defect Band Characteristics in and Pressure Die Castings, Metall. Mater. Trans. A - Phys. Metall. Mater. Sci. 38 (2007) 18331844.

J.M. Runge, The Metallurgy of Anodizing Aluminum, Springer, Cham, 2018.

G. Scampone, G. Timelli, Anodizing Al-Si Foundry Alloys: A Critical Review, Adv. Eng. Mater. (2022), 2101480.

J.M. Runge, L. Chesterfield, The science of successfully anodizing die cast substrates, Alum. Int. Today. (2010) 30-36.

S. Otarawanna, C.M. Gourlay, H.I. Laukli, A.K. Dahle, Microstructure Formation in AlSi4MgMn and AlMg5Si2Mn High-Pressure Die Castings, Metall. Mater. Trans. A - Phys. Metall. Mater. Sci. 40 (2009) 1645-1659.

D. Caliari, G. Timelli, B. Zabala, A. Igartua, Microstructural and tribological investigations of diecast and hard anodized AlSiCu alloys, Surf. Coat. Technol. 352 (2018) 462-473.

B. Feng, Effects of surface roughness on scratch resistance and stress-strain fields during scratch tests, AIP Advances 7 (2017), 035217.

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The original version of this eBook is available on https://www.scientific.net/book/12th-aluminium-two-thousand-world-congress-7th-international-conference-on-extrusion-and-benchmark/978-3-0364-1380-8

Materials Science Forum, ISSN: 1662-9752, Vol. 1129, pp 83-90

doi: 10.4028/p-1SlQcx

© 2024 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2023-12-10

Revised:
2024-03-18

Accepted:
2024-04-08

Online:
2024-10-30

Influence of Gas Oven Temperature on the Microstructure and Extrudability of AI-Mg-Si Aluminium Alloys

The original version of this paper is available on https://www.scientific.net/MSF.1129.83.pdf

Andreas Schiffl , Serena Tourey and Patrick Riepler

Hammerer Aluminium Industries Extrusion GmbH, Research and Development, Lamprechtshausenerstraße 69, 5282 Ranshofen, Austria,

andreas.schiff!@hai-aluminium.at, serena.tourey@hai-aluminium.at, patrick.riepler@hai-aluminium.at

Keywords: billet quality, extrudability, Al-Mg-Si alloys, homogenisation, heat treatment

Abstract

The microstructure of aluminium alloys is well characterised when it comes to the initial state of the cast billet or the artificially aged profile. Typically, a gas oven coupled with an induction oven is used to preheat the billets before extrusion. Preheating reduces the flow stress of the aluminium and dissolves the phases that have precipitated during cooling after homogenisation. In this paper, the influence of the gas oven temperature on the partial or complete dissolution of the precipitated phases will be shown. In addition, the negative effect of a wrong choice of parameters on the volume content of the MgSi phases will be shown.

Introduction

The original version of this paper is available on https://www.scientific.net/MSF.1129.83.pdf

The automotive industry is the main driving force for our developments. On the one hand, the mechanical properties shall become better, the ductility coupled with high strength. This allows to design and build light weight cars in special light and save battery frames and housing. On the other hand, the total consumption has come into the focus of the OEM's. The consumption of an alloy depends mainly on the recycling content. The needed energy for remelting and cleaning is less than of the energy to produce smelted aluminium [1]. The recycled alloys are as good as the smelted alloys. The small difference is hidden in the background, by comparing the of the chemical elements and the distribution of these. The content is mostly on the upper end of the specification and the distribution width is not so narrow compared to the smelted alloy.
Royset et al. [2] showed that small reductions of Mg and Si can enhance the extrusion speed. On the basis of these results, a narrow distribution of the alloying elements on the lower limit of the specification is desired.

To characterise this phenomenon, the break through pressure can work as a comparison parameter or the flow stress determined from a lab deformation test [3]. The assumption is that all MgSi phases are dissolved during the extrusion process. This assumption is true on condition that all parameters like alloy composition, homogenisation and extrusion parameters were set perfectly. Perfect extrusion parameters launch with the alloy composition, chosen well at the lower limit of the specification and the used casting equipment is state of the art. The homogenisation is done at the highest alloy dependent temperature and the soaking time is long enough to fulfill the transformation of the AlFeSi phases. The cooling gradient from homogenisation temperature should be the same as the reheating gradient to extrusion temperature. The extrusion speed is set to the maximum speed, shortly before generating surface tear caused by overheating of the profile during deformation. But under industrial process conditions this is not fulfilled exactly.

For instance, the alloy chemistry is chosen at the upper end of the alloy specification for Si and Mg to get high mechanical properties and a better machinability [1,5]. During solidification of the alloy this leads to more primary MgSi phases on the grain boundary or between the dendrite arms, shown in Fig. 1. Also, the microsegregation of Mg and Si in the grains is high.

