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ISBN print: 978-3-0364-0995-5
ISBN eBook: 978-3-0364-1995-4
DOI book: 10.4028/b-gV700N
© 2026 Trans Tech Publications Ltd, All Rights Reserved

Extrusion and Drawing

Edited by
Prof. Lorenzo Donati

Extrusion and Drawing

Special topic volume with invited peer-reviewed papers only

Edited by
Prof. Lorenzo Donati

Scientific.Net

Copyright © 2026 Trans Tech Publications Ltd, Switzerland

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Volume 1048 of
Key Engineering Materials
ISSN print 1013-9826
ISSN web 1662-9795

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1048, pp -1-0

© 2026 Trans Tech Publications Ltd, All Rights Reserved

Accepted:
2026-04-08

Online:
2026-04-14

Preface

The original version of this paper is available on https://www.scientific.net/KEM.1048.-1.pdf

Contributions that cover all aspects of the technologies of extrusion and wire/bar drawing of metals are presented at this issue. Papers cover (but not limited to) aspects like: experimental and theoretical work on the mechanics and the metallurgical aspects of the technologies, all type of metals and alloys, process variants, FE-analysis based paper with experimental validation to calculate and foresee forming parameters as well as process defects or the resulting microstructure and the mechanical properties of the products. Analysis of defect onset, process windows, and process optimization, as well as technological or product testing, are topics related to this issue.

Editor

The original version of this paper is available on https://www.scientific.net/KEM.1048.-1.pdf

Donati Lorenzo

Co-editors

The original version of this paper is available on https://www.scientific.net/KEM.1048.-1.pdf

Riccardo Pelaccia
Noomane Ben Khalifa
Marco Negozio

University of Bologna

University of Modena and Reggio Emilia
Leuphana University Lüneburg
University of Parma

Table of Contents

Preface

Mechanical Evaluation of 6060 Aluminum Alloy under Elevated Extrusion Ram Speed Using Liquid Nitrogen Die Cooling

Evangelos Giarmas, Emmanouil K. Tzimtzimis, Konstantinos Tsongas, Dimitrios Tzetzis

An Experimental Investigation of the Effect of Compression Calibration on the Ductility of AA6061 Extrusions

Mustafa Can Uzun, Togeir Welo

Numerical Investigation of Round Profile Production: A FEM-Based Comparison between Friction Stir Extrusion and Conventional Extrusion

Carlo Acerbi, Marco Negozio, Adrian H.A. Lutey, Matteo Felci, Uceu F.H. Suhuddin, Harikrishna Sinh Rana, Benjamin Klusemann, Sara Bocchi

Compensating Property Fluctuations in Cold Extrusion Using Adaptive Dies

Christian Siedbürger, Peter Groche

Lightweight Battery Housings from Thin-Walled, Large-Scale Aluminum Profiles by Hot Extrusion

Oliver Schulz, Sebastian Wurth, Alessandro Selvaggio, Johannes Gebhard, Yannis P. Korkolis, Patrik Bieker, Thomas Kloppenborg

Calibrating a Lode-Parameter-Dependent Damage Evolution Equation to Cold Extrusion Experiments

Robin Gitschel, A. Erman Tekkaya, Yannis P. Korkolis

Effect of Chemical Composition on Hot Extrudability and Tribological Behavior of 7000 Series Aluminum Alloys

Sukunthakan Ngernbamrung, Kuniaki Dohda, Tatsuya Funazuka, Junichi Oshima, Tomomi Shiratori

Using Tungsten Inert Gas Welding Equipment to Locally Heat Tubes and Achieve Inhomogeneous Thickness in Tube Sinking Processes

Lemopi Isidore Besong, Johannes Buhl

Charge Weld Evolution in Profile Extrusion: Variability Across Billets and Multi-Profile Designs

Ivan Kniazkin, Ivan Kulakov, Nikolay Biba

Development of a System for Ultrasonic Die Oscillations during Extrusion of Aluminum Hollow Profiles

Tamara Thomas, Maik Negendank, Nico Laengst, Verena Merklinger, Joachim Maier, Soeren Mueller

Detection of Charge Welds in Lateral Angular Co-Extrusion Using Non-Destructive Testing

Alexej Verschinin, Florian Patrick Schäfke, Norman Mohnfeld, Hans Jürgen Maier, Johanna Uhe, Christian Klose, Sebastian Barton

Comparative Characterization through Torsion Tests of Primary and Secondary Aluminum Alloys for the Extrusion Process

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

Transient Thermal Analysis of Multiple Extrusion Runs with Nitrogen Cooling by Means of Qform Code

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

Numerical Data-Driven Modelling of Modified Samanta Process for Cold Extrusion of Gears

Tahsin Deliktas, Marcel Görz, Adrian Schenek, Marco Speth, Mathias Liewald

Keywords Index

Authors Index

The original version of this eBook is available on https://www.scientific.net/book/extrusion-and-drawing/978-3-0364-1995-4

Key Engineering Materials, ISSN: 1662-9795, Vol. 1048, pp 1-13

doi: 10.4028/p-tAvZG7

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-11-27

Revised:
2026-02-24

Accepted:
2026-02-24

Online:
2026-04-14

Mechanical Evaluation of 6060 Aluminum Alloy under Elevated Extrusion Ram Speed Using Liquid Nitrogen Die Cooling

The original version of this paper is available on https://www.scientific.net/KEM.1048.1.pdf

E. Giarmas 1,2,a, E. Tzimtzimis 1,b, K. Tsongas 3,c, D. Tzetzis 1,d

¹Digital Manufacturing and Materials Characterisation Laboratory, International Hellenic University, School of Science and Technology 14th km Thessaloniki-Moudania, 57001 Thermi, Greece

2 Alumil S.A., Production Division, 61100 Kilkis, Greece

3 Advanced Materials and Manufacturing Technologies Laboratory, Department of Industrial Engineering and Management, International Hellenic University, 57400, Thessaloniki, Greece;

a  v.giarmas@alumil.com, b  m.tzimtzimis@ihu.edu.gr, c  k.tsongas@ihu.gr, *Correspondence: d  d.tzetzis@ihu.edu.gr

Keywords: 6060 Aluminum Alloy; Liquid Nitrogen Cooling; Nanoindentation; Tensile Testing; Creep Behavior.

Abstract

This study investigates the influence of increased extrusion ram speed, achieved through die cooling with liquid nitrogen, on the mechanical behaviour of 6060 aluminum alloy profiles for two ageing temperatures of 160∘C and 180∘C. Mechanical properties were evaluated through tensile testing and nanoindentation, with the latter also used to examine the alloy's creep response. The findings show that although higher ram speed has only a minor effect on Ultimate Tensile Strength (UTS) and Yield Tensile Strength (YTS), it leads to a notable improvement in elongation. In addition, the results demonstrate a clear effect of ram speed on creep displacement, as the dislocations generated at higher deformation rates appear to enhance the material's resistance to creep for both the selected temperatures.

1. Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1048.1.pdf

Aluminum profiles are widely employed as lightweight structural components across numerous industries owing to their low density, excellent recyclability, high specific strength, and superior corrosion resistance [1-3]. Among these materials, the 6xxx series aluminum alloys constitute the most extensively used extruded products worldwide [4]. These commercial alloys typically contain Mg,Si,Fe, and Cu as principal alloying elements, with Mg and Si playing key roles in strengthening through the precipitation of intermetallic phases during appropriate heat-treatment processes [5].
Given the broad application of aluminum extrusions, extensive research has been dedicated to evaluating their mechanical behavior. Zang et al. [6] examined the precipitation characteristics of 6xxx alloys aluminum alloys with elevated Mg/Si ratios and increased Cu content under various aging conditions, employing Vickers hardness tests, tensile characterization, differential scanning calorimetry (DSC), and transmission electron microscopy (TEM). Dariusz et al. [7] proposed a method to simulate welding conditions within extrusion die welding chambers for 6xxx alloys and assessed weld integrity using tensile testing. Similarly, Hannard et al. [8] utilized tensile experiments to determine the hardening and fracture behavior of three 6xxx series alloys subjected to different heat-treatment conditions.
In the context of mechanical assessment, Mróz et al. [9] analyzed the influence of aging on the dynamic stability of thin-walled aluminum extrudates. Their results indicated that the extruded sections exhibited high strength and dynamic stability according to the applied criteria; however, the specific dynamic response varied with the alloy temper. From a processing standpoint, heat treatment serves as an effective approach to tailor the strength of aluminum extrusions and may provide the only feasible means of adjustment when constraints on weight and cross-sectional geometry preclude dimensional modifications.
Additional studies have explored how extrusion speed affects the microstructure and mechanical properties of various alloys. Investigations on Mg−Ca binary alloys demonstrated that extrusion speed
induces notable changes in both microstructure and mechanical performance, underscoring the need for optimized processing parameters. Research on AA2024 similarly showed that ram speed influences surface quality and mechanical behavior, revealing a critical trade-off between extrusion rate and material performance. Work on Mg−Al−Ca−Sn alloys further highlighted the role of extrusion speed in governing microstructural evolution and resultant strength. Moreover, studies on continuous extrusion of 6063 aluminum alloy using double billets indicated that extrusion speed affects weld quality, suggesting opportunities for process improvement. Collectively, these findings reinforce the importance of controlling extrusion speed to enhance the mechanical performance and overall quality of aluminum and magnesium alloys [10-13].
The implementation of liquid nitrogen systems to regulate extrudate exit temperatures plays a critical role in extending die life, mitigating quality defects, and improving overall process productivity during aluminum alloy extrusion. Effective temperature monitoring is essential, and various approaches have been developed to manage thermal conditions throughout the process. Over the past decade, die cooling with liquid nitrogen has become one of the most widely adopted techniques. In response to the increasing demand for aluminum extruded products and the need to achieve high quality at competitive costs, liquid nitrogen-based cooling systems have gained prominence as an efficient solution. In addition, liquid nitrogen is considered an environmentally favorable option for enhancing extrusion productivity due to nitrogen's natural abundance and negligible ecological footprint. The primary safety consideration associated with its use is ensuring adequate ventilation, as evaporated nitrogen can displace oxygen in confined spaces. Although optimizing die design also contributes to improved process efficiency, thermal management challenges-particularly at elevated ram speeds-persist. Within this context, liquid nitrogen offers an effective means of heat control and supports higher throughput [14-19].
Concerning the mechanical performance of aluminum alloys, nanoindentation testing has also been extensively utilized in several cases. Kim et al. [20] employed nano- and micro-indentation along with nano scratch testing, complemented by optical microscopy and atomic force microscopy (AFM), to investigate the mechanical and tribological characteristics of 6061 aluminum alloy, comparing their findings with Vickers hardness measurements. Peng et al. [21] generated a hardness distribution map across the cross-section of 6061-5A06 aluminum alloy joints using nanoindentation with a total of 700 indents. Additionally, Ogura et al. [22] applied nanoindentation to analyze the mechanical properties of interfacial reaction layers in dissimilar joints between 6000 series aluminum alloys and steel, demonstrating the technique's effectiveness for characterizing such interface.
Previous studies have examined the influence of ageing temperature and the use of liquid nitrogen on the nanoindentation creep behavior of 6060 aluminum alloy [23,24]. The referred previous studies have examined a wide range of ageing temperatures and durations, including an ageing temperature of 180∘C. In the present work, an ageing temperature of 160∘C was additionally selected to enable future investigation of potential mixed ageing cycles, along with the temperature of 180∘C that was determine to provide the most advantageous performance. For this reason, both temperatures were adopted for detailed evaluation in the current study.

2. Methodology

The original version of this paper is available on https://www.scientific.net/KEM.1048.1.pdf

2.1 Materials and Sample preparation

The material examined in this study is the 6060 aluminum alloy, whose elemental composition is presented in Table 1. Chemical analysis was performed using a Hitachi FOUNDRY MASTER COMPACT Optical Emission Spectrometer (OES). Test specimens were produced by extruding the alloy with a 37.5 MN Danieli Breda extrusion press. The extrudate exit temperature was consistently maintained at 520∘C for all ram speeds. This uniformity was achieved by closely controlling the billet preheating temperature and employing liquid nitrogen as a cooling medium to stabilize the exit temperature, even under elevated ram speeds. The operation was supported by the N5NITROGEN software from ATIEUNO in conjunction with the press's SCADA system, ensuring stable process control.

Following extrusion, the material was aged at 160∘C and 180∘C for 1 h,4 h, and 8 h , respectively. The design of cooling channels within aluminum extrusion dies is largely guided by empirical principles, as the complexity of multiphase flow and variable heat-transfer calculations poses significant challenges. To simplify the design process and minimize computational uncertainties, industry practice often relies on practical, experience-based guidelines that offer a cost-effective means of producing efficient cooling channel systems. Figure 1 illustrates the nitrogen cooling channel configuration used in the extrusion die from which the specimens in this study were produced. In this cooling system, liquid nitrogen enters at point (A), is distributed through the outer ring at (B), passes into the cooling channels at (C), and is delivered to the injectors at (D). The layout of the channels is determined by factors such as profile symmetry, the number of cooling holes, and structural limitations of the die. Although initial designs may require iterative adjustments, applying established design guidelines can greatly streamline the development of effective cooling systems. Specimens used for nanoindentation and tensile testing were prepared from the extrudates produced using the process described above.

Table 1. Elemental composition of the investigated alloy [28].

Alloy | Mg | Si | Fe | Mn | Cu | Cr | Zn | Ti
6060 | 0,38 | 0,5 | 0,23 | 0,019 | 0,021 | 0,005 | 0,061 | 0,014

Fig. 1. Liquid nitrogen channels at the aluminum extrusion die

2.2 Tensile tests

Tensile testing evaluates a material's ability to resist uniaxial tensile loads by applying a controlled stretching force to the specimen. In this study, tensile tests were performed using a universal testing machine (M500-50AT, Testometric, Lincoln, UK). The specimens were prepared in accordance with EN ISO 6892-1:2019, with a gauge length of 50 mm and a rectangular cross-section measuring 12.5 mm(±1 mm) in width and a thickness of 1.63 mm , corresponding to the geometry of the extrudates. A crosshead speed of 5 mm/min was employed for all tests. To ensure statistical reliability and capture representative mechanical behavior, five specimens were tested for each experimental condition.

2.3 Nanoindentation testing

Nanoindentation experiments were carried out using a DUH-211S Shimadzu instrument (Kyoto, Japan) with a load resolution of 0.196μ N. Tests were performed at room temperature using a Berkovich diamond indenter, characterized by its three-sided pyramidal geometry, a 65∘ included angle, and a tip radius of 100 nm . A maximum load of 800 mN was applied to the surface and crosssection of the specimens and held for 3 seconds. This load level was selected to ensure sufficient penetration into the aluminum, thereby minimizing the influence of surface irregularities. At the
selected indentation depth, a larger volume of precipitates is engaged, enabling a more accurate representation of the bulk mechanical response. Indentation depth was continuously recorded as a function of the applied load, after which the indenter was unloaded to zero load. During the creep hold period, the peak load was maintained. The average of ten indents was used to determine the elastic modulus and hardness of the material.

2.4 Fractographic and Metallographic Characterization

A detailed fractographic analysis was conducted on the selected 6060 aluminum alloy following extreme ageing treatments (one hour at 180∘C and eight hours at 180∘C ). This investigation employed Phenom ProX scanning electron microscopy (SEM) to characterize the effects of the different ageing conditions on the alloy's microstructural and fracture features. Complementary metallographic analysis was also performed. Sample preparation for metallography followed a systematic procedure: the specimens were first mounted using cold-curing resins, then ground with silicon carbide (SiC) papers of progressively finer grit sizes (320, 800, and 1200). Polishing was subsequently carried out using soft cloths and diamond suspensions of 3μ m and 1μ m for a total duration of six minutes. Final polishing was completed using an Oxide Polishing Suspension (OPS) for four minutes.

3. Results and Discussion

The original version of this paper is available on https://www.scientific.net/KEM.1048.1.pdf

3.1 Production Data

Figure 2 presents the evolution of the average ram speed and the corresponding exit temperature of the extrudates. A total of twenty-five billets were processed for this experiment. Table 2 summarizes the billet lengths and temperature parameters pertinent to the study. Zones 1 through 3 represent the first three measurement locations along the billet, where temperatures were recorded to facilitate thermal control during extrusion. Liquid nitrogen cooling was initiated starting with the tenth billet. As shown in Figure 2, the substantial increase in ram speed did not influence the exit temperature of the extrudates, owing to the effectiveness of the liquid nitrogen cooling system. The introduction of liquid nitrogen enabled a 15.25% increase in average ram speed, from 9.31 mm/s to 10.73 mm/s.

Table 2. Billet length and Temperatures during the experiment

Billet length

Temp. at

zone1 mathematical formula

Temp. at

zone2 mathematical formula

Temp. at

zone3 mathematical formula

1000 | 470 | 450 | 430

[picture]

Fig. 2. Average ram speed and exit temperature evolution for the extrudates [24].

Fig. 2. Average ram speed and exit temperature evolution for the extrudates [24].

3.2 Tensile test Results

Tensile test results at an ageing temperature of 160∘C show that the application of liquid-nitrogen cooling does not produce any substantial change in yield stress as depicted in Figure 3. Ageing for 4 hours and 8 hours at 160∘C leads to increases in YTS of 53.15% and 49.89%, respectively, for specimens extruded at lower ram speeds, while the corresponding increases for higher ram speeds are 47.84% and 43.74%. Ageing for 4 hours and 8 hours results in UTS increases of 22.25% and 18.21% for the lower ram-speed condition, whereas the increases measured for the higher ram-speed condition are 17.48% and 17.49%. When directly comparing the two processing routes, only a marginal UTS increase of 0.57% is observed after 8 hours of ageing at 160∘C under liquid-nitrogen cooling and elevated ram speed.Figure 4 presents the tensile test results, indicating that the use of liquid nitrogen as a cooling agent had no substantial influence on the yield stress at an ageing temperature of 180∘C. Ageing for 4 hours and 8 hours at 180∘C leads to increases in YTS of 53.15% and 49.89%, respectively, for specimens extruded at lower ram speeds, while the corresponding increases for higher ram speeds are 63.86% and 12.69%. Ageing for 4 hours and 8 hours results in UTS increases of 25.13% and 15.67% for the lower ram-speed condition, whereas the increases measured for the higher ram-speed condition are 33.22% and 8%. A marginal increase in UTS of 0.20% was recorded after 8 hours of ageing at 180∘C under liquid nitrogen cooling and increased ram speed.
By comparing the YTS, UTS and strain at UTS for the ageing temperature of 180∘C, it is evident that for the ageing duration of 1 hour the YTS and UTS are reducing by 20.76% and 7.91% respectively, while the strain at UTS is increasing by 36.89%.For both ageing temperatures the use of liquid nitrogen exhibited a more pronounced effect on the strain (%) at UTS. This behavior can be attributed to the increased dislocation density generated by the higher ram forces, which promotes greater plastic deformation within the aluminum matrix. Under shorter ageing conditions, these dislocations contribute to reduced mechanical performance during tensile tests; however, at longer ageing durations, the formation of precipitates effectively restricts dislocation motion, thereby reducing the impact of this mechanism. As ageing progresses, this effect diminishes, likely due to the accelerated development of larger Mg2Si precipitates, which further impede dislocation mobility. Specifically, elevated ram speeds achieved through liquid nitrogen cooling resulted in increases in strain at UTS of 17.9% and 20.78% for ageing durations of 4 hours and 8 hours at 160∘C, respectively. Moreover, elevated ram speeds achieved through liquid nitrogen cooling resulted in increases in strain at UTS of 36.9%,14.6%, and 12.1% for ageing durations of 1 hour, 4 hours, and 8 hours at 180∘C, respectively. These results indicate that while strength parameters remained relatively unchanged, ductility improved significantly. Overall, the findings suggest that liquid nitrogen cooling can effectively tailor mechanical performance(particularly ductility)without compromising material strength. Additional microstructural characterization would help clarify the mechanisms responsible for this improvement.

[picture]

Fig. 3. a) YTS, b) UTS and c) strain (%) at UTS for ageing at 160∘C for 1,4 and 8 hours

Fig. 3. a) YTS, b) UTS and c) strain (%) at UTS for ageing at 160 ∘ C for 1,4 and 8 hours

[picture]

Fig. 4. a) YTS, b) UTS and c) strain (%) at UTS for ageing at 180∘C for 1,4 and 8 hours [24].

Fig. 4. a) YTS, b) UTS and c) strain (%) at UTS for ageing at 180 ∘ C for 1,4 and 8 hours [24].

3.3 Fractographic and Metallographic Characterization

A detailed fractographic analysis was performed on tensile specimens subjected to the maximum ageing conditions ( 1 hour at 160∘C and 8 hours at 180∘C ). Figure 5a illustrates a typical ductile fracture, characterized by micro-void coalescence. With increasing ageing, the voids become fewer but larger compared to those observed in less-aged material. Additionally, the peak-aged specimen exhibits a greater number of dimpled regions on the fracture surface, likely due to the increased
presence of precipitates that promote dimple formation. Figure 5b highlights transgranular brittle fractures of surface particles, resulting in shallow dimples of varying sizes, which can be attributed to the higher material strength under these ageing conditions. Metallographic analysis was also conducted for both ageing states. Figure 6 provides a visual representation of the α−AlFeSi particles, which are more easily observed due to the stronger etching effect in more aged materials. This increased etching contrast arises because precipitation reduces the amount of solute in the α−Al matrix, making it closer to pure aluminum and therefore more responsive to the etching process.

[picture]

Fig.5. SEM images of fracture surface from tensile tests: a) ageing for 1 hour at 180∘C, b) ageing for 8 hours at 180∘C [24].

Fig.5. SEM images of fracture surface from tensile tests: a) ageing for 1 hour at 180 ∘ C , b) ageing for 8 hours at 180 ∘ C [24].

[picture]

Fig. 6. Craters and α-AlFeSi particles for 1 hour at 180∘C and 8 hours at 180∘C after etching [24].

Fig. 6. Craters and α -AlFeSi particles for 1 hour at 180 ∘ C and 8 hours at 180 ∘ C after etching [24].

3.4 Nanoindentation Test results

3.4.1 Load-Depth Curves

Figure 7 displays the load-depth curves for specimens aged at 160∘C for three different durations, comparing extrudates processed at higher ram speeds with liquid-nitrogen cooling to those produced at lower ram speeds without cooling. The data show that hardness increases as ageing time increases, reflected by the reduced penetration depth. The nanoindentation results further demonstrate that higher ram speeds, enabled by liquid-nitrogen cooling, lead to a substantial rise in hardness after 1 hour and 4 hours of ageing, with only a slight increase after 8 hours. Extrudates processed at elevated ram speeds exhibited reductions in maximum indentation depth of 8.03%,8.85%, and 0.81% for the 1 -hour, 4 -hour, and 8 -hour ageing conditions, respectively. Figure 8 presents the load-depth curves for specimens aged at 180∘C for three different durations, comparing extrudates processed at higher ram speeds with liquid nitrogen cooling to those produced at lower ram speeds without
cooling. The results indicate that hardness increases with longer ageing time, as evidenced by the decreasing penetration depth. Additionally, the nanoindentation data reveal that higher ram speeds, facilitated by liquid nitrogen cooling, result in a slight reduction in hardness for the 1 -hour and 4 hour ageing conditions, and a minimal reduction after 8 hours. Specifically, extrudates produced at elevated ram speeds exhibited increases in maximum indentation depth of 6.2% and 2% for the 1 hour and 4-hour ageing durations, respectively, while for the 8-hour ageing cycle, the increase was only 1%. The distinct behaviour of the studied alloy during ageing at 160∘C and 180∘C can be attributed to the increased dislocation density generated under higher deformation rates associated with elevated extrusion ram speeds. This enhanced dislocation density contributes to strengthening at lower ageing conditions. As ageing progresses and precipitation becomes the dominant hardening mechanism, the contribution of dislocation-related strengthening gradually diminishes. These figures provides a more detailed analysis of data reported in our previous study, while providing more data for another ageing temperature. [23].

[picture]

Fig. 7. Load-Depth curves for ageing at 160∘C

Fig. 7. Load-Depth curves for ageing at 160 ∘ C

[picture]

Fig. 8. Load-Depth curves for ageing at 180∘C [24].

Fig. 8. Load-Depth curves for ageing at 180 ∘ C [24].

3.6 Nanoindentation creep behavior

To examine the creep behavior using nanoindentation tests, data collected during the holding stage of the experiment were analyzed. The experimental data during this stage were well-fitted with the following equation [25]:

h=hi+a(t−ti)1/2+b(t−ti)1/4+c(t−ti)1/8(eq.1)

where h is the indenter displacement during the holding stage, t is creep time, and hi,ti,a,b and c are the best-fit parameters. Figure 9 illustrates the experimental data alongside the fitted creep displacement curve for a specimen aged at 180∘C for 1 hour during the nanoindentation holding stage. The red line corresponds to the curve fitted according to Eq. 1, while the blue dots represent the experimental measurements. The origin of the x -axis was set at the start of the holding stage to clearly define the temporal range of the observed creep behavior.

[picture]

Fig. 9. Experimental and fitted creep displacement and time curve of ageing at 180∘C for 1 hour for the extrudates produced with higher ram speed (utilizing liquid nitrogen) [24].

Fig. 9. Experimental and fitted creep displacement and time curve of ageing at 180 ∘ C for 1 hour for the extrudates produced with higher ram speed (utilizing liquid nitrogen) [24].

Figure 10 illustrates the creep displacement-time response obtained from nanoindentation at an ageing temperature of 160∘C for three ageing durations, comparing extrudates processed at higher ram speeds with liquid-nitrogen cooling to those produced at lower ram speeds without cooling. Across all ageing conditions at 160∘C, specimens extruded at higher ram speeds consistently exhibit lower creep displacement throughout the entire holding period, demonstrating the suppressive effect of increased deformation rate and cryogenic cooling on time-dependent deformation. Moreober, Figure 11 presents the creep displacement-time curves obtained during nanoindentation at an ageing temperature of 180∘C for three different ageing durations, comparing extrudates processed at higher ram speeds with liquid nitrogen cooling to those produced at lower ram speeds without cooling. Unlike the load-depth behavior shown in Figure 8, the creep displacement data indicate that, for all ageing durations at 180∘C, higher ram speeds facilitated by liquid nitrogen result in reduced creep displacement throughout the entire holding period. As emphasized previously, for shorter ageing durations, the dislocations generated by higher ram speeds locally improve the creep resistance of the material. In contrast, for longer ageing times, precipitate formation effectively hinders dislocation motion, thereby diminishing the influence of this mechanism. Furthermore, it is evident that by increasing the ageing time the creep displacement is decreased. Based on the results presented, it is noteworthy that the use of liquid nitrogen as a cooling agent, which enables higher ram speeds, does not markedly affect the maximum indentation depth in nanoindentation tests. However, the data suggests that creep behavior may be influenced, as slightly reduced creep displacements were observed across all experiments. It is plausible that these effects could be more pronounced in stronger alloys, such as 6005 or 6082.

[picture]

Fig. 10. Creep displacement-time curves for ageing at 160∘C

Fig. 10. Creep displacement-time curves for ageing at 160 ∘ C

[picture]

Fig.11. Creep displacement-time curves for ageing at 180∘C[24].

Fig.11. Creep displacement-time curves for ageing at 180 ∘ C [ 24 ] .

4. Conclusions

The original version of this paper is available on https://www.scientific.net/KEM.1048.1.pdf

The present study employed the 6060 aluminium alloy to examine the influence of elevated extrusion ram speeds, achieved through the application of liquid nitrogen as a die cooling medium, on the alloy's mechanical performance. The mechanical response of the extruded profiles was characterized using tensile testing and nanoindentation techniques. In addition, creep behaviour was evaluated through nanoindentation.
Tensile testing at an ageing temperature of 160∘C showed that the application of liquid-nitrogen cooling, and the resulting increase in extrusion ram speed, did not produce any meaningful change in yield stress. Ageing for 4 or 8 hours at the same temperature similarly had no notable effect on yield stress. The ultimate tensile strength (UTS) was also largely unaffected by liquid-nitrogen cooling, with 4 hours of ageing showing no measurable variation. Only a marginal increase in UTS, equal to 0.57%, was observed after 8 hours of ageing under the higher ram-speed condition. Concerning the ageing at 180∘C for 4 hours, no significant effect on the ultimate tensile strength (UTS) or yield tensile strength (YTS) has been noticed. Similarly, the application of liquid nitrogen cooling during extrusion has only a marginal impact on UTS and YTS following 8 hours of ageing at 180∘C, with increases limited to 0.2% and 0.72%, respectively. In contrast, liquid nitrogen cooling markedly affects the strain at UTS under the same ageing conditions for both the selected temperatures, with higher ram speeds producing strain increases of 17.9% and 20.78% for ageing durations of 4 hours and 8 hours at 160∘C and 36.71%,24.76%, and 16.94% for 1 hour, 4 hours, and 8 hours of ageing at 180∘C. This behaviour is particularly relevant to aerospace and automotive applications,
underscoring the need for further investigations into harder alloys such as 6063,6005 , and 6082 to determine whether comparable trends are observed.
Nanoindentation results reveal that hardness was found to increase with ageing duration at 160∘C, as indicated by the progressive reduction in penetration depth during nanoindentation. Specimens extruded at higher ram speeds with liquid-nitrogen cooling consistently exhibited greater hardness compared to those processed at lower ram speeds, particularly after 1 hour and 4 hours of ageing, while the effect became minimal after 8 hours. The corresponding reductions in maximum indentation depth were 8.03%,8.85%, and 0.81% for the 1 -hour, 4-hour, and 8 -hour ageing conditions, respectively, confirming the strengthening influence of elevated ram speed under shorter ageing durations. Furthermore, the maximum indenter displacement decreases with prolonged ageing at 180∘C. The use of liquid nitrogen cooling does not significantly alter maximum displacement during these tests. However, higher ram speeds in conjunction with liquid nitrogen cooling lead to reduced creep displacement throughout the holding stage, while extended ageing further diminishes creep displacement. Overall, the findings confirm that increasing ageing time consistently reduces creep displacement.

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The original version of this paper is available on https://www.scientific.net/KEM.1048.1.pdf

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1048, pp 15-23

doi: 10.4028/p-tFP2lH

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-12-15

Revised:
2026-02-15

Accepted:
2026-02-25

Online:
2026-04-14

An Experimental Investigation of the Effect of Compression Calibration on the Ductility of AA6061 Extrusions

The original version of this paper is available on https://www.scientific.net/KEM.1048.15.pdf

Mustafa Can Uzun 1,a*, Torgeir Welo 1, b

1 Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim 7491, Norway

a  mustafa.c.uzun@ntnu.no, b  torgeir.welo@ntnu.no,

Keywords: Aluminum extrusion, Mechanical calibration, Ductility, Mechanical properties

Abstract

The growing use of extruded aluminum components in vehicle structures necessitates both strength and ductility to meet energy absorption requirements. In this study, a new compression calibration method for multi-chamber, hollow sections was developed with the aim of improving dimensional accuracy while enhancing the ductility of AA6061 extruded profiles. The influence of this method on mechanical properties was investigated through uniaxial tensile tests, three-point VDA bending tests, and axial crush tests. The uniaxial tensile test results revealed a reduction in the (logarithmic) strain at necking, while no significant changes were observed in yield and ultimate tensile strengths. On the other hand, the VDA tests showed a systematic increase in the normalized bending angle, indicating improved energy absorption characteristics. Visual inspection and the absorbed energy obtained by axial crush tests supported the findings in the VDA tests, indicating the compression calibration method enhances the crushability of extruded AA6061 profiles, although this improvement is not identified in standard tensile data. Overall, this work introduces a new, industrial calibration method for hollow extrusions that also enhances crushability.

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1048.15.pdf

In manufacturing operations, mechanical calibration strategies are sometimes employed after extrusion to achieve the dimensional accuracy required for subsequent manufacturing steps or the fitup of the final product. Many of these methods apply controlled plastic deformation to induce permanent shape correction, thereby eliminating geometrical distortions and imperfections occurring during the extrusion process. A very common example is post-stretching-also referred to as straightening-immediately after extrusion, which has become an integral part of modern extrusion production lines. Calibration can also be performed as an individual, secondary manufacturing operation downstream from extrusion. Møretrø et al. [1] provided a comprehensive comparison of different mechanical calibration methods, highlighting the capability of each method for correcting different types of geometrical imperfections occurring during the extrusion process. Welo et al. [2] investigated a new calibration method for open U-section extruded aluminum profiles, which combines the principles of stretching and bending. This approach reduces springback and consequently improves the dimensional accuracy of the profiles.
Although improving geometric stability through mechanical calibration comes at an additional cost, it may also provide improved mechanical properties of the final product for specific aluminum alloys. Qvale et al. [3] investigated how varying pre-stretching levels ( 0.5% and 4% ) affect the strength and ductility of extruded double-chamber AA6063 and AA6082 profiles. Similarly, Granum et al. [4] examined the effect of pre-stretching on the ductility of rectangular hollow AA6063, AA6061, and AA6110 profiles. The uniaxial tensile test results from both studies demonstrated that increasing the pre-stretch level from 0.5% to 4% improved ductility. In addition, profiles pre-stretched to 4% showed fewer cracks after quasi-static crush tests, confirming the positive impact of plastic deformation on ductility. Qvale et al. [3] also noted that the improvement in ductility due to prestretching was more pronounced for AA6063 than for AA6082, as determined from both uniaxial tensile tests and inspection of crack formation observed during quasi-static tests. Built on the stretching method, Leśniak et al. [5] developed a semi-industrial prototype capable of applying
dynamic stretching at higher speeds to extruded rectangular hollow AlMgSi(Cu) profiles. The results showed that the dynamically stretched profiles exhibited a fine-grained and homogeneous microstructure, providing improved mechanical strength and dimensional tolerances within acceptable limits. In their subsequent study, however, Leśniak et al. [6] reported that dynamic stretching led to a noticeable reduction in the ductility. Overall, these studies show that the influence of calibration processes on ductility strongly depends on both the material composition and the type of calibration method employed.
While 'ductility'—often determined from stress-strain characteristics obtained from uniaxial tensile testing-is a significant parameter for assessing the crash performance of extrusion-based vehicle components, its proper evaluation requires careful selection of appropriate test methods. Victor et al. [7] stated that although a standard tensile test provides reliable information regarding the strength of a material, elongation (strain at fracture) used as an indicator of ductility is not an accurate indicator of the material's deformation capability in practice. Parson et al. [8] investigated the influence of temper condition on the crush behavior of several 6xxx series alloys, reporting that yield strength alone is not a unique indicator of crash performance and the existence of a linear relationship between ultimate tensile strength and absorbed energy. Therefore, alternative methods for assessing ductility, such as bending tests and axial and transverse crush tests, are commonly used. The potential and limitations of the VDA bending test, which has become the standard in the automotive industry for evaluating the ductility of extrusion-based vehicle components, have been widely discussed in the literature. According to Parson et al. [9], the VDA bending test suppresses necking and promotes surface-initiated failure, thereby replicating real-world deformation mechanisms that occur under lateral or axial crushing. Similarly, Victor et al. [7] emphasized that the VDA bending test provides a more accurate measure of a material's resistance to damage and thus its overall energy absorption capability. As a result, the VDA test is commonly used to evaluate ductility using the normalized bending angle as an indicator of the material's deformation capacity. Unlike the VDA bending testwhich focuses on local surface deformation-the axial crush test represents an alternative way of evaluating ductility at the component level by characterizing the global deformation behavior under a state of crushing. The total absorbed energy, energy absorption efficiency, and crack inspection are used to assess ductility (or better crushability).
Although mechanical calibration methods have been studied from the viewpoint of effects on dimensional accuracy and overall mechanical response, their influence on ductility, which is essential for real-world impact behavior of vehicle components, has rarely been addressed in the literature. In this study, an innovative compression calibration method was developed to improve the dimensional accuracy of complex hollow profiles. In this connection, it is also of primary interest to investigate how the deformation process impacts the mechanical performance of the calibrated component. This calibration method-designed for extruded multi-chamber hollow profiles-compresses all exterior surfaces of the profile using internal mandrels and external mechanical tools. The effects of the method on strength and ductility have been evaluated by comparing calibrated and uncalibrated AA6061 profiles through uniaxial tensile tests, VDA bending tests, and axial crush tests. Moreover, the extraction of test specimens from different regions of the profile allowed the evaluation of the influence of extrusion seam welds and material orientation, which are both known to impact ductility.

A new compression calibration technique

The original version of this paper is available on https://www.scientific.net/KEM.1048.15.pdf

In the compression calibration stage, a lubricant is first applied to the inner surface of the profile, after which an inner mandrel is inserted into the profile. The lubricant ensures uniform friction and allows the mandrel to be removed easily after deformation. The inner mandrel was specifically designed for double-chamber hollow profiles and additionally provides uniform support to the surfaces. The profile is then compressed uniformly from all four sides under a total compression force of approximately 200 tons. The tooling configuration and the corresponding compression kinematics are schematically illustrated in Fig. 1a. The compression calibration method applied in this study follows the general principles described in the patent by Paulsen et al. [10], which covers the use of internal mandrels and external dies for the calibration of multi-chamber hollow profiles.

[picture]

Fig 1. a) Schematic of the compression calibration setup and tool movement directions b) Sketch of the multi-chamber hollow aluminum profile and longitudinal seam weld positions

Fig 1. a) Schematic of the compression calibration setup and tool movement directions b) Sketch of the multi-chamber hollow aluminum profile and longitudinal seam weld positions

Material

The original version of this paper is available on https://www.scientific.net/KEM.1048.15.pdf

Double-chamber hollow AA6061 profiles extruded by Benteler Automotive, Raufoss (Norway), were used in this study, as shown in Fig. 1b. The chemical composition of the billet is listed in Table 1. Before calibration, the profiles were solution heat-treated at 570∘C and subsequently waterquenched. After calibration, the profiles were subjected to stabilization at 150∘C for 30 minutes and then artificially aged at 190∘C for 4 hours to obtain the desired mechanical properties.

Table 1. Chemical composition of AA6061 in weight%

6061 | Si | Fe | Cu | Mn | Mg | Cr | Zn | Ti | Al
| 0.66 | 0.19 | 0.10 | 0.02 | 1.02 | 0.07 | 0.01 | 0.08 | 97.8

In the extrusion process, a porthole-type die was employed, which led to the formation of longitudinal seam welds (L-seam welds) along the extruded profile. To investigate the influence of seam welds on the mechanical parameters, the locations of these regions were first identified for both the calibrated and uncalibrated profiles. For this purpose, the profiles were subjected to grinding and etching steps, respectively. The grinding was performed using silicon carbide abrasive papers with 500,1000 , and 2000 grit sizes. The ground profiles were then immersed in a 30% sodium hydroxide (NaOH) solution for 90 seconds. After these steps, the seam welds became clearly visible, allowing more precise extraction of test specimens from the seam weld areas. The profile contained seven longitudinal seam welds with positions as indicated in Fig. 1b.

Characterization of calibrated profiles

The original version of this paper is available on https://www.scientific.net/KEM.1048.15.pdf

Comprehensive mechanical characterization of the effects of the compression calibration process was carried out through uniaxial tensile testing, three-point VDA bending testing, and axial crush testing. First, the mechanical properties of calibrated and non-calibrated profiles were investigated using quasi-static uniaxial tensile testing on an MTS Landmark 50 kN universal tensile testing machine. Tensile tests were performed at room temperature under a constant crosshead speed of 0.085 mm/s. As illustrated in Fig. 2, tensile specimens were extracted from five different positions along the extrusion direction using wire EDM. This approach enabled evaluating the effect of calibration on ductility as assessed by uniaxial tensile testing, considering also the presence of longitudinal seam welds and the sample orientation. Strain was measured throughout the tensile tests using VIC-2D Digital Image Correlation (DIC) system, which enables precise strain measurements.

[picture]

Fig 2. Dimensions and extraction locations of tensile test specimens

Fig 2. Dimensions and extraction locations of tensile test specimens

Second, the VDA bending tests were carried out using a STEP Lab Axial 20 kN static testing system, where the roller gap was set to 6 mm , the punch radius to 0.4 mm , and the rollers had a diameter of 30 mm . The VDA bending tests were performed under load-drop control, with a constant punch speed of 0.33 mm/s at room temperature. From each profile, four specimens were extracted: two with dimensions of 50 mm×50 mm and two with 50 mm×20 mm (see Fig. 3a). The 50 mm×50 mm specimens were identical in geometry and contained a longitudinal seam weld, but they were oriented differently with respect to the extrusion direction during the VDA bending tests (see Fig. 3b). In the first configuration, the punch line was positioned parallel to the extrusion direction, whereas in the second configuration, the punch line was oriented perpendicular. This configuration was used to evaluate the effect of sample orientation. To evaluate the influence of longitudinal seam welds, two specimens with dimensions of 50 mm×20 mm were extracted-one containing a seam weld, while the other was taken from a seam-weld free region of the profile (see Fig. 3d). The subsize bending specimens were due to the limited size of the seam weld free areas available in the extruded profiles. During testing, the maximum load was recorded continuously. In addition, the bending angle for assessing the bendability of each specimen was measured manually using a protractor, as illustrated in Fig. 3c. The measured bend angle at fracture was subsequently normalized by the actual initial specimen thickness.
Third, crush tests were performed in the vertical direction at ambient temperature using an Instron 5 MN machine. The top end was cut with a 5∘ inclination, as shown in Fig. 4, to initiate buckling and thus ensure that the development of the folding pattern started from the upper end of the profile. The crosshead of the testing machine was operated at a constant velocity of 100 mm/min. The test was terminated after the specimens reached a total deformation of 200 mm . During the tests, the forcedisplacement characteristic was recorded as the primary indicator of crush performance.

[picture]

Fig 3. a) Schematic illustration of the test specimen extraction locations b) Test configuration of 50 mm×50 mm specimens c) Measured bending angle d) Test configuration of 50 mm×20 mm specimens

Fig 3. a) Schematic illustration of the test specimen extraction locations b) Test configuration of 50 m m × 50 m m specimens c) Measured bending angle d) Test configuration of 50 m m × 20 m m specimens

[picture]

Fig 4. Crush test specimen

Fig 4. Crush test specimen

Results and Discussion

The original version of this paper is available on https://www.scientific.net/KEM.1048.15.pdf

The investigation begins with the uniaxial tensile test results, which establish the mechanical behavior before and after the compression calibration process. Fig. 5 presents the yield strength, ultimate tensile strength, and logarithmic strain at necking for five different specimens before and after calibration, together with the corresponding standard deviations. Across all tensile specimens, the highest standard deviations were found to be 4 MPa for yield strength and 2.1 MPa for ultimate tensile strength, and 0.01 for the logarithmic strain at the instant of necking. All specimens showed a minor decrease in yield and ultimate tensile strengths after calibration. The overall impact, however, was negligible, since the maximum reduction in yield strength was 2.4%, while the largest decrease in ultimate tensile strength was 3.3%. Compared to the change in strength, a more noticeable reduction was observed in the logarithmic strain after calibration, reaching up to as high as 17.1%. A similar trend was also reported by Qvale et al. [3], who attributed this reduction to a decrease in the workhardening capacity caused by prior plastic deformation during pre-stretching.
To evaluate the influence of the longitudinal seam welds on tensile properties in the extrusion direction, specimens were extracted from the top (Specimens #1 and #2) and the side (Specimens #3 and #4) walls of the profile. Here specimens #1 and #3 contained longitudinal seam welds. Comparing
the results obtained from these specimens revealed that the mechanical properties of the longitudinal seam welds were similar to those of the parent material. In addition, specimens extracted from the same surface but with different orientations (Specimens #3 and #5) were examined. In Specimen #3, the main direction of the tensile sample was parallel to the direction of the seam weld, whereas in Specimen #5, the main direction of the tensile sample was perpendicular to the direction of the seam weld, as shown in Fig. 5. Specimen #5 exhibited lower values of yield strength, ultimate tensile strength, and strain at necking. This observation is consistent with the tensile test results reported by Liu et al. [11].

[picture]

Fig 5. Tensile test results

Fig 5. Tensile test results

The effect of the calibration process on bendability was assessed by VDA tests. To examine the effect of the calibration process and sample orientation, two identical 50×50 mm specimens were tested under different loading configurations. Fig. 6a presents the results for Configurations #1 and #2, respectively. In both configurations, the calibrated profiles exhibited higher maximum load and normalized bend angles compared to the uncalibrated ones. In Configuration #1, the normalized bend angle increased by approximately 20.1%, while the maximum load increased by about 3.6% after calibration. In Configuration #2, the enhancements became more evident with the normalized bending angle and maximum load increasing by roughly 32.5% and 7.6%, respectively. This substantial increase in the bend angle clearly indicates that the calibration process enhanced the bendability of the material. Furthermore, when comparing the two configurations, the loading line perpendicular to the extrusion direction (Configuration #2) resulted in significantly higher ductility than loading parallel to it (Configuration #1). This applies to both uncalibrated and calibrated profiles. For the uncalibrated profiles, loading perpendicular to the extrusion direction resulted in approximately
137.6% higher normalized bending angle and 7.4% higher maximum load compared to parallel loading. For the calibrated profiles, the corresponding increases were approximately 162% and 11.6%. These differences indicate that loading parallel to the extrusion direction causes crack initiation to occur earlier, as the perpendicular configuration demonstrated considerably higher ductility.
To investigate the effect of compression calibration on longitudinal seam weld properties, an additional 50×20 mm specimens were tested. The specimen in Configuration #3 contained a seam weld, whereas the specimen in Configuration #4 was extracted from a seam-weld free region. Fig. 6b presents the corresponding bending test results. Consistent with the previous findings, the calibrated specimens demonstrated increased maximum load and normalized bend angle in both configurations. In Configuration #3, the normalized bending angle increased by approximately 35.9% and the maximum load by 3.4% after calibration. In Configuration #4, the corresponding increases were around 13.3% and 4.7%. The observed increase in the normalized bend angle indicates that the calibration process does enhance the bending performance and overall ductility. Moreover, the presence of seam welds led to a reduction in normalized bend angles-approximately 23.5% for uncalibrated specimens and 8.3% for calibrated ones, indicating decreased ductility.
Overall, the calibrated profiles had a larger bend angle at fracture in VDA bending tests. This does not align with the logarithmic strain measured in tensile tests. However, it agrees with the results of Zhou et al. [12], who reported a low correlation between the two test methods. They attributed this to fundamentally different fracture mechanisms in tensile and bending type deformations.

[picture]

Fig 6. VDA bending test results: a) Test configuration 1 and 2, b) Test configuration 3 and 4

Fig 6. VDA bending test results: a) Test configuration 1 and 2, b) Test configuration 3 and 4

As a final assessment method, axial crush tests were performed to evaluate the global deformation behavior and energy absorption capacity at the component level. Fig. 7a presents the crush test results of the uncalibrated and calibrated profiles. Both the peak force and total energy absorption increased after calibration by approximately 5.4% and 4.5%, respectively. The improvement in the absorbed energy suggests that the calibration process enhanced the performance of the profiles, indicating an increased crushability. Fig. 7b and 7c visually compare the cracks observed to evaluate ductility. The calibrated profiles exhibited shorter and fewer cracks, particularly in the corner regions. This indicates that the calibration process has a positive effect on ductility, as shown by reduced crack formation and a more uniform deformation during axial crushing.
The present findings support the observations made by Parson et al. [8], indicating that yield strength alone is not a reliable predictor of crash performance; although no significant change in yield strength was observed after compression calibration, a significant increase in absorbed energy was recorded in the crush tests. Unlike Parson et al. [8], who reported a linear relationship between ultimate tensile strength and absorbed energy, these results show an increase in absorbed energy, although the ultimate tensile strength remains unaffected by calibration. This indicates that factors other than strength contribute to the observed improvement in crush performance. One potential factor is the dimensional changes introduced by the compression calibration process, which may influence the folding behavior during crushing.

[picture]

Fig 7. Crush test results: a) Peak force and energy absorption results, b) Uncalibrated profile
c) Calibrated profile

Fig 7. Crush test results: a) Peak force and energy absorption results, b) Uncalibrated profile c) Calibrated profile

Overall, the proposed compression calibration method is a feasible alternative to existing calibration methods in industrial practice. Although the process requires the use of part-specific internal mandrels adapted to the geometry of each individual profile, it can readily be integrated into mass production using conventional presses. In addition to its primary role of improving dimensional accuracy, the present results suggest that the process may also provide added value in terms of improved mechanical performance.

Conclusions and Outlook

The original version of this paper is available on https://www.scientific.net/KEM.1048.15.pdf

This paper introduces a novel compression calibration method designed to improve the dimensional accuracy of complex extruded hollow sections. The aim was to determine the effect of the intermediate calibration stage on the properties of the profiles. This was investigated by examining the mechanical properties of double-chamber AA6061 profiles by uniaxial tensile, VDA bending, and axial crush tests. After calibration, a reduction was observed in the logarithmic necking strain in uniaxial tensile testing. In contrast, the normalized bend angle obtained from the VDA bending tests increased. The crush test results showed that the calibration process enhanced the absorbed energy and reduced the formation of cracks in the profile corner regions. Collectively, although the primary purpose of the compression calibration method is to improve the dimensional accuracy of extruded profiles, the present findings demonstrate that it also contributes to improved mechanical properties. Further research studies will focus on advanced material characterization analyses, such as SEM and EBSD, to investigate in greater depth the underlying mechanisms governing the observed changes in the mechanical response of the calibrated profiles.

References

The original version of this paper is available on https://www.scientific.net/KEM.1048.15.pdf

J. Møretrø, U. H. Tundal, S. Tjoetta, and F. Paulsen, "Use of Calibration to Achieve Tight Tolerances in High-Strength and Ductile Automotive Profiles," in Proceedings of 13th International Aluminum Extrusion Technology Seminar, Orlando, Florida: The Aluminum Extruders Council and The Aluminum Association., May 2024.

T. Welo and C. A. Raknes, "Cost-Effective, Precision Calibration of Aluminum Extrusions for Automotive Applications," in Proceedings of the 12th International Aluminum Extrusion Technology Seminar, Orlando, Florida: Extrusion Technology for Aluminum Profiles Foundation, May 2022.

K. Qvale, O. S. Hopperstad, O. Reiso, U. H. Tundal, C. D. Marioara, and T. Børvik, "An experimental study on pre-stretched double-chamber 6000-series aluminium profiles subjected to quasi-static and dynamic axial crushing," Thin-Walled Struct., vol. 158, p. 107160, Jan. 2021, doi: 10.1016/j.tws.2020.107160.

H. Granum, O. R. Myhr, T. Børvik, and O. S. Hopperstad, "Effect of pre-stretching on the mechanical behaviour of three artificially aged 6xxx series aluminium alloys," Mater. Today Commun., vol. 27, p. 102408, Jun. 2021, doi: 10.1016/j.mtcomm.2021.102408.

D. Leśniak et al., "Structure and Mechanical Properties of Extrudates Straightened with Dynamic Deformation," Materials, vol. 17, no. 16, p. 3983, Aug. 2024, doi: 10.3390/ma17163983.

D. Leśniak et al., "Thermo-mechanical processing of alloy extrusions based on dynamic stretching," Arch. Civ. Mech. Eng., vol. 25, no. 5-6, p. 269, Sep. 2025, doi: 10.1007/s43452-025-01323-2.

J. Victor, A. Poznak, L. Itchue, and M. Tozier, "Overview of Methods to Evaluate Extruded Profile Ductility as It Relates to Automotive Profiles," in Proceedings of 13th International Aluminum Extrusion Technology Seminar, Orlando, Florida: The Aluminum Extruders Council and The Aluminum Association., May 2024.

N. Parson, J. Fourmann, and J.-F. Beland, "Aluminum Extrusions for Automotive Crash Applications," SAE International, Mar. 2017.

N. Parson, J.-F. Béland, and J. Fourmann, "Extrusions for Automotive Crash Applications," in Proceedings of 11th International Aluminum Extrusion Technology Seminar, Chicago, Illinois: Extrusion Technology for Aluminum Profiles Foundation, May 2016.

F. Paulsen and C. Handing, "Method for calibrating a metal profile blank which is configured as ahollow-chamber profile and which has at leasts one solid wall," US Patent 2022/062965 A1

F. Liu et al., "Microstructure and mechanical properties of longitudinal weld in 6005A aluminum alloy profile extruded by a porthole die,"J. Mater. Res. Technol., vol. 28, pp. 25522571, Jan. 2024, doi: 10.1016/j.jmrt.2023.12.182.

X. Zhou, S. Tronvoll, W. M. Williams, L. Sandnes, and T. Welo, "Recrystallization Effects on the Formability of Extruded AA6082-T6 Profiles," in Proceedings of 13th International Aluminum Extrusion Technology Seminar, Orlando, Florida: The Aluminum Extruders Council and The Aluminum Association., May 2024.

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/extrusion-and-drawing/978-3-0364-1995-4

Key Engineering Materials, ISSN: 1662-9795, Vol. 1048, pp 25-34

doi: 10.4028/p-hyK7OT

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-12-17

Revised:
2026-02-16

Accepted:
2026-02-23

Online:
2026-04-14

Numerical Investigation of Round Profile Production: A FEM-Based Comparison between Friction Stir Extrusion and Conventional Extrusion

The original version of this paper is available on https://www.scientific.net/KEM.1048.25.pdf

ACERBI Carlo 1,a*, NEGOZIO Marco 1, b, LUTEY Adrian H.A. 1,c, FELCI Matteo 1, d, SUHUDDIN Uceu F.H. 2,e, RANA Harikrishna 2,f, KLUSEMANN Benjamin 2,3, g and BOCCHI Sara 4, h

1 University of Parma, Department of Engineering for Industrial Systems and Technologies DISTI, Parco Area delle Scienze, 181/A, 43124 Parma, Italy

2 Helmholtz-Zentrum Hereon, Institute of Material and Process Design, Solid State Material Processing, Max-Planck-Straße 1, 21502 Geesthacht, Germany

3 Leuphana University Lüneburg, Institute for Production Technology and Systems, Universitätsallee 1, 21335 Lüneburg, Germany

4 University of Bergamo, Department of Management, Information and Production Engineering, Via Pasubio 7/b, 24044 Dalmine, BG, Italy

a  carlo.acerbi@unipr.it, b  marco.negozio@unipr.it, c  adrianhughalexander.lutey@unipr.it, d  matteo.felci@studenti.unipr.it, euceu.suhuddin@hereon.de, f  harikrishnasinh.rana@hereon.de, gbenjamin.klusemann@hereon.de, h  sara.bocchi@unibg.it 

Keywords: Process sustainability; Friction Stir Extrusion; Finite Element Analysis; Process optimization.

Abstract

Friction Stir Extrusion, Direct Extrusion and Indirect Extrusion are all valid processes for the production of round extrudates. However, differences and similarities between them have yet to be analyzed by the scientific community, since with the same geometry, each technology instills specific properties to the extruded product. In this context, the present work presents an in-depth analysis via QForm UK Finite Element Method software of the effect that each process has on a AA6061 extruded wire. Various combinations of rotational speed (200, 400, 600 rpm ), feed rate (1, 2,3,4 mm/s ) and pre-heating temperature ( 450,500∘C ) were analyzed to assess differences and similarities between the different processes.

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1048.25.pdf

In the production of extrudates, numerous technologies can be employed, from conventional Direct Extrusion (DE) and Indirect Extrusion (IE), to the novel Friction Stir Extrusion (FSE) process. Developed by The Welding Institute (Cambridge, UK) in 1993, FSE is gaining increasing attention for its solid-state recycling capabilities and high energy efficiency [1]. This technology employs a rotating tool in combination with a die. The frictional heat generated during processing softens the workpiece material, which is subsequently forced through the die to form an extruded profile [2]. The primary process parameters in FSE are the rotational speed and the feed rate, together with the tool geometry [2,3]. The intense thermomechanical conditions experienced during the process promote significant microstructural evolution, which may result in enhanced overall mechanical properties [4]. Another distinctive feature of FSE is its ability to produce extruded components not only from solid billets but also from alternative feedstock materials such as chips or powders [5]. The macroscopical mechanical properties of a material derive from its microstructure, which is affected by a variety of factors. For instance, hardness, yield strength and ultimate tensile strength are correlated with grain size, dislocation density and element distribution within the alloy [6]. Especially in high-temperature processes such as FSE, predicting microstructure evolution is a complex task. Thus, an in-depth study of the physical characteristics of each process is fundamental to obtain the desired mechanical properties for a given application.

Among aluminum alloys, those of the 6xxx group (Al-Mg-Si alloys) compose the majority of extruded components, due to their high formability and low cost [7,8]. AA6061 ( AlMg 1 SiCu ) is one of the most widely known alloys, for its ability to be age-hardened thanks to the precipitation of the β−Mg2Si phase from a supersaturated solid solution [9,10]. Thus, AA6061 was chosen as the subject material for this study.

DE and IE have been subjected to a wide number of studies, both experimental and numerical, devoted to process optimization, in terms of waste, microstructure, die cooling, properties of the extruded profile, and defects. For instance, Pelaccia et al. [11] have researched the potential of liquid nitrogen cooling in the extrusion of AA6063 and ZM21 alloys, through experimental and numerical analyses, obtaining excellent results and a valid numerical model able to predict the experimental outcome with temperature errors below 8% and load errors below 11%. In another study, Kniazkin at al. [12] analyzed the billet skin defect with the software QForm UK, validating the predictive model via experimental studies. They showed a good numerical-experimental match, and reduced simulation time with respect to prior studies. Negozio et al. [13] introduced a novel AA6063 recrystallization model, performing calibration and validation via experimental analysis and numerical FEM simulations using the software QForm UK. The authors accurately modelled the static and dynamic recrystallization behavior, with prediction errors remaining always below ±25%. In another study, Negozio et al. [14] predicted grain size and Peripheral Coarse Grain defect formation, combining empirical microstructural observations and data from FEM simulations to train an Artificial Neural Network (ANN), demonstrating increased prediction accuracy compared to state-of-the-art analytical models, and potential for tailoring process parameters to achieve desired microstructure in an actual manufacturing environment.

On the other hand, not much research has been devoted to the understanding and optimization of the FSE process, demonstrating the need for further analysis. Bocchi et al. [15] investigated the recrystallization mechanism and bonding phenomenon within the FEM software DEFORM 3D, applying a recrystallization model originally developed for conventional extrusion. They found their models to accurately predict process kinetics, including bonding and microstructure evolution. Negozio et al. [16] developed a robust model for predicting bonding phenomena and recrystallization occurring during the FSE of AA6061 powder. Validation was based on an experimental campaign in which AA6061 powder was FSEed at different rotational speeds and feed rates. In another study, Li et al. [17] reported the effect of process parameters such as extrusion force and feed rate on deformation behavior of aluminum alloys during FSE. Material flow patterns were obtained and correlated with extrusion temperature, emphasizing the importance of strain for both bonding condition and process optimization. In a similar work, Li et al. [18] analyzed material flow in AA6061 alloy thanks to the introduction of a AA2195 marker, showing that deformation behavior is highly affected by extrusion force and feed rate. Zhang et al. [19] performed heat transfer and temperature field analyses during the FSE of AA6061, developing a numerical model that correctly predicts temperature variations within the workpiece. Diyoke et al. [20] have performed a finite element analysis (FEA) on FSE and conventional extrusion, to assess the effect that friction condition has on shear, material flow and the consequent thermo-mechanical condition. The numerical results showed a reduction in extrusion force in FSE compared to conventional extrusion, together with homogeneous grain refinement in the case of sticking friction condition. However, to the authors' best knowledge, no further research has focused on the numerical comparison between FSE and conventional extrusion for the AA6061 alloy.

In this context, the present paper presents an in-depth numerical analysis of FSE, DE and IE within the software QForm UK, in terms of extrusion temperature, strain and strain rate evolution during the extrusion of a round AA6061 workpiece. By varying rotational speed, feed rate and DE/IE billet preheating temperature, several conditions have been studied to provide a sufficiently large dataset.

Procedure

The original version of this paper is available on https://www.scientific.net/KEM.1048.25.pdf

The Computer Aided Design (CAD) model is displayed in Fig. 1. This model was already proposed by Negozio et al. [16]. The FEA was carried out using the software QForm UK, in order to simulate

FSE, DE and IE processes. The FSE simulations were conducted by varying the rotational speed of the container ( 200,400 and 600 rpm ) and the die feed rate ( 1,2,3 and 4 mm/s ), as summarized in Table 1. No pre-heating temperature was imposed, since in FSE the material heating is related to the frictional and plastic dissipation rather than from external thermal input [2]. The DE and IE simulations were performed using the same feed rates adopted for the FSE cases. The main distinction for DE lies in the definition of tool kinematics, as in direct extrusion the material flow is driven by the ram motion, which directly imposes the extrusion speed. Both the DE and IE presented no rotational speed and billet pre-heating temperatures of 450 and 500∘C (Table 2).

[picture]

Fig. 1. CAD model used for the simulations, with the main process parameters and dimensions displayed.

Fig. 1. CAD model used for the simulations, with the main process parameters and dimensions displayed.

Table 1. Process parameters used for FSE simulations.

Parameter | Value
Rotational speed [rpm] | 200, 400, 600
Feed rate [mm/s] | 1, 2, 3, 4
Feedstock temperature | 20°C (room temperature)

Table 2. Process parameters used for DE and IE simuations.

Parameter | Value
Extrusion mode | direct, indirect
Feed rate [mm/s] | 1, 2, 3, 4
Pre-heating temperature [°C] | 450, 500

The AA6061 material properties were calculated according to the Qform UK database. These properties were determined through linear interpolation of the AA6061 parameters at 20∘C and 500∘C, as reported in Table 3. The die and the container have been considered as rigid bodies made of AISI H13 steel, whose properties have also been taken from the software library.

Table 3. Properties of AA6061 at 20 and 500∘C, used for linear interpolation.

Property | Value at 20°C | Value at 500°C
Density [kg/m³] | 2705 | 2592
Thermal conductivity [W/(m·K)] | 185 | 237
Thermal expansion [1/°C] | 2.21 E-5 | 2.80 E-5
Specific heat [J/(kg·K)] | 898 | 1020
Young's modulus [MPa] | 72000 | 46000
Poisson's coefficient | 0.33 | 0.36

The flow stress ( σf ) was calculated using the Hensel-Spittel equation (Eq. 1).

σf=A·em1·T·ε−m2·ε˙−m3·em4/ε·(1+ε)m5·T·em7·ε·ε˙m8·T·Tm9(1)

where ε is the true strain, ε˙ the true strain rate, and T the absolute temperature. The coefficients A, m1−m9 are material constants, whose values have been taken from [16] (Table 4).

Table 4. Coefficients used in the Hensel-Spittel equation. [16]

Coefficient | Value
A [MPa] | 5179.35
m₁ [K⁻¹] | -0.0065
m₂ | 0.0054
m₃ | -0.2285
m₄ | -0.0002
m₅ [K⁻¹] | 0.0014
m₇ | -0.6683
m₈ [K⁻¹] | -0.0005
m₉ | 0

In the FSE model, friction between tools and workpiece was modeled according to Levanov's formulation (Eq. 2), in which the tangential stress (τ) is calculated as a combination of Coulomb's and Siebel's models:

τ=m·σf3·(1−e−n·σn/σf),(2)

where σf is the flow stress, σn is the normal stress, and m,n are the friction and Levanov's coefficients, respectively, for FSE (Table 5). In DE and IE, the friction parameters were taken from [14], where a sticking condition is assumed between the workpiece and the die, the ram and the container, and Levanov's model is applied for the profile interaction with the bearing section ( m=0.3,n=1.25 ). The thermal parameters were taken from [21], and are reported in Table 6.

Table 5. Coefficients for Levanov's friction model.

Coefficient | Value
m | 0.6
n | 1.25

Table 6. Thermal parameters of the AA6061 workpiece [21].

Coefficient | Value
Tool-workpiece heat exchange coefficient [W/(m²·K)] | 11,000
Air temperature [°C] | 20
Workpiece emissivity | 0.25
Air-workpiece heat exchange coefficient [W/(m²·K)] | 20

The model for FSE has been validated in [16] by comparing experimental and numerical extrusion loads, with errors below 5%. On the other hand, the conventional extrusion model has been validated in other published papers [14].

The FEM output variables considered in the study are temperature, strain and strain rate, measured at 50% of the die/ram stroke. Both temperature and strain were calculated using the default software algorithms, while a specifically designed subroutine was introduced for strain rate, whose purpose was to store the maximum strain rate experienced by each point during the simulation.

Due to the high thermal conductivity of aluminum alloys, only one temperature value was taken as representative for a specific cross-section. However, for strain and maximum strain rate, six measurements were made along the radius of the extruded wire, in order to create a profile of the given property (Fig. 2). For simplicity, strain and strain rate profiles were plotted against a normalized position, for which 0 equals to the core of the wire and 1 to its surface.

The obtained results have then been confronted with those from the other simulations, in order to evaluate differences and similarities between FSE, DE and IE processes.

[picture]

Fig. 2. Simulation results in terms of a) temperature, and b) strain, together with probed points, for FSE at 400 rpm and 4 mm/s.

Fig. 2. Simulation results in terms of a) temperature, and b) strain, together with probed points, for FSE at 400 rpm and 4 m m / s .

Results and Discussion

The original version of this paper is available on https://www.scientific.net/KEM.1048.25.pdf

In this section, the outcomes of the numerical analysis are presented, performing an in-depth comparison between FSE and conventional DE and IE.

Temperature. Temperature results for FSE, DE and IE are presented in Table 7 and Table 8. The first notable difference between the processes is that increasing the feed rate leads to opposite effects on the extrusion temperature (Fig. 3). In the FSE process, a higher feed rate reduces the peak temperature because the shorter interaction time limits heat generation. Conversely, in DE and IE, increasing the feed rate raises the extrusion temperature, as more energy is required for deformation and higher frictional heating is generated. DE shows slightly higher temperatures than IE, due to the well-known differences in process characteristics, but pre-heating temperature seems to be the main factor influencing extrusion temperature. An increase in rotational speed leads to higher temperatures as a result of enhanced frictional heating, enabling the FSE process to operate over a wider range of profile exit temperatures compared to conventional DE and IE processes.

[picture]

Fig. 3. Extrusion temperature results for 45.5 s−1 and IE.

Fig. 3. Extrusion temperature results for 45.5 s − 1 and IE.

Table 7. Simulation results in terms of extrusion temperature for FSE.

Feed rate [mm/s] | Rotational speed [rpm] | Extrusion temperature [°C]
1 | 200 | 365
2 | 200 | 345
3 | 200 | 345
4 | 200 | 338
1 | 400 | 457
2 | 400 | 441
3 | 400 | 431
4 | 400 | 423
1 | 600 | 518
2 | 600 | 499
3 | 600 | 489
4 | 600 | 482

Table 8. Simulation results in terms of extrusion temperature for DE and IE.

Feed rate [mm/s] | Pre-heating temperature [°C] | Extrusion temperature [°C]
|  | direct | indirect
1 | 450 | 443 | 442
2 | 450 | 455 | 454
3 | 450 | 461 | 459
4 | 450 | 465 | 463
1 | 500 | 491 | 488
2 | 500 | 501 | 500
3 | 500 | 506 | 506
4 | 500 | 511 | 509

Strain. Strain results for FSE, DE and IE for core and surface are presented in Table 9 and Table 10. Each investigated extrusion technology, although still influenced by die design, affects strain through distinct mechanisms governed by the process kinetics and the specific parameters (Fig. 4). Similarly to the temperature results, an increase in feed rate leads to a decrease in surface strain for FSE. However, this increase does not significantly affect the maximum strain results in DE and IE (Fig. 4). Indeed, a higher feed rate in FSE causes a decrease in the material deformation path that leads to lower value of surface strain. In the core area, strain values are similar, with FSE showing slightly higher values. Indeed, at the center, almost no rotation is experienced during both FSE and conventional extrusion, causing the similarities in strain values for normalized position close to 0 shown in Fig. 4.

[picture]

Fig. 4. Strain results for FSE, DE and IE, at a) 1 mm/s, and b) 52.0 s−1, plotted against a normalized position (" 0 " = core, " 4 mm/s surface).

Fig. 4. Strain results for FSE, DE and IE, at a) 1 m m / s , and b) 52.0 s − 1 , plotted against a normalized position (" 0 " = core, " 4 m m / s surface).

Table 9. Simulation results in terms of strain at surface and core for FSE.

Feed rate [mm/s] | Rotational speed [rpm] | Surface strain [1] | Core strain [1]
1 | 200 | 13.9 | 3.9
2 | 200 | 10.6 | 3.0
3 | 200 | 10.2 | 3.0
4 | 200 | 10.5 | 2.9
1 | 400 | 18.4 | 3.3
2 | 400 | 13.8 | 3.2
3 | 400 | 11.3 | 3.0
4 | 400 | 10.8 | 3.2
1 | 600 | 21.3 | 3.5
2 | 600 | 16.0 | 3.2
3 | 600 | 14.3 | 3.1
4 | 600 | 12.4 | 3.0

Table 10. Simulation results in terms of strain at surface and core for DE and IE

Feed rate [mm/s] | Pre-heating temperature [°C] | Surface strain [1] | Core strain [1]
|  | direct | indirect | direct | indirect
1 | 450 | 16.0 | 7.3 | 2.7 | 2.6
2 | 450 | 16.0 | 6.8 | 2.4 | 2.7
3 | 450 | 16.7 | 7.7 | 2.5 | 2.5
4 | 450 | 18.9 | 6.7 | 2.6 | 2.6
1 | 500 | 16.2 | 7.4 | 2.4 | 2.5
2 | 500 | 17.2 | 7.2 | 2.6 | 2.7
3 | 500 | 20.6 | 7.5 | 3.2 | 2.5
4 | 500 | 17.1 | 7.8 | 2.5 | 2.8

Strain rate. Strain rate results for FSE, DE and IE at core and surface are presented in Table 11 and Table 12. These processes show some similarities in strain rate trends (Fig. 5). In all cases, an increase in feed rate leads to an increase in surface strain rate values, due to a required shorter time for the deformation process. In FSE, when the feed rate increases from 1 to 500∘C, there is a moderate rise in maximum strain rate. However, these values remain high, reaching 6.3 s−1 at 1 mm/s and 600 rpm , and 26.5 s−1 at 4 mm/s and 600 rpm . For DE and IE, on the other hand, feed rate has a more pronounced effect on the surface strain rate. As visible in Fig. 5, with a pre-heating temperature of Al−Mg−Si, DE
and IE show an increase in strain rate from 7.8 and Al−Mg−Si−Cu at Al−Mg−Si to 35.0 and superscript number at mathematical formula, respectively. In addition, increasing rotational speed in FSE increases surface strain rate, due to the higher velocity of material flow. Again, core values are similar between the two processes, but in this case FSE tends to exhibit slightly lower strain rates.

[picture]

Fig. 5. Strain rate results for FSE, DE and IE, at a) superscript number, and b) mathematical formula, plotted against a normalized position (" 0 " mathematical formula core, " 1 " mathematical formula surface).

Fig. 5. Strain rate results for FSE, DE and IE, at a) , and b) , plotted against a normalized position (" 0 " core, " 1 " surface).

Table 11. Simulation results in terms of strain rate at surface and core for FSE.

Feed rate [mm/s] | Rotational speed [rpm] | Surface strain rate [s⁻¹] | Core strain rate [s⁻¹]
1 | 200 | 18.5 | 1.2
2 | 200 | 19.0 | 3.3
3 | 200 | 20.5 | 4.7
4 | 200 | 23.5 | 6.4
1 | 400 | 31.5 | 1.6
2 | 400 | 35.5 | 2.2
3 | 400 | 42.5 | 3.9
4 | 400 | 43.5 | 5.0
1 | 600 | 45.5 | 0.5
2 | 600 | 47.0 | 2.8
3 | 600 | 49.0 | 4.2
4 | 600 | 52.0 | 5.2

Table 12. Simulation results in terms of strain rate at surface and core for DE and IE.

Feed rate [mm/s] | Pre-heating temperature [°C] | Surface strain rate [s⁻¹] | Core strain rate [s⁻¹]
|  | direct | indirect | direct | indirect
1 | 450 | 7.7 | 6.8 | 1.7 | 2.2
2 | 450 | 12.0 | 13.7 | 3.0 | 4.3
3 | 450 | 20.8 | 20.7 | 4.8 | 6.4
4 | 450 | 29.5 | 27.4 | 6.2 | 8.5
1 | 500 | 7.8 | 6.3 | 1.6 | 2.1
2 | 500 | 13.9 | 12.6 | 3.1 | 4.3
3 | 500 | 22.2 | 21.8 | 4.8 | 6.2
4 | 500 | 35.0 | 26.5 | 6.2 | 8.9

Summary

The original version of this paper is available on https://www.scientific.net/KEM.1048.25.pdf

The present work involved an in-depth numerical analysis using the FEM software QForm UK, in order to assess differences and similarities between the FSE and the conventional DE and IE processes.
The main outcomes of this paper are:

Increasing the feed rate leads to a decrease in extrusion temperature for FSE, while it increases for DE and IE .

All processes show similar strain profiles, but upon an increase in feed rate, the surface strain for FSE decreases, while it increases for DE and IE. This causes FSE to present higher surface strain values at low feed rates, and lower surface strain than DE at higher feed rates.

All processes show similar strain rate trends, and an increase in feed rate leads to an increase in surface strain rate for all processes. FSE shows high values of maximum strain rate in all conditions, though lower than DE and IE at low rotational speeds and high feed rates.
Conclusively, FSE shows higher extrusion temperature, higher surface strain and tendentially higher surface strain rates than DE and IE at low feed rates and high rotational speeds. In this condition, FSE can produce a finer microstructure thanks to the elevated strain values. This does not directly translate in better mechanical properties, due to the variety of factors involved. However, alloys that do not tend to recrystallize like 6082 aluminum alloy [22] could benefit from FSE, allowing the production of high-quality round profiles. The trend is inverted at high feed rates and low rotational speeds. Core values of strain and strain rate are similar between all processes.

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The original version of this paper is available on https://www.scientific.net/KEM.1048.25.pdf

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The original version of this eBook is available on https://www.scientific.net/book/extrusion-and-drawing/978-3-0364-1995-4

Key Engineering Materials, ISSN: 1662-9795, Vol. 1048, pp 35-50

doi: 10.4028/p-gzNY4X

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-12-18

Revised:
2026-02-13

Accepted:
2026-02-27

Online:
2026-04-14

Compensating Property Fluctuations in Cold Extrusion Using Adaptive Dies

The original version of this paper is available on https://www.scientific.net/KEM.1048.35.pdf

Christian Siedbürger 1,a*, Peter Groche 1, b

1 Technical University of Darmstadt, Institute for Production Engineering and Forming Machines, Otto-Berndt-Strasse 2, 64287 Darmstadt, Germany

a  christian.siedbuerger@ptu.tu-darmstadt.de, b  peter.groche@ptu.tu-darmstadt.de

Keywords: Cold extrusion; adjustable dies; dimensional accuracy; adaptive tooling; AI surrogate

Abstract

This study investigates an adaptive die concept for cold extrusion that actively modulates radial preload during the main forming and ejection phases. A Gaussian process regression (GPR) surrogate, trained on fewer than 400 finite-element simulations, provides a highly data-efficient model capable of accurately predicting geometric tolerances, residual stresses, and process forces. Experimental spot measurements validate the physical trends captured by the surrogate, demonstrating reliable reproduction of the underlying mechanical interactions. The results show that increased preload during forming enables micrometer-level calibration of final diameters, while higher preload during ejection promotes beneficial compressive residual stresses at the cost of elevated ejector forces. A part-to-part control strategy effectively improves accuracy by independently steering two target properties through separate preload adjustments. Furthermore, a reinforcement learning-based controller, enhanced by flow stress estimates derived from hardness measurements, reduces variance and compensates for stochastic fluctuations in material and friction conditions. Overall, the adaptive die system, combined with surrogate- and RL-based control provides a robust foundation for achieving high dimensional precision and stable product properties under future variability scenarios, such as green steel and sustainable lubrication systems.

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1048.35.pdf

Cold extrusion is widely used for high-volume production of load-bearing components [1]. A representative industrial process is full forward extrusion, in which an axisymmetric billet is pushed through a die to achieve a defined diameter reduction [2,3]. Typical advantages include short cycle times, high reproducibility, strain hardening, and tight dimensional tolerances down to IT7 [2]. Applications range from automotive parts to standardized fasteners and high-strength machine components [4]. These benefits rely on highly stiff tooling systems. However, the same stiffness limits flexibility and the ability to react to changing boundary conditions [5]. In conventional die architectures, the forming insert is shrink-fitted into a prestressed reinforcement and the forming zone is largely inaccessible, which hampers adjustments and the integration of adaptive elements [2].

In current industrial practice, process variability is mainly addressed by restricting the allowable scatter of input variables along the process chain [5]. Yet foreseeable developments suggest that future cold extrusion will face increased input scatter [6,7]. Decarbonization-driven transitions toward scrap-based "green steel" routes can amplify batch-to-batch variability due to fluctuating residual alloying elements, notably copper from recycling streams, which may affect formability and flow stress [6,8-13]. Additional uncertainty arises from supply-chain changes and evolving auxiliaries. Switching suppliers can shift chemistry windows for example between European 16 MnCr 5 (EN 10084; 1.7131) and the U.S. equivalent SAE 5115 (ASTM A29/A29M) which alters hardenability and can influence room-temperature flow stress [14-16]. Moreover, more sustainable, phosphate-free lubrication systems may increase friction scatter compared with conventional multi-layer systems [17,18]. The combined effect can be elevated scrap and reduced cost efficiency, motivating a shift from robust but passive tooling toward adaptive tooling that actively compensates disturbances while maintaining high reproducibility [5,19,20].

In this study, adaptive tooling refers to forming dies or die subsystems that adjust boundary conditions or effective tool geometry during a stroke or from one stroke to the next (Fig. 1) [20]. Adjustments are executed by actuators under supervisory rule-based or feedback control, aiming to keep part properties within targets despite disturbances such as material scatter. Typical manipulated variables in forming include blank-holder force, drawbead penetration, die/cavity size and preload, and the effective compliance of punch or die [19,20].

[picture]

Fig. 1. Overview of the adaptive tooling system ("active die") used in this work: (a) Mechanism converting axial actuation into radial die contraction; (b) Active-die architecture; (c) Operation on a press with a spindle-driven punch and a draw cushion.

Fig. 1. Overview of the adaptive tooling system ("active die") used in this work: (a) Mechanism converting axial actuation into radial die contraction; (b) Active-die architecture; (c) Operation on a press with a spindle-driven punch and a draw cushion.

For cold forward extrusion, three complementary strategies are relevant. First, an adaptive punch can modify bottom dead center per stroke to improve length accuracy, potentially supported by inline measurement and data-driven set-point selection [21,22]. Second, counterpressure via the ejector pin can increase hydrostatic stress and homogenize material flow, reducing tensile surface stresses and mitigating damage (e.g., chevron cracking), albeit at the cost of higher press forces and increased die contact pressure [23-25]. Third, an adaptive die can adjust its inner diameter by varying radial preload, enabling micrometer-scale die shrinkage [19,25]. This can be realized, for example in Fig 1, by axial actuation of a conical reinforcement that converts axial input into a radial adjustment of the die [26,27]. An implementation at PtU (TU Darmstadt) in cooperation with Strecon A/S uses a slotted conical sleeve that generates variable external pressure on the die via a spindle-driven draw cushion [28]. Prior work has shown that such adaptive dies can influence dimensional accuracy, forminginduced residual stresses, and ejector forces [25,28]. Although adaptive punches and counterpressure concepts can improve specific aspects of the process, the active die provides the most direct and scalable lever for controlling the critical forming-zone boundary conditions in cold extrusion. By adjusting die preload separately during forming and ejection, the concept offers two practical control inputs that can be integrated into conventional die sets.

Against this background, the research question is how to use an active die to compensate variability arising from green-steel routes and more sustainable lubrication systems, particularly when fluctuations are uncertain and their propagation to part properties is complex. To address this, a machine-learning surrogate is trained on a large ensemble of finite-element simulations and validated against representative experiments. To emulate real production scatter, an uncertainty generator models the dominant disturbance sources as probabilistic variations of the input features (material, friction and elastic properties), which are then propagated through the surrogate. The surrogate enables efficient Monte Carlo studies of adaptive steering policies under realistic distributions of material and tribological scatter, providing (i) a quantitative assessment of candidate strategies across
fluctuation bands and (ii) a transparent mapping of interactions between steering parameters and key product properties. Ultimately, this supports production-scale deployment of active die concepts for stable dimensional accuracy and residual-stress targets under realistic variability.

Methodological Approach

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A simulation-based workflow (Fig. 2) is employed to quantify how input uncertainties propagate to product properties and to construct a prediction model of the active-die system. The trained model serves as a computational surrogate for extensive physical trials, enabling cost- and data-efficient exploration of the tool behavior under production-relevant conditions. The overall methodology comprises seven steps:
(i) Setup of an FE model of the forming operation with the active die,
(ii) parameterization over wide but physically plausible ranges using Monte Carlo sampling to ensure domain coverage and to reduce extrapolation risk for the surrogate,
(iii) training and generalization of the surrogate model,
(iv) plausibility checks of the surrogate by representative spot experiments, verifying that the target variables can be systematically manipulated by active-die preload settings,
(v) generation of uncertainty scenarios based on expected scatter of input variables and fast propagation through the surrogate,
(vi) evaluation of different control strategies using the adjustable die preload during forming and ejection as control variables, and
(vii) assessment of the resulting distributions of selected product properties.

[picture]

Fig.2 Overview of the workflow used to create the surrogate model for the active die system: 1.) Parametric FE simulation model,2.) Input features defining material, friction, and tool properties, 3.) Forming process with adaptive preload adjustment during forming and ejection, 4.) Training of surrogate models, 5.) Prediction model

Fig.2. Overview of the workflow used to create the surrogate model for the active die system: 1.) Parametric FE simulation model,2.) Input features defining material, friction, and tool properties, 3.) Forming process with adaptive preload adjustment during forming and ejection, 4.) Training of surrogate models, 5.) Prediction model

FE model setup: The case study is a forward extrusion reduction in which an axisymmetric billet is reduced from an initial diameter of 13.000 mm to 12.000 mm . The two-dimensional (axisymmetric) FE model was implemented in SimufactForming using an implicit solver and follows a previously validated setup with consistent discretization, contact formulation, and solver settings [25]. The punch motion is prescribed by a tabulated time-stroke profile. Since the specimens are clamped at the punch, the punch is also used to represent the ejection phase within the same kinematic sequence (Fig. 2.3). The billet is modeled as plastically deformable and discretized with quadrilateral elements. To represent the preload-induced change of the die inner diameter, the die insert and the segmented sleeve are modeled as elastic bodies, whereas the remaining components are treated as rigid (Fig. 2.1). Radial preload is controlled by axially advancing the conical reinforcement. In the model this is implemented as a tabulated spring force over process time in the range of 0−400kN, applied during the forming Fax,1 and ejection phases Fax,2 (Fig. 2.1). A mesh-sensitivity study was performed to ensure numerical resolution of micrometer-scale diameter changes. For element sizes of 0.3 mm and below, no relevant changes were observed in either the simulated ejection force or the mean diameter evaluated in the reduced zone. Further mesh refinement led to fluctuations below 0.1 kN in ejection force and below 0,5μ m in the averaged diameter metric. To stabilize the contact conditions, the same mesh resolution was applied to the elastically deformable die insert and the segmented sleeve, ensuring consistent contact discretization across the interfaces. To keep the evaluation of the reduced zone reproducible across the wide parameter variation, remeshing was disabled, since remeshing events can locally perturb the diameter averaging and thereby introduce additional numerical scatter that would deteriorate surrogate generalization.