If Fe , Mn and/or Cr are present in the alloy, a second group of phases will appear, the phases (further designated as AlFeSi ). These phases precipitate directly out of the
melt at the end of the solidification process [6]. The AlFeSi phases look like needles (low Fe content) or like Chinese characters at a higher Fe, Mn and Cr content [7].
A homogenisation treatment offers a solution for both problems. During homogenisation the transformation of the into takes place [8]. The bigger transform into smaller . This is favoured for the extrusion process, the phases are more rounded compared to the needle shaped phases in low alloyed 6XXX alloys, therefore, the deformation of the alloy without any surface issues is enhanced [9]. The cooling speed after the homogenisation has no influence on the transformation of the AlFeSi phases [10-12].
Additionally, the homogenisation temperature lower than the solidus and higher than the solvus line levels the microsegregation in the grains. Simultaneously, the primary MgSi phases at the grain boundary are dissolved. For the MgSi phases, the cooling rate down to room temperature plays an important role. If the cooling rate is low, the MgSi phases start to reprecipitate during the cooldown. Some bigger MgSi phases nucleate at the AlFeSi phases as needles. Even if there are some dispersoids present, they can act as nucleation sites and the MgSi phases precipitate comparatively smaller [13]. If the samples were water quenched, only the transformed AlFeSi phases are visible. There are so many different possibilities how the microstructure of a homogenised billet can look like depending on the temperature, time and cooling speed. Fig. 2 shows an example for a 6005(A) alloy homogenised at for 4 hours and cooled with .

The characterisation of the homogenised state of the billet is common. Therefore, many billet quality specifications are out there. One of the topics is the transformation of the AlFeSi phases and another topic is the size of the MgSi phases. Billet quality specifications from different extrusion companies mention that the size of the MgSi phases should be less than . One of the leading quality documents is the BQA (billet quality assessment) from Hydro. According to this document, is a standard value. But the definition is not clear. Are the primary or secondary MgSi deemed? But for the metallurgical effect, re-melting or dissolution, only the size counts.

Fig. 1. Cast microstructure of a 6005(A) alloy, the AlFesi phases (grey) have a Chinese character structure. MgSi (blue, purple) is visible at the grain boundaries or the triple points. The measuring bar corresponds to

Fig. 1. Cast microstructure of a 6005(A) alloy, the AlFesi phases (grey) have a Chinese character structure. MgSi (blue, purple) is visible at the grain boundaries or the triple points. The measuring bar corresponds to

Fig. 2. The more rounded AlFeSi phases after homogenisation are visible. The small black dots in the grains are secondary MgSi phases. The big black ones on the grain boundary are the undissolved primary MgSi phases. The measuring bar corresponds to

Fig. 2. The more rounded AlFeSi phases after homogenisation are visible. The small black dots in the grains are secondary MgSi phases. The big black ones on the grain boundary are the undissolved primary MgSi phases. The measuring bar corresponds to

For the small MgSi phases it is postulated that they can re-dissolve during the extrusion process [7,14]. Fig. 3 shows the sketch of the extrusion process. The homogenised billet is reheated via a gas and induction oven to the start temperature for extrusion. After reheating, the MgSi phases should dissolve. Or as shown, the deformation and coupled increase of the temperature generates vacancies and, therefore, the MgSi phases have the ability to dissolve quickly. The highest break through pressure will be reached with all Mg and Si in solid solution. This is best practice if the extrusion press has enough power [14,15]. For this reason, less power of the press, one option is to increase the size of MgSi phases in the billet and try to dissolve the MgSi phases directly in front of the die. The billet is softer than in the state of a fully solid solution. When something went wrong with the extrusion parameters, some MgSi phases can survive and appear in the extruded profile. These phases are too big to enhance the mechanical properties. They tie up so much Mg and Si that the mechanical properties are dramatically decreased. Furthermore, big surviving MgSi phases can lead to local melting and a poor surface quality [16]. In the case of excessive surviving MgSi phases it could lead to hot tearing cracks. They look like melted grain boundaries. If all Mg and Si is in solid solution and the profile is cooled appropriately, it is capable of reaching the maximum strength, see Fig. 3. Therefore, the homogenisation treatment is mandatory before hot extrusion of 6XXX aluminium alloys [8,12,17,18].

It is assumed that during the reheating of the billets with the gas and induction oven the MgSi phases will dissolve. In the past, there was a big lack of knowledge with regard to what really happens in these process steps. The billets were hot sheared and transported to the press as soon as possible and loaded into the container. The accessibility to samples from the industrial environment is very low. Quenching of a hot sheared billet with approximately 200 kg is very risky and not a suitable way to get some proper homogeneous cooled samples. By adapting the gas oven and the hot saw placed directly behind, samples can be taken out of the process in a simple way. With the installation of the new gas oven line from extrutec, the problem was solved [19]. Slices with a thickness of 80 mm can be taken directly out of the process. The time between cutting and quenching is less than 20 seconds. During this short period there is no negative influence on the microstructure.

Samples were also produced on a laboratory scale because this is where the extreme tests can be carried out that are not possible on an industrial scale. But these tests are needed for the further development of the equipment. The samples obtained in that way will show that they differ from the expected results. The aim of this work is to investigate these microstructural dependencies of MgSi phases under consideration of the time and temperature regime of the gas and induction oven under industrial conditions. These approaches will help to explain some extrusion defects such as reduced mechanical properties or surface melting.

Fig. 3. Sketch of the ideal extrusion process. The development of the MgSi phases is pointed out in a oversubscribed way

Fig. 3. Sketch of the ideal extrusion process. The development of the MgSi phases is pointed out in a oversubscribed way

Experiments

The original version of this paper is available on https://www.scientific.net/MSF.1129.83.pdf

The used alloys are in the 6005(A) group. They have a billet diameter of 254 mm and were cast on an industrial scale at our plant in Ranshofen by using Wagstaff equipment. The homogenisation was done with a continuous homogenisation oven from Hertwich. The homogenisation was done at a soaking temperature of and a duration of 4 h . Afterwards, the billets were cooled by a turbo air cooling system. The cooling rate was set to . The chemical composition of the alloys was determined with the help of an optical spark emission spectroscope SPECTROMAXx [20] and shown in in Table 1. DSC examination was made with the help of a Netzsch DSC204F1. The heating rate was set to .

The samples for all our further experiments were taken out of a serial extrusion process from a very complex side sill. The hourly capacity for this product was approximately . The selection is based on the conspicuous low productivity, therefore, following a long dwell time in the gas oven. The gas oven has a length of 7000 mm , and a billet with the diameter of 254 mm has 970 kg . Hence, the dwell time of the log was approximately 50 minutes. The settings of the gas oven were: end temperature and the previous heating zone gradually colder. The log was heated in the last zone to . Directly after reaching the temperature, the slices were cut and quenched. All samples were taken from half the radius of the billet slices. The small samples had a diameter of 40 mm and a thickness of 25 mm . To simulate the further holding time in the gas oven, the small samples were reheated very quickly in 20 seconds in a salt bath oven. The samples were then kept in the salt bath oven for further 160 seconds, afterwards, the samples were directly water quenched (WQ). The salt bath oven was tempered to and . The samples were grinded with SiC paper and polished with diamond paste up to . The final polishing step was with an oxide polishing suspension from Struers to visualise the MgSi phases. The photos for the analysis were taken with an Olympus BX53 light microscope at a magnification of 1000 times. Smaller particles than 10 pixels were ignored. They were much smaller than the resolution of the light microscope.

The metallographic samples were analysed to get quantitative results of the amount of MgSi and AlFeSi phases. A trained person can conduct the examination of a single picture in approximately 60 minutes. To get a reliable quantitative result you have to analyse 15 pictures ( 124 x ) or more in a row if the grain size is approx. . Therefore, you do not over or underestimate the uneven distribution of the MgSi and AlFeSi phases depending on the grain boundaries. Consequently, the analysis of the metallographic samples is very time-consuming. To solve the problem an AI was trained to differentiate between MgSi and AlFeSi phases. The AI is capable of analysing 15 pictures in less than 60 seconds.

The AI is based on semantic segmentation, this is a crucial task in machine learning where each pixel in an image is assigned a class. We used a U-Net architecture with a transformer-based approach to pretrain the encoder in an unsupervised way. By leveraging the transformer's sequence processing capabilities, we reduced the required training data. The network was trained using image patches and
an A100 GPU, a powerful hardware component for ML tasks. Training took around 8 hours, varying based on dataset size, model complexity and available resources. Hyperparameter tuning and data augmentation techniques were guided by Wightman et al.'s paper on improved training procedures in time [21]. Our approach yielded a robust semantic segmentation solution.

Table 1. Chemical composition of the used alloy, all contents are given in

Si | Fe | Cu | Mn | Mg | Cr | Zn | Ti | Al
0.7 | 0.5 | 0.25 | 0.35 | 0.45 | 0.04 | 0.07 | 0.04 | rest

Results

The original version of this paper is available on https://www.scientific.net/MSF.1129.83.pdf

The samples were kept at the mentioned temperatures to simulate the holding time in the gas oven. The normal capacity of an extrusion line is 20 to 30 billets per hour; therefore, the dwell time was set to 3 minutes. The standard production of automotive products also includes the tapering step of the billet in the induction oven to reach an even and stable temperature for the profile directly after the die. This step is very fast compared to all other steps at higher temperatures. The tapering for the billets were at the end and at the beginning of the billet. This additional time and temperature load was captured with the temperature step of the . Higher temperatures were selected to see the influence of a possible overheating of the billet [22,23].