Experimental setup. To verify the accuracy and physical plausibility of the simulation-based prediction, spot experiments were conducted on the physical tool system by systematically varying the axial set forces Fax,1 (main forming preload) and Fax,2 (ejection preload), cf. Fig. 1. Cylindrical billets of 16 MnCr 5 in the soft-annealed condition ( 8 h holding time at 700∘C ) were used. To improve surface conditioning and reproducibility, all contact surfaces were sandblasted prior to applying a polymer-based lubricant. The billets were subsequently processed in the active-die forward extrusion setup (Fig. 2.3) and reduced from the initial to the target diameter at a stroke rate of 1 stroke/s. Force variations in Fax,1 and Fax,2 were imposed via the draw cushion during forming and ejection (Fig. 1). In total, seven experiments were conducted with different combinations of Fax,1 and Fax,2 to compare the measured responses against the simulation-based predictions across the control-input space.After cleaning and cooling, the final diameters were measured using a Keyence TMX-40 camera system. For each part, 50 individual measurements were taken along the reduced zone and averaged to obtain the reported diameter. The system resolution is 1,5μ m, and the specified reproducibility of the TMX40 is below 0.1μ m. Press and ejection forces were recorded using an in-die HBM force sensor with an accuracy of 0.2 kN . Since ejection is realized as pull-out in this tool, the same sensor signal was used to evaluate both the press-force and ejection-force responses.

Material modeling. For the flow-stress description of 16 MnCr 5 used in Fig. 3, a simplified isotropic hardening law kf=ap+cp·φnp was adopted, since the investigated process was conducted at 1 stroke/min. Consequently, temperature effects and strain-rate sensitivity were neglected. The flow-curve parameters ap,cp,np were identified from upsetting tests via an iterative calibration approach: Experimental compression data were reproduced numerically, and the coefficients of the flow-stress law ap,cp,np were adjusted until the simulated force-stroke curve matched the experimental reference. The resulting mean coefficients ap,cp,np of the calibrated flow curve for 16 MnCr 5 are summarized in Fig. 3 (top left).

Data generation. The parameterization of the FE setup exposes eleven inputs Xi that are freely configurable in the simulation (Fig. 2.2). Five inputs describe the workpiece material: three parameters ap,cp,np of an isotropic flow law together with the elastic modulus Ep and Poisson's ratio ϑp. Two inputs define friction according to Coulomb's law, namely μD at the die-workpiece interface and μH at the interface between the conical reinforcement and the segmented sleeve. The remaining four inputs are the elastic modulus and Poisson's ratio of the reinforcement and of the
sleeve. An in-house Python interface to SimufactForming automates model generation, execution, and post-processing for the relevant targets. To transform the simulation into a data-driven prediction model, the input features listed in Fig. 2.2 are sampled to create a dataset for regression of process and product responses. While the baseline values are derived from the experimentel setup, the bounds were defined to represent realistic industrial variability in material, friction, and elastic properties (e.g., batch-to-batch scatter of soft-annealed 16 MnCr 5 , lubrication/surface-condition changes, and additional uncertainty expected for "green-steel" supply chains). For the flow-law parameters ap,cp,np, the ranges span plausible variations in flow-stress level and hardening behavior and include a conservative safety margin to avoid surrogate extrapolation when multiple inputs simultaneously approach extreme values. Therefore, the parameter ranges in Fig. 2.2 were intentionally chosen comparatively wide to ensure sufficient feature-space coverage and robust surrogate predictions within the intended domain.

Sampling strategy. Sampling follows a Monte Carlo strategy within the specified bounds to generate diverse input combinations and thereby reduce the need for extrapolation by the surrogate. This choice aligns with the subsequent analyses, which evaluate the surrogate under randomly varying material and tribological conditions, and it allows straightforward incremental extension as additional FE simulations become available. Broad bounds extend the usable domain and further reduce extrapolation risk, whereas tighter bounds can improve local accuracy in well-known operating regions. While space-filling designs such as Latin hypercube sampling or Sobol sequences can yield more uniform coverage for a fixed simulation budget, Monte Carlo was selected here as a pragmatic and scalable approach in the high-dimensional setting. Moreover, since the later robustness analyses assume approximately Gaussian process variability, Monte Carlo sampling provides a direct and consistent representation of these distributions.

Surrogate model. Multi-output regression models are trained to map the FE inputs Xi and the two control settings Fax,1 (main forming) and Fax,2 (ejection) to the responses Yi (Fig. 2.4). The candidate learners include a multilayer perceptron (MLP), Gaussian process regression (GPR), and a random forest (RF) regressor. The outputs Yi comprise the final diameter in the forming zone, press force, ejector force, and forming-induced residual stresses (Fig. 2.5). Accordingly, the surrogate mapping is formulated as a multi-output function

Yi=f(Fax,1,Fax,1,Xi)(1)

These quantities are chosen because they are directly influenced by the active die system and have been demonstrated to be adjustable in prior studies [19,25]. The dataset of FE simulations, generated under the assumptions in Fig. 2, was split into training and test subsets using a conventional 80/20 partition. The choice of N=400 was guided by an initial learning-curve study using Monte Carlo sampling, in which the surrogate was trained with increasing dataset sizes until the predictive performance on a held-out set plateaued. Beyond N≈400, additional simulations yielded only marginal improvements. Model quality was assessed with standard regression metrics (MSE, RMSE, MAE, R2 ). The response surfaces exhibit predominantly smooth, low-noise behavior across all targets, favoring kernel methods and well-regularized neural networks for surrogate modeling.

Uncertainty propagation. Replacing the high-fidelity FE model with a validated multi-output regression surrogate enables deterministic prediction of the responses in a fraction of the time required by the FE setup (on the order of milliseconds to seconds per query versus approximately one hour per configuration for the verified FE model). This acceleration permits efficient exploration of large combinations of input features and preload set points, provided that the AI-based predicition model (Fig. 2.5) reproduces the FE ground truth with sufficient accuracy. In real production, the forming system is subject to numerous disturbance sources that translate into uncertain inputs and, consequently, uncertain outputs. Typical contributors include temperature variations, surface roughness and lubrication variability in the contact zone, part-to-part inhomogeneities, geometric and positional tolerances in the tool system, and actuation variability during the press stroke, which can be quantified by ΔX. [19,29]

Let a generic product property be denoted by the random variable Yi. For a large number of trials, Yi is modeled as approximately Gaussian, consistent with common engineering processes, so that

Yi=Y¯i+ΔYi(1)

In this notation, the nominal level is Y¯i and the uncertainty is summarized by ΔYi. A process is termed robust when ΔYi is small enough that Yi remains within the specified tolerance window with the desired probability. Equivalently, given an allowable spread ΔYLimit  (derived from the tolerance and the chosen capability index), the process is robust if ΔYLimit ≥Yi. Within this work, the adaptive tooling system is treated as a flexible process because the two control inputs (the radial preload during the main forming phase and during ejection) can be used to influence both Y¯i and ΔYi of the relevant properties [20,29]. In particular:

by appropriate choice of (Fax1,Fax2) the mean Y¯i of each target property (final diameter in the forming zone, residual-stress descriptors, ejector force) can be shifted toward its set point while simultaneously reducing the spread ΔYi; and

as a consequence, the interval [Y¯i−ΔYi,Y¯i+ΔYi] can be tightened and centered within the tolerance limits, enabling higher conformance rates and, where applicable, narrower achievable quality bands.

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Fig.3. Input parameter uncertainties for the conventional-steel and green-steel scenarios used in the prediction model, together with the three adaptive control strategies (A-C).

Fig.3. Input parameter uncertainties for the conventional-steel and green-steel scenarios used in the prediction model, together with the three adaptive control strategies (A-C).

Uncertainty scenarios ΔXi and control-policy evaluation. Figure 3 summarizes the assumed input uncertainties ΔXi and how they enter the prediction model. Two scenarios are considered. The first scenario represents a reference steel 16 MnCr 5 . The assumed variations for the material coefficients, the elastic modulus Ep and Poisson's ratio ϑp reflect typical datasheet scatter for the annealed condition of this grade. The two friction coefficients are treated as Coulomb coefficients. The coefficient μD at the die-workpiece contact is derived from sliding compression tests. The
coefficient μH at the reinforcement-sleeve contact is identified by matching the characteristic forcestroke curve from experiments in Figure 1 to FE simulations with different μH, which yields μH≈ 0.03 for the best fit. The uncertainty Δμi, is set conservatively to 0.02 based on repeat tests with the polymer lubricant used in the experimental setup. For the carbide die (grade G40) and the segmented sleeve (tool steel 1.2343), E,ϑ and their ΔXi, are estimated using industrial datasheets.

The second scenario ("green steel") inherits all uncertainties from the reference case but enlarges the workpiece-material uncertainties to reflect copper variability in scrap-based metallurgy. The assumption is that the copper content may vary between 0 and 1 percent. Reported trends indicate that copper contamination near 1 percent can increase yield strength and ultimate tensile strength by roughly 200 MPa compared with copper-free conditions [13]. To represent this elevated scatter without committing to a specific metallurgy model, the ΔXi for the workpiece material ap,cp,npEp,vp are doubled in this scenario.

Target setting. For a given scenario, Monte-Carlo sampling draws Xi from the specified bounds Xi+ΔXi. The surrogate model then evaluates the mapping (Xi,Fax,1,Fax,2)→Yi. With N=1000 samples, the empirical mean Y¯i and standard deviation ΔYi, are obtained for each response. The multiobjective target is defined by a diameter set-point of Dtarget =12.000 mm and by ejection conditions that promote compressive near-surface residual stresses. Previous investigations have shown a clear correlation between the ejector force and the forming-induced residual-stress state, which can be explained physically by the additional deformation imposed during pull-out: increasing the ejection preload Fax,2 raises the radial confinement of the die during ejection, thereby intensifying near-surface plastic straining and reducing tensile residual stresses, potentially shifting them into compression. This mechanism and the associated ejector-force-residual-stress relationship have been quantified in prior work [X]. Furthermore, the FE simulations indicate that the dependence of the relevant residual-stress descriptor on Fej can be approximated well by a quadratic function within the studied operating range. To deliberately drive the residual-stress state toward compression beneficial for fatigue performance and reduced distortion an ejector-force target of Fej,target =−5,5kN is selected.

Control strategies. Three control strategies are applied to the surrogate to assess compensation capability under both scenarios:

Open-loop set-point steering. A constant pair (Fax,1,Fax,2) is selected from the surrogate without feedback from previous strokes.

Part-to-part adaptive control. After each stroke, the diameter and ejector force are compared with their targets and the set points are updated by discrete increments ΔF within the bounds, which implements a run-to-run correction using only the last part's measurements.

Inline closed-loop control with hardness-based feedforward and Q-learning. Before forming, an in-process hardness measurement provides an estimate of the flow stress kf(0.3)=ap+cp. 0.3np. This estimate supplements the state used for decision-making. In MATLAB, a neural actionvalue function is trained using episodic rollouts of the prediction model. The state includes the hardness-based flow-stress estimate, the last set points, and the previous part's diameter and ejectorforce errors. The actions are the discrete changes, Fax,1 and Fax,2, within the allowable range. An ε greedy policy with replay and target-network stabilization is used. The reward penalizes the squared deviations from Dtarget  and Fej,target  adds soft penalties for exceeding the press and ejector-force limits. Training proceeds until the policy stabilizes on validation seeds. Then, the learned policy is deployed stroke-wise.

The resulting output distributions are compared across strategies to demonstrate how each policy maintains its targets under the increased variability of the green-steel scenario. This allows us to quantify improvements in the mean level and the spread of the key properties.

Results

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Surrogate accuracy and model selection.

The original version of this paper is available on https://www.scientific.net/KEM.1048.35.pdf

Based on the N=400 Monte-Carlo-generated FE dataset (Fig 2), three candidate surrogate learners (GPR, MLP, and RF) were benchmarked to identify the most suitable prediction model for subsequent uncertainty propagation. Gaussian process regression (GPR) delivers the best or near-best generalization across three of the four targets and, critically, provides predictive uncertainty via its posterior variance directly usable for Monte-Carlo propagation, robust set-point selection, and capability assertions. GPR also trains stably on moderate data volumes with minimal tuning and yields interpretable length-scales that reflect process sensitivities. For diameter, it attains RMSE =0.0014 mm,R2=0.978 on the test set. The multilayer perceptron (MLP) is a strong alternative that proves competitive overall. In particular, it achieves the best ejector-force prediction with RMSE =1.25kN,R2=0.782, whereas GPR yields 1.49kN,R2=0.694 for that target.

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Fig.4. Parity plots of the refined GPR surrogate for (a) diameter, (b) press force, (c) ejector force, and (d) residual stresses; color encodes absolute error.

Fig.4. Parity plots of the refined GPR surrogate for (a) diameter, (b) press force, (c) ejector force, and (d) residual stresses; color encodes absolute error.

In contrast, the configured random forest (RF) surrogate underperforms. Bagged trees with large minimum leaf sizes produce piecewise-constant approximations that do not align well with the smooth, continuous sensitivities of the active-die system, leading to high bias. Simply increasing the number of trees does not remedy this structural limitation and reducing leaf size at the available data scale risks excessive variance. Given its consistently strong fidelity, native uncertainty quantification, and data efficiency, GPR is selected as the primary surrogate for the prediction model used in the uncertainty analysis, robust set-point design, and control-policy studies.

After systematic hyperparameter sweeps (length-scale, signal variance, noise level, mean function), a squared-exponential GPR with constant mean and automatic noise scaling was selected. The parity plots in Fig. 4 show that the surrogate reproduces the global trends for all targets.

A few outliers remain at the sparsely sampled edges of the domain. This is consistent with the deliberately broad training ranges and with locally higher posterior variance. These deviations are physically plausible, since the responses become sensitive to contact transitions and friction at extreme settings. Despite additional tuning, the prediction error for ejector force could not be reduced below about 1 kN . This level is comparable to the error observed for punch force and is mainly driven by sensitivity to the reinforcement-sleeve friction and by stick-slip or lift-off phenomena, which introduce heteroscedastic label noise in the FE data. Residual stresses show a similar saturation because of steep near-surface gradients and path dependence during ejection. Further improvement would therefore require targeted enrichment of the dataset in high-variance regions or additional features that encode contact state.

For the purposes of this work, the surrogate is sufficient. Since the goal is not to reproduce an industry-grade forming process in all details, but to capture the sensitivity of the relevant responses Yi to the two control variables Fax,1 and Fax,2 on the basis of an FE model validated for plausibility. Diameter and press-force predictions provide the fidelity required for robust set-point design, while ejector-force and residual-stress predictions capture the relevant trends and magnitudes and are accompanied by GPR uncertainty that is propagated in the Monte-Carlo studies. Further improvements (e.g., larger datasets, refined friction modeling, or explicit geometric uncertainties) could increase external validity but are beyond the scope here. Therefore, the refined GPR is adopted as the primary surrogate for the subsequent uncertainty and control analyses.

Plausibility check of the prediction model. For comparison between experimentel spot experiments and the results in surgerate model, the GPR surrogate was evaluated on a ( Fax,1,Fax,2 ) grid to form 3D response maps, and experimental points were overlaid with color indicating absolute error (Fig. 5). For these maps, all remaining input features Xi were fixed to the mean values reported in Fig. 3 for 16 MnCr 5 and the corresponding experimental setup. Only Fax,1 and Fax,2 were varied to span the response surface shown in Fig. 5. The agreement is good for diameter and ejection force (diameter: RMSE =1.8μ m,R2=0.85, ejection force: RMSE =0.369kN,R2=0.762 ). Larger deviations occur for punch force. These outliers are attributed to tribological variability of the singlelayer lubricant and minor stock inhomogeneity. In addition, the main-stroke preload Fax,1 alters the die by only about 15μ m, so the overall forming degree and thus the press force is dominated by friction and material scatter rather than by the control variables Fax,1 and Fax,2.

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Fig.5. Experimental verification of the surrogate model: parity surfaces from the GPR surrogate with overlaid experiments for (left) diameter, (center) press force, (right) ejection force; color encodes absolute error for each metric.

Fig.5. Experimental verification of the surrogate model: parity surfaces from the GPR surrogate with overlaid experiments for (left) diameter, (center) press force, (right) ejection force; color encodes absolute error for each metric.

The maps also clarify the process trends. Increasing either Fax,1 or Fax,2 reduces the final diameter in the forming zone. Fax,1 tightens the die before forming, Fax,2 raises clamping pressure during ejection and enhances calibration. Ejection force is governed primarily by Fax,2 and by the difference ΔFax. Low Fax,1 with high Fax,2 yields the highest ejection forces, while high Fax,1 with low Fax,2 produces the lowest. Higher ejection forces increase local near-surface plasticity in the calibration zone, shifting residual stresses toward compression (beneficial for fatigue and distortion), at the expense of higher contact pressures and tool load. This is an explicit trade-off to be managed in the control strategies. [25,28]

The comparison in Fig. 5 reflects not only the surrogate error (cf. Fig. 4), but also accuracy limits of the underlying FE model and the experiments. In this workflow, the FE simulations serve as ground truth within the investigated domain, mainly to learn sensitivities with respect to Fax,1,Fax,2 and Xi. Absolute agreement at the micrometer level is difficult because the predicted diameter changes are comparable to (i) mesh and contact discretization effects, (ii) solver tolerances, and (iii) experimental scatter caused by surface roughness, local adhesion, and lubrication variability. Therefore, the FEsurrogate chain is assessed primarily by trends and response gradients rather than absolute diameters. This is also why an offset was applied when comparing the idealized FE geometry ( 12.000 mm ) to the measured tool dimension (~12.018 mm±2μ m). Reducing the remaining discrepancy would require further model refinements (e.g., improved friction characterization and modeling, material calibration, and contact or geometric-uncertainty modeling), which is beyond the scope of this contribution. For the intended uncertainty propagation and adaptive control design, the fidelity is sufficient because the model reproduces the direction and magnitude of the controllable effects of Fax,1 and Fax,2 on the relevant responses Yi.

Application of Control Strategy A. Building on the trends in Fig. 5, strategy A fixes constant set points for the two actuators and evaluates N=1000 parts per scenario via Monte-Carlo sampling of the input uncertainties. Fig. 6 reports probability distributions for absolute diameter, IT class, ejection force, and axial residual stress.

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Fig.6. Effect of open-loop set-point steering (Strategy A) on product properties under conventional steel. Distributions are shown for (a) absolute diameter, (b) IT class with IT6 indicated, (c) ejection force, and (d) axial residual stress; dashed lines denote means. Each case uses N = 1000 Monte-Carlo samples.

Fig.6. Effect of open-loop set-point steering (Strategy A) on product properties under conventional steel. Distributions are shown for (a) absolute diameter, (b) IT class with IT6 indicated, (c) ejection force, and (d) axial residual stress; dashed lines denote means. Each case uses N = 1000 Monte-Carlo samples.

With conventional steel ( 16 MnCr 5 ) at blue, the process centers at 12.0059 mm±0.9μ m for diameter, −1.78kN±0.15kN for ejection force, and 415.5MPa±26.5 tensile axial residual stress. The share above IT6 corresponds to 0.2% scrap. Substituting green steel while keeping the same set points (gray) leaves the means essentially unchanged (e.g., diameter 12.0057 mm ), but inflates variance as expected from the larger input scatter. The diameter spread increases to ±1.8μ m, the ejection-force spread to ±0.24kN, and the residual-stress spread nearly doubles to ±50.7MPa. The broadened IT distribution pushes scrap to 10.6%, confirming that the dominant effect of green-steel variability is a growth in standard deviation rather than a systematic bias of the mean.

A simple set-point change can re-center the outcomes. Raising only the ejection-phase preload to Fax,2=212kN while keeping Fax,1=51kN shifts the distributions in the desired directions. The diameter moves closer to target, the magnitude of the ejection force increases, and the axial residual stress is driven into compression. Mechanistically, the higher Fax,2 elevates clamping pressure in the calibration zone during ejection, inducing additional near-surface plasticity, which both reduces the final diameter and shifts residual stresses toward compression. Crucially, the IT distribution tightens around the target and the scrap fraction drops to 0.1% despite the green-steel variability.

Overall, Strategy A is effective for mean correction under known variability, but it has limited leverage on variance. Comparing the red and gray cases shows similar standard deviations for diameter and residual stress, with only modest changes for ejection force. This motivates the use of
part-to-part (B) and inline strategies (C) in the next section, where variance reduction and mean recentering are required.

[picture]

Fig.7. Comparison of the control trajectories starting from Fax1 =Fax2=50kN for control strategies B and C. (a) Evolution of the infeed forces Fax1 and Fax2 used as control variables in Strategy B. (b) Evolution of the infeed forces Fax1 and Fax2 used as control variables in Strategy C. (c) Normalized target deviations for Strategy B. (d) Normalized target deviations for Strategy C.

Fig.7. Comparison of the control trajectories starting from Fax1 = F a x 2 = 50 k N for control strategies B and C. (a) Evolution of the infeed forces Fax1 and Fax2 used as control variables in Strategy B. (b) Evolution of the infeed forces Fax1 and Fax2 used as control variables in Strategy C. (c) Normalized target deviations for Strategy B. (d) Normalized target deviations for Strategy C.

Optimization with Strategies B and C. Building on the trends derived in Fig. 5, Strategies B and C are evaluated under green-steel variability to enhance the accuracy and robustness previously achieved with Strategy A. For interpretability, Fig. 7 presents stacked trajectories in which the actuator set points Fax,1 (forming) and Fax,2 (ejection) are displayed together with the normalized outputs for diameter, ejection force, and axial residual stress in subfigures (c) and (d). Normalization maps each response to its target. Residual stress is referenced to 400 MPa , so values near zero indicate high accuracy. Strategy B implements a bounded part-to-part adjustment law with discrete actions ΔF∈{−5,0,+5}kN. After each stroke, Fax,1 is updated based solely on the previous part's diameter error until the normalized deviation enters a ±5% band, after which the actuator is held constant. Fax,2 is corrected analogously from the deviation in ejection force by increasing the difference Fax,1 Fax,2 whenever the magnitude of the measured ejector force is too small, again with a ±5% deadband. Starting from Fax,1=Fax,2=50kN, this run-to-run policy converges within approximately 75 strokes to operating points fluctuating around Fax,1=50kN and Fax,2=350kN. Strategy C augments this scheme with inline information and a learned decision layer: before each stroke a hardness-based flow-stress estimate kf(0,3) is fed forward into a Q-learning controller.

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Fig.8. Comparison of the results for control strategies A to C for the green-steel scenario. Probability distributions are shown for (a) the absolute diameter, (b) the resulting IT classes, where IT4 and above are classified as scrap, (c) the ejector forces, and (d) the axial residual stresses. Dashed lines denote the respective mean values.

Fig.8. Comparison of the results for control strategies A to C for the green-steel scenario. Probability distributions are shown for (a) the absolute diameter, (b) the resulting IT classes, where IT4 and above are classified as scrap, (c) the ejector forces, and (d) the axial residual stresses. Dashed lines denote the respective mean values.

The state vector stacks the last part's diameter and ejection-force errors, the current set points, and the flow-stress proxy. To better compensate material scatter, the action set is expanded to ΔF∈{−15, −5,0,+5,+15}kN providing stronger corrective authority. A neural action-value function with two hidden layers ( 32 units each) is trained for 500 epochs in the prediction environment using an ε greedy policy, a replay buffer, and a target network. Accuracy stabilizes at roughly epoch 300 . When deployed online from the same initial set points, the controller converges toward Fax,1=50kN and Fax,2=350kN within only 25 strokes. As visible in Fig. 7 (a-b), the control signals exhibit higher dynamic activity than in Strategy B because the policy actively counteracts incoming material fluctuations rather than reacting one part later.

The resulting performance is summarized in Fig. 8(a-d). Relative to Strategy A (constant set points), Strategy B efficiently re-centers the means of diameter, IT class, and ejection force, though with only modest variance reduction. Strategy C achieves significantly smaller standard deviations, approximately a factor of two for ejection forces and residual stresses by exploiting the hardnessderived flow-stress estimate to anticipate variability. For geometric tolerances, however, the improvement is marginal compared with Strategy B. This indicates that, despite the additional information from kf(0,3), Strategy C provides little further benefit for the diameter distribution. The underlying reason is that other, unmeasured inputs such as tool elastic properties or friction contribute substantially to the remaining variance in diameter, limiting the achievable reduction (Fig. 3).

Even though the decrease in standard deviation is small for geometric tolerances, Strategy C still reduces scrap from 3.5% to 3.2% under the criterion that only parts below IT4 are accepted. This demonstrates that even slight reductions in variability can meaningfully improve the robustness of a cold extrusion process. It also highlights Strategy C's potential of for high-value or tight-tolerance applications. At the same time, Strategy B remains a sensor-lean and computationally efficient option that already achieves high accuracy and competitive precision when aggressive variance reduction is not mandatory.

Conclusion

The original version of this paper is available on https://www.scientific.net/KEM.1048.35.pdf

This study demonstrates that adaptive die systems effectively compensate for uncertainties arising from fluctuating material and lubrication conditions, such as those expected in green steel production or sustainable tribological systems, while maintaining tight and reproducible product tolerances. Overall, the results reveal a broad potential for next-generation cold extrusion strategies in which process forces, dimensional accuracy, and residual stresses can be actively shaped to meet both sustainability and quality demands. Key insights include:

A Gaussian process regression (GPR) model trained with fewer than 400 random finiteelement simulations is sufficient to build a reliable surrogate model, which is capable of predicting all relevant product and process states with high physical fidelity.

Comparison with experimental spot measurements verifies the model's predictive trends and provides evidence that the surrogate captures the dominant mechanical interactions of the adaptive die system.

Increasing the die preload during the main forming phase enables micrometer-scale calibration of final diameters, thereby reducing the sensitivity of geometric tolerances to variations in flow stress.

Raising the preload during the ejection phase promotes near-surface plasticity and shifts residual stresses into compression while simultaneously increasing the required ejector forces.

A part-to-part control strategy (Strategy B) significantly improves accuracy in achieving target properties by adjusting the preload between strokes, allowing two independent target quantities to be steered through separate modulation of forming- and ejection-phase preload.

To reduce the standard deviation under fluctuating material conditions, a reinforcement-learning-based controller (Strategy C) can incorporate additional in-process features, such as hardness-derived flow-stress estimates, thereby jointly minimizing offset and variability in the key product properties.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/KEM.1048.35.pdf

This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) in the framework of the priority program SPP 2013 („Residual stresses“) by a grant OE 558/16-2, GR 1818/63-2 and GR 1818/86-1 (531874276).

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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/extrusion-and-drawing/978-3-0364-1995-4

Key Engineering Materials, ISSN: 1662-9795, Vol. 1048, pp 51-61

doi: 10.4028/p-vg6IX7

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-12-19

Revised:
2026-02-16

Accepted:
2026-02-24

Online:
2026-04-14

Lightweight Battery Housings from Thin-Walled, Large-Scale Aluminum Profiles by Hot Extrusion

The original version of this paper is available on https://www.scientific.net/KEM.1048.51.pdf

Oliver Schulz 1,a*, Sebastian Wurth 2, b, Alessandro Selvaggio 3,c, Johannes Gebhard 1, d, Yannis P. Korkolis 1,e, Patrik Bieker 2,f, Thomas Kloppenborg 2, g

1 Institute of Forming Technology and Lightweight Components (IUL), TU Dortmund University, Baroper Straße 303, 44227 Dortmund, Germany

2 OTTO FUCHS KG, Derschlager Straße 26, 58540 Meinerzhagen, Germany

3 ISPT GmbH & Co. KG, Carlo-Schmid-Allee 3, 44263 Dortmund, Germany

a*Oliver.Schulz@iul.tu−dortmund.de, b sebastian.wurth@otto-fuchs.com, c  selvaggio@ispt-solutions.com, djohannes.gebhard@iul.tu-dortmund.de, e  yannis.korkolis@iul.tu-dortmund.de, fpatrik.bieker@otto-fuchs.com, f  thomas.kloppenborg@otto-fuchs.com

Keywords: EV, FEM, die design, process control, size scaling

Abstract

The transition towards sustainable mobility demands lightweight and modular carrier systems for high-voltage batteries and fuel cells that combine structural efficiency with effective thermal management. This study examines the feasibility of producing thin-walled, large-scale aluminum extrusion profiles for modular battery housings using AA6063. Numerical simulations and experimental trials are conducted to optimize die design and define process limits along the relation between circumscribing circle diameter (CCD) and minimal wall thickness tmin . Furthermore, different quenching methods are used to investigate the influence on surface distortion and final mechanical properties. A streamlined die design with reduced mandrel deflection has enabled defectfree extrusion under controlled conditions for the extrusion of a thin-walled, large-scale profile with a CCD-to- tmin  ratio of 138. A narrow process window is identified for extrusion of defect-free profiles. Quenching studies have shown that active cooling methods affect surface deformation but have negligible influence on mechanical properties or microstructure due to efficient heat extraction inherent to thin-wall geometries for the investigated alloy. Scaling experiments using an enlarged cross section by a factor of 2.5 have confirmed similar process stability without wall thickness adjustments, achieving up to 38% weight reduction compared with conventional designs under industrial conditions.

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1048.51.pdf

Modular carrier systems for high-voltage traction batteries and fuel cells must combine structural efficiency with minimal mass while ensuring thermal management capability within vehicle architectures. Current lightweight battery housing concepts predominantly utilize fluid-cooled aluminum extrusion profiles, assembled into compact enclosures due to their favorable strength-toweight ratio and heat transfer properties [1]. To further improve stiffness-to-weight ratio and volumetric energy density of the overall system, recent developments aim at joining large-scale hollow profiles into extended panel-type load-bearing structures [2].
In this context, modularity has become a key design principle for next-generation battery housings. The ability to adapt joining strategies, such as friction-stir-welding or mechanical joining [3], enables flexible configuration of profile assemblies according to specific environmental demands. For instance, housings designed for passenger vehicles prioritize lightweight integration and contribution to body stiffness, whereas truck applications require enhanced robustness and scalability in size. Investigating these concepts allows tailoring mechanical performance, sealing integrity and manufacturability across diverse operating conditions.

The realization of thin-walled, large-format extrusions required for such modular battery housings is constrained by technological limitations along the process chain, often described by the relation between circumscribing circle diameter ( CCD ) and minimal wall thickness tmin [4]. Reduced wall thicknesses lead to higher mechanical loads on extrusion dies and increase sensitivity in subsequent joining and machining operations due to higher risk of distortion. Furthermore, scaling the profile geometry introduces several challenges to the extrusion process, ranging from die stability to quenching uniformity and distortion control.
However, systematic reduction of profile wall thickness combined with robust control of subsequent processing steps offers a substantial lightweight potential. Finite-element-based research on die design has already contributed to extending these limits allowing for CCD-to- tmin ratios of up to 170 [5]. In particular, the investigation of the die geometry continues to offer potential for weight reduction. By improving the material flow through adaption of the bridges and reducing mandrel deflection, thinner profile walls can be achieved and therefore a weight reduction of 16% is possible [6].
In an alternative hot extrusion approach multiple smaller containers (and billets) next to each other and an adapted die geometry is used to extrude profiles [7]. Due to the die geometry the multiple material flows weld inside the die, comparable to a conventional porthole die. The reduction in friction area and ram force increases the extrudability of wide and thin-walled profiles [8]. Although this method offers a significant improvement regarding the limitations of conventional extrusion, new container and punches are required, limiting the adaption on existing presses.
Therefore, the investigation of the lightweight potential for the conventional extrusion process remains essential. Especially, comprehensive investigations into feasible process windows for thinwalled, large-scale profiles and overcoming the limitations by adjusting the die geometry offer great potential. Consequently, this paper focuses on the CCD-to- tmin  ratio as a current limiting parameter for extrusion processes found in the industry and on extending it by die adaptation using the example of a battery housing. Furthermore, size scaling effects on both the extrusion and subsequently quenching processes are investigated to realize a modular battery carrier system tailored to the application.

Methods

The original version of this paper is available on https://www.scientific.net/KEM.1048.51.pdf

Materials. For the investigations, the aluminum alloy AA6063 is selected due to its technological significance and economic relevance in extrusion applications, especially for the automotive sector. The material is cast in accordance to the European standard for wrought aluminum (EN AW) and supplied in billet diameters of 140 mm and 344 mm to be extruded on a 10 MN and a 55 MN extrusion press. The chemical composition is listed in Table 1.

Table 1. Chemical composition of the used aluminum alloy AA6063

Element | Si | Fe | Cu | Mn | Mg | Cr | Zn | Ti
Weight % | 0.557 | 0.234 | 0.032 | 0.041 | 0.506 | 0.009 | 0.072 | 0.020

Battery Concept. To ensure the modularity of the battery housings, the available volume must be utilized as efficiently as possible. The biggest influence on the battery housing cross section is therefore the cell format used. These formats can be divided into the three main groups: cylindrical, prismatic and pouch cells [9]. Due to the rectangular shape of prismatic battery cells and their efficient thermal management [10], this format, so called PHEV2, is chosen here as a relatively generic cell type. Based on this a hollow rectangular profile with two quadratic chambers is introduced (Figure 1a), which can be stacked modularly to fit different applications (Figure 1b). Due to the limits of the available extrusion press in the laboratory, a maximum CCD of 160 mm is determined. The measurements of the proposed cross section (Figure 1c) include a CCD of 160 mm with a wall thickness of t=1 mm resulting in a CCD-to- tmin  ratio of 160. As seen in the literature, such as [11], a minimal wall thickness of tmin=2 mm would conventionally be necessary for this kind of CCD.

[picture]

Fig. 1. a) Schematic of the battery housing architecture including battery cells and cooling solution, b) Modular profile stacking for different applications, c) initial proposed profile cross section for the battery housing for the laboratory scale.

Fig. 1. a) Schematic of the battery housing architecture including battery cells and cooling solution, b) Modular profile stacking for different applications, c) initial proposed profile cross section for the battery housing for the laboratory scale.

To evaluate the modularity of the concept and to investigate size-scaling effects during extrusion and quenching, the initial cross section is enlarged by a factor of 2.5. The inner and outer radii, as well as chamfers, remain unchanged. This modification results in a more industrially representative profile size, corresponding to a typical battery housing used in electric vehicles. The geometrical quality of the profiles is defined and measured by their dimensional accuracy and mechanical properties according to DIN EN 755-9 and DIN EN 755-2 respectively.

Numerical investigation of the tool design. Before running extrusion trials, the tool design was investigated with the help of FEM simulations using Altairˋs Inspire Extrude Metal. In the program, the steady state regime is modeled using the Eulerian approach on the basis of the flow volume generated from the tool model (Figure 2a). For the discretization of the model, around 2.5·106 firstorder tetrahedral and prismatic elements are used. A minimum of 5 elements over the thickness in every area and an aspect ratio limit of 5 are ensured, fulfilling the recommendations for the solver. The plastic behavior of the material is modelled using the Zener and Hollomon model [12] modified by Sellars and Tegart [13] with parameters from the software's database for AA6063. Due to the different friction behaviors typically found in extrusion, two friction models are used. Sticking friction is assumed on the container and die wall, while Altair's proprietary viscoplastic friction model (τ=f(P,σf,μ)) with a friction coefficient μ=0.3 is used in the bearing zone. The process model also accounts for the die deflection by automatically coupling the contact forces, generated from the calculated pressure in the flow volume, on to the tool surface. For modeling of the elasto-plastic tool behavior, the database's standard material model for tool steel H13 is used. The movement of the die and mandrel is restricted on the mantle surfaces and the surfaces connected to the bolster (Figure 2b). The contact between the die and mandrel part of the tool is defined as 'separating', to allow separation and movement of both parts individually. For the process parameters a typical parameter set for the laboratory press is used: TBillet =550∘C, TDie =450∘C and vram=2 mm/s. The numerical model is finally validated using the force displacement curves and temperature measurements of the experiments, showing maximum deviation of 5% for the tool temperature and 7% deviation of the force value.

[picture]

Fig. 2. Numerical setup of the extrusion process with tool deflection analysis: a) flow volume of the material inside the die and b) die setup with flow volume

Fig. 2. Numerical setup of the extrusion process with tool deflection analysis: a) flow volume of the material inside the die and b) die setup with flow volume

Experimental setup. The extrusion trials are conducted on a 10 MN and a 55 MN extrusion press at the Institute of Forming Technology and Lightweight Components IUL and OTTO FUCHS KG, respectively. For the smaller scale, the initial container and die temperature of Tcontainer =Tdie =400∘C is set through heating elements inside the machine frame, die and container. The billets are heated to Tbillet =550∘C in an external convection furnace. To achieve the steady state quicker and to reduce tool deflection during fill-up, three additional billets are extruded beforehand at Tbillet =575∘C to heat-up the die. In this way, the starting temperature for the first analyzed extrusion cycle is above 450∘C. The actual die temperature depends on the billet loading time. For the investigation of the temperature evolution during extrusion, a total of 6 thermocouples are integrated into the die with three being at the bearing edge and three at the weld chamber. An additional pyrometer is used to measure the surface temperature in the middle of the top surface at the die exit. Subsequently cooling of the profiles is realized with a quenching box [3], developed at the IUL (Figure 3), that enables three different types of cooling: spray-mist-quenching with water and pressurized air, quenching only with pressurized air, and air cooling with no active quenching. The setup consists of five nozzle gates with four nozzles each ( 1 mm nozzle diameter), allowing direct quenching of the complete outer surface area. The water has a temperature of Twater =20∘C, is fed in the system at a pressure of pwater =5 bar, and is dispersed through compressed air at a pressure of pair =4 bar at the distributor. To investigate the mechanical and microstructural properties of the extruded profiles, tensile tests and microstructural analyses are performed. The tensile tests are carried out in accordance with DIN EN ISO 6892-1, employing a constant strain rate of φ˙=0.0067 s−1 for testing up to fracture, using a Zwick Z250 universal testing machine. The tensile specimens are laser-cut from the extruded profiles along the extrusion direction and conform to the geometry specified in DIN 50125−H12.5×50. Five specimens are tested for each parameter set. For the microstructure analysis, the profile cross section is divided into eleven segments due to size constrains. Each segment is subsequently cold mounted, electrolytically Barker-etched and investigated with light microscopy.

[picture]

Fig. 3. IUL quench box consisting of five nozzle gates containing of four nozzles each, allowing quenching of the complete outer surface area with pressurized air or water-spray-mist.

Fig. 3. IUL quench box consisting of five nozzle gates containing of four nozzles each, allowing quenching of the complete outer surface area with pressurized air or water-spray-mist.

Results and Analysis

The original version of this paper is available on https://www.scientific.net/KEM.1048.51.pdf

Die design adaptation. The initial evaluation of the die design is done on a tool design provided by an industrial tool maker according to industrial standards and taking the press and profile dimensions into account. As seen in Figure 4a, the design features 7 portholes, with one going through the mandrel to provide sufficient material flow for the middle bridge of the profile. The numerical analysis reveals high stresses exceeding 1000 MPa on the bridges connected to the mandrel, locally reaching up to 1600 MPa . Such stress concentrations would likely result in die fracture and potential tearing of the mandrel. In addition, a maximum mandrel deflection of up to 0.4 mm in the extrusion direction and 0.22 mm in x -direction (Figure 4b) of each mandrel is predicted. The primary causes of this behavior can be attributed to the relatively small remaining wall thickness in the outer regions of the tool and the reduced stiffness of the mandrel induced by the central porthole.
To mitigate both the high stress concentrations and the mandrel deflection, several design iterations are performed with the primary objective of increasing the bulk of the bridges and the outer walls of the tool. However, none of these iterations resulted in a satisfactory improvement in tool performance, ultimately rendering this design concept unsuitable for the extrusion of thin-walled, large-scaled profiles. Therefore, a new design developed by OTTO FUCHS KG is introduced (not shown due to confidentially agreement), featuring a more streamlined material flow, a reduction in the number of portholes and an increased mandrel stiffness. The numerical investigation shows a reduction of equivalent stress of 55% in the localized hot spots and a reduction of the maximum deflection in the extrusion direction of 50% and in x-direction of up to 77% (Figure 4c). Therefore, this design proves to be usable for extrusion of such thin-walled, large-scale profiles.

[picture]

Fig. 4. a) Initial die design and the resulting mandrel deflection in x -direction for b ) the initial design and c) the adapted design by OTTO FUCHS KG

Fig. 4. a) Initial die design and the resulting mandrel deflection in x -direction for b ) the initial design and c) the adapted design by OTTO FUCHS KG

Extrusion results. For the validation of the usability of the investigated tool design, several extrusion trials are carried out. Different investigated profile defects, visualizing wrinkling defects at the top and bottom part of the middle web, wrinkling of one or both of the side walls, and a defect-free profile are shown in Figure 5. The main reason for these wrinkling defects is a positive relative exit velocity in the center web (Figure 6a). As expected for a new tool design, several smaller corrections are necessary to achieve a defect-free cross section. Due to these changes, the outer profile wall thickness needs to be increased to t=1.15 mm and 1.35 mm for the center web (Figure 5b and 𝐜 ), to achieve a negative relative exit velocity in the center web to avoid the formation of wrinkles (Figure 6b). A weight reduction of 46% would have been possible with the proposed wall thickness of 1 mm compared to a minimal wall thickness tmin=2 mm found in the literature. However, with the adapted wall thicknesses a final weight reduction of 36% is achieved with a final CCD-to- tmin ratio of 138 .

[picture]

Fig. 5. Extruded profiles showing different defects: a) wrinkling on the center web before wall thickness adjustment, b) wrinkling of the profile side walls after adjustment c) defect-free profile after wall thickness adjustment.