Fig. 4 shows the results of the metallographic analysis of the samples. The as-cast state exhibits that the MgSi phases have . The homogenised sample reveals the highest content, it has more than doubled and shows . The quenched sample and the sample held for 160 seconds at 435 display a decrease of the MgSi volume content. A further increase of the temperature to and exhibits only another slight decrease of the volume content. The two highest temperatures point out a steep decrease of the MgSi content down to below .

The used AI also allows the classification of each MgSi phase. The optical auxiliary conditions gave us the lowest limit of . The classification steps were . Fig. 5 exhibits the results of the two main interesting conditions. The homogenised state and the gas oven reheated state. The MgSi phases in the homogenised sample indicate a small size and narrow size distribution. In contrast, the reheated samples via the gas oven have bigger MgSi phases with a much broader distribution. Some further MgSi particles were bigger than .

Fig. 6 exhibits the DSC measurement of the examined alloy. The first visible precipitation peak for MgSi phases is located approximately at . The second peak is the dissolution peak placed at , followed by the next precipitation peak at around . At the end of the DSC sequence there is the dissolution peak.

Fig. 4. Results for the counts for each classification slot for the MgSi phases

Fig. 4. Results for the counts for each classification slot for the MgSi phases

Discussion

The original version of this paper is available on https://www.scientific.net/MSF.1129.83.pdf

From the point of view of the extrusion process, it does not matter if the MgSi phases in the reheated billet were ` or ``phases. They all should dissolve during the extrusion process to reach the solute saturated state (SSS). Afterwards, this SSS can be quenched in to get the highest mechanical properties after the heat treatment or to prevent some surface issues caused by the remelting of big MgSi phases. The extrusion parameter was set to a gas oven temperature of . This gas oven temperature was formerly considered a good compromise between the mechanical properties and the possibility to hot shear the billet. The hot shearing process was mandatory before the use of the hot saw to cut the log into the desired billet length for the extrusion process. From the literature it is well known that the or phases prefer to precipitate at the temperature range from up to [24-26].

Fig. 5. DSC measurement of the used 6005(A) alloy. The curve presents the temperature range were the MgSi phases should precipitate or dissolve

Fig. 5. DSC measurement of the used 6005(A) alloy. The curve presents the temperature range were the MgSi phases should precipitate or dissolve

The tested 6005(A) alloy exhibits a shift to a higher temperature range up to . This effect could be influenced by the used initial state. Normally, the alloys were tested in the solutionised state, therefore, the driving force to precipitate is higher and the phases precipitate at lower temperatures.

The tested sample was in the as-cast state, therefore, the major percentage of the MgSi phases was captured in the big primary MgSi phases. Hence, the favoured precipitation temperature was higher.

The DSC results exhibit that exactly in that temperature range of the precipitation of the MgSi phases has its maximum. From this perspective, the used gas oven temperature of 435 is not the perfect match. The MgSi phases have the best conditions to precipitate and to grow. This can be seen in Fig. 4 and Fig. 5, the MgSi phases were not dissolved. The smaller MgSi phases from the homogenised state were dissolved and were consumed from the bigger ones supported from the Ostwald ripening process. These bigger phases need, in the further processing steps, more time or more deformation at higher temperatures to dissolve. The MgSi volume content is very stable in the range of up to . The slight temperature increase to indicates a small reduction of the volume content. The exposure time at and 160s was too short to dissolve the bigger particles, only the small ones were gone. But with the used billet quantity per hour, the holding time cannot increase further. The only way to reduce the MgSi content was to raise the temperature up to and . Only some big MgSi phases survive at this high temperature at an exposure time of 160s. But the residual volume content is very low compared to all other conditions, . A temperature rise to or higher is not possible in the industrial extrusion process. Additional heat arises during the deformation of the billet. This additional temperature plus the temperature of the reheated billet is too high for a good extrusion process. If all other parameters of the extrusion process are held constant. This leads to transverse tears of the profile melting of the grain boundaries [27].
The and higher were only a test to see a correlation between time and temperature and the dissolution of MgSi phases. The was the same temperature as to homogenisation temperature. The exposure time of 160 seconds was enough for both temperatures to dissolve the MgSi phases which precipitate during the cooling after the homogenisation heat treatment and the coarsened MgSi phases during reheating of the billet. This is good to know. If a taper quench is installed, the overheating and quenching of the billet can solve the problem of the undissolved MgSi phases and enhance the extrusion process [28].

Conclusion

The original version of this paper is available on https://www.scientific.net/MSF.1129.83.pdf

The experiments with the gas oven were done in industrial scale to get the perfect samples as starting point for the heat treatment experiments. Afterwards the samples were treated in a salt bath oven to simulate different temperatures in the gas oven at a constant duration. This chosen experimental setup is very close to the industrially possible processes.

The use of a trained AI has proven to be useful to analyse the MgSi phases. The time to label and count the different phases was decreased significantly. At the same time, the number of analysed pictures can be increased to get reliable results.

The historical approach of using the same parameters at the gas oven with new equipment has proven to be false. The parameters used in the past were set to this high temperature to shear the log. Using a hot saw and keeping the DSC results of the used alloy in mind, the temperature range was set perfectly into the precipitation maximum of the MgSi phases. The homogenised sample shows the highest volume content of MgSi phases but the smallest ones. Reheating of the billet makes the smallest MgSi phases dissolve, but the bigger ones grow of the expanse of them. A further increase of the billet temperature was not useful. The raised temperature leads to tearing of the profile.

On the one hand considering the DSC measurements, the temperature could be decreased down to to prevent the excessive precipitation of the MgSi phases. On the other hand, the use of a taper quench at temperature higher than can be useful to enhance the extrudability in perspective of reducing the volume content of undissolved MgSi phases in the billet.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/MSF.1129.83.pdf

The AI was developed by the light metal competence center Ranshofen under the guidance of the R&D department of Hammerer Aluminium Industries Extrusion GmbH.

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The original version of this paper is available on https://www.scientific.net/MSF.1129.83.pdf

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Keywords Index

#

6xxx Alloys A Modelling Framework for Rapid Evaluation of Speed Limitations During Extrusion of Aluminium Profiles

A

Aa5754 Chips Feasibility Study of Solid-State Recycling through Direct Hot Rolling of AA5754 Aluminum Chips for Automotive Applications

Aa6082 Extrusion Benchmark 2023: Effect of Die Design on Profile Speed, Seam Weld Quality and Microstructure of Hollow Tubes; Hot Torsion Tests of AA6082 Alloy

Aizn In Situ Composites AlZn-Matrix Cast Composites Reinforced with Ti Aluminides

Al-mg-si Alloys Influence of Gas Oven Temperature on the Microstructure and Extrudability of AI-Mg-Si Aluminium Alloys

Aluminium Antimicrobial Performance of Different Metals

Aluminum Hot Torsion Tests of AA6082 Alloy

Aluminum Alloy Microstructure Evolution and FEM Prediction on AA6XXX Alloys

Analytical Modelling Study of Direct Metal Extrusion by the Upper-Bound and Finite Volume Methods

Anodizing; Al-si Alloy; High Pressure Diecasting; Microstructure; Surface Mechanical Properties The Influence of Surface Segregation on the Anodizing of AlSi11Cu2(Fe) Diecastings

Antimicrobial Performance Antimicrobial Performance of Different Metals

Automotive Feasibility Study of Solid-State Recycling through Direct Hot Rolling of AA5754 Aluminum Chips for Automotive Applications

B

Billet Quality Influence of Gas Oven Temperature on the Microstructure and Extrudability of AI-Mg-Si Aluminium Alloys

C

Casting Aluminum Alloy Prediction of Damage Behavior of Casting Aluminum Components Considering Inhomogeneous Properties

Characterization Prediction of Damage Behavior of Casting Aluminum Components Considering Inhomogeneous Properties

Charge Welds Extrusion Benchmark 2023: Effect of Die Design on Profile Speed, Seam Weld Quality and Microstructure of Hollow Tubes

Cooling Channels; Extrusion Dies Recent Trends in Nitrogen Cooling Modelling of Extrusion Dies

D

Damage Model Prediction of Damage Behavior of Casting Aluminum Components Considering Inhomogeneous Properties

Die Design Automated Optimum Extrusion Die Design and Profile Quality Control Based on Simulation

Direct Hot Rolling Feasibility Study of Solid-State Recycling through Direct Hot Rolling of AA5754 Aluminum Chips for Automotive Applications

Dispersoids Effect of Homogenization Cycle on Precipitation of Dispersoids and Abnormal Grain Growth in an Al-Mg-Si Alloy

Ductility AlZn-Matrix Cast Composites Reinforced with Ti Aluminides

E

E Coli Antimicrobial Performance of Different Metals

En Aw 6082 Simulation of Quench Rate by Jominy End Quench Test for 6082 Aluminium Alloys

Extrudability Influence of Gas Oven Temperature on the Microstructure and Extrudability of AI-Mg-Si Aluminium Alloys