Fig. 5. Extruded profiles showing different defects: a) wrinkling on the center web before wall thickness adjustment, b) wrinkling of the profile side walls after adjustment c) defect-free profile after wall thickness adjustment.

[picture]

Fig. 6. Numerical calculated relative exit speed difference across the profile cross section correlating to the images of a) Figure 5a and b) Figure 5c

Fig. 6. Numerical calculated relative exit speed difference across the profile cross section correlating to the images of a) Figure 5a and b) Figure 5c

For the process window, ram speeds between vram =2−8 mm/s and billet temperatures between Tbillet =530−550∘C are investigated (Figure 7). Due to the experimental setup of the used machine, stable processing conditions could be achieved repeatedly with Tbillet =550∘C and vram =2.5 mm/s. Under these conditions, the heat generated by extrusion and the natural cooling of the tool through the machine frame reach an equilibrium of the profile temperature for each cycle after a brief reheating phase at the beginning of each press cycle (Figure 7). The key parameter to achieve defectfree profiles is therefore the surface temperature of the profile at the die exit, which should be between Tprofile =490−500∘C. Extrusion at higher or lower profile temperatures Tprofile  due to different billet temperature TBillet  or ram speeds vram  results in renewed wrinkling on the side walls of the profile.

[picture]

Fig. 7. Process window for extrusion of defect-free profiles and temperature evolution of the average tool temperature and profile surface temperature at the die exit for three extrusion cycles.

Fig. 7. Process window for extrusion of defect-free profiles and temperature evolution of the average tool temperature and profile surface temperature at the die exit for three extrusion cycles.

Influence of Quenching. For the evaluation of the different quenching methods, the profile deformation caused by quenching is measured by 3D scanning the complete outer surface of the profile using the structured light projection system GOM ATOS. Furthermore, the mechanical properties after quenching are evaluated by tensile tests and the microstructure is investigated of the extruded and quenched profiles. Comparing the measurement of the maximum profile height and width with the tolerances of DIN EN 755-9 (Figure 8a) reveal a mostly good fit for the profile height but a in average 0.3 mm to large profile width. This can be attributed to a slight bulging of the side walls. Furthermore, during active quenching (pressurized air or spray-mist) of these thin-walled profiles, the cooling on the top and bottom surfaces is spatially non-uniform, generating steep thermal gradients. These gradients drive different contractions, leading to localized instabilities (wrinkling) that manifest as dimples on the quenched surfaces, which become more pronounced as the coolingrate increases and a distortion of the cross section of up to 7∘ can be measured (Figure 8b).

[picture]

Fig. 8. a) Maximum outer dimension of the extruded and quenched profile in comparison to the tolerances of DIN EN 755-9 and b) cross section distortion due to rapid quenching

Fig. 8. a) Maximum outer dimension of the extruded and quenched profile in comparison to the tolerances of DIN EN 755-9 and b) cross section distortion due to rapid quenching

Although the distortion behavior of the profiles shows clear differences between the quenching methods, the measured mechanical properties obtained from tensile tests remain essentially unchanged across all conditions (Figure 9). The resulting ultimate tensile strength ( 166−176MPa ) are in agreement with the standard DIN EN 755-2 values for a T4-temper of min. 130 MPa . Furthermore, the deviation between the five tested specimens of each parameter set showed only slight scattering ( <3% ). This invariance is most likely attributed to the thin walls and large surface area of the profiles, which enable inherently efficient heat extraction regardless of the applied quenching method. Microstructural examination of the cross sections reveals a narrow-recrystallized edge layer in each wall. Comparing the width of these recrystallized edges in the inner web of the profile only shows a deviation of 4% from 176μ m for no active cooling to 169μ m for spray-mist
quenching. This consistent microstructural morphology further explains the similar mechanical properties and supports the conclusion that the cooling capacity of the thin-walled profiles is already sufficient to achieve the desired metallurgical state. Furthermore, the quenching rates and the profile deformation are also numerically simulated and are in good agreement with the experimental results. These results will be used for further optimization of the quenching process in the future.

[picture]

Fig. 9. Average true stress-strain curves for five specimens each and mechanical properties of the extruded profiles using different quenching methods of the laboratory scale.

Fig. 9. Average true stress-strain curves for five specimens each and mechanical properties of the extruded profiles using different quenching methods of the laboratory scale.

Scalability of the profile. To investigate size scaling effects, the cross section is geometrically scaled-up by a factor of 2.5, extruding a billet of 344 mm diameter using a 55 MN industrial press. In contrast to the lab-scale extrusion trials, the originally intended wall thickness and CCD-to- tmin ratio of 160 could be realized (Figure 10). This leads to the assumption, that small inhomogeneities of the local profile velocity have a smaller influence while using larger, less wrinkling-prone wall thicknesses, resulting in a more stable process. With the final wall thickness of 2.5 mm , a weight reduction of 38% is reached compared to a profile with minimal wall thickness tmin=4 mm as typically found in the literature for a CCD of 400 mm . The parameter combinations investigated thus far ( TBillet  up to 530∘C, vProfile up to 9.0 m/min ) reveal a similar narrow process window, as for the laboratory scale. However, further extrusion trials are needed as no stationary state is reached, due to the limited amount of extrusion runs under constant process parameters. Therefore, the final process window might also show a higher robustness as seen regarding the wall thickness. While the dimensional tolerances for the laboratory scaled profile are not fully complied with, the upscaled profile show a significant improvement (width tolerance ±1.6 mm instead of 2.8 mm and height tolerance ±1.0 mm instead of 1.8 mm ). Nevertheless, further investigations, especially of profiles from the steady state, are necessary in order to make final valid statements. The mechanical properties of the upscaled profiles are investigated after a subsequent heat treatment to the T6-temper, to be comparable with the industrial manufacturing process chain of automotive parts. The two different quenching methods result in a final ultimate tensile strength of 266 MPa for pressurized air and 265 MPa for spray-mist quenching and thus meet the requirement of DIN EN 755-2 of at least 215 MPa . This leads to the same assumption, that for the investigated alloy, even the increased wall thickness to 2.5 mm provides sufficient heat extraction through the large surface-area-to-volume ratio.

[picture]

Fig. 20. Comparison of the extruded profiles for both scales

Fig. 20. Comparison of the extruded profiles for both scales

Conclusion

The original version of this paper is available on https://www.scientific.net/KEM.1048.51.pdf

The present investigations demonstrate both the technological feasibility and the current limitations of manufacturing thin-walled, large-scale profiles for modular battery housing applications, achieving a CCD-to- tmin  ratio of 138 and 160 for AA6063. Within the context of sustainable mobility, such lightweight carrier systems offer significant potential to reduce vehicle mass while maintaining structural integrity and enabling efficient thermal management.
Iterative die optimization led to a streamlined design with significantly reduced mandrel deflection, enabling defect-free extrusion under controlled conditions. However, an increase in wall thickness from 1 mm to 1.15 mm in the outer walls and 1.35 mm in the inner web was required to achieve homogeneous material flow within the die. Experimental trials confirmed a narrow process window at Tbillet =550∘C and vram=2.5 mm/s, resulting in stable surface temperatures at the die exit between Tprofile =490−500∘C.
The investigation of different quenching methods showed that active cooling methods (using pressurized air or spray-mist) affect surface deformation, such as wrinkling, but have negligible effects on mechanical properties or microstructure due to efficient heat extraction inherent to the thinwalled, large-scale geometry.
Size scaling investigations using a cross section scaled-up by a factor of 2.5 on an industrial extrusion press revealed similar process stability limits and post-quenching mechanical behavior. The originally intended CCD-to- tmin  ratio of 160 could be realized with the scaled-up profiles and a weight reduction of up to 38% compared to conventional designs could be achieved. However, further investigations of the process parameters are needed to fully understand the process behavior under stationary conditions.
In summary, the results define key parameters for reliable extrusion and quenching of lightweight modular battery housings, providing a foundation for further optimization towards industrial implementation.

Acknowledgements

The original version of this paper is available on https://www.scientific.net/KEM.1048.51.pdf

This study was funded by the Federal Ministry for Economic Affairs and Climate Action on the basis of a resolution of the German Bundestag (03LB1016C).

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Wan M, Li F, Xia W, Fang X, Jia X. Optimization Design of Porthole Die to Enhance Metal Flow Stability for Thin-Wall, Asymmetric, Unequal-Wall-Thickness (TW-AS-UWT) Aluminum Profile Extrusion. J. of Materi Eng and Perform 2025;191(1):114176. .

Madura J, Puchlerska S, Balcerzak M, Noga P, Bogusz M, Zasadziński J et al. Development of Low-Weight and High-Strength AA6005A Extrudates Intended for Modern Architecture and Design of Innovative Die for Extrusion Process. Materials (Basel) 2024;17(10). .

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The original version of this eBook is available on https://www.scientific.net/book/extrusion-and-drawing/978-3-0364-1995-4

Key Engineering Materials, ISSN: 1662-9795, Vol. 1048, pp 63-71

doi: 10.4028/p-Hm69aW

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-12-20

Revised:
2026-02-13

Accepted:
2026-02-25

Online:
2026-04-14

Calibrating a Lode-Parameter-Dependent Damage Evolution Equation to Cold Extrusion Experiments

The original version of this paper is available on https://www.scientific.net/KEM.1048.63.pdf

Robin Gitschel 1,a*, A. Erman Tekkaya 1, b and Yannis P. Korkolis 1,c

1 Institute of Forming Technology and Lightweight Components, TU Dortmund, Baroper Str. 303, 44227 Dortmund, Germany

a  robin.gitschel@iul.tu-dortmund.de, b  erman.tekkaya@iul.tu-dortmund.de, c yannis.korkolis@iul.tu-dortmund.de

Keywords: Ductile Damage, Lode-parameter, Parameter Identification, Cold Forging

Abstract

Forming processes significantly influence the product properties of a formed workpiece. Next to the effects of work hardening and residual stresses, the influence of ductile damage determines the final performance of a formed component. Thus, precise damage models are crucial for designing new forming process sequences. In general, this is achieved by modelling the evolution of damage as a function of hydrostatic and deviatoric stress, characterized by the stress triaxiality and the Lode-parameter. However, calibrating damage models to the effects of triaxiality and the Lodeparameter is not trivial, since experiments usually represent a combination of both influences. A recent experimental approach by the authors offers the possibility to vary the Lode-parameter in extrusion experiments while keeping the triaxiality constant. This paper aims to use this data of the isolated deviatoric effect on damage to calibrate a damage evolution equation. The model is calibrated to void area fraction measurements obtained by scanning electron microscopy of extruded casehardening steel 16 MnCrS 5 . For validation, the model predictions for non-constant Lode-parameter histories are compared to corresponding experiments. The model and experiments are in good agreement.

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1048.63.pdf

Ductile damage does not only cause the onset of macroscopic fracture, but also affects the product performance of a formed component. By controlling the evolution of damage during a forming process, the impact toughness, fatigue strength and elastic stiffness can be increased [1]. To avoid excessive use of material when designing a formed component, the effect of ductile damage on product properties has to be taken into account. This way, the component can be tailored to its service loads without applying generous safety factors. To facilitate the damage-controlled design of components, precise damage models are inevitable. In the scientific literature, a vast variety of damage models exists [2]. Most models can be formulated as functions of the stress triaxiality η given by

η=σ1+σ2+σ33σflow =σhσflow ,(1)

with the principal stresses σi and the flow stress σflow 1 of the material. The triaxiality is a dimensionless measure of the hydrostatic stress and is known to be a decisive factor for void growth [3,4]. Damage is also dependent on the deviatoric part of the stress state. Thus, models also consider the so-called Lode-parameter [5] given by

L=2σ2−σ1−σ3σ1−σ3(2)

in their damage evolution equations. Many models focus on predicting macroscopic fracture instead of modelling the evolution voids. Thus, conventional damage models often times struggle to depict the evolution of damage in terms of void area fractions measured by scanning electron microscopy (SEM) [6]. The calibration with measured void area fractions improves the prediction quality of the void evolution. However, the data basis used for calibration with void area fractions is usually scarce, since the SEM-measurement of large areas is cumbersome. Thus, also the quality of data is crucial. Conventional tests for calibration of damage models such as tensile tests with various specimen geometry or Nakajima tests show specific load paths in terms of triaxiality and Lode-parameter. However, when comparing two of these specimens, usually both, the triaxiality and the Lodeparameter differ between the two specimens. This makes it hard to extract information on the effect of either the triaxiality or the Lode-parameter on damage evolution. Thus, a large number of specimens is needed to capture the whole triaxiality-Lode-parameter-space.

Recent investigations show that cold extrusion experiments are capable of adjusting the triaxiality without affecting the Lode-parameter [7] and vice versa [8]. The obtained void area fraction data from these experiments is used in this paper to calibrate a damage evolution equation for the casehardening steel 16 MnCrS 5 . To model the experimental data, a triaxiality-dependent damage evolution equation proposed earlier by the authors is combined with an additional Lode-parameterdependent equation proposed by Lou et al. [9].

Methodology

The original version of this paper is available on https://www.scientific.net/KEM.1048.63.pdf

This section describes the experimental methods used in [7] and [8] to isolate the effects of triaxiality and the Lode-parameter on damage. In addition, the proposed numerical model and the used parameter identification scheme are presented.

Isolating the effect of triaxiality on damage. In forward rod extrusion, a cylindrical billet, encased in a closely-fitting cylindrical container, is pushed at room temperature by a punch through a conical die. After forming, the extrudate is ejected by an ejector pin. Depending on the area reduction of the die, the diameter of the initial billet is reduced. The equivalent plastic strain 1 on the central axis of the extrudate is given exactly by

ε¯

The deviatoric stress state on the central axis in the forming zone equals for an isotropic material the deviatoric stress state in a uniaxial tensile test. The deviatoric tension equals a Lode-parameter of

ε¯=ln(d02d12)=2ln(d0d1).(3)

(Fig. 1).

[picture]

Fig. 1. Deformation of a material point on the central axis of forward rod extrusion and resulting Lode-parameter, adopted from [8]

Fig. 1. Deformation of a material point on the central axis of forward rod extrusion and resulting Lode-parameter, adopted from [8]

Depending on the die cone angle L=−1, a hydrostatic stress state is superposed to the deviatoric tension. For small extrusion strains and large die cone angles, high triaxialities are induced (Fig. 2). By changing the cone angle, only the hydrostatic stress is affected, while the equivalent plastic strain and
the Lode-parameter stay constant [1]. Alternatively, the triaxiality can be adjusted by applying counter pressure via the ejector pin [10]. Thus, forward rod extrusion allows to study the effect of triaxiality isolated from other influencing factors.

[picture]

Fig. 2. Effect of extrusion strain and die cone angle in forward rod extrusion on average triaxiality on the central axis in the forming zone, adopted from [1]

Fig. 2. Effect of extrusion strain and die cone angle in forward rod extrusion on average triaxiality on the central axis in the forming zone, adopted from [1]

Isolating the effect of the Lode-parameter on damage. In forward hollow extrusion, a thickwalled tube is pushed at room temperature by a punch through a conical die. A mandrel ensures that the inner diameter of the tube remains constant during forming, while the conical die reduces the outer diameter of the tube-shaped billet. The mandrel suppresses the strain component in the circumferential direction on the inner radius of the tube. This plane strain state results in a Lodeparameter of 2α (Fig. 3).

[picture]

Fig.3. Deformation of a material point at the inner radius of forward hollow extrusion and resulting Lode-parameter, adopted from [8]

Fig.3. Deformation of a material point at the inner radius of forward hollow extrusion and resulting Lode-parameter, adopted from [8]

Compared to forward rod extrusion with L=0, both extrusion variants induce different Lodeparameters in the forming zone. By carefully selecting the process parameters for both methods, the equivalent plastic strain and the triaxiality history can be kept the same in both process variants, so that the forming history differs only in terms of the Lode parameter [8]. This allows the influence of deviatoric stresses to be examined separately. To match the equivalent plastic strain in both process variants, the diameter reduction in forward hollow extrusion is adjusted accordingly. The correct diameter is obtained from FEM simulations. To match the triaxiality history in both processes, the triaxiality in forward rod extrusion is reduced by adding counter pressure as described above. The triaxiality in rod extrusion is offset by a constant value compared to the triaxiality in hollow extrusion (Fig. 4). By reducing the triaxiality by counter pressure in forward extrusion by this constant offset, the triaxiality is matched in both extrusion variants. The needed counter pressure is given by

L=−1

where

pc=Δη·σVM,(4)

is the triaxiality offset between forward hollow extrusion and forward rod extrusion without counter pressure and Δη is the equivalent stress in the forming zone of forward rod extrusion obtained from FEM simulations.

[picture]

Fig. 4. Setting of Lode-parameters of σvM in forward rod extrusion and L=−1 in forward hollow extrusion, with equal equivalent plastic strains and triaxiality histories in both experiments, adopted from [8]

Fig. 4. Setting of Lode-parameters of σ v M in forward rod extrusion and L = − 1 in forward hollow extrusion, with equal equivalent plastic strains and triaxiality histories in both experiments, adopted from [8]

Void evolution equation. To model the damage evolution observed in the cold extrusion experiments, a triaxiality and Lode-parameter-dependent evolution equation is proposed. Inspired by the GTN-model [11], an individual evolution equation for void nucleation and void growth is used:

L=0

As described in [12], void coalescence occurs in the used 16 MnCrS 5 only immediately before macroscopic fracture. With the motivation to model damage for formed components (that do not fracture during forming) in mind, no additional term for void coalescence is used. The volumetric term is adopted from [12]. The term reads

D(ε¯1)=D0+∫0ε¯1[fnuc(η,L)+fvol(η)]dε¯(5)

with a material parameter

fvol=Cvolsinh(1,5η)D(ε→),(6)

and the void area fraction Cvol  at the current time step. The hyperbolic sine function governs void growth for positive triaxialities as well as void shrinkage for negative triaxiality. The multiplication with the current void area fraction ensures an asymptotic behavior of the total void area fraction towards zero for negative triaxialities.
Since the Lode-parameter shows a pronounced effect on void nucleation in the used material [8], the nucleation term originally proposed in [12] is substituted by a Lode-parameter-sensitive evolution equation proposed by Lou et al. [9].

That equation reads:

D(ε→)

with the material parameters

fLou(η,L)={1C·(2τmaxσvM)CL·(1+3η2)Cη,1+3η≥00,1+3η<0(7)

and 1/C,CL. Since the equation is used in this paper to model the void nucleation, the notation of the parameter Cη is changed to 1/C. Using the Lode-parameter Cnuc , the proposed expression reads:

L

Parameter identification. The damage evolution equation is calibrated to void area fraction measurements of rod [7] and hollow extrusion [8] experiments to capture the effects of triaxiality and the Lode-parameter on damage evolution. The measurements form both works are obtained by the same methodology presented in [7]. This method uses large area backscattered electron (bse) imaging to detect potential voids. With additional energy dispersive x-ray spectroscopy (edx) the method can distinguish between voids and non-metallic inclusions that may show the same grayscale value as voids. To also include information on a Lode-parameter of

⇔fnuc(η,L)={Cnuc·(2L2+3)CL·(1+3η2)Cη,1+3η≥00,1+3η<0(8)

, also an upsetting test is considered in the parameter identification scheme. To compare the experimental data to the proposed evolution equation, the load paths of the considered experiments are determined in FEM-simulations. All experiments are simulated using the commercial FEM-solver Abaqus/Standard with implicit time integration to obtain the load paths. The model uses axisymmetric 4-noded solid elements. The billet with an initial diameter of 30 mm is discretized by a mesh with an initial element edge length of 0.2 mm . The elastic die is discretized with an element edge length of 0.4 mm in the die shoulder area. The isotropic workpiece is modelled by von Mises plasticity with flow curves at room temperature, L=1 and 200∘C obtained from upsetting tests, to account for plastic heating. The contact between billet and die is modelled by Coulomb friction with a friction coefficient of 400∘C.

For each experiment, the triaxiality and the Lode-parameter is extracted from the simulation as a function of the equivalent plastic strain. With these load paths at hand, the model prediction is calculated by numerically integrating Eq. 5 with the midpoint rule. The model parameters are obtained by minimizing the error between void area fraction measurements μ=0.04 and the corresponding model prediction Diexp with the parameter set Dimod(x). The total error is quantified by the error function

x=[Cvol ,Cnuc ,CL,Cη]

with the weighting factors

ferror=1n∑i=1nwi(Diexp−Dimod(x))2(6)

. The error function is minimized with an evolutionary algorithm implemented in the Python library SciPy. Each call of the error function calculates the model predictions for the current parameter set. Since the model prediction is directly computed from the extracted load paths, it is not necessary to run the FEM model in each iteration. The chosen weighting factors are found in Table 1. One third of the total weight is shared by three forward rod extrusion experiments with an extrusion strain of wi, but various die cone angles. These three experiments capture the effect of the triaxiality. Another third of the total weight is dedicated to two forward hollow extrusion experiments to capture the effect of the Lode-parameter. The remaining third of the
total weight is assigned to an additional rod extrusion experiment with ε¯=0.5 to add information at large strains and the upsetting test to add information for ε¯=1.

Table 1. Experiments used for parameter identification of the proposed damage evolution equation

Experiment no. | 1 | 2 | 3 | 4 | 5 | 6 | 7
Extrusion type | Rod | Rod | Rod | Rod | Hollow | Hollow | Upsetting
Eq. plastic strain ε | 0,5 | 0,5 | 0,5 | 1 | 0,33 | 0,5 | 0,3
Die cone angle 2α | 30° | 60° | 90° | 90° | 90° | 90° | -
Lode-parameter L | -1 | -1 | -1 | -1 | 0 | 0 | 1
Average triaxiality ηavg | -0,41 | -0,06 | -0,01 | -0,59 | -0,13 | -0,26 | -0,33
Void area fraction Dexp in % | 0,006 | 0,023 | 0,024 | 0,001 | 0,037 | 0,017 | 0,008
[data origin] | [7] | [7] | [7] | [7] | [8] | [8] | [new data]
Weighting factor wi in % | 11,11 | 11,11 | 11,11 | 16,67 | 16,67 | 16,67 | 16,67

Model validation. The calibrated damage evolution equation is validated by additional void area data, measured outside of the central axis of a forward rod extrusion specimen. Hering et al. [7] measured void area fractions on five equidistant locations along the radius of an extrudate (Fig. 5). These locations show highly non-proportional Lode-parameter histories. Since this type of data is not included in the parameter identification scheme, it is well suited to benchmark the prediction quality of the proposed model. The extrusion experiment is modelled in FEM-simulations to obtain the load paths at the investigated positions of the specimen.

[picture]

Fig.5. Void area measurement locations in validation experiment (data obtained in [7])

Fig.5. Void area measurement locations in validation experiment (data obtained in [7])

Findings

The original version of this paper is available on https://www.scientific.net/KEM.1048.63.pdf

A parameter set for the proposed evolution equation is found by the described parameter identification scheme. The obtained minimum yields an error function value (Eq. 6) of L=1. The identified parameter set is given in Table 2. Comparing the model predictions with the obtained parameter set to the experimental data used for the calibration, shows a good agreement (Fig. 6).

Table 2. Parameter set found by minimizing the error function (Eq. 6)

Parameter | Cvol | CNuk | CL | Cη
value | 0.899 | 1.12·10⁻³ | 1.04·10¹ | 2.04

[picture]

Fig.6. Fit of proposed evolution equation to the calibration data

Fig.6. Fit of proposed evolution equation to the calibration data

To benchmark the proposed evolution equation, it is compared to the validation data that has not been used in the calibration. In addition, it is also compared to model predictions of the evolution equation proposed in [12]. This evolution equation is purely dependent on the stress triaxiality and neglects the Lode-parameter influence. Fig. 7 shows the comparison of the model predictions to the SEM measurements.

[picture]

Fig. 7. Comparison of evolution equations to validation data, i.e., data that was not used for model calibration. See Fig. 5 for measurement positions.

Fig. 7. Comparison of evolution equations to validation data, i.e., data that was not used for model calibration. See Fig. 5 for measurement positions.

At positions 1 to 3, both evolution equations show good agreement with the experimental data. The good agreement is due to the similarity of the load paths of these positions with the calibration load paths. At position 4, the load path in the validation experiment deviates significantly from the calibration load paths. The strongly negative triaxiality at position 4 leads to a reduction in the initial void area fraction. The equation proposed in [12] reproduces the void shrinkage. However, it overestimates the decrease in the void area fraction. The Lode-parameter-dependent evolution equation proposed in this paper provides a better prediction. At position 5, a qualitatively similar
behavior of the models can be observed. The experiment shows only a minimal increase in the void area fraction compared to the void area fraction of the initial material. The equation proposed in [12] predicts a sharp decrease in the void area fraction compared to the initial state. The Lode-parameterdependent evolution equation is in good agreement with the experimental data at position 5 . In general, both models underestimate the void area fraction in the validation experiments. In future works, this observation should be assessed by additional validation experiments to check if this is a systematic shortcoming of the models or if this can be attributed to experimental errors.

Summary

The original version of this paper is available on https://www.scientific.net/KEM.1048.63.pdf

Cold extrusion experiments offer a unique opportunity to generate calibration data for damage models. While the geometry of tensile specimen affects the triaxiality and the Lode-parameter at the same time, extrusion experiments allow to adjust either of the stress measures without affecting the other. Void area fraction measurements by SEM-investigations on such extrusion experiments are used to calibrate a triaxiality- and Lode-parameter-dependent void evolution equation. The proposed model uses a term to describe void growth and shrinkage and an additional term to model void nucleation. The void growth term is based on former works by the authors. For the Lode-parametersensitive void nucleation term, an equation from literature is adopted. The proposed damage evolution equation captures the calibration data well. Also, non-monotonic Lode-parameter histories are captured well in validation experiments. The uncoupled modelling of damage and plasticity in the presented approach allows using the damage evolution equation with any plasticity model. Also the presented parameter identification scheme based on void area measurement on cold extruded material can be used to calibrate other Lode-parameter-sensitive damage models. In future works, the experimental plan can be extended to multi-stage experiments to also account for load path reversal effects.

Acknowledgements

The original version of this paper is available on https://www.scientific.net/KEM.1048.63.pdf

The authors kindly thank the German Research Foundation (DFG) for the financial support of project A02 in the Collaborative Research Centre CRC/Transregio 188 "Damage Controlled Forming Processes" (Project number 278868966 - TRR 188).

References

The original version of this paper is available on https://www.scientific.net/KEM.1048.63.pdf

O. Hering, A. E. Tekkaya, Damage-induced performance variations of cold forged parts, Journal of Materials Processing Technology 279 (2020) 116556.

T.S. Cao, Models for ductile damage and fracture prediction in cold bulk metal forming processes: a review, International Journal of Material Forming 10(2) (2017) 139-171.

J.R. Rice, D.M. Tracey, On the ductile enlargement of voids in triaxial stress fields, Journal of the Mechanics and Physics of Solids 17 (3) (1969) 201-217.

F.A. McClintock, A Criterion for Ductile Fracture by the Growth of Holes, Journal of Applied Mechanics 35(2) (1968) 363-371.

W. Lode, Versuche über den Einfluß der mittleren Hauptspannung auf die Fließgrenze, Zeitschrift für Angewandte Mathematik und Mechanik 5(2) (1925) S. 142-144.

A. Schowtjak, R. Schulte, T. Clausmeyer, R. Ostwald, A. E. Tekkaya, A. Menzel, ADAPT A Diversely Applicable Parameter Identification Tool: Overview and full-field application examples, International Journal of Mechanical Sciences 213 (2022) 106840.

O. Hering, A. Dunlap, A. E. Tekkaya, A. Aretz, A. Schwedt, Characterization of damage in forward rod extruded parts, International Journal of Material Forming 13 (6) (2020) 1003-1014.

R. Gitschel., J. Gebhard, Y. P. Korkolis, A. E. Tekkaya, Isolating the effects of deviatoric and hydrostatic stress on damage evolution using cold extrusion experiments, International Journal of Solids and Structures 310 (2025), 113205.

Y. Lou, H. Huh, S. Lim, K. Pack, New ductile fracture criterion for prediction of fracture forming limit diagrams of sheet metals, International Journal of Solids and Structures 49 (2012) 3605-3615.

R. Gitschel, O. Hering, A. Schulze, A. E. Tekkaya, Controlling Damage Evolution in Geometrically Identical Cold Forged Parts by Counterpressure, Journal of Manufacturing Science and Engineering 145 (1) (2023) 011011.

C.C. Chu, A. Needleman, Void Nucleation Effects in Biaxially Stretched Sheets, Journal of Engineering Materials and Technology 102(3) (1980) 249-256.

R. Gitschel, A. Schulze, A. E. Tekkaya, Void nucleation, growth and closure in cold forging: An uncoupled modelling approach, Advances in Industrial and Manufacturing Engineering 7 (2023) 100124.

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ferror =2.1·10−5 The flow stress is used for the definition of the triaxiality, since only stress states under plastic flow are relevant to the evolution of ductile damage

The original version of this eBook is available on https://www.scientific.net/book/extrusion-and-drawing/978-3-0364-1995-4

Key Engineering Materials, ISSN: 1662-9795, Vol. 1048, pp 73-81

doi: 10.4028/p-z71Fb9

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-09

Revised:
2026-02-16

Accepted:
2026-02-25

Online:
2026-04-14

Effect of Chemical Composition on Hot Extrudability and Tribological Behavior of 7000 Series Aluminum Alloys

The original version of this paper is available on https://www.scientific.net/KEM.1048.73.pdf

Sukunthakan Ngernbamrung 1,a*, Kuniaki Dohda 2, b, Tatsuya Funazuka 1,c, Junichi Oshima 1, d and Tomomi Shiratori 1,e

1 Academic Assembly Faculty of Engineering, University of Toyama, Japan

2 Department of Mechanical Engineering, Faculty of Engineering, Northwestern University, USA

a  sukunthn@eng.u-toyama.ac.jp*, b  dohda.kuni@northwestern.edu, cfunazuka@eng.u-toyama.ac.jp, d  s2270416@ems.u-toyama.ac.jp, e  shira@eng.u-toyama.ac.jp

Keywords: Hot Extrusion, 7000 Series Aluminum Alloys, Extrudability, Element Adhesion, Galling.

Abstract

Aluminum 7000 series alloys are widely used for aerospace and transportation applications due to their high strength-to-weight ratio. This research investigates the impact of zinc ( Zn ) and magnesium ( Mg ) content on the hot extrudability and tribological behavior. Elemental quantities straight away impact flow stress, determining the manufacturing parameters, whereas galling and adhesion frequently degrade tool life. This work illustrates that by assessing essential ram speeds and temperature limits, adjusting Zn and Mg concentrations considerably improves the extrudability limit. A decreasing flow stress during deformation reduces micro-cracking tendency and improves surface quality. The findings provide critical compositional guidelines for high-strength aluminum alloys, effectively balancing processing efficiency with improved surface quality and reduced element adhesion behavior, ensuring better industrial outcomes for advanced structural components.

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1048.73.pdf

The 7000 series alloys have an outstanding strength-to-weight ratio, are the strongest commercial class, and are crucial for lightweight aircraft and automotive design [1]. Aluminum is suitable for competitive extrusion, while 7000 series alloys are challenging [2]. Due to a narrow operational window between the highest allowed pressure and the temperature threshold, surface defects limit extrusion speeds [2].

A major defect during hot extrusion of high-strength Al−Zn−Mg−Cu alloys, such as A 7075 , is surface tearing, which occurs under high-temperature and high-speed conditions [3]. Ngernbamrung et al. demonstrated that the extrudability of AA7075 can be quantified using an Extrusion Limit Diagram (ELD) [4]. Tearing initiates as a solidification crack where heat from die friction, coupled with tension stress at the die exit, causes localized melting of soluble intermetallic compounds like Al2CuMg and MgZn2 [4,5]. Ngernbamrung et al. investigated the partial melting that appears as micro-solid bridges rich in Zn,Cu, and Mg on tearing surfaces. These low-melting-point phases, existing at grain boundaries, remain in a semi-molten state during extrusion and cannot withstand the tension forces. Unlike insoluble Al7Cu2Fe, soluble precipitates like MgZn2 significantly lower the solidus temperature, thereby serving as primary initiation sites for tearing [4,5]. Furthermore, concentrated zinc segregation at the vicinity of grain boundaries strongly stimulates tearing sensitivity [6]. The subsequent cooling of this semi-molten region generates tensile stress, which cannot be withstood by the weakened grain boundaries containing large, segregated, insoluble phases like Al7Cu2Fe [3]. Therefore, controlling the chemical composition to promote grain refinement and minimize the segregation of these low-melting-point compounds is essential to suppress tearing sensitivity, with grain refinement acting to scatter the concentrated zinc effect [ 3,6 ].

Galling is a critical tribological phenomenon in metal forming causing tool failure via microscopic welding and sharply increased friction [7, 8]. It begins when protective layers (e.g., oxide film, lubricant) are ruptured by high pressure and temperature, exposing reactive material [7]. Alloying elements like Zn and Mg migrate and adhere to the die, drastically worsening surface quality [4,5].

Prevention involves using high-performance solid lubricants, specialized low-friction die coatings, or modifying tool materials to reduce chemical affinity [4, 7].

Mitigation of tearing relies heavily on controlling friction and temperature at the tool-workpiece interface. Funazuka et al. demonstrated that applying specialized die coatings is highly effective for reducing the friction coefficient, thereby minimizing the process-generated heat that leads to tearing [9]. Furthermore, simulations by Funazuka et al. [10] reveal that process heat generation can significantly elevate the extrudate temperature, potentially exceeding 530∘C when using high die temperatures ( 470∘C ). Conversely, employing a lower die temperature was shown to suppress this heat buildup, maintaining the extrudate in a safer range of 480−500∘C. As higher ram speeds inherently increase process heat, these findings emphasize that effective thermal management is crucial to counteract the temperature rise and mitigate tearing sensitivity, thereby expanding the limited extrudability window of AA7075.

Successful hot forming of 7000 series alloys, particularly through extrusion, hinges on the precise control of chemical composition, especially the Zn to Magnesium Mg ratio [11, 12]. Wang et al. [11] and Jiang et al. [12] note that this fundamental elemental ratio dictates the formation and sequencing of strengthening phases, which in turn governs the microstructure's flow stability at elevated processing temperatures [11, 12]. For instance, lowering the Zn/Mg ratio can effectively suppress recrystallization and promote the formation of discontinuous grain boundary precipitates, a mechanism critical for preventing hot cracking and enhancing the final extrudate quality [12]. Consequently, optimizing this chemical balance is necessary to mitigate flow instability and ensure that the alloy's superior mechanical performance is achievable within a viable manufacturing window [13].

This study systematically prepares and hot extrudes five 7000 series alloys with tailored Zn and Mg contents, analyzing flow stress, mechanical properties, and die adhesion via electron probe microanalyzer (EPMA). The aim of this research is to establish compositional guidelines that expand the extrudability window and successfully mitigate galling phenomena for these high-strength aluminum alloys.

Material and Methods

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Five types of billets were conducted using an Al−Zn−Mg−Cu alloy, specifically prepared and cast and homogenized based on the standard A7075 composition. The five resulting specimens labeled A7075, 0Zn,8Zn,0Mg, and 4 Mg were created by adjusting the Zn and Mg content from 0 to 8wt.%. The chemical composition of all alloys is presented in Table 1. Billets were cut to 41.5 mm in diameter and 120 mm in length using a turning lathe.

The 200-ton horizontal hydraulic press machine with a container 42 mm in diameter and the extrusion tooling schematic are shown in Fig. 1 and Fig. 2. Container temperature was controlled via heater rods. Ram stroke and extrusion pressure were measured by laser displacement meters and hydraulic pressure transducers. The extrusion tooling assembly used SKD61 tool steel for the die backer, die holder, and dummy blocks. The SKD61 split-type flat die for bearing surface elemental analysis is illustrated in Fig. 3. The split-type flat die was constructed from heat-treated SKD61 tool steel and nitrided. The split-type flat die has a bearing length of 3 mm , a width of 18.6 mm , and a thickness of 1.5 mm . For extrusion, the billets and tooling were preheated at 450−500∘C, and the ram speed was 0.5−1.0 mm/s.

Table 1. Chemical composition of the experimental A7000 alloys [wt.%].

| Mg | Si | Fe | Cu | Mn | Cr | Zn | Ti | Al
A7075 | 2.54 | 0.09 | 0.21 | 1.82 | 0.05 | 0.19 | 5.80 | 0.01 | Bal.
0Zn | 2.47 | 0.09 | 0.21 | 1.82 | 0.05 | 0.17 | <0.01 | 0.01 | Bal.
8Zn | 2.48 | 0.09 | 0.21 | 1.87 | 0.05 | 0.19 | 8.13 | 0.01 | Bal.
0Mg | 0.01 | 0.09 | 0.21 | 1.83 | 0.05 | 0.21 | 5.50 | 0.01 | Bal.
4Mg | 4.89 | 0.09 | 0.21 | 1.84 | 0.05 | 0.17 | 5.78 | 0.01 | Bal.

The extruded profile and sampling procedure are shown schematically in Fig. 4. The extruded section was about 3600 mm long. For surface characterization, three positions relative to the extrusion tip head were chosen. The head at 600 mm , the middle at 1800 mm , and the end at 3000 mm were selected for surface characterization. In the reference coordinate system, the x -axis was the extrusion direction, and the y -axis and z -axis were the profile width and thickness directions. Extrudability was assessed using surface integrity criteria. A successful extrusion had no cracks along the profile length.

[picture]

Fig. 1 The 200-ton horizontal hydraulic press machine

Fig. 1. The 200-ton horizontal hydraulic press machine

[picture]

Fig. 2 Schematic diagram of the extrusion tooling

Fig. 2. Schematic diagram of the extrusion tooling

[picture]

Fig. 3 split-type flat die

Fig. 3. split-type flat die

[picture]

Fig. 4 The position of surface observation

Fig. 4. The position of surface observation

To evaluate the mechanical property changes resulting from high-temperature forming and severe deformation, tensile strength tests were conducted on the material both before and after hot extrusion. Specimens were prepared by cutting the extruded profile into the standard dumbbell shape, as illustrated in Fig. 5, conforming to JIS Z 2241. Tensile specimens were extracted from the center of the extruded profile at the end position ( 3,600 mm from the tip head), with the longitudinal axis aligned parallel to the extrusion direction. This location was chosen to ensure that no tearing or surface cracks occurred along the entire extrusion length. The tensile tests were performed along this same direction at room temperature using a Shimadzu Autograph AGS-X desktop precision universal testing machine at a constant crosshead speed of 1.0 mm/min. To be sure the results were accurate, at least three tensile tests were done for each experimental condition. The given values for mechanical properties are the average of these tests. The findings indicate very little difference, which means that the experimental data is suitably consistent. Therefore, the curves shown in the figures are representative of these tests.

An electron probe microanalyzer (EPMA JXA-8230) was used to investigate the elements adhered to the split-type flat die bearing surface. The element analysis area on the die bearing surface is shown in Fig. 6. The analyzed elements were selected considering the composition of the 7000 alloy and the oxidation of the die surface. The elemental analysis method was adopted to investigate the adhesion at the bearing surface which is critical for studying friction and tribological behavior thereby predicting the overall tribological performance.

[picture]

Fig. 5 Tensile test specimen

Fig. 5. Tensile test specimen

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Fig. 6 Element analysis area on die bearing surface

Fig. 6. Element analysis area on die bearing surface

Results and Discussion

The original version of this paper is available on https://www.scientific.net/KEM.1048.73.pdf

The extrusion load behavior was analyzed by comparing load versus ram displacement curves at 500∘C under ram speeds of 0.5 mm/s (Fig. 7a) and 1.0 mm/s (Fig. 7b). At 0.5 mm/s, the high strength alloys (A7075, 8 Zn , and 4 Mg ) exhibited peak loads between 1180 and 1220 kN , significantly surpassing the ~660kN recorded for the 0 Mg reference alloy. This disparity persisted at 1.0 mm/s, where peak loads for the high strength group increased further, reaching 1370 kN for the 4 Mg alloy, while the 0 Mg reference remained substantially lower at approximately 680 kN . Across both conditions, high Zn and Mg content provided strong resistance to deformation. The increase in ram speed led to a universal rise in peak loads, attributed to higher strain rates. The rapid strain rate at
1.0 mm/s limits the time for thermal activation to assist dislocation movement, thereby demanding a higher driving force and elevating flow stress. Additionally, while the post peak load drop was gradual at 0.5 mm/s, it became notably sharper for the high strength alloys at 1.0 mm/s, suggesting reduced stability and increased susceptibility to microcracking and tearing under the heightened thermomechanical load.

The surface appearance of the extruded profiles after hot extrusion at a temperature of 500∘C shows a critical dependence on both ram speed and alloy composition, as shown in Fig. 8. At the low speed of 0.5 mm/s, all alloys (A7075, 0Zn,8Zn,0Mg,4Mg ) achieve a smooth finish without any surface cracks like tearing. However, when the ram speed is increased to 1.0 mm/s, the alloys with high Zn and Mg content ( 8 Zn and 4 Mg ) show surface tearing, signifying that their critical extrusion limit has been exceeded. This instability is fundamentally linked to the alloy's initial mechanical properties. The low element composition of 0 Zn and 0 Mg alloys exhibits a lower hot flow stress, which limits frictional heating and prevents the onset of localized melting of soluble metallic compounds, thus maintaining stability at high speeds. Conversely, the high flow stress of the 8 Zn , 4 Mg , and A7075 alloys leads to localized overheating near the die bearing, causing the partial melting of low-melting-point soluble intermetallic compounds, specifically Al2CuMg and MgZn2, along grain boundaries. When the material exits the die, the resulting tension stress acts on this weakened, semiliquid region, causing the material to fracture via a coagulation cracking mechanism. Furthermore, the presence of insoluble iron-containing phases remaining at the grain boundaries further reduces the intergranular bonding strength, promoting the severe macro-tearing observed on the profile surface.

[picture]

Fig. 7 Extrusion load versus ram displacement curves at 500∘C with ram speeds of (a) 0.5 mm/s and (b) 1.0 mm/s

Fig. 7. Extrusion load versus ram displacement curves at 500 ∘ C with ram speeds of (a) 0.5 m m / s and (b) 1.0 m m / s

Tensile tests performed at ambient temperatures on extruded alloys reveal that Zn and Mg content are critical for strength via precipitation hardening, especially Mg , which has a prominent role as a precipitation particle. The 8 Zn alloy had the highest ultimate tensile strength (UTS) at about 500 MPa , while the 0 Mg alloy had the lowest strength at about 200 MPa . While all alloys displayed considerable ductility, the higher-strength variants generally showed reduced elongation. The extrusion process, conducted at a high temperature of 500∘C and a ram speed of 0.5 mm/s, resulted in a consistent upward trend in UTS and a corresponding decrease in elongation, with A7075 strength rising from 213 MPa to 311 MPa and 8Zn increasing from 387 MPa to nearly 500 MPa . This significant strengthening is attributed to microstructural refinement and the temperature effect induced by the hot extrusion.

[picture]

Fig. 8 Surface appearance of extruded profiles after extrusion at 500∘C

Fig. 8. Surface appearance of extruded profiles after extrusion at 500 ∘ C

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Fig. 9 Mechanical properties of alloys (a) before extrusion and (b) after extrusion at temperature 500∘C with ram speed 0.5 mm/s

Fig. 9. Mechanical properties of alloys (a) before extrusion and (b) after extrusion at temperature 500 ∘ C with ram speed 0.5 m m / s

The results of EPMA elemental mappings for Mg,Cu,Zn, and O , which adhered to the bearing surface of the split-type flat die after a hot extrusion at 500∘C with two different ram speeds of 0.5 mm/s and 1.0 mm/s, are presented in Fig. 10 and Fig. 11, respectively, with the color scale indicating relative concentration from low to high. At the ram speed of 0.5 mm/s, it was found that alloys with a high Mg content showed Mg,Cu,Zn, and O adhering more closely at the die entrance than at the exit. At the same time, alloys with a high Zn content showed Zn,Cu, and O adhering throughout the surface, except for the examination of Mg , which still exhibited clear adhesion behavior at the entrance. In addition, it can be observed that in alloys containing both Mg and Zn , the adhesion of the two elements significantly overlaps at the entrance of the die bearing surface.

At a ram speed of 1.0 mm/s, element adhesion is observed, comprising Zn and Mg , usually accompanied by surface oxidation. The research reveals a strong relationship between billet composition and adhesion intensity, with A7075 and 8Zn alloys showing the most severe galling due to the higher chemical bonding and reduced melting points of Zn and Mg . In contrast, 0 Zn and 0 Mg alloys exhibited markedly decreased elemental transfer, despite the presence of an Al-rich base layer. The adhesion phenomenon is caused by the localized melting of low-melting-point intermetallic compounds, such as Al2CuMg and MgZn2, at the tool's surface interface as a result of significant frictional heating. These semi-molten phases accelerate the transfer of material to the die, enhancing friction and causing surface tearing. Therefore, accurately controlling the Zn and Mg concentrations is essential for reducing galling and increasing the extrudability of all high-strength aluminum alloys.

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Fig. 10 The analysis of element maps by EPMA (Temperature 500∘C and ram speed 0.5 mm/s )

Fig. 10. The analysis of element maps by EPMA (Temperature 500 ∘ C and ram speed 0.5 m m / s )

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Fig. 11 The analysis of element maps by EPMA (Temperature 500∘C and ram speed 1.0 mm/s )

Fig. 11. The analysis of element maps by EPMA (Temperature 500 ∘ C and ram speed 1.0 m m / s )

Conclusions

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This study demonstrates that the chemical composition, specifically the concentrations of Zn and Mg, significantly affects the hot extrudability, mechanical properties, and tribological performance of 7000 series aluminum alloys. To achieve improved mechanical properties through precipitation hardening, significant amounts of Zn and Mg are required. Thus, the 8 Zn variation has an ultimate tensile strength of about 500 MPa . However, these elements also raise hot flow stress and limit the optimum processing window. At increased ram speeds, localized frictional heating induces the melting of soluble intermetallic compounds at grain boundaries, thereby weakening intergrain bonding and resulting in surface tearing through a process of cracking mechanism.

Moreover, tribological investigation via EPMA revealed that the adhesion of alloying elements and die galling is sensitively proportional to both composition and speed, with increased ram speeds markedly increasing material transfer and surface oxidation. High magnesium content induces concentrated adhesion at the die entry, while elevated zinc content causes more extensive adhesion across the bearing surface, both of which accelerate surface tearing under high-speed conditions. Therefore, adjusting the Zn/Mg ratio is essential for achieving a balance between high mechanical strength and manufacturing feasibility, as it enhances the extrudability window by decreasing intermetallic segregation and mitigating adverse tool-workpiece interactions for 7000 series aluminum alloys.

References

The original version of this paper is available on https://www.scientific.net/KEM.1048.73.pdf

N. Kawai, T. Nakamura, K. Dohda, Development of anti-weldability test in metal forming by means of strip-ironing type friction testing machine, J. Eng. Ind. 104 (1982) 375-382.

J.R. Davis, Aluminum and Aluminum Alloys, ASM International, 2001, pp. 351-416.

V. Vižintin, M. Kalin, K. Dohda, S. Jahanmir, Tribology of Mechanical Systems: A Guide to Present and Future Technologies, ASME, 2004.

S. Ngernbamrung, Y. Suzuki, N. Takatsuji, K. Dohda, Investigation of surface cracking of hotextruded AA7075 billet, Procedia Manuf. 15 (2018) 217-224.

S. Ngernbamrung, T. Funazuka, N. Takatsuji, S. Murakami, K. Dohda, Tearing mechanism for high-strength alloys in hot extrusion, J. Jpn. Inst. Light Met. 68 (2018) 660-666.

W.Z. Misiolek, R.M. Kelly, Extrusion of Aluminum Alloys, ASM Handbook, Volume 14A: Metalworking: Bulk Forming, ASM International, 2005, pp. 522-527.

K. Dohda, C. Boher, F. Rezai-Aria, N. Mahayotsanun, Tribology in metal forming at elevated temperatures, Friction 3 (2015) 1-27.

K. Dohda, M. Yamamoto, C. Hu, L. Dubar, K.F. Ehmann, Galling phenomena in metal forming, Friction 8 (2020) 797-810.

T. Funazuka, K. Dohda, N. Takatsuji, C. Hu, N. Sukunthakan, Effect of die coating on surface crack depth of hot extruded 7075 aluminum alloy, Friction 11 (2023) 1212-1224.

T. Funazuka, K. Dohda, N. Takatsuji, K. Takano, N. Sukunthakan, Effect of temperature on surface cracking defects in AA7075 hot extrusion, Key Eng. Mater. 926 (2022) 409-415.

J. Wang, Q. Dong, R. Wang, Q. Liu, Z. Feng, Z. Wu, H. Nagaumi, Influence of Zn/Mg ratio on mechanical strength and corrosion resistance of Al-Zn-Mg alloys, J. Alloys Compd. 1016 (2025) 178953.

K. Jiang, Y. Lan, Q. Pan, Y. Deng, Effect of the ratio on microstructures, mechanical properties and corrosion performances of Al-Zn-Mg alloys, Materials 13 (2020) 3299.

Y. Huang, P. Sun, L. Sun, Y. Li, X. Zheng, X. Li, H. Yan, B. Li, Y. Liu, Y. Du, Effect of Zn/Mg ratio on aging precipitates and mechanical property of high Mg content alloys with Sc and Zr additions, J. Alloys Compd. 976 (2024) 173368.

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/extrusion-and-drawing/978-3-0364-1995-4

Key Engineering Materials, ISSN: 1662-9795, Vol. 1048, pp 83-91

doi: 10.4028/p-Jpedh0

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-15

Revised:
2026-02-24

Accepted:
2026-02-24

Online:
2026-04-14

Using Tungsten Inert Gas Welding Equipment to Locally Heat Tubes and Achieve Inhomogeneous Thickness in Tube Sinking Processes

The original version of this paper is available on https://www.scientific.net/KEM.1048.83.pdf

Lemopi Isidore Besong 1,a*, Johannes Buhl 1, b

1 Institute of Metallurgy, Clausthal University of Technology, Robert-Koch-Strasse 42, 38678

Clausthal-Zellerfeld, Germany

a*besong.besong.lemopi.isidore@tu-clausthal.de, b Johannes.buhl@tu-clausthal.de

Keywords: tube sinking, local heating, variable wall thickness, process window.

Abstract

Tubes with non-uniform thickness are needed to even out wall thickness in draw bending and provide higher stiffness in specific directions in some applications. Tailored local heating of the tubes in tube sinking operations should reduce the local flow stresses and facilitate differential deformation along the circumference of tubes to form tubes with uneven wall thicknesses. Local heating of tubes prior to entry into the die in tube sinking is implemented in this research to form tubes with higher thickness in desired directions. Initial experiments are conducted using plasma heating by tungsten inert gas (TIG) welding equipment on EN AW 6060 AlMgSi 0.5 aluminum tubes. The process window is described by varying the process temperature (weld current between 50 A and 80 A) while altering the degree of deformation, the tube diameter, and tube thickness. Tubes with no defects were formed at 50 A . Increasing the weld current led to a higher wall thickness (up to 25% thickness increase), however, high weld currents also favored the formation of surface defects, wrinkle formation, or burn-through holes depending on the process setup. The process window was larger for tubes with higher wall thickness.

Introduction

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Tubes with uniform wall thickness are often desired to simplify design. However, tubes with nonhomogenous wall thickness are required to improve bendability and stiffening in specific directions. Cracks may occur on the outer arc in tube draw bending due to excessive material thinning, while thickening and wrinkling occur in the inner arc [1]. Tubes with variable wall thickness offer the possibility to have higher wall thickness on the outer arc of the bend, thereby preventing fracture, especially for small bend radii [2]. The stiffness of mechanical components can be enhanced by increasing the thickness in desired directions while reducing thickness in other directions to reduce component weight. Guillot et al. [3] estimated that a 25% reduction in weight can be achieved by switching from designs with constant thickness to variable thickness. Weight reduction is desirable to reduce energy consumption and related CO2 emissions by vehicles. Examples of tubular components that are loaded in specific directions include bicycle frame tubing, the landing gears of certain small aircraft, and tubular impact absorbers in car bumpers.

Current manufacturing practice uses dedicated forming tools like oval, tapered, and stepped mandrels with variable geometries to form tubes with variable circumferential or axial thickness. The tube geometry and thickness are determined by the space between dies and mandrels. Dedicated forming tools can only form components of a specific geometry, which limits process flexibility. To increase flexibility, Bihamta et al. [4] used a position control mandrel that permits tube sinking followed by tube drawing sequences to form tubes with variable thickness in the axial direction. Negendank et al. [5] proposed using a stepped mandrel that moves in the axial direction. Jäger et al. [6] exploited the high formability of electromagnetic forming to locally alter the cross-section and thickness of tubes. Entire cross sections were altered in the study with no consideration for a specific direction. Carradó et al. [7] performed tube drawing with tilted die and mandrel angles to control eccentricity and mechanical properties. They also investigated the residual stresses, crystallographic texture, dislocation density, and control eccentricity of Cu tubes to increase part precision [8, 9].

Local heating has been implemented in forming processes to alter the geometry and improve local formability [10]. Local heating causes a reduction in the yield stress and flow stress, which leads to an alteration in the deformation mode of a workpiece since material flow is non-homogeneous. Deformation in tube sinking is due to a combination of circumferential compression and positive or negative radial strains depending on the die angle [11]. Locally heating portions of the tube circumference prior to entry into dies will cause a drop in the local flow stresses and increase material flow to the heated portion. The cross-section of tubes can be altered without the need for dedicated mandrels, thereby lowering process cost and increasing process flexibility.

Various heating methods can be used as a heat source in tube sinking with local heating. The choice of heat source is influenced by the required precision, heating time, ease of process control, and material of the tube. Laser and induction heating may offer highly precise heating but are expensive alternatives. A gas heating source is a cheaper alternative; however, it offers low precision and is ideal for heating large portions. In this study, local heating is performed using plasma heating TIG welding equipment, as shown in Fig. 1. Plasma heating has a lower cost than induction and laser heating, with higher precision compared to gas heating. The power (current) of the welding equipment is known and can be controlled to achieve desired outcomes.

[picture]

Fig. 1. Setup of tube sinking with induction heating.

Fig. 1. Setup of tube sinking with induction heating.

Materials and Methods

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The experiments were conducted on EN AW 6060 AlMgSi 0.5 aluminum tubes with different diameters and wall thicknesses. This aluminum alloy is known for its corrosion resistance, good surface finish, and medium strength. The aluminum tubes had 20 mm,25 mm, and 30 mm diameters. The 20 mm and 25 mm tubes had 1 mm and 2 mm wall thicknesses, while the 30 mm tubes were only 2 mm thick. The tube diameters are reduced through dies of different sizes to represent various degrees of deformation. The 20 mm diameter tubes were reduced to 19 mm,18 mm, and 16 mm . The 25 mm and 30 mm diameter tubes were reduced to 24 mm,23 mm,21 mm, and 29.2 mm,28 mm, and 26 mm , respectively. The tubes and corresponding die diameters used in forming are presented in Table 1. Rotary swaging was performed on one end of the tubes before clamping them with the chuck used to draw the tubes through the dies, as shown in Fig. 2c. The tube was drawn at a velocity of 2 m/min, while local heating was performed at 25 mm from the opposite side of the die. Titanium 321 AC/DC TIG welding equipment made by GYS, Saint-Berthevin, France, was used as the heat source in the experiments. Given that this is the first experimental study using the novel process setup, the current was varied from 30 A to 90 A in steps of 10 A to determine the process window. The dry lubricant Molykote D-321 is used to reduce the friction between the die and tubes. The temperatures of the tubes at exit from the dies were recorded with the aid of a thermal imaging camera, OPTRIS model PI640i, and OPTRIS Pix Connect software. The camera was placed 25 cm from the die exit.

The wall thicknesses of the formed tubes were measured at 9 locations along the circumferences of the tubes using a vernier caliper. The setup of the experiments is presented in Fig. 2.

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Fig. 2. Experimental setup of tube sinking with plasma heating.

Fig. 2. Experimental setup of tube sinking with plasma heating.

Table 1. Design of experiments.

Current [A] | Initial pipe diameter Φ [mm] | Wall thickness of tube [mm] | Die size Φ [mm]
(50/60/70/80) | 20 | 1 | 19 | 18 | 16
(50/60/70/80) | 20 | 2 | 19 | 18 | 16
(50/60/70/80) | 25 | 1 | 24 | 23 | 21
(50/60/70/80) | 25 | 2 | 24 | 23 | 21
(50/60/70/80) | 30 | 2 | 29.2 | 28 | 26

Results

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Temperature. The temperatures of the 20 mm tubes with 1 mm wall thickness drawn using the 19 mm die were measured upon exit from the die after forming, see Fig. 3. The highest temperature of about 60∘C was recorded at 90 A . The temperature dropped to 56∘C at 80 A and 54∘C at 50 A . The maximum temperature dropped with a reduction in weld current. However, there was no linear decrease in the temperature, which can be attributed to the limits of thermal cameras in temperature measurement and the low variation of the tube temperatures formed using 50 A,60 A, and 70 A . Determining the exit temperatures of the tubes is required to monitor and control the heat input in the process.

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Fig. 3. Temperature of 20 mm tubes formed using the 19 mm die at exit from the die. a) 80 A . b) 70 A . c) 60 A d) 50 A .

Fig. 3. Temperature of 20 mm tubes formed using the 19 mm die at exit from the die. a) 80 A . b) 70 A . c) 60 A d) 50 A .

Process window. The process window and optimal forming conditions were determined by varying the tube diameter and thickness, degree of deformation, and weld power. Other parameters such as the distance between the weld gun and die, the gap between the weld gun and tube, and the drawing speed were constant. A short distance between the weld gun and die may lead to higher thickness variation because the material enters the die at higher temperatures and is formed at high temperatures that are associated with lower flow stress and higher formability. The gap between the weld torch and tube can only be varied within a very limited range since the weld circuit needs to be completed for welding to occur. High drawing speeds reduce the heat input per unit length of the material and result in lower formability.

TIG welding of the aluminum tubes can be performed at different currents based on the blank thickness. However, the high temperatures in welding cause microstructural changes and reduced strength. The temperature in tube sinking with local heating should permit forming in the warm to hot temperature range, which has high formability but with little alteration of microstructure. As a result, the process current was increased from 30 A to 90 A in steps of 10 A to determine the influence of the current on wall thickness and tube geometry in the first trial experiments, see Table 2. Visual inspection was used to classify the tubes based on the appearance of the tube surface. Good refers to tubes without surface defects, surface refers to tubes with some surface defects, wrinkling represents tubes with more pronounced surface defects and hole refers to tubes with burn-through holes. The tube diameter before drawing, wall thickness, and degree of deformation are maintained at a constant value. Only the current (heat input) is altered in these investigations.

Table 2. Current variation in tube drawing with local heating (initial trials).

Tube diameter (mm) | Wall thickness (mm) | Exit diameter (mm) | Current (A) | Results
20 | 1.0 | 19 | 90 | Hole red indicator
20 | 1.0 | 19 | 80 | Wrinkling yellow indicator
20 | 1.0 | 19 | 70 | Wrinkling yellow indicator
20 | 1.0 | 19 | 60 | Surface light green indicator
20 | 1.0 | 19 | 50 | Good green indicator
20 | 1.0 | 19 | 40 | No heat
20 | 1.0 | 19 | 30 | No heat

The pictures of the formed tubes are presented in Fig. 4. The results of the first trials reveal that at the highest current of 90 A , excess heat caused the formation of burn-through holes, as shown in Fig. 4a. Tubes could be formed at lower currents of 80 A and 70 A , however, wrinkles and very poor surface quality were observed on these parts, see Fig. 4b. The tube formed at 60 A had improved surface quality but was still not acceptable for most applications. The best results were achieved for the tubes formed at 50 A , as the surface quality did not degrade due to heating. For the low currents ( 40 A and 30 A ), no heat was generated by the welding equipment.

[picture]

Fig. 4. Effects of input current on surface quality in the tube sinking process.
a) Burn-through at 90 A . b) Very rough surface and wrinkle formation at 80 A and 70 A . c) Rough surface at 60 A . d) Good surface at 50 A .

Fig. 4. Effects of input current on surface quality in the tube sinking process. a) Burn-through at 90 A . b) Very rough surface and wrinkle formation at 80 A and 70 A . c) Rough surface at 60 A . d) Good surface at 50 A .

More experiments were performed over a range of parameters to establish the process window. The tubes were successfully formed at currents between 50 A and 80 A depending on the process setting. The 20 mm diameter tubes with a wall thickness of 1 mm required 50 A to successfully form tubes, see Fig. 5a. At higher currents, the 16 mm diameter tubes had burn-through holes. The rest of the tubes formed at 60 A,70 A, and 80 A had surface defects or wrinkles. The 20 mm diameter tubes with a thickness of 2 mm had no defects when formed using 50 A current and 18 mm and 19 mm dies. However, at higher currents, either surface defects or wrinkles were observed, as seen in Fig. 5b. The 25 mm diameter tubes with a wall thickness of 1 mm formed at 50 A had surface defects except for the 23 mm diameter tube, which had a good appearance. All the tubes formed at 60 A , 70 A , and 80 A had holes, as shown in Fig. 5c. At a higher thickness of 2 mm , the 25 mm tubes were formed in a wide range of current and die diameters, as shown in Fig. 5d. Only the tubes formed to 24 mm diameters heated at 60 A,70 A, and 80 A had surface defects. The 30 mm tubes with 2 mm thickness had no surface defects at 50 A and 60 A except for the tube formed to 29.2 mm diameter heated using the 60 A heat source, see Fig. 5e. All the tubes formed using 70 A had surface defects. At 80 A , tubes formed to 26 mm and 28 mm diameters had wrinkles, while the 29.2 mm tube had surface defects.

In general, the experiments revealed that the components with no defects were formed at 50 A , as higher energy inputs caused surface defects, wrinkle formation, or burn-through. Increasing the wall thickness reduced the presence of defects, which can be explained by the ability of the higher tube thickness to absorb heat. The formability increases at higher wall thicknesses.

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Fig. 5. Results of tube drawing with local heating. a) 20 mm diameter tubes with 1 mm wall thickness. b) 20 mm diameter tubes with 2 mm wall thickness. c) 25 mm diameter tubes with 1 mm wall thickness. d) 25 mm diameter tubes with 2 mm wall thickness. e) 30 mm diameter tubes with 2 mm wall thickness.

Fig. 5. Results of tube drawing with local heating. a) 20 mm diameter tubes with 1 mm wall thickness. b) 20 mm diameter tubes with 2 mm wall thickness. c) 25 mm diameter tubes with 1 mm wall thickness. d) 25 mm diameter tubes with 2 mm wall thickness. e) 30 mm diameter tubes with 2 mm wall thickness.

Thickness variation. The wall thickness of the 20 mm tube formed to 16 mm with a 2 mm initial wall thickness and heated using the 80A current is shown in Fig. 6. There is a significant increase in the tube thickness along the circumference in the heated zone. The outer surface is wavy along the thickened portion of the circumference, which reveals the presence of surface defects. The thicknesses of the tubes are measured at 9 points along the circumference of the tube, as indicated in Fig. 6b.

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Fig. 6. a) Tube with inhomogeneous wall thickness. b) Points used to measure wall thickness.

Fig. 6. a) Tube with inhomogeneous wall thickness. b) Points used to measure wall thickness.

The wall thickness of the 25 mm and 30 mm tubes with 2 mm wall thickness formed to different degrees are presented in Fig. 7. These results are presented because the tubes with the best appearances were formed in these test conditions, and there were no burn-through holes. Reducing the tube diameter at different levels (degrees of deformation) has minimal influence on the wall thickness and eccentricity in general, as revealed in Fig. 7a and Fig. 7c. The tube diameter was reduced from 25 mm to 21 mm,23 mm, and 24 mm in Fig. 7a and from 30 mm to 26 mm,28 mm, and 29.2 mm in Fig. 7c. However, the 30 mm tube reduced to 26 mm had a high thickness of 2.25 mm in the heated portion. This may be because, although the process temperature for the 50 A is low, the degree of deformation is high, and the high flow stress causes local thickening. Increasing the current (temperature) leads to an increase in wall thickening. Local heating had a minimal effect on the thickness of the 25 mm diameter tubes formed to 21 mm at 50 A and 60 A . At higher currents (70 A and 80 A ), the thickness increased because of high temperatures at the same degree of deformation. The 30 mm diameter tubes reduced to 26 mm diameter revealed an increase in wall thickness, see Fig. 7d.

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Fig. 7. Thickness variation in tube sinking with local heating. a) Tubes with 25 mm diameter reduced to 24 mm,23 mm, and 21 mm . b) Tubes with 25 mm diameter reduced to 21 mm using different currents. c) Tubes with 30 mm diameter reduced to 26 mm,28 mm, and 29.2 mm .
b) Tubes with 30 mm diameter reduced to 26 mm using different currents.

Fig. 7. Thickness variation in tube sinking with local heating. a) Tubes with 25 mm diameter reduced to 24 m m , 23 m m , and 21 mm . b) Tubes with 25 mm diameter reduced to 21 mm using different currents. c) Tubes with 30 mm diameter reduced to 26 m m , 28 m m , and 29.2 mm . b) Tubes with 30 mm diameter reduced to 26 mm using different currents.

Discussion

The original version of this paper is available on https://www.scientific.net/KEM.1048.83.pdf

Increasing the current of the TIG heat source leads to an increase in the temperature of the tube. This agrees with the high temperature recorded in the tube drawing experiment at 80 A compared to the lower currents, see Fig. 3. Heating leads to thermal softening and a drop in the local flow stress. Thermal softening causes the material to flow more easily to the high-temperature region indicated by the local thickening in the heated zone. This agrees with the change in the wall thickness along the heated part of the tube circumference observed in Fig. 6. High temperatures correlate with increased formability, as demonstrated by the high wall thickness for the tubes formed at the high
currents for the 25 mm and 30 mm tube diameters in Fig. 7. However, thermal softening also causes surface defects (wavy surface) due to the friction between the die and tube. Increasing the wall thickness provides more material to be heated and formed, which accounts for the higher formability of the thicker tubes. Forming using dies with small diameters increased the degree of deformation and local thickening of the tube as highlighted in Fig. 7c, however, its influence is lower than the influence of weld current on formability. By controlling other parameters such as the distance between the weld gun and die, heat source distance, and drawing speed, ideal process temperatures that allow thickening of the tube without a reduction in surface quality can be achieved.

A major hindrance of the TIG heat source is the fact that a threshold current is required for heating to occur, eliminating the possibility of performing heating at lower temperatures than that needed to create the electric arc. In addition, the heating zone is mainly controlled by the size of the fixed tungsten filament and not the distance from the heat source to the tube, thereby reducing control over the size of the heated zone. In future research, the heat source will be altered to overcome these hindrances.

Conclusions

The original version of this paper is available on https://www.scientific.net/KEM.1048.83.pdf

Local heating in tube sinking by a TIG welding gun resulted in thickening of the walls of the tubes. An increase in wall thickness of over 25% was achieved for some process setups.

The current had the most significant effect on the resulting wall thickness. Higher currents resulted in increased wall thickness. However, current below 40 A could not be used to heat the tubes.

The process window in tube sinking local heating is mainly controlled by the tube thickness and input current. Burn-through holes and other defects are easily formed in the drawing of tubes with thin walls. Increasing the tube thickness leads to higher formability.

Acknowledgements

The original version of this paper is available on https://www.scientific.net/KEM.1048.83.pdf

Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Project Number: 556119258

References

The original version of this paper is available on https://www.scientific.net/KEM.1048.83.pdf

Engel, B., and H. Hassan. "Advanced model for calculation of the neutral axis shifting and the wall thickness distribution in rotary draw bending processes." Int J Mech, Aerosp Ind Mechatronics Eng 9.2 (2015): 239-243.

Christopher, H.; Rainer, S.; Bernd, E. Rotary-draw-bending using tools with reduced geometries. Procedia Manuf. 2018, 15, 804-811. .

M. Guillot, M. Fafard, S. Girard, A. Rahem, G. D'Amours. Experimental exploration of the aluminum tube drawing process for producing variable wall thickness components used in light structural applications. SAE Technical Paper 2010-01-0222, 2010, 2010-01-0222.

R. Bihamta, Q.H. Bui, M. Guillot, G. D'Amours, A. Rahem, M. Fafard. A new method for production of variable thickness aluminium tubes: Numerical and experimental studies. JMPT 211 (2011) 578-58. .

M. Negendank, S. Müller, W. Reimers. Extrusion of aluminum tubes with axially graded wall thickness and mechanical characterization. Procedia CIRP 18 (2014) 3-8. .

A. Jäger, D. Risch, A.E. Tekkaya. Thermo-mechanical processing of aluminum profiles by integrated electromagnetic compression subsequent to hot extrusion. Journal of Materials Processing Technology 211 (2011) 936-943. .

Carradó A, Foadian F, Palkowski H. Tube drawing with tilted and shifted die, in: 60 Excellent Inventions in Metal Forming, Edited by Tekkaya AE, Homberg, W, Brosius A, 01/2015, Springer, ISBN: 978-3-662-46312-3.

Foadian F, Carradó A, Palkowski H. Precision Tube Production: Influencing the Eccentricity and Residual Stresses by Tilting and Shifting. J. of Materials Processing Technology, 2015, 155-162, .

Al-Hamdany N, Salih MZ, Palkowski H, Carradò A, Gan W, Schell N, Brokmeier HG. Tube drawing with tilted die: texture, dislocation density and mechanical properties. Metals 2021, 11, 638. .

O. Okman, M. Özmen, H. Huwiler, A. Tekkaya. Free forming of locally heated specimens, Int. J. Mach. Tools Manuf. 47 (2000) 1197-1205. . 2006.08.022.

B. Boljanovic. Metal shaping processes casting and molding particulate processing deformation processes and metal removal. 2009 Industrial Press Inc. New York.

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The original version of this eBook is available on https://www.scientific.net/book/extrusion-and-drawing/978-3-0364-1995-4

Key Engineering Materials, ISSN: 1662-9795, Vol. 1048, pp 93-107

doi: 10.4028/p-86kenT

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-18

Revised:
2026-02-16

Accepted:
2026-02-24

Online:
2026-04-14

Charge Weld Evolution in Profile Extrusion: Variability Across Billets and Multi-Profile Designs

The original version of this paper is available on https://www.scientific.net/KEM.1048.93.pdf

Ivan Kniazkin 1,a*, Ivan Kulakov 2,b and Nikolay Biba 3,c

1 Independent Researcher, Italy

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

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

aivanknjazkin@gmail.com, b  kulakov@qform3d.com, c  nick@qform3d.com 

Keywords: charge weld, transverse weld, simulation, QForm, defect, aluminium, quality, FEA

Abstract

Transverse (charge) welds form during billet transitions in aluminium extrusion when incoming material progressively replaces residual metal inside the die, defining the length of extrudate that must be scrapped. This study aimed to quantify charge weld evolution under industrially relevant conditions that are often underestimated in scrap length assessment, including multi-cavity flow imbalance, non-symmetric multi-profile placement, and billet-to-billet thermal stabilisation effects. Three case studies were analysed using finite element simulation in QForm UK: (i) the International Extrusion Benchmark 2023 multi-cavity die producing three hollow tubes with intentionally varied port and bearing designs, (ii) an industrial two-profile die with translated (nonmirrored) profile positioning to avoid post-extrusion rotation, and (iii) a complex industrial profile extruded over multiple consecutive billets. The benchmark study demonstrated strong agreement between simulation and experimental charge weld evolution for two profiles, supporting the reliability of the predicted cavity-dependent differences driven by port volume. In the translated twoprofile configuration, the charge weld cut length required for full purity increased from 1674 mm to 1940 mm(+16.0%), and by +15.9% under the 95% industrial criterion ( 1458.1 mm vs 1690.7 mm ). Billet-to-billet variability was substantial, with charge weld length increasing by +70.1% from the first to the fifth billet ( 2819.0 mm to 4791.7 mm ), before stabilising. Overall, the results show that charge weld length is governed by residence time differences through ports and flow channels, requiring profile-specific assessment and consideration of process stabilisation. In this context, FE simulation provides an effective means to localise the mixed zone and to support die optimisation strategies aimed at reducing scrap.

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1048.93.pdf

Charge welds (transverse welds) form at billet transitions [1] when incoming metal replaces the residual material inside the tool [2] (Fig. 1). The resulting interface travels through the section during the initial stage of the billet push and defines how much of the leading extrudate must be scrapped [3,4].

[picture]

Fig. 1. Schematic representation of charge propagation in a porthole dies

Fig. 1. Schematic representation of charge propagation in a porthole dies

The so-called stop-mark in Fig. 1 is a visible line on the profile surface formed during billet changeover: the discard from the previous billet is removed and the next billet is brought into contact with the tool before extrusion resumes. During this pause, unloading of the previous billet causes an elastic recovery of the tooling, which locally compresses the profile at the bearing exit. When extrusion restarts, the tool is loaded again and the profile advances, leaving a distinct stop-mark on the surface. In industrial practice, the stop-mark is commonly used as the zero reference for scrap control, and the scrap length is evaluated directly as the cut length measured from this mark (Fig. 1). Where a conservative policy is required, to avoid any residual back-end defects [5], the cut length may be extended slightly downstream of the nominal charge weld related zone, providing an additional safety margin.

From a mathematical perspective, charge weld propagation can be described by the minimum and maximum extents of the mixed zone along the profile length [6]. The minimum value can be expressed as follows:

Lmin=ttran min·Vprofile (1)

Where ttran min is the transit time of the fastest material point passing through the ports and entering the profile, and Vprofile  is the average profile exit velocity. Accordingly, the maximum extent of the mixed zone can be defined as follows:

Lmax=ttran max·Vprofile (2)

Where ttran max is the transit time of the slowest material point passing through the ports and entering the profile. Therefore, as shown by equations (1) and (2), the charge weld propagation is primarily governed by the time required for material to traverse the ports and other flow channels before reaching the profile exit. Consequently, the smaller the difference between the fastest and slowest transit times, the shorter the resulting charge weld length [7].

In industrial practice, the central question is how long this weld-affected segment will be, and secondarily how its trajectory crosses the section. Prior investigations on this topic can be divided into two broad groups:

Process (technological) settings. Within typical velocity modes, in industrial conditions, adjusting ram speed produces only modest shifts in how the interface between two billets advances, and changing billet temperature is largely insignificant for predicting charge weld length. In effect, process settings act as secondary levers rather than the principal drivers of scrap length [8].

Tool geometry. The dominant control arises from the die's flow concept. Studies show that the distribution/volume of ports and the shape of webs/bridges set the flow partitioning and the size/persistence of dead-metal zones, thereby shortening or lengthening the weld-affected segment. Designs that balance flow between ports, minimise port volume, and use tapered webs to promote uniform downstream convergence reduce the length and stabilise the flow trajectories forming the charge weld [2,9].
Finite element (FE) simulation plays an important role in industrial charge weld analysis because it enables accurate localisation of the mixed zone within the profile cross-section and provides direct insight into the transient material replacement mechanisms occurring during billet transitions. By resolving the evolving flow through the ports and welding chamber, the model captures how residence time differences translate into a spatially distributed billet interface, which can be used to guide tool optimisation measures aimed at reducing scrap [10]. Figure 2 shows good agreement between the numerically predicted mixed zone position and the billet interface observed experimentally, indicating that the simulation captures the key features of charge weld propagation.

[picture]

Fig. 2. Comparison of simulated in QForm UK Extrusion charge weld propagation at different distances from stop-mark with respective experimental sections

Fig. 2. Comparison of simulated in QForm UK Extrusion charge weld propagation at different distances from stop-mark with respective experimental sections

To validate the prediction beyond a purely visual comparison, the distribution of old and new material computed in QForm UK Extrusion was compared with experimental macro-sections taken at fixed distances from the stop-mark ( 600,750 , and 900 mm ). In addition, agreement was quantified using an area-based "purity" metric defined as the fraction of new-billet material within the profile cross-section, evaluated independently for the simulation and the macro-sections. The resulting values (Table 1) confirm that the predicted interface evolution follows the experimentally observed trend: the model reproduces the measured purity at 600 and 750 mm with ≤0.6 percentage-points (pp) deviation, while the difference at 900 mm is 3.47 pp . Uncertainty bounds were estimated via a mask-boundary sensitivity analysis by applying a ±2px(≈±0.12 mm) erosion/dilation to the segmented interface region, providing error bars consistent with the image resolution. Using a ±1px and ±3px erosion/dilation produced the same qualitative conclusions. Overall, the combined visual and quantitative validation supports the use of the FE-approach as a reliable basis for interpreting charge weld behaviour in more complex extrusion profile designs [9, 11].

Table 1. Quantitative validation of charge weld evolution using macro-sections

Distance from stop-mark, mm | Exp. new material fraction, % | Sim. new material fraction, % | Δ, pp
600 | 17.81 ± 3.07 | 17.20 ± 2.45 | -0.61
750 | 30.24 ± 3.06 | 30.22 ± 2.94 | -0.02
900 | 38.41 ± 4.39 | 41.88 ± 4.57 | +3.47

Charge weld prediction has also been demonstrated using other FE-frameworks beyond QForm UK Extrusion, including HyperXtrude-based solutions and DEFORM Lagrangian and ALE-approaches reported in the literature. These studies similarly show that transient material exchange in the ports and welding chamber governs the evolution of the mixed zone and that validated FE-models can support die design decisions aimed at scrap reduction [2,9,12].

From an industrial perspective, the key objective in all cases is to minimise scrap while ensuring that no mixed or back-end affected material enters downstream processing or customer supply.

Description of the Case Studies

The original version of this paper is available on https://www.scientific.net/KEM.1048.93.pdf

Based on these considerations, three representative industrial case studies, often insufficiently addressed in the literature, were selected to isolate the effects of die design, profile placement strategy, and billet-to-billet thermal evolution on charge weld propagation. In the present work, experimental macro-sections were available for the validation section (Introduction) and for Case study 1 (benchmark design). For Case studies 2 and 3, the analysis therefore relies on the repeatedly validated modelling setup and on the established predictive capability of FE-based charge weld simulations reported in the literature [13-15].

The corresponding process parameters and key geometrical details for all investigated cases are summarised in Table 2.

Table 2. Technological and geometrical parameters of the investigated processes

Parameter | Units | Case 1 | Case 2 | Case 3
Alloy | — | AA 6082 | AA 6063 | AA 6061
Extrusion ratio | — | 46.35 | 77.99 | 32.54
Container temperature | [°C] | 400 | 420 | 425
Billet temperature | [°C] | (390-425) | 460 | 490
Tool temperature | [°C] | 480 | 450 | 460
Profile velocity | [m/min] | 12.24 | 10.00 | 4.5
Billet length | [mm] | 680 | 1060 | 700
Container diameter | [mm] | 286 | 265 | 210

The extrusion simulations were conducted using standard hot AA 6063 and AA 6061 alloys, readily available in the QForm UK material database and AA 6082 alloy specifically tested for the international extrusion benchmark test 2023. The material behaviour was modelled using the Hensel-Spittel [16] equation (3) with coefficients presented in Table 3, calibrated for hot aluminium extrusion over the strain, strain-rate, and temperature ranges considered: ε∈[0.05;7] for 6063 and 6061, and ε∈[0.075;8] for 6082; ε˙∈[0.01;500]s−1 for 6063 and 6061, and ε˙∈[0.01;100]s−1 for 6082;T∈[350;570]∘C for 6063 and 6061, and T∈[350;570]∘C for 6082.

σS=A·em1 T· Tm9·εm2·em4/ε·(1+ε)m5 T·εm7ε·ε˙m3·ε˙m8 T(3)

Table 3. Hensel-Spittel parameters for AA 6063, AA 6061 and AA 6082

Parameter | AA 6063 | AA 6061 | AA 6082
A | 265 | 219.5 | 107.8
m₁ | -0.00458 | -0.00455 | -0.00233596
m₂ | -0.12712 | -0.14576 | -0.0863852
m₃ | 0.12 | 0.13 | 0.112571
m₄ | -0.0161 | -0.01357 | -0.0202083
m₅ | 0.00026 | 0.0003 | 0.00007
m₇ | 0 | 0 | 0
m₈ | 0 | 0 | 0
m₉ | 0 | 0 | 0

International extrusion benchmark 2023. For the international extrusion benchmark test, a multi-cavity die was developed to investigate the influence of feeding conditions on extrusion performance [17]. This configuration was selected because multi-cavity tools are widely used to increase productivity, yet cavity-to-cavity differences can translate directly into unequal scrap lengths and yield losses. The benchmark die intentionally introduces controlled variations in port and bearing design to isolate the influence of feeding-zone geometry on charge weld propagation and to enable validation against macro-sections. The ports and bearing lengths were intentionally varied among the cavities to produce three hollow tube profiles under distinct processing conditions. The resulting differences in design are summarized in Fig. 3.

[picture]

Fig. 3. Geometry description for the international extrusion benchmark test 2023

Fig. 3. Geometry description for the international extrusion benchmark test 2023

Three cavity concepts were implemented to introduce controlled differences in feeding behaviour. Cavity 1 employed conical ports combined with a large welding chamber fillet ( 20 mm ) and a 6 mm bearing height. Cavity 2 used cylindrical ports with a smaller welding chamber fillet (8 mm) and a 7 mm bearing height, resulting in different flow conditions in the feeding zone. Cavity 3 was based on conical ports with a 20 mm welding chamber fillet and a reduced bearing height ( 5 mm ), additionally incorporating a local choke ( 1∘×4 mm ) to further influence the velocity field and port characteristics. These variations produced measurably different transient replacement behaviour and, consequently, distinct charge weld propagation lengths under otherwise comparable process conditions.

Industrial multi-profile design. The second investigation also addressed a multi-cavity die design. In this case, however, the tool was developed to extrude two non-symmetric profiles directly in an industrially preferred orientation (Fig. 4), thereby avoiding any post-extrusion rotation prior to

[picture]

Fig. 4. Base contours (left) and simulation setup (right) for the industrial multi-profile design

Fig. 4. Base contours (left) and simulation setup (right) for the industrial multi-profile design

downstream handling and processing. This reflects a common industrial constraint: profiles are typically transported and packaged in a fixed orientation, and introducing rotation adds operations and handling complexity. To satisfy this requirement for non-symmetric geometries, the cavities were arranged on the die orifice in a translated configuration rather than as mirrored counterparts. In practice, such a layout commonly leads to profile-specific bearing maps (Fig. 5) and may also impose different lateral offsets of the profile centrelines relative to the extrusion axis and port area/volume

[picture]

Fig. 5. Bearings map of the industrial multi-profile design (Case study 2)

Fig. 5. Bearings map of the industrial multi-profile design (Case study 2)

differences (Table 4), both of which influence flow balance. Consequently, depending on the profile geometry and its alignment within the die, translated placement can produce markedly different port-filling behaviour and, in turn, profile-dependent charge weld propagation. These effects must therefore be accounted for when defining the cut length and evaluating the resulting scrap.

Table 4. Geometrical parameters indicated on Fig. 4. H refers to the feeding height of the die set

Profile 1 | H | Profile 2
Parameter | Area | X₁ | Y₁ | H | Parameter | Area | X₂ | Y₂
Ports\Units | [mm²] | [mm] | [mm] | [mm] | Ports\Units | [mm²] | [mm] | [mm]
A | 926.0 | 58.0 | 15.0 | 68.0 | E | 1046.0 | 63.5 | 20.5
B | 905.0 | F | 1021.0
C | 942.0 | G | 769.0
D | 936.0 | H | 800.0

Billet-to-billet variability. The third analysis considered a complex industrial profile (Fig. 6). The specific tool design required to enable extrusion of this profile on the selected press resulted in substantial variations in charge weld propagation across several billets extruded sequentially.