Extrusion Microstructure Evolution and FEM Prediction on AA6XXX Alloys; Study of Direct Metal Extrusion by the Upper-Bound and Finite Volume Methods; Automated Optimum Extrusion Die Design and Profile Quality Control Based on Simulation

Extrusion Benchmark Extrusion Benchmark 2023: Effect of Die Design on Profile Speed, Seam Weld Quality and Microstructure of Hollow Tubes

Extrusion Simulations A Modelling Framework for Rapid Evaluation of Speed Limitations During Extrusion of Aluminium Profiles

Extrusion Speed A Modelling Framework for Rapid Evaluation of Speed Limitations During Extrusion of Aluminium Profiles

F

Fatigue Prediction of Damage Behavior of Casting Aluminum Components Considering Inhomogeneous Properties

Fem Microstructure Evolution and FEM Prediction on AA6XXX Alloys

Finite-volume Method Study of Direct Metal Extrusion by the Upper-Bound and Finite Volume Methods

Flow Stress Hot Torsion Tests of AA6082 Alloy

G

Grain Refinement AlZn-Matrix Cast Composites Reinforced with Ti Aluminides

H

Hardness Simulation of Quench Rate by Jominy End Quench Test for 6082 Aluminium Alloys

Heat Treatment Influence of Gas Oven Temperature on the Microstructure and Extrudability of AI-Mg-Si Aluminium Alloys

Homogenisation Influence of Gas Oven Temperature on the Microstructure and Extrudability of AI-Mg-Si Aluminium Alloys

Homogenisation Heating Rate Effect of Homogenization Cycle on Precipitation of Dispersoids and Abnormal Grain Growth in an Al-Mg-Si Alloy

Hot Torsion Test Hot Torsion Tests of AA6082 Alloy

I

Inhomogeneity Prediction of Damage Behavior of Casting Aluminum Components Considering Inhomogeneous Properties

J

Jominy End Quench Test Simulation of Quench Rate by Jominy End Quench Test for 6082 Aluminium Alloys

K

Knuckle Prediction of Damage Behavior of Casting Aluminum Components Considering Inhomogeneous Properties

L

Liquid Nitrogen Recent Trends in Nitrogen Cooling Modelling of Extrusion Dies

M

Material Characterization Hot Torsion Tests of AA6082 Alloy

Microstructure Extrusion Benchmark 2023: Effect of Die Design on Profile Speed, Seam Weld Quality and Microstructure of Hollow Tubes; Microstructure Evolution and FEM Prediction on AA6XXX Alloys; Feasibility Study of Solid-State Recycling through Direct Hot Rolling of AA5754 Aluminum Chips for Automotive Applications

Mrsa Bacteria Antimicrobial Performance of Different Metals

O

Optimisation Automated Optimum Extrusion Die Design and Profile Quality Control Based on Simulation

P

Peripheral Coarse Grain (pcg) Effect of Homogenization Cycle on Precipitation of Dispersoids and Abnormal Grain Growth in an Al-Mg-Si Alloy

Process Monitoring Extrusion Benchmark 2023: Effect of Die Design on Profile Speed, Seam Weld Quality and Microstructure of Hollow Tubes

Q

Quench Rate Simulation of Quench Rate by Jominy End Quench Test for 6082 Aluminium Alloys

Quench Sensitivity Simulation of Quench Rate by Jominy End Quench Test for 6082 Aluminium Alloys

R

Recrystallization Microstructure Evolution and FEM Prediction on AA6XXX Alloys

S

Seam Welds Extrusion Benchmark 2023: Effect of Die Design on Profile Speed, Seam Weld Quality and Microstructure of Hollow Tubes

Simulation Prediction of Damage Behavior of Casting Aluminum Components Considering Inhomogeneous Properties; Automated Optimum Extrusion Die Design and Profile Quality Control Based on Simulation

Simulation Validation Extrusion Benchmark 2023: Effect of Die Design on Profile Speed, Seam Weld Quality and Microstructure of Hollow Tubes

Solid-state Recycling Feasibility Study of Solid-State Recycling through Direct Hot Rolling of AA5754 Aluminum Chips for Automotive Applications

Strength And Wear Properties AlZn-Matrix Cast Composites Reinforced with Ti Aluminides

T

Temperature Models A Modelling Framework for Rapid Evaluation of Speed Limitations During Extrusion of Aluminium Profiles

Testing Antimicrobial Performance of Different Metals

Ti Aluminides AlZn-Matrix Cast Composites Reinforced with Ti Aluminides

Topological Optimization Recent Trends in Nitrogen Cooling Modelling of Extrusion Dies

U

Upper-bound Method Study of Direct Metal Extrusion by the Upper-Bound and Finite Volume Methods

W

Wheel Prediction of Damage Behavior of Casting Aluminum Components Considering Inhomogeneous Properties