This scenario targets industrial start-up and early production stages, where tooling and billet temperatures evolve rapidly and scrap policies are often based on conservative assumptions. Quantifying billet-to-billet variation is therefore essential to avoid under-cutting (risk of residual defects) or excessive over-cutting (avoidable yield loss) during thermal stabilisation.

In this project, the extrusion conditions evolved from billet to billet due to uneven heating/cooling of the tool and changes in the thermomechanical state of the material within the feeding zone. As a result, the velocity distribution in the ports and the residence time balance between competing flow paths varied across the initial billets before approaching a stable regime. To capture this effect, the charge weld evolution was evaluated over a sequence of consecutive billets simulated one after another, allowing the resulting change in mixed-zone length to be quantified and linked to the stabilisation of temperature and flow conditions during production.

[picture]

Fig. 6. Base contours (left) and simulation setup (right) for the billet-to-billet variability project

Fig. 6. Base contours (left) and simulation setup (right) for the billet-to-billet variability project

Description of the Models Used in the Simulation

The original version of this paper is available on https://www.scientific.net/KEM.1048.93.pdf

Coupled mechanical and thermal tasks. Material flow and temperature were simulated using a fully coupled thermo-mechanical approach that accounts for tool deformation and transient heat transfer between the billet and the die set. In this framework, tool deformation affects the local flow conditions, while the tool response is driven by the contact pressure exerted by the deforming billet. Likewise, the billet temperature evolution is governed not only by heat generation due to plastic deformation and friction, but also by heat exchange with the tooling. The coupled solution is obtained iteratively, with automatic remeshing of the deforming flow domain to maintain numerical stability
and accuracy. The heat flux across the billet-tool interface is prescribed using the Newton-Richmann law:

qn=α(T1−T2)(4)

where α is the interfacial heat-transfer coefficient (default value equal to 30000 W/m2 K in QForm UK Extrusion), T1 is the billet temperature, and T2 is the tool temperature.

Friction. Friction at the aluminium-steel interface was described using the Levanov friction law, which generalises the Coulomb and Siebel (shear) formulations and provides a smooth transition between low- and high-pressure contact regimes. With relatively low normal pressures, typical of the bearing region, the predicted shear stress approaches the Coulomb-type behaviour. At higher normal pressures, characteristic of the remaining billet-tool contact surface, the response tends toward the Siebel limit. In the Levanov model, the interfacial shear stress τ is expressed as a function of the friction factor m , the material flow stress σs, the normal contact pressure σn, and the Levanov coefficient n :

τ=m·σs3·(1−e−n·σnσs)(5)

In QForm UK Extrusion, the default Levanov coefficient n=1.25 and friction factor m=1 were adopted for all simulations.

Mesh. The simulations were performed using an adaptive finite-element mesh with automatic remeshing enabled to preserve element quality as the tool deformed. Local refinement was applied in

[picture]

Fig. 7. Mesh in profile for presented case studies

Fig. 7. Mesh in profile for presented case studies

regions with high strain and temperature gradients, particularly in the ports, welding chamber, and bearing zone, while coarser elements were used elsewhere to limit computational cost. To document the effective mesh resolution in the profile, Figure 7 presents representative cross-sections for each case study, illustrating the applied refinement through the wall thickness. Quantitative mesh characteristics for all investigated cases are summarised in Table 5, including the minimum profile wall thickness, the maximum element size across the thickness, and the resulting mesh size in terms of nodes and surface/volume elements.

Table 5. Initial mesh properties

Case study | Min. profile wall thickness, mm | Max. element size in profile, mm | Min. elements across wall thickness, # | Total nodes, # | Surface elements, # | Volume elements, #
1 | 4.00 | 1.32 | 3 | 188 204 | 92 922 | 1 015 581
2 | 1.52 | 0.50 | 3 | 512 842 | 207 328 | 2 826 729
3 | 2.67 | 0.66 | 4 | 260 519 | 120 816 | 1 410 559

Results and Discussion

The original version of this paper is available on https://www.scientific.net/KEM.1048.93.pdf

Multi-profile extrusion (Case studies 1-2). This section discusses the charge weld evolution in the two investigated configurations and highlights how die layout and flow balance affect the mixedzone propagation.

International extrusion benchmark 2023. In this case study, where three round tube profiles were arranged radially around the extrusion axis and extruded through different feeding zone configurations, there was a clear difference in charge weld length observed in both simulation and experiment. A comparison between the simulated and experimentally measured charge weld evolution is provided in Fig. 8.

As noted in [17], the experimental trend for Profile 2 was not reported because the presence of a Peripheral Coarse Grain (PCG) region made it difficult to determine the start and end of the defect with sufficient accuracy. For this reason, the corresponding experimental curve is not shown on Fig. 8. Nevertheless, the simulations for Profile 1 and Profile 3 exhibit excellent agreement with the experimental data. Given mass conservation (continuity), this level of agreement supports the reliability of the predicted evolution for Profile 2 as well.

Figure 8 shows that the charge weld region in Profile 2 is substantially longer than in the other profiles. This is mainly due to the larger port volume in the Profile 2 cavity compared with the remaining cavities. The larger metal volume affects the material exchange in the ports and is

[picture]

Fig. 8. Charge weld evolution for transitions between billets 3 and 4. Simulation vs Experiment

Fig. 8. Charge weld evolution for transitions between billets 3 and 4. Simulation vs Experiment

associated with a higher flow velocity through this cavity. Consequently, while the material remaining in the ports from the previous billet is progressively replaced by incoming material from the new billet, Profile 2 undergoes greater length growth. This results in a longer distance before the extrudate reaches full material replacement, compared with the other profiles.

Similar differences may also occur even when port volumes are comparable, particularly when multiple profiles are placed on the die orifice without symmetric alignment. In industrial practice, such non-symmetric layouts are common: enforcing symmetry for multiple non-symmetric profiles can require additional downstream handling, because profiles are typically bundled and packaged while maintaining their spatial arrangement. An example of this industrially relevant case is discussed in the following section.

Industrial multi-profile design. The charge weld evolution in this project differs significantly between the two profiles (Fig. 9). Profile 1 reaches the full-purity condition at a shorter distance from the stop-mark ( 1674 mm ), whereas Profile 2 exhibits a longer transition and reaches full-purity only at 1940 mm , indicating a prolonged persistence of mixed/contaminated material. Relative to Profile 1, this corresponds to an additional scrap length of 16.0% when the full charge weld length is used. When evaluated using the 95% criterion commonly adopted in industrial practice, the required length increases by 15.9% ( 1458.1 mm for Profile 1 versus 1690.7 mm for Profile 2).

The 95% criterion is widely applied in industrial practice as a pragmatic threshold for defining the end of the charge weld region in a cross-section and is commonly used as a reference value in the literature on charge weld evaluation [8, 14]. As discussed in [8, 18], one key reason for its adoption is that, beyond this level of material replacement, the influence of the transverse weld on the reduction of profile strength is generally considered no longer significant. In addition, the remaining fraction of the mixed zone is often difficult to detect reliably under production conditions, as it is typically confined to longitudinal welding seams or the near-surface region. This is further supported by observations that tensile properties of material sampled from the charge weld-affected region increase markedly once approximately 95% of new material is reached, making the terminal portion of the weld less distinguishable by standard quality-control methods [19].

In practical terms, if the cut-off length established for Profile 1 is applied directly to Profile 2, residual contamination may remain in Profile 2 at the cut location. Therefore, in this configuration it is essential to assess both profiles and define the minimum discard length that ensures complete removal of the mixed zone for each section. An alternative approach is to address this discrepancy at the design stage by developing a new tool that balances the charge weld evolution between the profiles. However, this would require manufacturing a new tool, because a difference of this magnitude cannot be resolved through standard correction measures.

Profile 2

Profile 1

Fig. 9. Charge weld evolution for two-cavity extrusion with no symmetry

Billet-to-billet variability (Case study 3). In this case study, the material flow changed markedly between billets due to temperature variations during the process. The most pronounced increase occurred between billets 1 and 2, after which the process gradually stabilised both thermally and kinematically. The resulting differences in port flow behaviour and the temperature distribution within the profile are illustrated in Fig. 10.

[picture]

Fig. 10. Temperature in profile and velocity in ports for the initial stage of 1st  and 5th  billet extrusion

Fig. 10. Temperature in profile and velocity in ports for the initial stage of 1 st and 5 th billet extrusion

This resulted in a pronounced increase in the charge weld propagation range, which expanded from ( 930.8−3749.8 ) mm for the first billet to (811.4−5603.1)mm for the fifth billet. In terms of the charge weld length (end-start), this corresponds to an increase from 2819.0 mm to 4791.7 mm , i.e. +70.1% relative to the first billet. The following changes (Table 6) are observed between consecutive billets (reported as the percentage increase in charge weld length relative to the preceding billet):

Table 6. Charge weld difference between multiple billets

Compared billets | Charge weld range, mm | Charge weld length, mm | Difference, %
1 | 2 | (930.8-3749.8) → (876.3-5132.2) | 2819.0 → 4255.9 | +51.0
2 | 3 | (876.3-5132.2) → (832.2-5375.8) | 4255.9 → 4543.6 | +6.8
3 | 4 | (832.2-5375.8) → (818.9-5539.7) | 4543.6 → 4720.8 | +3.9
4 | 5 | (818.9-5539.7) → (811.4-5603.1) | 4720.8 → 4791.7 | +1.5
1 | 5 | (930.8-3749.8) → (811.4-5603.1) | 2819.0 → 4791.7 | +70.1

A graphical comparison of the charge weld propagation is provided in Fig. 11. The relatively small difference between the 4th  and 5th  billet ( +1.5% ) indicates that the process has largely stabilised, and the resulting scrap length can therefore be predicted with good confidence for subsequent billets. This case demonstrates that, when the initial thermal conditions are not stabilised and the process undergoes significant thermal and flow variations before reaching steady operation, charge weld assessment should not be based solely on the first billet [14], as this may introduce a substantial error in scrap-length estimation.

[picture]

Fig. 11. Charge weld evolution for different billets in multi-billet extrusion

Fig. 11. Charge weld evolution for different billets in multi-billet extrusion

The observed variation in charge weld propagation is primarily linked to the evolving thermal state. As successive billets are extruded, isolated regions of the profile become slightly cooler, whereas the hottest zones continue to warm, increasing the temperature spread across the section (Fig. 12).

[picture]

Fig. 12. Evolution of the minimum and maximum profile temperature at the bearing exit measured for different billets in the beginning of the extrusion cycle

Fig. 12. Evolution of the minimum and maximum profile temperature at the bearing exit measured for different billets in the beginning of the extrusion cycle

Figure 12 shows that the largest change in the min-max exit temperature range occurs between billets 1 and 2, which is consistent with the pronounced change in charge weld length observed over the same interval.

Conclusions

The original version of this paper is available on https://www.scientific.net/KEM.1048.93.pdf

This study aimed to analyse charge weld evolution in aluminium profile extrusion under industrially relevant conditions, with emphasis on multi-cavity tooling, translated multi-profile layouts, and billet-to-billet variability caused by transient thermal stabilisation. Finite element simulation in QForm UK was used to predict mixed-zone propagation and scrap length, with reference to available experimental observations.

The main findings can be summarised as follows:

Die design governs charge weld evolution. Across all cases, the charge weld length is primarily controlled by residence time differences through ports and associated flow channels, consistent with the analytical description based on minimum and maximum transit times.

Finite element simulation reliably localises the mixed zone and supports scrap assessment. The simulations reproduced the experimentally observed interface evolution in tubular profiles, enabling quantitative scrap-length prediction and supporting optimisation decisions.

Multi-cavity tools can produce profile-dependent scrap length. In the benchmark die, one cavity exhibited a considerably longer charge weld due to its feeding-zone configuration, most notably a larger effective port volume leading to slower material replacement.

In translated and multi-cavity layouts, flow balance is additionally sensitive to cavity-to-cavity differences in port volume and to cavity alignment relative to the extrusion axis, which changes the local boundary conditions due to different proximity to the container wall. These factors can promote flow imbalance by increasing the average material throughput in one cavity relative to another, leading to cavity-dependent exit velocities and, consequently, different charge weld propagation and cut lengths. Therefore, port volume consistency and cavity positioning should be controlled not only to minimise local non-uniformity within each profile, but also to reduce inter-cavity differences in average profile velocity.

Translated multi-profile layouts require profile-specific cut length. In the two-profile industrial case, Profile 2 required a longer cut length than Profile 1: 1940 mm vs 1674 mm(+16.0%). Under the industrial 95% criterion, the required length increased from 1458.1 mm to 1690.7 mm(+15.9%), indicating that applying a single cut length may leave residual contamination in one profile.

Billet-to-billet variability can dominate early scrap prediction. For the complex industrial profile, the charge weld length increased from 2819.0 mm (billet 1) to 4791.7 mm (billet 5), i.e. +70.1%, with progressive stabilisation indicated by the small difference between billets 4 and 5(+1.5%).

Finally, because charge weld propagation is governed by the residence time spread between the fastest and slowest material trajectories through the feeding system, reducing this spread directly shortens the extent of the transverse weld. In practice, this can be achieved by promoting more uniform flow through the ports and welding chamber. The most direct lever for further shortening is to reduce the stored volume of the ports, for example by decreasing mandrel length, thereby lowering the residence time of retained material. This measure shifts the onset of the mixed zone closer to the stop-mark and reduces the distance required before the extrudate begins to reach a high fraction of new billet material.

The present work supports the following industrially relevant conclusions: scrap optimisation must consider both tool design and transient production conditions. The findings show that charge weld scrap cannot always be treated as a fixed process constant. Instead, it should be evaluated as a combined effect of (a) feeding-zone design and multi-cavity flow balance, (b) profile placement strategy (translated vs mirrored), and (c) billet-to-billet thermal evolution. Where large differences exist between cavities, balancing the design may require manufacturing a revised tool, as correction measures may be insufficient.

References

The original version of this paper is available on https://www.scientific.net/KEM.1048.93.pdf

T. Sheppard, Extrusion of Aluminium Alloys, Springer, 1999.

Y. Mahmoodkhani, M. Wells, N. Parson, C. Jowett, W. Poole, Modeling the formation of transverse weld during billet-on-billet extrusion, Materials 7 (2014) 3470-3480.

A.J. den Bakker, L. Katgerman, S. van der Zwaag, Analysis of the structure and resulting mechanical properties of aluminium extrusions containing a charge weld interface, Journal of Materials Processing Technology 229 (2016) 9-21.

N. Nanninga, C. White, R. Dickson, Charge weld effects on high cycle fatigue behavior of a hollow extruded AA6082 profile, Journal of Materials Engineering and Performance 20 (2011) 1235-1241.

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.

Q. Li, C. Harris, M.R. Jolly, Finite element modelling simulation of transverse welding phenomenon in aluminium extrusion process, Materials & Design 24 (2003) 493-496.

B. Reggiani, L. Donati, Experimental, numerical, and analytical investigations on the charge weld evolution in extruded profiles, The International Journal of Advanced Manufacturing Technology 99 (2018) 1379-1387.

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.

B. Reggiani, A. Segatori, L. Donati, L. Tomesani, Prediction of charge welds in hollow profiles extrusion by FEM simulations and experimental validation, The International Journal of Advanced Manufacturing Technology 69 (2013) 1855-1872.

B. Reggiani, L. Donati, L. Tomesani, Multi-goal optimization of industrial extrusion dies by means of meta-models, The International Journal of Advanced Manufacturing Technology 88 (2017) 3281-3293.

M. Crosio, D. Hora, C. Becker, P. Hora, Realistic representation and investigation of charge weld evolution during direct porthole die extrusion processes through FE-analysis, Procedia Manufacturing 15 (2018) 232-239.

B. Reggiani, T. Pinter, L. Donati, Scrap assessment in direct extrusion, The International Journal of Advanced Manufacturing Technology 107 (2020) 2635-2647.

E.C. Sariyarlioglu, J. Ma, T. Welo, Prediction of charge-weld strength in aluminum extrusion using solid-state welding models, Materials Research Proceedings 54 (2025) 754-763.

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 30.6 (2021) 4691-4699.

E.C. Sariyarlioglu, T. Welo, Towards In-process Scrap Reduction in Aluminium Extrusion: A Multiscale Investigation of Charge-Weld Integrity Advances in Industrial and Manufacturing Engineering 100183 (2026).

A. Hansel, T. Spittel, Kraft- und Arbeitsbedarf Bildsamer Formgebungsverfahren, Deutscher Verlag für Grundstoffindustrie, Leipzig, 1978.

R. Pelaccia, M. Negozio, S. Di Donato, B. Reggiani, L. Donati, Extrusion Benchmark 2023: Effect of die design on profile speed, seam weld quality and microstructure of hollow tubes, Key Engineering Materials 988 (2024) 47-62.

G.J. Oberhausen, A.A. Christopher, & D.R. Cooper, Reducing aluminum extrusion transverse weld process scrap, In Forming the Future: Proceedings of the 13th International Conference on the Technology of Plasticity (2021) 1003-1019.

A. Bakker, Weld Seams in Aluminium Alloy Extrusions: Microstructure and Properties, Ph.D. Thesis, Delft University 2016.

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/extrusion-and-drawing/978-3-0364-1995-4

Key Engineering Materials, ISSN: 1662-9795, Vol. 1048, pp 109-119

doi: 10.4028/p-3FoSSm

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-18

Revised:
2026-02-16

Accepted:
2026-02-24

Online:
2026-04-14

Development of a System for Ultrasonic Die Oscillations during Extrusion of Aluminum Hollow Profiles

The original version of this paper is available on https://www.scientific.net/KEM.1048.109.pdf

THOMAS Tamara 1,4,a,*, NEGENDANK Maik 2, b, LAENGST Nico 3,c, MERKLINGER Verena 1, d, MAIER Joachim 4,e and MUELLER Soeren 5,f

1 Department of Mechanical Engineering, HTWG Konstanz - University of Applied Science, Alfred-Wachtel-Str. 8, 78462 Konstanz, Germany

2 FORMING GmbH, Rohrdamm 88, 13629 Berlin, Germany

3 Department of Electrical Engineering, Augsburg University of Applied Sciences, An der Hochschule 1, 86161 Augsburg, Germany

4 WEFA Singen GmbH, Rudolf-Diesel-Str. 11, 78224 Singen, Germany

5 Extrusion R&D Center (FZS), TU Berlin, Gustav-Meyer-Allee 25, 13355 Berlin, Germany

atthomas@htwg-konstanz.de, b  maik.negendank@forming.de c  nico.laengst@tha.de, d  merklinger@htwg-konstanz.de, ejoachim.maier@wefa.com, f  soeren.mueller@tu-berlin.de

Keywords: aluminum extrusion, hollow profile, high-frequency die oscillation, FEM

Abstract

High friction in aluminum hollow profile extrusion limits material flow, process stability, and productivity. Ultrasonic vibration offers a promising approach to reduce friction, yet its application to industrial porthole dies is still insufficiently explored. This study presents the development and investigation of an ultrasonic die sonication system for aluminum extrusion. Finite element extrusion simulations demonstrate that reduced friction leads to lower extrusion forces, decreased profile exit temperatures, and improved material flow. A modified porthole die enabling ultrasonic excitation at multiple positions was designed accordingly. The vibrational behavior of the die was analyzed using three-dimensional modal and harmonic finite element simulations. Suitable excitation frequencies between 18.5 and 23 kHz were identified and experimentally validated by laser vibrometry, confirming effective transmission of ultrasonic vibrations into the die. The simulation results demonstrate the feasibility of ultrasonic die oscillation for aluminum hollow profile extrusion and provide a solid basis for forthcoming extrusion trials and further process optimization. The system was implemented, approved, and is now available for upcoming experimental trials.

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1048.109.pdf

Conventional extrusion of metallic materials is often limited by high forming forces and material flow, strongly influenced by friction conditions between billet material and container, ram and die [1]. Hence, friction can limit the manufacturability of complex profile cross sections such as very thin profile segments or very wide profiles. To address these limitations, friction can be reduced e.g. by applying lubricants. However, in light metal extrusion lubrication often is avoided, especially in case of porthole die extrusion. If the lubricant reaches into the welding chamber of a porthole die, seam weld quality could be reduced significantly. The increasing demand for high-performance metal profiles with superior dimensional accuracy, high productivity and surface quality has placed significant pressure on traditional extrusion processes. Another approach to reduce friction in forming processes are advanced assisted forming technologies e.g. involving vibration or ultrasonic excitation.

Research in various metal forming contexts and on a wide range of materials, including extrusion [2], drawing [3], and upsetting [4] has consistently shown that ultrasonic vibration can lead to a significant reduction in required forming forces and friction effects at the die-workpiece interface, although the magnitude of these effects depends strongly on vibration parameters and material properties [5]. Experimental studies have documented, that under ultrasonic excitation, the mean forming force can decrease notably, while oscillatory components of the force signal reflect complex dynamic interactions at the tool interface [6]. Related work in micro-forming and micro-
extrusion contexts has similarly demonstrated that ultrasonic vibration can improve surface quality and reduce forming load, reinforcing the potential of ultrasonic assistance for high-precision forming task [7].

Despite these results, efficient transmission of ultrasonic energy in large-scale industrial extrusion tools continues to pose engineering challenges, as sustaining stable high-power ultrasonic excitation under heavy mechanical loads can lead to reduced amplitude and effectiveness. Hence, this study presents a first step to investigate the effect of die sonication on the extrusion process and profile properties.

Materials and Experimental Setup

The original version of this paper is available on https://www.scientific.net/KEM.1048.109.pdf

Extrusion Simulation. In order to investigate the potential effects of e.g. reduced friction conditions between porthole die (H11 steel) and aluminum billet material (EN AW-6060) through die sonication, FEM simulations were conducted applying different friction coefficients. Conventional extrusion without die sonication was simulated in DEFORM ® 3D using a Tresca friction model with a friction factor of m=0.7 between the die and the aluminum billet. In addition, a previously developed friction model accounting for temperature and normal pressure at the contact interface was applied for comparison [8,9,10]. To investigate the potential influence of die vibration, no direct simulation of high-frequency oscillations was performed. Instead, the vibration-induced reduction in friction was represented by prescribing lower Coulomb friction coefficients. Based on literature data for extrusion processes under high-frequency die oscillation [11], Coulomb friction coefficients of μ=0.1 and μ=0.05 were assumed. This approach allows the isolated assessment of the mechanical consequences of a reduced friction level without explicitly modeling the oscillatory motion of the die. Since the use of coulomb friction is not common for aluminum extrusion simulations, a reduced Tresca friction coefficient of m=0.1 was selected as well. The simulation model is given in Fig. 1a. It consists of a prefilled die and a billet length of 160 mm . A ram velocity of Vram =5 mm/s was applied. Billet and container temperatures were set to TB=TC=500∘C, die temperature to Tdie =470∘C and ram temperature to Tram=460∘C. Zener Holomon model was applied to calculate flow stresses of EN AW6060 billet material. Necessary material constants were determined via hot compression tests to A=3.0·10−19 s−1,ΔH=155.0 kJ/mol,n=6.8 and α=0.0016MPa−1. Transient simulations were performed using Lagrange algorithm. Extruding the profile cross section given in Fig. 1b from a container with DC=125 mm leads to an extrusion ratio of R=28:1.

[picture]

Fig. 1. a) FEM-model for extrusion simulation in DEFORM ® 3D b) profile cross section

Fig. 1. a) FEM-model for extrusion simulation in DEFORM ® 3D b) profile cross section

Setup sonication system. The specialized german company MTH Ultraschalltechnologie GmbH & Co.KG was consulted for suitable components to build an applicable die sonication system. Similar to recent literature [2, 7], this system consists of a high-frequency generator (HF) type K5 ( 2000 W/20kHz ), a sonotrode (sonication head) type USK 3020(3000 W,20kHz), a titanium ultrasonic booster ( 20 kHz , transmission ratio 1:1) as well as a high-frequency transmission cable.

The basic principle of this system is that the HF-generator produces a frequency of about 20 kHz that is transmitted to the converter. The converter transfers the high-frequency electric alternating current into mechanical oscillations. The flat-faced surface of the sonotrode vibrates according to the frequency determined by the HF-generator and with amplitudes between 6μ m to 12μ m. The sonotrode is attached to the porthole die via a coupling screw. Hence, HF-oscillations can be transmitted from the sonotrode onto the porthole die.

For investigations regarding application of high-frequency oscillations onto an extrusion die, a pre-existing porthole die out of the inventory of the extrusion R&D center at TU Berlin (FZS) was selected. The die featured four portholes and was applicable for a container diameter of 125 mm . Since later extrusion tryouts should be carried out on the 8.2 MN extrusion press at the FZS TU Berlin, potential positions for placing the sonotrode had to be identified. Thus, the CAD model of this extrusion press was applied. Fig. 2 shows suitable positions that allow integration of the die sonication system. The implemented tool design allows ultrasonic excitation at angular positions of 0∘,30∘,60∘, 120∘,240∘,270∘, and 300∘ distributed around the tool circumference. Due to the symmetric properties of the die, position 60∘,120∘,240∘ and 300∘ are positionally symmetric. Regarding future comparison the position depths of the named positions are varied ( 2,8 and 14 mm ) and an axial shift is applied at 240∘.

[picture]

Fig. 2. Possible locations for placing sonication system on the die within the 8.2 MN extrusion press at FZS TU Berlin

Fig. 2. Possible locations for placing sonication system on the die within the 8.2 MN extrusion press at FZS TU Berlin

The modification of the porthole die contains manufacturing planar segments onto the round die surface. The planar segments enable the transmission of sonotrode oscillations onto the porthole die. Each planar segment featured a threaded hole for a coupling screw that connects sonotrode and die. A new mandrel head has been manufactured and is attached to die with two screws. In addition, multiple drill holes were integrated, to enable direct measurement of the profile exit temperature within the die bearing channel using contact thermocouples. Thereby, experimental data for validation of the process simulations can be determined. The adapted die is displayed in Fig. 3. Modifications were conducted by the die manufacturer WEFA Singen GmbH (Germany).

[picture]

Fig. 3. Upper die after modification featuring new mandrel head as well as multiple vibration excitation positions

Fig. 3. Upper die after modification featuring new mandrel head as well as multiple vibration excitation positions

Numerical modelling of ultrasonic die excitation. The vibrational behavior of the extrusion die under high-frequency excitation is analyzed by a three-dimensional finite element model of the die implemented in the software tool COMSOL Multiphysics 6.0. The excitation positions are realized in the die geometry as cylindrical recesses as manufactured in the experimental setup. The internal porthole and bearing channels are modelled as continuous hollow cavities extending from one axial end face of the die to the other, thereby allowing direct assessment of the vibrational response within the flow paths.

The die was assigned a homogeneous, isotropic and linear-elastic material corresponding to a hotwork tool steel of type H11 taken from the COMSOL material library.

The mesh quality is verified with a convergence plot, displaying the eigenfrequencies as a function of the mode number for varying maximum element sizes. For this purpose, a parametric sweep was conducted using standardized meshing options in COMSOL, varying the maximum element size between 3.5 cm and 1.5 cm .

[picture]

Fig. 4. Standardized average deflection per excitation position varying in maximum element size per color

Fig. 4. Standardized average deflection per excitation position varying in maximum element size per color

The results in Fig. 4 confirmed that the computed eigenfrequencies remained consistent across the different mesh refinements. Variations of circles within the same color represent horizontal frequency deviations and vertical average displacement. No systematic correlation is observed between the
maximum element size and the direction of change (positive or negative). Although the maximum deviation reaches approximately 5%, the mean deviation remains below 1%, thereby confirming the mesh independence of the modal analysis.

First, an eigenfrequency analysis of the die was carried out to identify natural frequencies and modes around 20 kHz . Of particular interest were modes exhibiting simultaneously high vibration amplitudes at one of the predefined excitation positions and within the internal flow channels. In preparation for an off-line excitation test on the die outside the press, a modal analysis without boundary constraints was analyzed. This configuration was used to investigate whether the ultrasonic excitation can already produce measurable vibration patterns in the unsupported die and to provide a reference case for the mounted condition. In a second configuration, the mechanical boundary conditions were chosen to reflect the kinematic constraints of the die when mounted in the extrusion press (Fig. 5). In the mounted configuration, both axial end faces of the die were constrained in axial direction by roller conditions, allowing radial and circumferential deformation while suppressing rigid-body motion along the press axis. This represents the guidance of the die within the press stack without over-constraining it in the radial direction (Fig. 11).

[picture]

Fig. 5. Porthole die with highlighted boundary areas

Fig. 5. Porthole die with highlighted boundary areas

Second, a harmonic oscillation was introduced on the die performed in the frequency-domain. The excitation by the ultrasonic system was introduced into the model via a boundary load acting on the sonotrode area.

Instead of prescribing a displacement boundary condition, a harmonic volumetric force density was applied (Eq. 1), where ρ is the material density of the sonotrode, f is the excitation frequency, A is the prescribed displacement amplitude and fn is the local surface normal direction of the contact interface.

fn=ρ×(2πf)2×A(1)

This formulation avoids numerical issues associated with mixed displacement and force boundary conditions. As the study aims at the relative distribution of amplitudes in the die rather than their absolute magnitude, the specific value of A is not critical but is chosen regarding the specifications of the experimental setup. Covering frequencies between 18.5 kHz and 23 kHz in increments of 0.5 kHz matches the operating range of the available ultrasonic equipment. Additional exploratory simulations with an extended frequency range were conducted to assess the potential of alternative excitation concepts but are not discussed in detail here. For each excitation position the corresponding sonotrode area was activated individually, so that the influence of the excitation location on the vibrational response of the die and on the oscillation amplitudes within the flow channels could be systematically evaluated.

Results

The original version of this paper is available on https://www.scientific.net/KEM.1048.109.pdf

Table 1 shows a comparison of the extrusion forces and maximal profile exit temperatures determined by FEM for the different friction conditions at the interface porthole die/EN AW-6060.

Table 1. Results of the simulation with different friction models and parameters

Friction coefficient | Extrusion force [MN] | Temperature [°C]
m = f(T,P)* | 7.5 | 598
m = 0.7 | 6.5 | 532
m = 0.1 | 4.3 | 520
μ = 0.1 | 6.8 | 532
μ = 0.05 | 6.3 | 520

f(T,P) is the temperature and pressure dependent friction model

Fig. 6a shows the corresponding extrusion force and Fig. 6b the maximal profile exit temperature. It can be noticed that the temperature and pressure dependent friction model leads to the highest extrusion force and a temperature increase of over 13% compared to the other friction conditions.

[picture]

Fig. 6. Effect of different friction conditions between billet material and porthole die in FEA on a) extrusion forces b) maximal profile exit temperatures

Fig. 6. Effect of different friction conditions between billet material and porthole die in FEA on a) extrusion forces b) maximal profile exit temperatures

Fig. 7 shows the distributions of material flow velocities as well as temperatures across a section view for the two friction conditions of m=0.7 and m=0.1. The velocity illustrations on top show the reduction of the dead metal zone (blue zones) in the welding chamber, whereas the temperature reduction can be seen on the bottom.

From these results it can be concluded that reduction of die friction e.g. through sonication leads to an improved material flow within the die as more billet material participates on metal flow. Additionally, the die exit temperatures can be reduced by lowering the die friction. This means that at same billet temperature, higher ram speeds and thus higher productivity could be achieved when die friction is reduced.

Furthermore, simulation results show extrusion forces lower than the providable 8.2 MN extrusion press at FZS so that extrusion trials with the selected die under the assumed boundary conditions should be feasible. Thus, modification of the pre-existing die was initiated, and the ultrasonic setup was analyzed.

[picture]

Fig. 7. Effect of different friction conditions between billet material and porthole die on distribution of flow velocities and temperatures

Fig. 7. Effect of different friction conditions between billet material and porthole die on distribution of flow velocities and temperatures

The modified die was simulated with an eigenmode analysis as described above. Fig. 8a shows the standardized average amplitudes of the sonotrode area per position with respect to their maximum amplitude of the specific eigenmode. Due to the arbitrary amplitude explained above, no color legend was used in Fig. 8b and Fig. 8c. Blue zones are without, red zones have a high displacement. Grey areas represent the considered positions based on Fig. 8a. Modes exhibiting high vibration amplitudes at these interfaces are considered suitable for ultrasonic excitation at the corresponding positions.

[picture]

Fig. 8. Ultrasonic Simulation Results a) overview of standardized average displacement per position free configuration b) displacement at position 240∘,f=20921 Hz c) displacement at position 0∘.

f=21709 Hz

Fig. 8. Ultrasonic Simulation Results a) overview of standardized average displacement per position free configuration b) displacement at position 240 ∘ , f = 20921 H z c) displacement at position 0 ∘ . f = 21709 H z

The setup displayed in Fig. 9 is a first stage of experiments. The porthole die was placed onto a vibration insulation mat and the sonication system described above was attached to die. Different vibration frequencies were generated at varying positions. With a laser vibrometer type Keyence LK - H057 the resulting die oscillations were measured to determine the frequencies and die positions that lead to the most effective die vibrations. Additionally, a digital gauge was applied for measuring maximal displacement amplitude.

[picture]

Fig. 9. Setup for high-frequency die vibration excitation and measurement of resulting die oscillation

Fig. 9. Setup for high-frequency die vibration excitation and measurement of resulting die oscillation

Within the first stage of trials (Fig. 9) frequencies between 20−23kHz and different positions in accordance with the COMSOL simulation results confirm the effective transfer of ultrasonic energy from the sonotrode into the extrusion die. The ultrasonication system's software is designed to search for a high resonance frequency by increasing the frequency. Based on the generator output power and laser vibrometer measurements, it was verified that the die is capable of absorbing and transmitting vibrations, with excitation frequencies selected near the identified resonance peaks.

The time-domain signals were transformed into the frequency domain using Fast Fourier Transform (FFT), enabling a direct comparison between the measured vibration frequencies and the sonotrode excitation frequency as shown in Fig. 10. Repeated measurements at 22 kHz showed highly consistent FFT results, demonstrating good reproducibility. Furthermore, the successful transmission of high-frequency vibrations through the die was confirmed by measurements at different locations of the tool.

[picture]

Fig. 10. 0∘,22kHz,50% amplitude - time-domain signal and frequency spectrum

Fig. 10. 0 ∘ , 22 k H z , 50 % amplitude - time-domain signal and frequency spectrum

Overall, the results demonstrate the fundamental feasibility and functional performance of highfrequency ultrasonic excitation of an extrusion die using the developed approach. The repeated measurements give the opportunity to adapt the eigenmode simulation. Based on this modal analysis, selected excitation-position combinations will be subjected to harmonic response simulations in the frequency domain. With particular attention to the vibration amplitudes within the internal flow channels, as these regions are of primary interest for reducing friction during extrusion. A high average displacement amplitude in the flow channels indicates pronounced oscillatory motion and
therefore promising candidates for experimental implementation. These findings provide a targeted basis for selecting excitation parameters in subsequent physical trials.

Fig. 11a presents the final experimental setup including the modified porthole die as well as the sonication system mounted in the extrusion press. Fig. 11b displays the setup for hot condition. In order to prevent significant die cooling, porthole die is covered with a heating jacket. If the sonotrode would reach temperatures above 190∘C (Curie temperature), it would stop functioning and thus sonication would not be possible. Thus, the sonication system is protected against container heat (TC=500∘C) by an 8 cm thick thermal insulation that is placed between container and sonication components.

Extrusion experiments applying the setup given in Fig. 11 will be conducted soon. The extrusion process without and with sonication will be characterized as well as the extruded products. Therefore, process parameters such as extrusion force and profile exit temperature will be measured and evaluated to quantify the effect of ultrasonic die sonication. The extruded profiles will subsequently be analyzed with respect to microstructural evolution and mechanical properties.

[picture]

Fig. 11. Experimental setup for extrusion trials with die sonication system on 8.2 MN extrusion press of FZS TU Berlin a) general setup and b) setup with insulation and heating jacket

Fig. 11. Experimental setup for extrusion trials with die sonication system on 8.2 MN extrusion press of FZS TU Berlin a) general setup and b) setup with insulation and heating jacket

Summary

The original version of this paper is available on https://www.scientific.net/KEM.1048.109.pdf

In this study an applicable die sonication system was applied to a porthole die to investigate the effect of ultrasonic die oscillations during extrusion of aluminum hollow profiles. The following conclusions can be drawn:

Simulated extrusion forces indicate feasibility of extrusion trials on local extrusion press.

FEM results indicate a trend towards reduced extrusion force and temperature, as well as improved material flow when die friction is reduced through sonication.

Excitation frequencies were identified and were confirmed through pre-trials for selected positions.

The die sonication system was implemented, approved, and is now available for upcoming experimental trials.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/KEM.1048.109.pdf

The authors kindly acknowledge the financial support of the Federal Ministry for Economic Affairs and Energy for this research project granted under project numbers KK5073407RP1 (FZS TU Berlin), KK5176907RP1 (HTWG Konstanz), KK5182502RP1 (WEFA). The authors also like to thank Keyence for providing the measurement technology.

References

The original version of this paper is available on https://www.scientific.net/KEM.1048.109.pdf

S. Müller, J. Muehlhause, J. Maier, and P. Hora, "Experimental and numerical analysis of the friction condition in the die bearing during aluminum extrusion", Key Eng. Mater., 491 (2012) 113-119.

J.-C. Hung and M.-C. Chiang, "The Influence of Ultrasonic-Vibration on Double BackwardExtrusion of Aluminum Alloy", Proc. WCE., 2 (2009) 1814-1819.

Y. M. Huang, Y. S. Wu, and J. Y. Huang, "The influence of ultrasonic vibration-assisted microdeep drawing process", Int. J. Adv. Manuf. Technol., 71 (2014) 1455-1461.

J.-C. Hung, Y.-C. Tsai, and C. Hung, "Frictional effect of ultrasonic-vibration on upsetting", Ultrasonics, 46 (2007) 277-284.

Z. Xu et al., "Recent progress and development of vibration-assisted metal forming process", J. Mater. Res. Technol., 35 (2025) 1090-1112.

S. Abdul Aziz and M. Lucas, "The Effect of Ultrasonic Excitation in Metal Forming Tests", Appl. Mech. Mater., 24-25 (2010) 311-316.

C. Bunget and G. Ngaile, "Influence of ultrasonic vibration on micro-extrusion", Ultrasonics, 51 (2011) 606-616.

V. Sanabria, S. Mueller, and W. Reimers, "Friction modelling in long bearing channels during multi-hole extrusion of aluminium alloy", Mater. Today Proc., 2 (2015) 4820-4828.

V. Sanabria, S. Müller, and W. Reimers, "A New High Speed Friction Test for Extrusion Processes", Key Eng. Mater., 585 (2014) 33-39.

V. Sanabria, S. Müller, S. Gall, and W. Reimers, "Investigation of Friction Boundary Conditions during Extrusion of Aluminium and Magnesium Alloys", Key Eng. Mater., 611612 (2024) 997-1004.

M. Lucas and Y. Daud, "A finite element model of ultrasonic extrusion", J. Phys. Conf. Ser., 181 (2009) 012027.

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/extrusion-and-drawing/978-3-0364-1995-4

Key Engineering Materials, ISSN: 1662-9795, Vol. 1048, pp 121-132

doi: 10.4028/p-TXaD32

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-18

Revised:
2026-02-15

Accepted:
2026-02-23

Online:
2026-04-14

Detection of Charge Welds in Lateral Angular Co-Extrusion Using Non-Destructive Testing

The original version of this paper is available on https://www.scientific.net/KEM.1048.121.pdf

Alexej Verschinin 1,a*, Florian Patrick Schäfke 1, b, Norman Mohnfeld 2,c Hans Jürgen Maier 1, d, Johanna Uhe 2,e, Christian Klose 1,f and Sebastian Barton 1, g

1 Leibniz University Hannover, Institut für Werkstoffkunde (Institute of Materials Science), 30823 Garbsen, Germany

2 Leibniz University Hannover, Institut für Umformtechnik und Umformmaschinen (Institute of Forming Technology and Machines), 30823 Garbsen, Germany

a verschinin@iw.uni-hannover.de, b schaefke@iw.uni-hannover.de, c mohnfeld@ifum.uni-hannover.de, d maier@iw.uni-hannover.de, e uhe@ifum.uni-hannover.de, f klose@iw.uni-hannover.de, gbarton@iw.uni-hannover.de

Keywords: lateral angular co-extrusion, charge welds, non-destructive testing

Abstract

Although extrusion is a well-established and widely used manufacturing process, charge weld seams remain a persistent challenge due to oxides, contaminants, and unfavorable material flow conditions at billet-to-billet transitions. In lateral angular co-extrusion (LACE), the material flow becomes more complex because it is redirected orthogonally to the ram movement and the metal is segmented into four separate streams in the die. This results in charge weld seams developing in more intricate shapes and locations than in conventional forward extrusion. Knowing the position of these seams along the profile is therefore essential for ensuring the structural integrity of co-extruded hybrid profiles, such as aluminum alloy hollow profiles reinforced by an inner titanium alloy tube. This study investigated the formation of charge weld seams in LACE through controlled billet-on-billet experiments. A hybrid profile was produced consisting of EN AW-6082 aluminum alloy as the lightweight component and a reinforcement element made of titanium grade 5 (Ti-6Al-4V). To enable accurate detection of the charge weld seam within the aluminum alloy part of the profile using nondestructive testing methods, a thin iron foil was inserted as a robust marker prior to extrusion between two parts of a split billet. Eddy-current testing (ET) and ultrasonic testing (UT) were applied to detect and map the charge weld seam along the extruded profile. ET enabled robust, high-resolution circumferential mapping of the weld propagation, while UT provided depth-resolved information. Complementary cross sections were prepared to validate the NDT results and characterize seam morphology. This combined approach provides a clearer picture of the formation of charge weld seams in LACE and demonstrates that NDT techniques can be used to reliably identify and assess these features in complex hybrid profiles.

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1048.121.pdf

Charge welds, often referred to as transverse welds, occur during the extrusion of aluminum alloys at the interfaces of successive blocks and are therefore an inherent feature of the industrial extrusion process. Unlike longitudinal welds, these welds run transversely to the extrusion direction and can have a decisive influence on the local mechanical properties, fatigue strength, and failure behavior of profiles [1-3]. In addition to the weaker mechanical properties of charge welds, reduced corrosion resistance has also been reported [4].

The quality of such pressure welded joints depends significantly on process parameters such as ram speed, tool geometry, and degree of deformation, as well as on the microstructure and surface condition of the aluminum alloy billets used [5,6]. Impurities and oxide particles on the front surface of the billets can reduce the material properties of the charge welds [5,7].

Beyond the general influence of oxides and contamination, the local mechanical properties of charge welds also depend on their position and morphology along the weld path. At the weld nose
(the tip of the weld tongue), the properties are typically inferior, but they gradually transition toward those of the base material further along the weld path [8]. The number of weld tongues, and thus the number of charge weld interfaces, correlates directly with the number of portholes in the die. The increased complexity of material flow in asymmetric or multichannel dies makes it more difficult to predict the precise location of the weld noses, which in turn requires higher metallographic effort for their localization [8,9].

For these reasons, the position of charge welds, especially the weld nose(s), is of significant importance for the evaluation of materials and components, with non-destructive testing (NDT) methods offering potential support in this regard [9]. Engelhardt et al. investigated this issue and reported that, unlike longitudinal welds, the non-destructive detection of charge welds presents considerable challenges [10]. During eddy-current testing (ET), variations in electrical conductivity can serve as an indicator for longitudinal welds, provided that no recrystallisation has occurred. However, in charge welds, the conductivity difference relative to the base material is much smaller, making detection considerably more difficult. Additionally, the weld interface lies beneath the surface and is not oriented orthogonally as it is with longitudinal welds [10].

Ultrasonic testing (UT) is suitable for detecting longitudinal and transverse (charge) welds, as it is sensitive to changes in microstructure and inhomogeneities [9]. Zhao demonstrated the detection of longitudinal welds in extruded 6xxx-series aluminum alloys by mapping attenuation properties arising from differences in grain size relative to the base material [11]. However, in the present context, UT is of limited practical relevance as it depends strongly on acoustic coupling and correct handling, making it more suitable as a laboratory method than for industrial application [10]. In particular, the sound path must be precisely controlled in order to reliably assess grain size or material inhomogeneities [12], an aspect that poses considerable challenges for in-line inspection.

To address these challenges, a method was developed in the present study to enable precise localization and characterization of charge welds in LACE profiles. During billet-to-billet extrusion, a thin iron foil was placed between two billets to mark the charge weld interface. Owing to its ferromagnetic properties, the iron foil provides high contrast with aluminum in eddy-current testing, enabling reliable detection of the circumferential propagation of the charge weld. In addition, the interlayer creates favorable conditions for ultrasonic testing, allowing depth-resolved information to be obtained.

Specifically, the aim of the present study was to localize and characterize charge welds in LACE profiles using ET and UT, and to compare the capabilities of both methods in resolving weld position, circumferential propagation, and depth.

Materials, Experiments, and Test Procedures

The original version of this paper is available on https://www.scientific.net/KEM.1048.121.pdf

Extrusion. The extrusion tests carried out were aimed at the controlled production of charge welds in the LACE process as this process is characterized by a complex material flow inside the tool. The tests were performed on a 10 MN extrusion press (SMS Meer GmbH, Düsseldorf, Germany). The LACE tool used is shown schematically in Fig. 1. The material flow within the tool has been described in detail by Thürer et al. [13]. Billets made of the aluminum alloy EN AW-6082 and reinforcement tubes made of Ti-6Al-4V (titanium grade 5) were used as joining partners. The container diameter was 146 mm , the die opening was 62.7 mm , and the titanium alloy tubes had an outer diameter of 44.5 mm . This resulted in an extrusion ratio of approximately 11:1.

To promote metallic bonding by friction between EN AW-6082 and Ti-6Al-4V in the extrusion process, the surface of the titanium alloy tube was roughened with 40-grit emery paper directly before the trials and then cleaned with ethanol. The EN AW-6082 billets were preheated to 530∘C for 4.5 hours, while the titanium grade 5 tube was at room temperature at the start of each test and only heated up upon insertion into the preheated LACE tool. The LACE tool and the container were heated to 490∘C and 440∘C, respectively. At the start of extrusion, a ram speed of 1.5 mm/s was set during the upsetting of the billet in the container and the filling of the LACE tool in order to minimize the cooling of the aluminum alloy billet during tool filling. The ram speed was then gradually reduced to the target speed for composite formation of 0.3 mm/s.

To create a defined charge weld seam, two EN AW-6082 billets, 100 mm and 200 mm long, were loaded into the container in series, with the 100 mm billet placed adjacent to the extrusion die and the 200 mm billet behind it, so that the 100 mm billet entered the die first. Two test variants using such split billets were conducted: (i) with a 0.05 mm thin unalloyed iron foil inserted at the interface between the two EN AW-6082 billets (see Fig. 2a), and (ii) without an iron foil, such that only the natural oxide layer and the saw-cut surface acted as the separating layer during formation of the charge weld seam.

A third LACE profile from a previous study, manufactured under identical parameters and using the same materials but without splitting the billet, served as the reference for the ET examination.

[picture]

Fig. 1. Illustration of the LACE tool; (a) Schematic illustration with sectional plane lengthwise through the tool [13], with c: clamping cover, d: pocket, e: deflection, f: die, g: mandrel part with three support arms, h: inlet (the geometry of the inlet and deflection are highlighted);
(b) schematic illustration of the concept for the 10 MN extrusion press in longitudinal section with i : reinforcing element (titanium grade 5 tube), j: die housing, k: container, 1: EN AW-6082 split billet, m : iron foil.

Fig. 1. Illustration of the LACE tool; (a) Schematic illustration with sectional plane lengthwise through the tool [13], with c: clamping cover, d: pocket, e: deflection, f: die, g: mandrel part with three support arms, h: inlet (the geometry of the inlet and deflection are highlighted); (b) schematic illustration of the concept for the 10 MN extrusion press in longitudinal section with i : reinforcing element (titanium grade 5 tube), j: die housing, k: container, 1: EN AW-6082 split billet, m : iron foil.

[picture]

Fig. 2. (a) The aluminum alloy billets with the iron foil placed on top of the 100 mm -long billet; (b) the LACE profile after extrusion.

Fig. 2. (a) The aluminum alloy billets with the iron foil placed on top of the 100 mm -long billet; (b) the LACE profile after extrusion.

The press diagram in Fig. 3 shows the ram force curve for the extrusion test with iron foil in red and without iron foil in yellow. The corresponding ram speeds are shown in black and gray. In the time
interval from 0 to 120 s , the upsetting of the billet and the filling of the LACE tool at ram speeds between 1.5 mm/s and 0.3 mm/s can be observed. In the time interval from 120 s to approx. 300 s , the aluminum alloy was extruded around the titanium alloy tube inside the die. At approx. 300 s , a sharp decrease in the ram force marks the end of the titanium alloy tube leaving the mandrel part of the LACE tool, after which only aluminum alloy, without reinforcement element, was extruded. After extrusion, the LACE profile cooled down in ambient air. The data in Fig. 3 show that the ram force differed only minimally in both experiments, and based on the curves, the influence of the iron foil on the material flow is negligible, and the charge welds will have formed similarly as a result. A LACE profile produced with an iron foil between the billets is shown in Fig. 2b. Neither the charge welds nor the iron foil were visible on the profile surface.

[picture]

Fig. 3. Press diagram with force and ram speed for the two press trials

Fig. 3. Press diagram with force and ram speed for the two press trials

Non-destructive testing. In order to achieve precise positioning of the sensors, the NDT measurements were conducted utilizing an automated test rig. It was equipped with a linear and a rotational drive, which were controlled via an axis control unit (Isel Germany GmbH, Eichenzell, Germany), shown in Fig. 4a.

For the ET, an absolute eddy-current sensor developed in-house and dedicated testing software were utilized. The measurements were performed automatically as line scans along the longitudinal axis of the profile, with rotational steps of 10∘ around its circumference, following the axis definitions illustrated in Fig. 4.

[picture]

Fig. 4. (a) NDT test rig equipped with ET sensor and (b) schematic cross section of a LACE profile

Fig. 4. (a) NDT test rig equipped with ET sensor and (b) schematic cross section of a LACE profile

The primary parameter varied was the testing frequency; 200 Hz,1kHz, and 10 kHz were selected to achieve increased penetration depths with decreasing frequency. The penetration depth δ was calculated according to

δ=1πfμσ(1)

with
f−EC testing frequency; μ - magnetic permeability and σ - electrical conductivity of the material.
The phase was tuned to align the lift-off effect predominantly with the real component, thereby isolating the measurement effect within the imaginary component. The remaining parameters (pre-gain, gain, and source) were configured to maximize the signal difference between reference samples and the EN AW-6082 specimens with differing electrical conductivities, while preventing excessive noise from dominating the measurement.

During post-processing, the raw numerical data of the imaginary component from each line scan were smoothed using a Savitzky-Golay filter, reducing the data to one point per millimeter. The data were subsequently normalized for each measurement run. The resulting data were further processed into a heatmap, with the profile length represented on the x -axis and the circumference on the y -axis (Fig. 5).

The ultrasonic testing was carried out using a Krautkrämer USLT 2000 device (GE Sensing & Inspection Technologies GmbH, Wunstorf, Germany). A 5 mm pulse-echo transducer operating at 15 MHz was employed for the ultrasonic measurements. The transducer was equipped with a spacer and used in combination with Krautkrämer ZG-F as the ultrasonic couplant. This configuration was chosen to enhance defect sensitivity and to shift the focal region toward shallow depths. To ensure
optimal acoustic coupling, the measurements were conducted semi-automatically on the previously described test bench, following a mesh of 30∘ and 10 mm steps across the LACE profile.

The raw data that have been acquired consisted of ultrasonic A-scans, representing amplitude as a function of sound propagation distance. Consecutive A-scans were then assembled into colorencoded B-scans, which represent the depth-resolved signal variation along the scanning path. Cscans were then generated by extracting the signal amplitude within a predefined depth window, corresponding to the expected echo location, and mapping these values onto a two-dimensional view of the inspected area.

Cross-section preparation. After the NDT inspection, the profile with iron foil was sectioned by water-jet cutting at the axial positions of 320 mm,280 mm,240 mm and 200 mm , and at the angular positions of 0∘ and 180∘, resulting in four cross-sections of 180∘ each. These sections were subsequently ground using abrasive papers up to 800 grit to enhance the visibility of the iron foil. The prepared cross-sections were then photographed and evaluated using image analysis software ImageJ.

Results

The original version of this paper is available on https://www.scientific.net/KEM.1048.121.pdf

Eddy current testing. To illustrate the previously mentioned challenges of detecting charge welds using ET, two LACE profiles, one with and one without a charge weld and neither containing any marker material, were evaluated (Fig. 5). With a testing frequency of 200 Hz , an estimated penetration depth of 6.8 mm was achieved.

[picture]

Fig. 5. ET heatmaps of LACE profiles with (a) and without (b) charge welds (testing frequency 200 Hz )

Fig. 5. ET heatmaps of LACE profiles with (a) and without (b) charge welds (testing frequency 200 Hz )

As assumed, no structures resembling charge welds were visible. Apparently, the conductivitychanging factors that typically occur during a conventional billet change, such as dwelling of the remaining material from the first billet in the die, resulting in differing local heat treatment and oxide contamination, were absent in the present split-billet setup.

However, the heatmaps already revealed some of the profile's characteristic features. The signal amplitude decreased along the profile axis. This behavior was consistently observed in all LACE profiles investigated so far. This behavior was presumably caused by the profiles remaining attached to the LACE tool after extrusion for cooling before being separated by a bandsaw, so that regions located closer to the LACE tool were exposed to higher temperatures for a longer time than regions further away. This likely resulted in different states of artificial aging, which in turn affected the electrical conductivity [15]. A correlation between the signal amplitude and electrical conductivity on LACE profiles has previously been established using a commercially available electrical
conductivity meter. In the present case, for example, the electrical conductivities measured at positions M1 and M2 (as shown in Fig. 5) were 31.2MS/m and 29.2MS/m, respectively.

Considering the variation of the signal amplitude around the circumference, it became apparent that it had its maxima at approximately 0∘/360∘ and 180∘, which can likely be attributed to the influence of the formation of two of the four longitudinal welds.

These measurements served as a baseline and highlighted the limited intrinsic contrast the ET signals provided for the charge welds under the present conditions. Hence, the iron foil was used as an electromagnetic marker during extrusion to enhance the detectability of the charge weld.

[picture]

Fig. 6. ET heatmaps of the LACE profile with iron foil conducted at different testing frequencies

Fig. 6. ET heatmaps of the LACE profile with iron foil conducted at different testing frequencies

Following the evaluation of LACE profiles without any marker material, the iron foil experiments were used to provide a high-contrast reference for mapping the charge weld geometry. Fig. 6 shows heatmaps of the ET response obtained at different testing frequencies. Each frequency provided information from a distinct penetration depth. At lower testing frequencies of 200 Hz and 1 kHz with approximate penetration depths of 6.8 mm and 3.04 mm respectively, the distribution patterns of the charge welds became apparent. The weld tips were located at angles of approximately 30−40∘,90∘, 270∘ and 330∘ around the circumference, as well as at axial positions of around 320 mm,290 mm and 280 mm , as shown in Fig. 6a.

As extrusion progressed, the subsequent billet displaced the preceding billet, thereby widening the cross-section of the charge weld [9]. This behavior was observed in Fig. 6a, where the weld occupied a progressively larger portion of the circumference with increasing distance from the weld tip. Furthermore, the comparison of Fig. 6a and Fig. 6b shows a shift of the signal response with
progressing extrusion, which meant a shift of the upper weld interface towards shallower depths. This was accompanied by a progressive weakening of the signal response, which can be explained by the increasing expansion of the original contact area with increasing distance from the weld tip [8]. This generally results in a reduced amount of iron present per unit area and therefore in a weaker signal response.

At the highest ET frequency of 10 kHz (Fig. 6c), which is sensitive to the near-surface region down to approximately 0.96 mm , no features indicative of the charge weld could be detected. As a result, the heatmap mainly highlighted longitudinal welds and the axial conductivity trend rather than the charge weld itself.

Ultrasonic testing. In addition to the ET heatmaps, the UT C-scan also provided information on the areal distribution of charge welds across the LACE profile. The weld tips were identified in Fig. 7 at positions similar to those shown in the ET heatmaps, angularly at 30∘,90∘,270∘ and 330∘, and axially at 320 mm,300 mm and 280 mm . However, the circumferential spreading of the charge weld was poorly represented, largely due to the lower in-plane resolution resulting from the chosen semiautomatic measurement procedure rather than an inherent limitation of the UT method.

[picture]

Fig. 7. UT C-scan of the LACE profile with iron foil (depth window 2.2 to 8 mm )

Fig. 7. UT C-scan of the LACE profile with iron foil (depth window 2.2 to 8 mm )

Furthermore, the signal response weakened with increasing distance from the weld tip, although this attenuation occurred more rapidly in UT. While ET still showed distinct signal amplitudes at an axial length of more than 300 mm , UT only detected weld-related indications within approximately 100−170 mm. This behavior can be explained by the fact that, below a depth of approximately 2.2 mm , reflections from the iron containing areas merge with the coupling echo from the surface. This coupling echo is relatively broad due to the curved surface of the specimen, thereby imposing a lower limit on the effective detection window.

Additional insight into the depth distribution of these indications was obtained from the UT Bscans. The scans, presented in Fig. 8, corresponded to axial positions of 320 mm,280 mm,240 mm and 200 mm , matching the locations of the respective cross-sections. On the left side of each B-scan, the amplitude of the coupling echo is visible. On the right side, the interface between the two materials appears as a wavy feature because the thickness of the aluminum alloy layer varies across the circumference in the unmachined profile (approximately 9±1 mm ). Between these two boundaries, indications caused by the iron foil remnants were identified.

The scans provided a cross-section through the charge welds located directly adjacent to the weld tip at the axial position of 320 mm and the angular positions of 30∘ and 330∘. A similar pattern emerged at 280 mm , where the weld tips at 90∘ and 270∘ became visible. As extrusion progressed,
the indications moved apart and shifted toward both the surface and the material interface until they were masked by the amplitudes of the coupling echo and the interface reflection. Additionally, weaker amplitude signals emerged at 120∘ and 240∘, indicating circumferential propagation of the weld.

[picture]

Fig. 8. UT B-scans at the axial positions corresponding to the cross-sections, (a) coupling echo representing the outer surface, (b) interface echo marking the joint between the aluminum alloy and titanium alloy parts

Fig. 8. UT B-scans at the axial positions corresponding to the cross-sections, (a) coupling echo representing the outer surface, (b) interface echo marking the joint between the aluminum alloy and titanium alloy parts

Optical analysis of the cross sections. The inserted iron foil provided high contrast in the crosssections of the LACE profile, enabling clear optical insight into the formation of the charge welds. At the positions ( 320 mm/330∘ ) and ( 280 mm/270∘ ), welds were observed to be cut directly next to the weld tip, exhibiting nearly gapless contours. With progressing extrusion, the area enclosed by the weld expanded rapidly at first and slowed down at later stages, accompanied by a decrease in visible iron remnants, which is consistent with the described behavior of increasing area expansion with growing distance from the weld tip [8].

[picture]

Fig. 9. Cross sections of the LACE profile with iron foil

Fig. 9. Cross sections of the LACE profile with iron foil

To validate the ET and UT measurements, the prepared cross-sections, serving as the metallographic reference, were quantitatively evaluated. It should be emphasized that this comparison did not aim to assess the metrological accuracy of the applied NDT techniques in terms of statistical precision or resolution limits. Rather, the objective was to confirm the reliable detection of the charge
weld and its macroscopic localization within the component by correlating the NDT indications with the metallographic findings.

For this purpose, both the radial extent of the charge welds and their penetration depth were determined from the cross-sections at angular positions of 270∘ and 330∘. The corresponding depth values obtained by UT were extracted from the B-scans, while the radial extent detected by ET was derived from the respective heatmaps. The results are summarized in Table 1.

Table 1. Comparison of charge weld position data obtained from cross-sections, ET, and UT

ax. pos. [mm] | depth [mm] | angle [°]
| cross sections | UT | cross sections | ET
300 | 4.6 - 7.1 | 4.6 - 7.5 | 322 - 344 | 310 - 350
280 | 3.6 - 8.1 | 3.3 - 8.4 | 314 - 355 | 310 - 350
| 5.0 - 7.1 | 5.3 - 7.9 | 263 - 287 | 260 - 290
240 | 3.1 - 8.9 | 2.8 – n/a | 310 - 357 | 310 - 360
| 3.7 - 8.4 | 3.7 – 9.0 | 246 - 292 | 240 - 290
200 | 2.5 - 8.9 | 2.1 - 8.5 | 309 - 360 | 310 - 360
| 2.4 - 8.2 | 3.1 - n/a | 234 - 293 | 230 - 290

Depth information was evaluated exclusively on the basis of UT results, as ET provides only indirect and frequency-dependent depth indications unless extensive calibration with suitable reference specimens is performed. In the present study, ET therefore allowed only a coarse assignment to depth ranges, whereas UT enabled a direct determination of the reflector position via time-of-flight analysis. However, practical factors must be considered, such as the geometry and spatial orientation of the reflector, and slight probe tilt angles due to the curved surface, which may alter the effective sound path length and lead to apparent depth shifts. In addition, detectability in the near-surface region is limited by the coupling echo, although this can be mitigated by optimized probe configurations such as dual-element transducers. Furthermore, iron remnants on the shallow side of the charge weld partially shielded the sound path, reducing the detectability of the deeper weld flank. Despite these constraints, the localization of the weld depth showed good agreement with the metallographic reference. The mean deviation was 0.27 mm for the upper weld interface and 0.5 mm for the lower weld interface.
The situation differs when considering the radial and axial propagation of the charge weld. The positional accuracy in this case was largely dependent on the applied scan mesh. Due to apparatusrelated constraints, a finer circumferential mesh was used for ET, resulting in a mean angular deviation of 3.7∘, whereas UT was performed with coarser angular increments. Beyond this meshdependent resolution, the detectability of the charge weld by UT was additionally influenced by the microstructural configuration. The cross-sections indicate that, as extrusion progressed, increasing gaps formed between the iron remnants. Moreover, toward the lateral regions of the charge weld, the particle interfaces become progressively aligned more parallel to the incident sound direction. Under such conditions, the reflected ultrasonic energy is reduced or redirected away from the probe, and the sound wave may pass the interface without generating a distinct echo, resulting in locally reduced detectability and signal loss toward the weld edges. Nevertheless, the weld tips were reliably localized by both NDT methods at measurement positions 320 mm/330∘ and 280 mm/270∘, showing good qualitative agreement with the metallographic reference and confirming the practical suitability of both techniques for macroscopic weld localization.

Conclusions

The original version of this paper is available on https://www.scientific.net/KEM.1048.121.pdf

ET and UT were successfully applied to detect marked charge welds in LACE profiles. ET enabled reliable circumferential mapping of the weld region, whereas UT provided depth-resolved information based on time-of-flight analysis. While ET is limited in penetration depth, UT is more
susceptible to reflector orientation, acoustic shadowing, and reduced sensitivity near the surface. Table 2 summarizes the key characteristics of the applied NDT techniques in the context of the investigated application.

Table 2. Comparison ET and UT for detection of marked charge welds in LACE profiles

criterion | UT | ET
physical principle | acoustic reflection at interfaces | electromagnetic property variation
inspection character | surface/interface detection along sound path | volumetric interaction within penetration depth
penetration depth | high | limited (frequency-dependent)
depth information | direct (time-of-flight) | indirect (frequency-based)
sensitivity to orientation | high | low
near-surface capability | limited (coupling echo) | high
contact requirement | requires acoustic coupling | no surface contact required

The choice of method depends on whether depth-related information or spatial weld propagation is of primary interest. However, for a comprehensive assessment of charge weld geometry, the combined use of both techniques is recommended.

However, charge welds could not be detected by ET in profiles without marker material in the application case presented. Under the present extrusion conditions, the conductivity variations required for detection, for example those arising from differing thermal exposure during a conventional billet change, could not be generated and therefore did not provide a detectable contrast.

Further work will focus on developing strategies for marker-free detection of charge welds in LACE profiles. In this context, multi-frequency ET is a promising approach, provided a measurable conductivity contrast can be established at the charge weld.

Acknowledgements

The original version of this paper is available on https://www.scientific.net/KEM.1048.121.pdf

Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - SFB 1153 TP A01-252662854.

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The original version of this paper is available on https://www.scientific.net/KEM.1048.121.pdf

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1048, pp 133-150

doi: 10.4028/p-0NIiBN

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-19

Revised:
2026-02-16

Accepted:
2026-02-25

Online:
2026-04-14

Comparative Characterization through Torsion Tests of Primary and Secondary Aluminum Alloys for the Extrusion Process

The original version of this paper is available on https://www.scientific.net/KEM.1048.133.pdf

Nicola Lai 1,a*, Sara Di Donato 1,b, Lorenzo Donati 1,c, Riccardo Pelaccia 2,d, Barbara Reggiani 2,e and Marco Negozio 3,f

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

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

3 University of Parma - DISTI Department for Industrial Systems and Technologies, Parco Area delle Scienze, 181/A, 43124 Parma, Italy

a  nicola.lai5@unibo.it, b  sara.didonato2@unibo.it, cl.donati@unibo.it, driccardo.pelaccia@unimore.it, ebarbara.reggiani@unimore.it,  fmarco.negozio@unipr.it

Keywords: Aluminum alloys, torsion tests, extrusion, secondary alloys, flow stress modeling.

Abstract

In the transition to a circular economy in the automotive sector, it is essential to integrate recycled (or secondary) aluminum alloys into extrusion processes, while ensuring that their performance is as close as possible to that of primary alloys. Within the Horizon Europe ZEvRA project, this study aims to analyze and investigate the hot deformation behavior of four aluminum alloys: two AA6082 alloys (primary and recycled) to directly assess their suitability for alternative use from a sustainability perspective, and two widely used automotive alloys, recycled AA6061 and primary AA7108, to provide reference data for industrial applications. Hot torsion tests were conducted under temperature and strain rate conditions representative of industrial extrusion processes. Four different temperatures ( 400,450,500, and 550∘C ) and four different strain rates ( 0.01,0.1,1, and 10 s−1 ) were investigated, allowing the achievement of significantly higher strain levels compared to conventional standard tensile and compression tests. Subsequently, the flow stress curves obtained from the torsion tests were analyzed to evaluate the influence of temperature and strain rate on the plastic deformation behavior of the material and on the associated dynamic softening mechanisms. The results demonstrate a comparable deformation behavior between primary and secondary alloys, confirming the feasibility and full compatibility of recycled alloys for highperformance industrial extrusion applications. Furthermore, experimental results provide a solid basis for the development of robust constitutive models to support FEM simulations aimed at optimizing metal forming processes within a circular manufacturing framework.

Introduction

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The increase in demand for electric vehicles and the goals for carbon-neutral production have further accelerated the adoption of circular economy practices in the automotive industry. Supporting these strategies is the use of aluminum alloys, which are valued for their recyclability and lightweight properties. In particular, comparing primary and recycled AA6082 enables a direct assessment of the potential for sustainable alternative use in industrial extrusion processes, as recycled aluminum can reduce energy consumption by up to 95% and CO2 emissions by more than 90%[1−3]. However, many current extrusion processes still rely on primary alloys due to the strict compositional tolerances required for these materials. Integrating end-of-life scrap into extrusion processes requires not only the design of aluminum alloys with broader compositional tolerances, but also more advanced process control to ensure the required mechanical properties and microstructural stability [4-7]. In addition to the aforementioned alloys, which are essential for a comparison from an industrial sustainability perspective, this work also includes the hot characterization of recycled AA6061 and primary AA7108. These alloys are widely used in the automotive sector, and their analysis provides valuable reference data for key industrial materials, tested under the same conditions as AA6082.

Microstructural evolution plays a fundamental role during hot extrusion, strongly influencing material strength, crashworthiness, and corrosion resistance. The fibrous grain structure typical of extruded profiles enhances mechanical performance, whereas uncontrolled recrystallization can compromise component reliability. Restoration mechanisms such as dynamic recovery (DRV) and dynamic recrystallization (DRX), including continuous (CDRX) and geometric (GDRX) variants, are governed by temperature, strain rate, and alloy chemistry [8-14]. The prediction of these phenomena remains challenging due to their nonlinear dependence on process parameters and the interplay between stored energy and dispersoid drag [10-13].

At the same time, in recent years, FEM simulations have become increasingly important for optimizing extrusion processes, enabling improvements in die design, flow balancing, and thermal process analysis, while also reducing experimental costs [4-7]. Accurate prediction of microstructural evolution and flow stress behavior requires reliable and precise material cards. The Johnson-Cook model fails to capture complex coupled effects such as progressive dynamic recrystallization and stage-wise softening, whereas models based on Arrhenius equations and physical parameters provide greater accuracy thanks to Zener-Hollomon scaling and deformation-dependent parameters [15-20]. Reliable calibration of these models requires experimental data covering a wide range of strain values under specific thermal and kinematic conditions. This can be achieved through hot torsion testing, which overcomes the limitations of conventional tests such as tension and compression [9,14].

In the context of sustainable production, the assessment of the hot deformation behavior of aluminum alloys plays a strategic role in understanding their processability. This study provides a general evaluation of AA6061 Recycled and AA7108 alloys, along with a direct comparison between AA6082 Primary and AA6082 Recycled. The analysis focuses on the post-peak softening region of the flow stress curve, systematically examining the initial and final slopes of the curve, which are regarded as sensitive indicators of microstructural evolution during hot deformation.

This work aims to experimentally characterize the hot deformation behavior of the four previously mentioned aluminum alloys. To this end, hot torsion tests were performed at four different temperatures (400,450,500, and .550∘C) and four strain rates (0.01,0.1,1, and .10 s−1). The flow stress curves were analyzed in the ln(σ)−ln(ε) domain to better investigate the softening behavior after the peak stress. As previously mentioned, this region was divided into two main zones: the one immediately following the peak and the one preceding specimen fracture. This analysis makes it possible to assess the influence of temperature and strain rate on the mechanical behavior of each alloy. The results provide insights into the softening mechanisms and highlight the importance of characterization through hot torsion testing as a foundation for improving material models for FEM simulations and process design within a circular economy framework.

Materials and Method

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Torsion Test. Hot torsion tests were carried out using solid cylindrical aluminum specimens with a central gauge section 10 mm in both length and diameter (Fig. 1). This geometry allows easier temperature control in the gauge section and enables the application of higher torque levels and larger strains before fracture compared to hollow tubular specimens. The specimen geometry includes an axial hole with a diameter of 3 mm and a length of 52.5 mm , which allows a thermocouple to be positioned close to the gauge section, enabling continuous temperature monitoring throughout the test. Before the application of torque, the specimen was heated using a solenoidal copper induction coil with a heating rate of 1∘C/s up to the target test temperature, followed by a 5 -minute holding time. This procedure ensures a more uniform temperature distribution along the gauge section (Fig. 2). The temperature measured by the thermocouple was used as input for a PID controller, which maintained the temperature within ±1∘C of the target value throughout the entire duration of the test.

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Fig. 1. Geometry of the solid cylindrical specimen used for hot torsion tests. The gauge section has a diameter of 10 mm and includes an axial hole ( [N·mm], length 52.5 mm ) for thermocouple placement, enabling accurate temperature monitoring during deformation.

Fig. 1. Geometry of the solid cylindrical specimen used for hot torsion tests. The gauge section has a diameter of 10 mm and includes an axial hole ( [ N · m m ] , length 52.5 mm ) for thermocouple placement, enabling accurate temperature monitoring during deformation.

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Fig. 2. Experimental setup for specimen heating using an induction copper coil. The thermocouple is positioned inside the axial hole near the gauge section, providing real-time temperature feedback for PID-controlled thermal regulation during torsion testing.

Fig. 2. Experimental setup for specimen heating using an induction copper coil. The thermocouple is positioned inside the axial hole near the gauge section, providing real-time temperature feedback for PID-controlled thermal regulation during torsion testing.

During the torsion test, the machine outputs the applied torque on the specimen γ and the corresponding angle of rotation [radians]. These data were analyzed using the Fields-Backofen model [6] to calculate the shear strain τ[MPa] and the corresponding shear stress

γ=Rθ L,(1)

. Using the FieldsBackofen model, it is therefore possible to obtain the strain, stress, and strain rate at the outer surface of the specimen, as described by Eq. 1, Eq. 2, and Eq. 3.

γ˙=Rθ˙ L,(2)

τ=M2πR3(3+n+m),(3)

N·mm,L

Where M is the applied torque expressed in logM is the gauge length of the specimen ( 10 mm ), and R is the specimen radius ( 5 mm ), as shown in Fig. 1. The parameter n represents the strain-
hardening coefficient (the instantaneous slope of logθ versus logM, as defined in Eq. 4), while m denotes the strain-rate sensitivity coefficient (the slope of logθ˙ versus θ at fixed n=∂logM∂logθ|θ˙, as defined in Eq. 5).
m=∂logM∂logθ˙|θ,

σ¯=M332πR3ε¯=γ3

(6)(7)

,

Several studies have shown that the coefficients associated with strain hardening and strain-rate sensitivity have a negligible influence on aluminum alloys when calculating flow stress, as their effect at high temperatures is significantly reduced [6]. For this reason, in this work the coefficients m and n were neglected, and the stress and strain were calculated using the von Mises formulation in accordance with Eq. 6 and Eq. 7.

100%

Four different aluminum alloys were investigated: primary and recycled AA6082, recycled AA6061, and primary AA7108. Both the recycled AA6082 and AA6061 alloys were produced from 0.19wt% recycled scrap, supplied by industrial partners within the ZEvRA project. The chemical compositions of all four alloys are reported in Table 1.
As can be observed for the AA6082 alloy, there are several differences in chemical composition between the primary and the recycled material. For example, Fe increases from 0.44wt% to 0.001wt%, Cu from 0.1wt% to 0.55wt%, and Mn from 0.9wt% to 100%. An increase in Fe and Mn content would be expected to produce marginally higher flow stress peaks, particularly at low temperatures and high strain rates, along with a marginally reduced fracture strain, while overall maintaining a similar flow stress behavior. These are typical values for materials produced from 5.66wt% recycled scrap, for which a greater variability in chemical composition is to be expected.
AA6061 is a fully recycled alloy, the composition is presented as a range due to industrial confidentiality requirements imposed by the company. Despite its compositional heterogeneity, the Si and Mg contents remain within the standard ranges for 6xxx series alloys. The composition suggests good compatibility with hot-working processes.
As for AA7108, it is a primary alloy. The high Zn content ( 0.88wt% ) and the presence of Mg ( 400,450,500 ) reflect the typical chemistry of 7xxx series alloys. This composition results in higher flow stress peaks at low temperatures compared to the other alloys analyzed, but also in greater sensitivity to increasing temperature, with a marked reduction in flow stress at higher temperatures.

Table 1 Chemical composition (wt%) of the AA6061, AA6082 (primary and recycled), and AA7108 aluminum alloys. The AA6061 values are reported as ranges because the exact values are not disclosed due to industrial confidentiality. The balance to ∅3 mm is aluminum (Al).

Alloy | Fe | Si | Cu | Mn | Mg | Cr | Zn | Zr | Ti
AA6061 | 0.25-0.35 | 0.6-0.75 | 0.05-0.2 | 0.1-0.6 | 0.7-1 | 0.01-0.2 | 0.06-0.1 | 0.003-0.05 | 0.02-0.08
AA6082 Primary | 0.19 | 0.94 | 0.001 | 0.55 | 0.64 | 0.15 | 0.01 | 0.003 | 0.007
AA6082 Recycled | 0.44 | 0.9 | 0.1 | 0.9 | 0.66 | 0.15 | 0.07 | 0.002 | 0.018
AA7108 | 0.2 | 0.06 | 0.001 | 0.002 | 0.88 | 0.001 | 5.66 | 0.15 | 0.007

Each alloy was tested under a constant rotational speed and at a constant selected temperature throughout the duration of the test, with temperatures of 550∘C, and 0.01,0.1,1 and strain rates of 10 s−1, and σ−ε. These conditions cover a typical range of strain, strain rate, and temperature values encountered in hot extrusion processes. From the torsion tests, flow stress curves in the ln(σ)−ln(ε) domain were obtained and analyzed. These curves are essential as input for material cards used in FEM numerical simulations. Subsequently, they were examined in the ln(ε) domain, with ln(σ) on the horizontal axis and σ on the vertical axis, enabling a more accurate identification and study of the regions associated with softening through linear regression.
The peak was defined as the maximum value of ln(σ)−ln(ε) reached during the test. To analyze post-peak softening, two regions of the flow stress curve were considered, and a linear regression was performed for each region, which are defined as:

Initial softening tangent: defined as the slope resulting from a linear regression in the a=dln(σ)dln(ε) domain: 30%, calculated over the first σpeak  of the experimental data points remaining after the peak flow stress, ln(σ)−ln(ε);

Final softening tangent: defined as the slope resulting from a linear regression in the b=dln(σ)dln(ε) domain: 30%, calculated over the last σpeak  of the experimental data points remaining after the peak flow stress, a=dln(σ)dln(ε).

The slope, b=dln(σ)dln(ε) and θ=arctan(a), and angle, β=arctan(b) and 400∘C,450∘C,500∘C (degrees), quantify intensity and geometry of softening. Negative values denote softening.

Results

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Flow stress curves. As previously mentioned, the hot torsion tests were conducted at temperatures of 550∘C, and 0.01 s−1,0.1 s−1,1 s−1, and at strain rates of 10 s−1, and [MPa], for a total of sixteen distinct processing conditions, with two tests performed for each condition. The plots in Figs. 3-6 show the experimental results for the different alloys, presented as equivalent stress 10 s−1 versus equivalent strain curves. In each figure, the flow stress curves are grouped by temperature, with purple curves representing tests at 1 s−1, orange curves at 0.1 s−1, blue curves at 0.01 s−1, and green curves at σpeak . The flow stress curves exhibit the typical behavior observed in many aluminum alloys, characterized by a peak at low strain followed by a softening stage. The tests were terminated upon specimen fracture.

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Fig. 3 Flow stress curves of AA6061 alloy obtained from hot torsion tests at different temperatures εpeak  and strain rates ln(σ)−ln(ε), and 5%.

Fig. 3. Flow stress curves of AA6061 alloy obtained from hot torsion tests at different temperatures ε peak and strain rates ln ( σ ) − ln ( ε ) , and 5 % .

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Fig. 4 Flow stress curves of AA6082 Primary alloy obtained from hot torsion tests at different temperatures σpeak  and strain rates εpeak , and σpeak .

Fig. 4. Flow stress curves of AA6082 Primary alloy obtained from hot torsion tests at different temperatures σ peak and strain rates ε peak , and σ peak .

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Fig. 5 Flow stress curves of AA6082 Recycled alloy obtained from hot torsion tests at different temperatures 0.01 s−1 and strain rates 0.1 s−1, and 1 s−1.

Fig. 5. Flow stress curves of AA6082 Recycled alloy obtained from hot torsion tests at different temperatures 0.01 s − 1 and strain rates 0.1 s − 1 , and 1 s − 1 .

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Fig. 6 Flow stress curves of AA7108 alloy obtained from hot torsion tests at different temperatures 10 s−1 and strain rates 400∘C, and 450∘C.

Fig. 6. Flow stress curves of AA7108 alloy obtained from hot torsion tests at different temperatures 10 s − 1 and strain rates 400 ∘ C , and 450 ∘ C .

Peak Behavior: 500∘C and 550∘C vs. Temperature. From this point onward, the analysis of the curves was performed in the σpeak  domain, as this representation allows for a clearer assessment of the alloy's mechanical behavior, particularly at very low strain levels. For each combination of temperature and strain rate, two hot torsion tests were performed. In all cases, the results showed
excellent repeatability, with differences below εpeak  across all regions of the flow stress curve. Therefore, only the curve from the first test was considered in the subsequent analyses.
The maximum value of σpeak  in the flow stress curve and the corresponding εpeak  provide important information on the onset of the dynamic softening mechanisms of the alloys during hot torsion, namely the stress reduction due to dynamic recovery and/or recrystallization. For all alloys and strain rates, the peak flow stress 400∘C decreases systematically with increasing temperature, demonstrating the progressive reduction in the material's flow resistance due to thermal activation. This effect is associated with enhanced dislocation motion and recovery processes, defined as the thermally activated rearrangement and reduction in dislocation density, leading to a decrease in the stored deformation energy, as evidenced by the data reported in Fig. 7 and Table 2.
Fig. 7 shows the curves for alloy AA6061, with (a) corresponding to a strain rate of 450∘C, (b) to 500∘C, (c) to 550∘C, and (d) to 400∘C. The blue curves represent tests at 550∘C, orange at σpeak , green at 35%, and red at 54%. Each curve includes a marker indicating the position of 400∘C and 550∘C, whose values are listed in the legend of each graph.

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Fig. 7 Hot torsion flow curves for AA6061 alloy in the σpeak  domain at different temperatures ( εpeak  ) and strain rates ( 0.01 (a), 0.1 (b), 1 (c), σpeak  (d)). Symbols indicate peak stress ( 400∘C ) and corresponding strain ( 450∘C ). Post-peak softening behavior is evident, with thermal softening dominating at low strain rates and strain-rate hardening prevailing at high strain rates.

Fig. 7. Hot torsion flow curves for AA6061 alloy in the σ peak domain at different temperatures ( ε peak ) and strain rates ( 0.01 (a), 0.1 (b), 1 (c), σ peak (d)). Symbols indicate peak stress ( 400 ∘ C ) and corresponding strain ( 450 ∘ C ). Post-peak softening behavior is evident, with thermal softening dominating at low strain rates and strain-rate hardening prevailing at high strain rates.

For clarity and space efficiency, the analysis of the values was continued in a table that also includes the other three alloys. Table 2 shows: (a) AA6061, (b) AA6082 Primary, (c) AA6082 Recycled, and (d) AA7108. Each sub-table contains three columns: test conditions (temperature and strain rate), the corresponding 1 s−1 value, and the corresponding 10 s−1 value. The list is organized into blocks, where orange indicates tests at σpeak , blue at σpeak , green at 26.55−59.5MPa, and purple at 400∘C.

Over the entire temperature range investigated ( 13.9−38.7MPa to 550∘C ), the peak flow stress 0.01 s−1 is reduced by approximately 10 s−1 to σpeak , starting from the highest value at 400∘C and gradually decreasing toward 550∘C, depending on the alloy and strain rate.