Wrought Aluminum Alloys; Heat Treatment; Microstructure; Mechanical Properties Influence of Different Aging Times and Temperatures on Microstructural and Mechanical Properties of EN AW-6XXX Aluminum Alloys

Authors Index

A

Agrawal, S. Effect of Homogenization Cycle on Precipitation of Dispersoids and Abnormal Grain Growth in an Al-Mg-Si Alloy

Aymerich, F. Feasibility Study of Solid-State Recycling through Direct Hot Rolling of AA5754 Aluminum Chips for Automotive Applications

B

Bharadwaj, B. Effect of Homogenization Cycle on Precipitation of Dispersoids and Abnormal Grain Growth in an Al-Mg-Si Alloy

Biba, N. Automated Optimum Extrusion Die Design and Profile Quality Control Based on Simulation

Bressan, J. Study of Direct Metal Extrusion by the Upper-Bound and Finite Volume Methods

Buonadonna, P. Feasibility Study of Solid-State Recycling through Direct Hot Rolling of AA5754 Aluminum Chips for Automotive Applications

C

Carminati, A. The Influence of Surface Segregation on the Anodizing of AlSi11Cu2(Fe) Diecastings

Carta, M. Feasibility Study of Solid-State Recycling through Direct Hot Rolling of AA5754 Aluminum Chips for Automotive Applications

Ci, X. Prediction of Damage Behavior of Casting Aluminum Components Considering Inhomogeneous Properties

D

Donati, L. Extrusion Benchmark 2023: Effect of Die Design on Profile Speed, Seam Weld Quality and Microstructure of Hollow Tubes; Microstructure Evolution and FEM Prediction on AA6XXX Alloys; Recent Trends in Nitrogen Cooling Modelling of Extrusion Dies; Hot Torsion Tests of AA6082 Alloy

Donato, S. Extrusion Benchmark 2023: Effect of Die Design on Profile Speed, Seam Weld Quality and Microstructure of Hollow Tubes; Microstructure Evolution and FEM Prediction on AA6XXX Alloys; Recent Trends in Nitrogen Cooling Modelling of Extrusion Dies; Hot Torsion Tests of AA6082 Alloy

Duzhev, A. Automated Optimum Extrusion Die Design and Profile Quality Control Based on Simulation

F

Fortini, A. Influence of Different Aging Times and Temperatures on Microstructural and Mechanical Properties of EN AW-6XXX Aluminum Alloys

Furu, T. A Modelling Framework for Rapid Evaluation of Speed Limitations During Extrusion of Aluminium Profiles; Optimization of Geometric Design of Extruded Products Incorporating Properties, Cost and CO2 Footprint

G

Gahlyan, S. Effect of Homogenization Cycle on Precipitation of Dispersoids and Abnormal Grain Growth in an Al-Mg-Si Alloy

Gracz, B. AlZn-Matrix Cast Composites Reinforced with Ti Aluminides

Greer, A. AlZn-Matrix Cast Composites Reinforced with Ti Aluminides

Gülbaba, O. Simulation of Quench Rate by Jominy End Quench Test for 6082 Aluminium Alloys

Gümüşsoy, A. Simulation of Quench Rate by Jominy End Quench Test for 6082 Aluminium Alloys

I

Iddberg, M. A Modelling Framework for Rapid Evaluation of Speed Limitations During Extrusion of Aluminium Profiles; Optimization of Geometric Design of Extruded Products Incorporating Properties, Cost and CO2 Footprint

J

Jayachandran, R. Antimicrobial Performance of Different Metals

K

Kawalec, M. AlZn-Matrix Cast Composites Reinforced with Ti Aluminides

Kaya, I. Simulation of Quench Rate by Jominy End Quench Test for 6082 Aluminium Alloys

Khalifa, N. Feasibility Study of Solid-State Recycling through Direct Hot Rolling of AA5754 Aluminum Chips for Automotive Applications

Kniazkin, I. Automated Optimum Extrusion Die Design and Profile Quality Control Based on Simulation

Kong, D. Prediction of Damage Behavior of Casting Aluminum Components Considering Inhomogeneous Properties

Krajewski, P. AlZn-Matrix Cast Composites Reinforced with Ti Aluminides

Krajewski, W. AlZn-Matrix Cast Composites Reinforced with Ti Aluminides

Kulakov, I. Automated Optimum Extrusion Die Design and Profile Quality Control Based on Simulation

L

Lang, Y. Prediction of Damage Behavior of Casting Aluminum Components Considering Inhomogeneous Properties

Loi, G. Feasibility Study of Solid-State Recycling through Direct Hot Rolling of AA5754 Aluminum Chips for Automotive Applications

M

Martins, M. Study of Direct Metal Extrusion by the Upper-Bound and Finite Volume Methods

Mehtedi, M. Hot Torsion Tests of AA6082 Alloy; Feasibility Study of Solid-State Recycling through Direct Hot Rolling of AA5754 Aluminum Chips for Automotive Applications

Merlin, M. Influence of Different Aging Times and Temperatures on Microstructural and Mechanical Properties of EN AW-6XXX Aluminum Alloys

Mohtadi, R. Feasibility Study of Solid-State Recycling through Direct Hot Rolling of AA5754 Aluminum Chips for Automotive Applications

Morales, C. Influence of Different Aging Times and Temperatures on Microstructural and Mechanical Properties of EN AW-6XXX Aluminum Alloys

Muzzani, R. The Influence of Surface Segregation on the Anodizing of AlSi11Cu2(Fe) Diecastings

Myhr, O. A Modelling Framework for Rapid Evaluation of Speed Limitations During Extrusion of Aluminium Profiles; Optimization of Geometric Design of Extruded Products Incorporating Properties, Cost and CO2 Footprint

N

Negozio, M. Extrusion Benchmark 2023: Effect of Die Design on Profile Speed, Seam Weld Quality and Microstructure of Hollow Tubes; Microstructure Evolution and FEM Prediction on AA6XXX Alloys; Recent Trends in Nitrogen Cooling Modelling of Extrusion Dies; Hot Torsion Tests of AA6082 Alloy

Nesse, A. A Modelling Framework for Rapid Evaluation of Speed Limitations During Extrusion of Aluminium Profiles; Optimization of Geometric Design of Extruded Products Incorporating Properties, Cost and CO2 Footprint

O

Olejnik, E. AlZn-Matrix Cast Composites Reinforced with Ti Aluminides

Orava, J. AlZn-Matrix Cast Composites Reinforced with Ti Aluminides

P

Pelaccia, R. Extrusion Benchmark 2023: Effect of Die Design on Profile Speed, Seam Weld Quality and Microstructure of Hollow Tubes; Microstructure Evolution and FEM Prediction on AA6XXX Alloys; Recent Trends in Nitrogen Cooling Modelling of Extrusion Dies; Hot Torsion Tests of AA6082 Alloy

Piwowarski, G. AlZn-Matrix Cast Composites Reinforced with Ti Aluminides

Q

Qiao, H. Prediction of Damage Behavior of Casting Aluminum Components Considering Inhomogeneous Properties

R

Reggiani, B. Extrusion Benchmark 2023: Effect of Die Design on Profile Speed, Seam Weld Quality and Microstructure of Hollow Tubes; Microstructure Evolution and FEM Prediction on AA6XXX Alloys; Recent Trends in Nitrogen Cooling Modelling of Extrusion Dies; Hot Torsion Tests of AA6082 Alloy

Riepler, P. Influence of Gas Oven Temperature on the Microstructure and Extrudability of AI-Mg-Si Aluminium Alloys

Russo, A. The Influence of Surface Segregation on the Anodizing of AlSi11Cu2(Fe) Diecastings

S

Saxena, M. Effect of Homogenization Cycle on Precipitation of Dispersoids and Abnormal Grain Growth in an Al-Mg-Si Alloy

Scampone, G. The Influence of Surface Segregation on the Anodizing of AlSi11Cu2(Fe) Diecastings

Schiffl, A. Influence of Gas Oven Temperature on the Microstructure and Extrudability of AI-Mg-Si Aluminium Alloys

Soffritti, C. Influence of Different Aging Times and Temperatures on Microstructural and Mechanical Properties of EN AW-6XXX Aluminum Alloys

Srivastava, V. Effect of Homogenization Cycle on Precipitation of Dispersoids and Abnormal Grain Growth in an Al-Mg-Si Alloy

Stebunov, S. Automated Optimum Extrusion Die Design and Profile Quality Control Based on Simulation

Sun, D. Prediction of Damage Behavior of Casting Aluminum Components Considering Inhomogeneous Properties

T

Timelli, G. The Influence of Surface Segregation on the Anodizing of AlSi11Cu2(Fe) Diecastings

Tourey, S. Influence of Gas Oven Temperature on the Microstructure and Extrudability of AI-Mg-Si Aluminium Alloys

W

Wang, W. Prediction of Damage Behavior of Casting Aluminum Components Considering Inhomogeneous Properties

Wanjari, P. Effect of Homogenization Cycle on Precipitation of Dispersoids and Abnormal Grain Growth in an Al-Mg-Si Alloy

Z

Zhu, Z. Prediction of Damage Behavior of Casting Aluminum Components Considering Inhomogeneous Properties

Ö

Özdoğru, E. Simulation of Quench Rate by Jominy End Quench Test for 6082 Aluminium Alloys