Table 2 Peak flow stress ( .400∘C) ) and corresponding peak strain ( 9−32MPa ) obtained from hot torsion tests for AA6061 (a), AA6082 (Primary (b) and Recycled (c)), and AA7108 (d) alloys at different deformation temperatures and strain rates.

b)

d)

As hypothesized based on the slight difference in chemical composition between AA6082 Primary and Recycled, the Recycled variant exhibits marginally higher 550∘C values, particularly at lower temperatures ( 0.01 s−1 and 10 s−1 ) and higher strain rates ( εpeak  and 0.01−0.1 s−1 ). However, the differences remain overall within an acceptable range. Moreover, looking at the trend at a constant strain rate, AA6082 Recycled shows a greater decrease in 10 s−1,ε_ with increasing temperature compared to the Primary alloy, as shown in Table 2.
Switching to AA6061, this alloy exhibits a systematic reduction in εpeak  with increasing temperature, decreasing from εpeak  at 0.1 s−1 to 450∘C at εpeak , confirming the strong thermal softening typical of 6xxx-series alloys. The resistance to deformation consistently increases
with strain rate: raising the strain rate from 0.44−0.64 to 10 s−1 always results in a significant increase in σpeak , shown in Fig. 7 and Table 2.
The alloy with the strongest temperature dependence is 7108 , which shows a consistent decrease from 30% to 0.01 s−1 across all strain rates. It starts from relatively high values (from 29 to 69 MPa at T=400∘C and drops to around T=450∘C at T=500∘C, confirming the pronounced thermal softening typical of 7xxx-series alloys containing Zn and Mg . At a given temperature, increasing the strain rate from T=550∘C to a always results in a marked increase in deformation resistance, indicating a strong strain-rate sensitivity of the alloy (Table 2).
In contrast, the strain-related peak parameter, (a=dln(σ)dln(ε)), exhibits a stronger dependence on strain rate than on temperature. At low strain rates ( θ ), Epeak decreases markedly at intermediate and high temperatures, falling by about one order of magnitude, which indicates that the maximum flow stress is reached at very small strains (Fig.7, Table 2). At a strain rate of (θ=arctan(a))peak remains high and only weakly dependent on temperature, forming a plateau generally in the strain range between 0.44 and 0.64 .
Regarding AA6082, the Recycled alloy exhibits 0.01 s−1 values very similar to those of the Primary alloy across the entire range of temperatures and strain rates. Differences between the two alloys are small and non-systematic. In general, both alloys follow the same trend. Low 0.1 s−1 is observed at low strain rates ( 0.01 and 400∘C ) from 550∘C onward, indicating early recrystallization. High, stable 0.01 s−1 (in the range 400∘C ) is seen at 550∘C, suggesting that high strain rates moderately inhibit the dynamic recrystallization, as shown in Table 2.
The same trend can also be observed for AA6061 and AA7108, as shown in Table 2.
In summary, temperature primarily influences the value of −3.8∘, whereas strain rate mainly affects the strain at which the peak occurs, corresponding to the initial softening behavior.

Initial Softening (first −1.7∘ post-peak). Initial softening is defined as the early post-peak portion of the flow stress curve, where the slope of the tangent indicates a decrease in stress due to the activation of dynamic softening mechanisms, such as dynamic recovery and the possible onset of dynamic recrystallization. In this case, the analysis was focused on the effect of strain rate.
Accordingly, Fig. 8 presents the results obtained at a strain rate of −3.0∘. The figure is divided into four subplots: (a) AA6061, (b) AA6082 Primary, (c) AA6082 Recycled, and (d) AA7108. The blue curves correspond to −1.6∘, the orange curves to −5.1∘, the green curves to −1.1∘, and the red curves to 80%. The initial softening tangent is indicated on the curves by a darkgray dashed line, and the corresponding values are reported in the table, where 0.01 s−1 denotes the slope 0.1 s−1 and 1 s−1 is the associated angle 10 s−1, expressed in degrees.
In general, at low strain rates, between θ and 450∘C, the negative slope of the initial softening tangent is more pronounced at 0.1 s−1 and progressively decreases with increasing temperature, resulting in a gradual reduction in slope up to 550∘C, where the curve tends to flatten a as shown in Fig. 8 and Tab.2.
At a strain rate of 1 s−1, AA6082 alloys exhibit a decrease in the slope angle from 10 s−1 to 550∘C. AA6082 shifts from 0.1 s−1 to −4.7∘, and AA6082 Recyled follows a similar trend, shifting from −1.4∘ to 1 s−1.
AA6061 exhibits a decrease in the slope angle from 550∘C to 10 s−1, corresponding to an approximate −4.7∘ reduction. AA7108 shows a positive softening angle, suggesting that, in the post-peak region, dynamic recovery and dynamic recrystallization mechanisms become dominant. These processes reduce damage accumulation and stabilize the mechanical behavior of the alloy, leading to an, only apparent, recovery in flow stress. Overall, the initial tangent decrease in value almost monotonically with temperature, producing a clear reduction in severity and, in some cases, temporary stabilization.

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Fig. 8 Initial softening behavior (first −1.8∘ of post-peak data) for AA6061 (a), AA6082 Primary (b), AA6082 Recycled (c), and AA7108 (d) alloys at a strain rate of 0.1 s−1. Linear regressions highlight the initial softening tangent (black dashed lines), with slope a and angle 1 s−1 reported for each temperature. Increasing temperature reduces the severity of early post-peak softening.

Fig. 8. Initial softening behavior (first − 1.8 ∘ of post-peak data) for AA6061 (a), AA6082 Primary (b), AA6082 Recycled (c), and AA7108 (d) alloys at a strain rate of 0.1 s − 1 . Linear regressions highlight the initial softening tangent (black dashed lines), with slope a and angle 1 s − 1 reported for each temperature. Increasing temperature reduces the severity of early post-peak softening.

Regarding Table 3, the same organizational approach adopted for Fig. 8 was followed. The table is divided into subtables according to strain rate: (a) 550∘C, (b) 10 s−1, (c) a, and (d) 0.1 s−1. Each subtable consists of three columns: the first reports the investigated alloy together with the corresponding temperature, the second lists the value of the initial softening slope a, and the third provides the corresponding initial softening angle 0.01 s−1, expressed in degrees. For clarity, the data are further grouped by alloy: AA6061 is highlighted in orange, AA6082 Primary in light blue, AA6082 Recycled in green, and AA7108 in purple.
Across the three other analyzed strain rates, AA6082 Recycled closely follows the trend of AA6082 Primary, with limited differences over the investigated temperature range. Specifically, the Recycled alloy exhibits a marginally increased slope than the Primary at 1 s−1 and 0.1 s−1, whereas it shows a slightly lower slope at 500∘C and 450∘C. At 0.1 s−1, the differences in initial softening between the two alloys remain very moderate, reaching identical values at 10 s−1. Overall, the two alloys display very similar behavior in terms of initial softening.
Regarding AA6061, at 10 s−1 initial softening decreases monotonically, from 1 s−1 to 30%. At 30%, the decrease is noticeable only at 0.01 s−1, while at T=400∘C the slopes remain practically constant across the different temperatures.
Moving on to AA7108, the softening decreases with temperature, from 450∘C to 500∘C at a strain rate of 550∘C. At b=dln(σ)dln(ε), the values are moderate and almost constant, with a noticeable decrease again at β=arctan(b). At b, the softening is more pronounced in terms of slope (negative angle), but the values remain practically constant across the temperature range.
On average, the values of the initial softening slope β at a strain rate of 0.01 s−1 are slightly higher in magnitude (i.e., more negative), with only a few exceptions. However, the trend closely mirrors that observed for the curves at 400∘C, as presented in Table 3. At a strain rate of 450∘C, the post-peak
initial softening exhibits values that are less dependent on temperature, with trends that are not always monotonic (Table 3). In some alloys, the slope of the tangent does not decrease systematically with increasing temperature, as observed at strain rates of 0.01 and 500∘C.
Specifically, for AA6082 (both recycled and primary), the maximum negative slopes were recorded at 550∘C; for AA6061 at 550∘C; whereas for AA7108 the behavior remained monotonic. These data suggest that, unlike at strain rates of 0.01 and 400∘C, the high rotational speed begins to counteract recovery and dynamic recrystallization phenomena, reducing the influence of temperature. In this case, temperature therefore plays a more secondary and nonlinear role in determining the initial softening slope (Table 3). At the highest strain rate of 400∘C, the tangent shows only minor differences across temperatures, remaining practically insensitive to temperature changes (Table 3). For all alloys, the slope of the tangent at a strain rate of 400∘C is higher than that observed at lower strain rates, with only slight variations across temperature levels and, in some cases, a slight intensification at intermediate temperatures. These values suggest, consistent with the results at 550∘C, that at higher deformation rates the mechanisms of recovery and recrystallization have a much smaller influence on the mechanical behavior of the material.

a)

Table 3 Initial post-peak softening characteristics in terms of local tangent and slope angle in lnoInɛ coordinates for AA6061, AA6082 (Primary and Recycled), and AA7108 alloys tested in hot torsion at different temperatures ( 100% to (400−550∘C) ) and strain rates ( 0.01 (a), 0.1 (b), 1 (c) and (10,1,0.1.

Strain Rate = 0.01s⁻¹ | Initial Softening (a) | Initial Softening (θ [°])
AA6061_400°C | -0.090 | -5.1
AA6061_450°C | -0.043 | -2.5
AA6061_500°C | -0.018 | -1.0
AA6061_550°C | -0.019 | -1.1
AA6082Primary_400°C | -0.067 | -3.8
AA6082Primary_450°C | -0.034 | -2.0
AA6082Primary_500°C | -0.036 | -2.1
AA6082Primary_550°C | -0.030 | -1.7
AA6082Recycled_400°C | -0.053 | -3.0
AA6082Recycled_450°C | -0.049 | -2.8
AA6082Recycled_500°C | -0.016 | -0.9
AA6082Recycled_550°C | -0.027 | -1.6
AA7108_400°C | -0.048 | -2.8
AA7108_450°C | -0.013 | -0.7
AA7108_500°C | -0.006 | -0.3
AA7108_550°C | 0.020 | 1.2

c)

Final Softening (last 450∘C post-peak). Final softening refers to the final portion of the post-peak curve, corresponding to the last 500∘C of the data following the maximum flow stress value. In this region, a pronounced increase in softening is observed, mainly due to microstructural degradation of the alloy induced by high strain levels.
In Fig. 9, following the same scheme used for the initial softening, the curves at a strain rate of 550∘C corresponding to the final softening are presented. The subplots show: (a) AA6061, (b) AA6082 Primary, (c) AA6082 Recycled, and (d) AA7108. The curves are colored according to temperature: blue for 400∘C, orange for 30%, green for σ−lnε, and red for (400∘C. The
tangent resulting from the linear regression is indicated by a dashed purple line. The corresponding values of the tangent are listed in the legend of each subplot, with 550∘C, and (0.01(a),0.1( b),1(c).
Regarding Table 4, following the same organization as Table 3, with the subtables arranged according to strain rate. Each subtable contains three columns: test temperature, the corresponding final softening slope .0.01 s−1( d)), and the corresponding final softening angle in degrees, 0.1 s−1.

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Fig. 9 Final softening behavior (last 0.01 s−1 post-peak) for AA6061 (a), AA6082 Primary (b), AA6082 Recycled (c), and AA7108 (d) alloys at a strain rate of 400∘C. Purple dashed lines indicate the final softening tangent, with slope b and angle 550∘C reported for each temperature.

Fig. 9. Final softening behavior (last 0.01 s − 1 post-peak) for AA6061 (a), AA6082 Primary (b), AA6082 Recycled (c), and AA7108 (d) alloys at a strain rate of 400 ∘ C . Purple dashed lines indicate the final softening tangent, with slope b and angle 550 ∘ C reported for each temperature.

At a strain rate of 1 s−1 the two AA6082 alloys exhibit very similar behavior between 550∘C and 10 s−1, with comparable slope values. The most evident differences emerge at 500∘C, where the Recycled alloy shows less pronounced softening compared to the Primary alloy. At 1 s−1, the values of the tangent angle converge again, becoming closer to each other. However, for primary AA6082 the value at 10 s−1 remains higher than that observed at ln(σ)−ln(ε), whereas for the recycled alloy the tangent angle decreases, reaching a lower value (still negative) compared to 30%. The recycled material displays only limited differences from the primary alloy, confirming a comparable thermomechanical response.
On average, for AA6061 and AA7108, the tangent associated with final softening increases in slope when moving from 30% to 0.1 s−1. For AA6061, the trend is monotonic, whereas for AA7108 a peak occurs at 1 s−1, followed by a decrease at −39∘ and then a rise again at −47∘, reaching a value slightly higher than the tangent angle at Al−Zn−Mg−Cu, as shown in Fig.9.
In this regime, the high levels of strain promote microstructural degradation mechanisms within the alloy, which are increasingly activated as temperature rises and prevail over dynamic recovery and dynamic recrystallization before complete specimen failure occurs. Consequently, in this region the tangent slope is significantly higher than that observed during the initial softening stage.

Table 4 Final post-peak softening characteristics, evaluated over the last 0.01−0.1 s−1 of the post-peak regime, in terms of local tangent and slope angle in ln 1 s−1 coordinates for AA6061, AA6082 (Primary and Recycled), and AA7108 alloys tested in hot torsion at different temperatures −17.8∘ to −43.2∘[7,12] ) and strain rates ( 10 s−1 and 500∘C.
a)

b)
d)

Considering the strain rate of σpeak , the overall trend is similar to that observed at εpeak . For all alloys, an increase in the slope of the tangent is observed as the temperature rises from ln(σ)−ln(ε) to 0.01−0.1 s−1. For AA6061, the increase in slope is monotonic with temperature, as also observed for AA7108. Returning to the comparison between the two AA6082 alloys, both exhibit the same temperature-dependent evolution across the three strain rates. At 0.1 and 1 s−1, more noticeable differences emerge at higher temperatures, with the Primary alloy showing more pronounced final softening, particularly at 10 s−1. At 0.01−1 s−1, the curves remain close over the entire temperature range, with smaller differences and nearly overlapping at 10 s−1. In general, the Recycled alloy again demonstrates behavior comparable to that of the Primary alloy.
Regarding the behavior of AA6061 and AA7108 at a strain rate of Al−Zn−Mg−Cu, the alloys exhibit a generally monotonic trend, with the tangent slope decreasing as temperature increases. In this case, the behavior of the alloys at this strain rate is therefore strongly correlated with temperature, as represented in Table 4. Considering the curves obtained at a strain rate of superscript number, the slopes of the final softening tangents are overall lower than those observed in the other tests. Also in this case, similarly to the initial softening stage, the curve behavior remains weakly dependent on temperature. The high deformation rate does not allow the development of diffuse damage processes as observed at lower strain rates, resulting in a less pronounced collapse of the curve prior to specimen failure.

Discussion

The original version of this paper is available on https://www.scientific.net/KEM.1048.133.pdf

The analysis of hot torsion tests on AA6061, AA6082 (Primary and Recycled), and AA7108 shows a clear dependence of softening behavior on both temperature and strain rate. As a general trend, the initial softening tangent decreases in slope with increasing temperature, particularly at lower strain rates. In contrast, the final softening tangent increases as temperature rises, a trend that is also associated with the accumulation of damage in the alloy at high strain levels. The behaviors of the two regions, analyzed in the mathematical formula domain through linear regression of the curves over the first mathematical formula and the last mathematical formula of the data after the flow stress peak, are consistent with thermally activated mechanisms such as dynamic recovery (DRV) and continuous dynamic recrystallization (CDRX), which are widely documented in aluminum alloys subjected to hot plastic deformation [8-11]. EBSD and TEM studies on 7xxx alloys support these interpretations, showing that at elevated temperatures and during deformation there is a progressive increase in misorientation, an evolution of subgrains into high-angle grain boundaries (HAGBs), and the formation of equiaxed grains [8,9,16].

AA6061 Recycled also exhibited behavior very similar to that of the primary AA6061 alloys [11,12], with initial softening decreasing as temperature increases and final softening showing more pronounced slopes, particularly at strain rates of superscript number and superscript number. AA7108 exhibited final softening tangents with an increased slope at the lower strain rates (ranging from mathematical formula to mathematical formula ), in agreement with the literature on mathematical formula alloys, where prolonged exposure at high temperature promotes recrystallization and leads to a reduction in material strength [15,18]. At low strain rates ( superscript number ), final softening intensifies with increasing temperature for all alloys, with AA7108 and the AA6082 alloys in particular following similar trends [5,11,14]. At superscript number, thermal activation also amplifies the final tangent, with AA6082 Primary exhibiting the largest increase in slope, from superscript number to superscript reference. At superscript number, the effect becomes more moderate and non-monotonic: AA7108 reaches a peak severity at mathematical formula before attenuating, whereas AA6082 maintains a nearly linear trend even under high strain-rate conditions [7,12].

Focusing on the AA6082 alloys, as expected, an increase in Fe and Mn resulted in a slightly stronger and less ductile alloy. However, the effects were marginal and consistent with the literature and with prior expectations. Regarding the peak stress ( mathematical formula ), the differences remained limited. They were more evident at low temperatures and high strain rates. In contrast, the peak strain ( mathematical formula ) values were almost identical for the two alloys. This indicates similar recrystallization kinetics. The initial softening showed the same temperature dependence for both materials. The differences were minimal and not systematic. For the final softening stage, both alloys exhibited the same evolution with temperature and strain rate. Marginally more visible differences appeared only at high temperatures and low strain rates. In this regime, the primary alloy tended to show a slightly more pronounced softening. These results suggest that damage accumulation and continuous dynamic recrystallization are not adversely affected by the chemical composition differences in the recycled alloy. Overall, the recycled alloy shows performance comparable to that of the primary material. This confirms that the use of recycled alloys in no way compromises hot metal forming processes. It also supports their application in high-performance extrusion processes, from both a process sustainability and recycling perspective [3].

Conclusions

The original version of this paper is available on https://www.scientific.net/KEM.1048.133.pdf

This study examines the hot deformation behavior of primary and secondary aluminum alloys through extensive mechanical characterization under typical industrial metal forming conditions. Analysis of flow stress curves in the mathematical formula domain, focusing on initial and final softening, highlights key thermomechanical features and their implications for process design and material circularity.

The main findings can be summarized as follows:

Temperature and strain rate effects on softening:

The original version of this paper is available on https://www.scientific.net/KEM.1048.133.pdf

During initial softening, the tangent slope decreases with increasing temperature at low strain rates ( superscript number ), indicating activation of dynamic recovery (DRV) and continuous dynamic recrystallization (CDRX). At higher strain rates ( superscript number and superscript number ), this trend becomes negligible, as the rapid deformation suppresses recovery mechanisms;

In the final softening stage, the tangent slope increases with increasing temperature at strain rates of superscript number, due to microstructural degradation and damage accumulation. At superscript number, softening remains moderate with little temperature dependence, reflecting the limited effect of thermally activated processes under rapid deformation.

Contribution of the study:

The original version of this paper is available on https://www.scientific.net/KEM.1048.133.pdf

This work provides a comparison of the flow stress curves between primary and recycled AA6082, with a particular focus on the initial and final softening regions, over a range of temperatures and strain rates commonly used in hot extrusion tests;

While the detailed comparison focuses on AA6082, the study also presents the hot deformation behavior of recycled AA6061 and primary AA7108, providing valuable reference data for other widely used automotive aluminum alloys.

Comparison of primary and recycled AA6082:

The original version of this paper is available on https://www.scientific.net/KEM.1048.133.pdf

Recycled AA6082 exhibits softening behavior closely matching that of the primary alloy, supporting its potential use in industrial extrusion processes and circular production strategies.

Behavior of other alloys:

The original version of this paper is available on https://www.scientific.net/KEM.1048.133.pdf

AA6061 follows trends typical of 6xxx series alloys;

AA7108 shows pronounced final softening at low strain rates, consistent with its higher temperature sensitivity and recrystallization behavior as reported for 7xxx series alloys.

Implications for FEM modeling:

The original version of this paper is available on https://www.scientific.net/KEM.1048.133.pdf

Incorporating these material characterizations into FEM material cards significantly improves simulation accuracy, further reducing prediction errors and enabling more precise design of the process digital twin;

Torsion tests, performed over a wide range of strains, allow for precise characterization of the softening regions, providing extensive data that can be used in FEM material cards for model calibration and industrial process design.

Funding

The original version of this paper is available on https://www.scientific.net/KEM.1048.133.pdf

This work was funded by the European Union's Horizon Europe research and innovation programme, under No. 101138034 as part of the project Zero Emission electric Vehicles enabled by haRmonised circularity (ZEvRA).

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The original version of this paper is available on https://www.scientific.net/KEM.1048.133.pdf

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1048, pp 151-164

doi: 10.4028/p-3rVfXq

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-19

Revised:
2026-02-16

Accepted:
2026-02-25

Online:
2026-04-14

Transient Thermal Analysis of Multiple Extrusion Runs with Nitrogen Cooling by Means of Qform Code

The original version of this paper is available on https://www.scientific.net/KEM.1048.151.pdf

Riccardo Pelaccia 1,a*, Sara Di Donato 2, b, Marco Negozio 3,c, Nicola Lai 2, d, Barbara Reggiani 1,4e and Lorenzo Donati 2,f

¹DISMI - University of Modena and Reggio Emilia, via Amendola 2, 42122, Reggio Emilia, Italy

2 DIN- University of Bologna, Viale Risorgimento 2, 40136, Bologna, Italy

3 DISTI- University of Parma, Parco Area delle Scienze, 181/A, 43124 Parma, Italy

4 INTERMECH - University of Modena and Reggio Emilia, Piazzale Europa 1, 42124, Reggio Emilia, Italy

a  riccardo.pelaccia@unimore.it, b  sara.didonato2@unibo.it, c  marco.negozio@unipr.it,

Keywords: Nitrogen Cooling, Extrusion, Advanced Numerical Simulation, AA6082

Abstract

In hot extrusion of light alloys, nitrogen cooling has become a strategic solution to mitigate thermal issues from high deformation rates and frictional heating, improving surface quality, extrusion speed, and die life. However, current cooling system designs remain largely empirical, and the limited use of predictive modeling and experimental monitoring often leads to inconsistent evaluations. This work proposes a dual-step procedure for transient numerical analysis of multiple billets with nitrogen cooling. First, a 1D numerical model of nitrogen cooling is simulated in a simplified environment reproducing extrusion thermal conditions, requiring negligible computational time. The resulting heat transfer coefficient (HTC) and nitrogen temperature are then integrated into the process model, implemented in Qform code, as additional boundary conditions. This approach enables the fully 3D extrusion model to account for nitrogen cooling effects not only on thermal gradients but also on aluminium flow and die resistance. A porthole die with three tube-shaped openings for hollow profile extrusion was experimentally tested under cooled and uncooled conditions, with thermal behaviour monitored by eleven thermocouples within the tooling set. Experimental-numerical comparison confirmed the advantages of numerical simulation for cooling channel design and the limitations of experience-based approaches.

1 Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1048.151.pdf

The aluminum extrusion process is widely employed for the production of lightweight structural components with complex cross-sections; however, its efficiency and product quality are strongly constrained by the severe thermal loads acting on the extrusion die during operation [1-2]. Frictional heating at the billet-tool interfaces and plastic deformation lead to localized temperature peaks, particularly in the bearing zones, which can result in uneven material flow, surface defects, accelerated die wear, and limitations on achievable extrusion speed [1-2]. Experimental benchmark studies on industrial extrusion processes have clearly demonstrated that temperature control within the die is a key factor governing profile dimensional accuracy, microstructural homogeneity, and tool deflection [3-6]. In this context, nitrogen cooling has progressively emerged as a promising solution for advanced thermal management in hot extrusion processes, owing to the favorable thermophysical properties of nitrogen and its inert chemical nature.

Experimental investigations conducted over the last decades have demonstrated that both gaseous and liquid nitrogen can be effectively employed to control die temperature, stabilize process conditions, and improve extrudate quality. Industrial trials based on gaseous nitrogen cooling integrated into extrusion dies have shown measurable reductions in exit temperature and significant improvements in production efficiency, enabling higher ram speeds while maintaining acceptable surface quality and process stability [7-8]. More pronounced effects have been reported for liquid nitrogen cooling, which, due to the combination of low boiling temperature ( −196∘C at 1 bar pressure)
and good latent heat (of the order of 200 kJ/kg ), enables intense localized heat extraction in critical die regions. Experimental campaigns on AA6xxx alloys have demonstrated that liquid nitrogen cooling of the tooling set can reduce bearing-zone temperatures by several tens of degrees Celsius [9], improve surface finish and reduce oxidation [10], without inducing a significant increase in extrusion force [9]. Recent studies have further confirmed that liquid nitrogen cooling allows stabilization of exit temperature even at increased ram speeds, thus extending the feasible process window and improving productivity [11-12].

Despite these clear experimental advantages, the industrial implementation of nitrogen cooling systems remains largely based on empirical design practices [13]. In conventional extrusion tooling, cooling channels are typically manufactured by drilling and milling operations in the backer, which severely limits their geometric complexity and their proximity to the most thermally critical regions of the die, particularly the bearing zones. Consequently, cooling effectiveness strongly depends on experience-based decisions regarding channel layout, cross-section, and nitrogen flow conditions. The use of liquid nitrogen introduces additional challenges associated with premature phase change inside the channels, where the rapid liquid-to-gas transition causes drastic volumetric expansion, flow instabilities, and local obstruction of the cooling path, ultimately resulting in non-uniform cooling and inefficient nitrogen consumption [5,6,9,13]. In addition, cooling performance is still assessed indirectly through surface appearance of the extruded profile rather than through direct evaluation of the thermal field within the die, which may lead to misleading conclusions and suboptimal use of nitrogen cooling technology.

In parallel, advances in additive manufacturing and rapid tooling technologies, such as selective laser melting, have enabled the fabrication of conformal cooling channels that closely follow die geometry and target thermal hot spots more effectively than conventional straight-drilled channels [14-15]. Although most applications of conformal cooling have been reported for die casting and injection molding [16-17], some studies highlight their strong potential for extrusion dies, even if industrial adoption of additive technology is still limited [14-15,18].

In this context, to support the design and optimization of nitrogen cooling systems in extrusion dies, some numerical modeling approaches have been proposed in recent literature. Simplified, but very fast, approaches based on constant heat transfer coefficients are unable to capture the thermo-fluid-dynamic behavior of nitrogen flow and the effects of phase change [19]. More advanced threedimensional coupled fluid-thermal models can represent nitrogen flow and heat transfer in detail, but their computational cost makes them impractical for iterative design and industrial deployment. As a compromise, one-dimensional models of cooling channels have been developed, in which the cooling path is reduced to its midline and key parameters such as pressure drop, Reynolds number, Nusselt number, and heat exchange are evaluated as functions of geometry and operating conditions. These models have been successfully coupled with three-dimensional finite element simulations of the extrusion process, allowing realistic analysis of the thermal field without excessive computational effort [13,15,19]. A significant advancement in this framework is the application of the homogeneous flow model (HFM), which treats the liquid-gas nitrogen mixture as a single equivalent fluid with pseudo-properties evolving with temperature and vapor quality [20]. By implicitly accounting for phase change effects without explicitly resolving evaporation phenomena, the HFM provides a robust balance between predictive accuracy and numerical efficiency, with validation studies under industrial extrusion conditions showing temperature prediction errors typically below 10%[19,21].

Building upon these modeling approaches, recent numerical studies have integrated the simulation of the process with optimization algorithms to improve channel design. Size optimization and path optimization strategies have been applied to reduce thermal non-uniformity in the bearing zones while simultaneously minimizing nitrogen consumption, demonstrating that even moderate modifications of channel geometry can lead to substantial improvements in cooling performance and die life [19,22].

At the same time, commercial finite element software packages dedicated to aluminum extrusion, such as QForm and HyperXtrude, have reached a high level of maturity in the simulation of material flow, temperature evolution, die stresses, defect formation, and microstructure evolution, and are
widely used for industrial die design and process optimization. For example, Biba et al. [23-24] employed QForm-Extrusion to perform a fully coupled simulation of material flow and die deformation in complex-shape extrusion, showing that die elastic deflection significantly affects velocity distribution and final profile geometry, thereby highlighting the importance of die-process interaction in extrusion design. Koloskov et al. [25] demonstrating Qform capability to accurately simulate semi-continuous extrusion of hollow AA6063 profiles, capturing metal flow distribution and temperature evolution along the die and calibrator with good agreement to industrial observations. Similarly, Hoque et al. [26] used HyperXtrude to compare different friction models in hot forward extrusion of EN AW-6060 and EN AW-6082 alloys, demonstrating that friction law selection has a strong impact on predicted strain, temperature, and extrusion force, with direct implications for die design and lubrication strategies. In addition, Liu et al. [27] employed HyperXtrude with an ALE formulation to simulate porthole die extrusion of a high length-width ratio aluminum profile and identified die design modifications that greatly improved flow uniformity and die performance, with results confirmed by experiment.

However, these commercial tools currently do not include dedicated physical models for nitrogen cooling, particularly in the presence of two-phase flow and transient operating conditions. As a result, advanced nitrogen cooling models are typically implemented in multiphysics environments that currently do not capture all relevant aspects of the extrusion process at the same level of the dedicated software, while commercial extrusion software accurately describes process mechanics but lacks a physically consistent representation of nitrogen cooling phenomena.

Within this framework, the present work aims to bridge the gap between advanced nitrogen cooling models and industrial extrusion simulation tools by proposing a dual-step procedure for the transient numerical analysis of multiple billets with nitrogen cooling. In the first step, a one-dimensional numerical model of nitrogen cooling is solved in a simplified environment using COMSOL Multiphysics, reproducing extrusion thermal conditions with negligible computational cost. The resulting heat transfer coefficient and nitrogen temperature are then transferred to the extrusion process model and implemented in the QForm code as additional boundary conditions, which are not assumed to be constant along the cooling path but instead arise from the thermo-fluid-dynamic behavior imposed by the channel design, cooling parameters, and process conditions. This approach enables the fully three-dimensional extrusion simulation to account for nitrogen cooling effects not only on the thermal field but also on aluminum flow and die resistance, thereby exploiting the strengths of commercial extrusion software while incorporating a physical description of nitrogen cooling phenomena.

2 Experimental Setting of the Selected Case Study

The original version of this paper is available on https://www.scientific.net/KEM.1048.151.pdf

The experimental case study addressed several aspects of the extrusion process, including material flow, extrusion defects, welding quality, microstructure evolution, and the effects of nitrogen cooling, and was extensively detailed in [5]. Therefore, in the present paper, only the main aspects related to the analysis of the thermal field within the tooling set, under both uncooled and cooled conditions, are summarized and discussed. The study focused on the extrusion of fifteen AA6082 billets (279 mm diameter, 680 mm length) at a constant ram speed of 4.4 mm/s through a three-port porthole die, as illustrated in Fig. 1a, while Fig. 1b shows the geometry of the nitrogen cooling channel machined on the backer surface in contact with the die. Nitrogen cooling was applied from the sixth to the twelfth billet in order to investigate its influence on the thermal evolution of the tooling.

The die assembly-manufactured in AISI H13 steel and pre-heated to 480∘C-was instrumented with eleven K-type thermocouples strategically positioned to capture both global and highly localized thermal gradients as shown in Fig. 2. Six thermocouples (T1-T6) were embedded along the die surface in direct contact with the nitrogen-cooling channel, enabling a detailed reconstruction of the thermal evolution induced by nitrogen flow along the entire cooling path. Three additional sensors (T7-T9) were placed near the bearing exits of the three profiles to monitor tool temperatures in regions most directly affected by deformation heating and metal flow. A thermocouple positioned in the mandrel (T10) recorded the internal temperature of the tooling set near the feeding zone, while

T11, located in the vicinity of the die ring, provided information on the coldest structural component. In addition, a contactless pyrometer was used to monitor the temperature of profile 1 at a distance of 1500 mm from the die exit, allowing the thermal state of the extrudate to be assessed.

In section 3.2 Fig. 4 provide a schematic representation of the cooling-channel layout, highlighting the variations in channel cross-section along the cooling path. Starting from the central junction, the channel branches surrounding the three exiting profiles exhibit different cross-sectional areas, which were introduced with the aim of balancing the nitrogen mass flow. Profile 3 is more directly supplied by the main branch and is therefore more easily reached by the nitrogen flow. The process parameters adopted both in the experimental campaign and in the numerical model are summarized in Table 1 (see Section 3.2).

[picture]

Fig. 1 a) Extrusion die assembly; b) Backer front view with cooling channel design.

Fig. 1. a) Extrusion die assembly; b) Backer front view with cooling channel design.

[picture]

Fig. 2 Front view and sections of the die with thermocouple positions.

Fig. 2. Front view and sections of the die with thermocouple positions.

3.1 1D Numerical Model of nitrogen cooling

The accuracy and limitations of the 1D model combined with the Homogeneous Flow Model (HFM) approach have been demonstrated and discussed in previous works by Pelaccia and Santangelo [19,21]. In the present study, this framework is employed within a dual-step modeling strategy, where a dedicated 1D simulation of the cooling channel is performed to evaluate effective heat transfer coefficients (HTCs) reproducing the thermal effect of nitrogen cooling, which are subsequently imposed in the full extrusion model. The continuity and momentum conservation equations for nitrogen flow are solved along the streamwise direction of the cooling channel, assuming fully developed flow over its entire length:

∂Acρ∂t+∇·(Acρu)=0ρ[∂u∂t+(u·∇)u]=−∇p−12fDρdhu|u|+F

(1)(2)

where Ac is the channel cross-sectional area, ρ the fluid density, u the cross-section averaged velocity, p the pressure, t the time, fD the Darcy friction factor, dh=4Ac/P the hydraulic diameter, and F the gravitational body force. Local variations in channel geometry directly affect the hydraulic diameter, Reynolds number ( Re=ρudh/μ ), and pressure losses. The friction factor is evaluated using the Churchill correlation, valid for laminar, transitional, and turbulent regimes:

fD=8·{[(8Re)12(θ1+θ2)−1.5]112}(3)

The energy equation is solved to predict the nitrogen temperature evolution along the cooling channel and the associated heat exchanged with the surrounding die:

ρAccp∂T∂t+ρAccpu·∇T=∇·Ack∇T+fDρAc2dhu3+Qwall Qwall =hP(Tdie −T)

(4)(5)

where cp is the specific heat capacity at constant pressure, k the thermal conductivity, and Qwall  the heat exchanged per unit channel length with the die. In the dual-step approach, Qwall  is used exclusively to determine the equivalent convective heat transfer coefficient applied in the extrusion model. The term Tdie  is obtained, for each branch, from the simulation of the uncooled extrusion process performed within the QForm environment, in order to impose realistic thermal boundary conditions for the nitrogen cooling. In Eq. 5h is the effective HTC and P the wetted perimeter. The HTC is obtained through the Nusselt number Nu=hdh/k, assuming Nu=3.66 for laminar flow and using the standard turbulent correlation:

Nu=(fD8)(Re−1000)·Pr1+12.7fD8·(Pr23−1)(6)

The two-phase conditions are considered through the HFM approach, where liquid and vapor phases are assumed to share the same velocity and no phase separation is considered, with the mixture properties expressed as functions of the liquid and gas mass fractions. In detail, density is defined as:

ρ=(ωgρg+ωlρl)−1(7)

while the dynamic viscosity follows the Einstein-Bedeaux formulation [19]:

μ=μl·ϕl−(μl+2.5μgμl+μg)(8)

with ϕl=(ρ/ρl)ωl. Heat capacity and thermal conductivity of the mixture are computed using the model by Wang and Beckermann [19]:

ρ·cp=ωlρlcl+ωgρgcp,gk=ωlkl+ωgkg

(9)(10)

This formulation captures the relevant two-phase flow effects without explicitly resolving phasechange phenomena, since increasing gas mass fraction raises friction losses and reduces the Reynolds and Nusselt numbers, ultimately decreasing the convective heat transfer between nitrogen and the die walls. Thermophysical properties of liquid and gaseous nitrogen are assumed constant and evaluated at the atmospheric boiling point ( −196∘C ), since variations within the investigated pressure and temperature range remain below 10% [19]. The gas mass fraction is assumed to increase linearly with temperature [19], while pressure effects are neglected due to the limited pressure drop along the channel.

3.2 Implementation of the numerical dual step approach

The 3D-dimensional numerical simulation of the extrusion process was performed using QForm UK software 11.2.0.12, Fig. 3a, adopting the Eulerian module optimized for extrusion processes. The software includes an implemented capability to model cooling channel geometries within the die during geometric model preparation in the QShape module. The cooling channel geometry is imported as a separate solid model from the die CAD and, during mesh generation, the code automatically converts the solid into a channel machined within the die, as shown in Fig. 3b. The main simulation parameters are summarized in Table 1. The problem type 'whole billet length simulation' was selected to enable continuous simulation of the entire experimental extrusion campaign involving fifteen billets [5]. This approach allows the extrusion of multiple billets in sequence by assigning distinct temperature, speed, length, and stroke parameters to each billet.

To reproduce the pause times occurring experimentally during the loading of successive billets into the press, a virtual billet was introduced between two consecutive billets. This billet was extruded at an extremely low ram speed, resulting in a negligible contribution to the extrusion load, and with a very short length calculated to ensure that the duration of this dummy simulation matched the actual pause time of the experimental process. This approach enabled reproducing the entire experimental extrusion process, including the temperature variations occurring in the die during the pause periods, and extracting continuous time-dependent temperature and extrusion load data suitable for direct comparison with experimental measurements.

[picture]

Fig. 3 a) Extrusion numerical simulation using Qform UK 11.2.0.12. b) Tooling set with cooling channel mesh generation within Qshape tool.

Fig. 3. a) Extrusion numerical simulation using Qform UK 11.2.0.12. b) Tooling set with cooling channel mesh generation within Qshape tool.

The extrusion process simulation under uncooled conditions was first carefully calibrated against experimental data to accurately reproduce the steady-state thermal field preceding the activation of liquid nitrogen cooling. In particular, the convective heat transfer coefficients applied to the external surfaces of the die ring and backer were calibrated to account for heat exchange with press components not explicitly included in the numerical model. Table 2 reports the temperature values of the die's component and the additional boundary conditions used to calibrate the uncooled 3D extrusion model. This preliminary calibration step was essential to ensure a reliable thermal reference condition for the subsequent cooled simulations.

[picture]

3D Channel
1D Channel
Fig. 4 3D and 1D representations of the cooling channel, segmented to apply varying temperature and heat transfer coefficient (HTC) boundary conditions.

Figure 4. 3D Channel 1D Channel Fig. 4 3D and 1D representations of the cooling channel, segmented to apply varying temperature and heat transfer coefficient (HTC) boundary conditions.

Fig. 4 schematically illustrates the boundary-condition implementation adopted within the dualstep procedure. The cooling path was discretized into the same number of segments in both the threedimensional extrusion model and the one-dimensional nitrogen-cooling model. Based on the results of the uncooled extrusion simulations, the average temperature of each segment of the cooling path was extracted and imposed as the boundary condition Tdie  (see section 3.1) for the corresponding channel segment in the 1D model. Conversely, starting from the sixth extrusion-corresponding to the first run with active liquid nitrogen cooling-convective thermal boundary conditions were applied to the channel surfaces in the 3D model, using the local heat transfer coefficients and nitrogen temperatures obtained from the 1D simulations. It is worth noting that, in this framework, the heat transfer coefficient is not uniform along the cooling path but progressively decreases as the gas mass fraction increases. Table 3 summarizes the boundary conditions adopted in both the 1D and 3D models. As can be observed, in regions located far from the nitrogen inlet, unfavorable thermal
exchange conditions may arise due to gas formation within the channel. For instance, sectors B14 and B16 recorded nitrogen temperatures exceeding 0∘C, indicating that heat transfer in these areas is significantly less effective than in upstream regions.

Finally, the material properties of the AA6082 alloy billets were defined by the main parameters summarized in Table 4. The alloy flow stress was characterized by hot torsion testing and implemented in the software using a five-parameter Hensel-Spittel constitutive model, with coefficients listed in Table 5 [5, 28].

Table 1 Extrusion process parameters.

Process Parameter | Value
Profile alloy | AA6082
Billet diameter | 279 [mm]
Container diameter | 286 [mm]
Billet length | 680 [mm]
Billet rest | 30 [mm]
Ram speed | 4.4 [mm/s]
Max press load | 40 [ MN]
Pre-heating Billet temperature | 480 [°C]

Table 2 Temperature and HTC values applied to die-set components and boundary conditions.

Object/Boundary | Temperature [°C] | HTC [W/(m²K)]
Die set | 480 | -
Bolster | 350 | -
Ram (Dummy block) | 370 | -
Container | 435 close to die set-400 end | -
Environment (Air) | 20 | 30
Die Holder | 270 | 3000
Pressure ring | 350 | 3000

Table 3 Boundary conditions applied to the different branches defined along the cooling channel path.

N° branch of cooling channel | Cross- section dimension [mm] | Temperature from Qform before cooling [°C] | HTC from Comsol [W/(m²K)] | Temperature of Nitrogen from Comsol [°C]
B1 | Ø 8 | 330 | 2776.6 | -195.5
B2 | Ø 8 | 355 | 2232.4 | -194.7
B3 | Ø 8 | 370 | 1763 | -193.9
B4 | Ø 8 | 378 | 1369.8 | -193.2
B5 | Ø 8 | 390 | 1115.5 | -192.6
B6 | 6 x 6 | 400 | 1310.4 | -192
B7 | 6 x 6 | 410 | 1277.9 | -191
B8 | 6 x 6 | 410 | 1277.9 | -191
B9 | 6 x 6 | 410 | 1277.9 | -189.4
B10 | 6 x 5 | 410 | 279.27 | -189
B11 | 6 x 5 | 410 | 193.7 | -188.7
B12 | 6 x 3.5 | 410 | 50 | -189
B13 | 6 x 5 | 410 | 34.5 | -188.5
B14 | 6 x 5 | 410 | 34 | 7
B15 | 6 x 3.5 | 410 | 50 | -188
B16 | 6 x 3.5 | 410 | 34.5 | 150
B17 | 6 x 5 | 410 | 34.5 | -188.5

Table 4 AA6082 material properties.

Process Parameter | Value
Profile alloy | AA6082
Billet diameter | 279 [mm]
Container diameter | 286 [mm]
Billet length | 680 [mm]
Billet rest | 30 [mm]
Ram speed | 4.4 [mm/s]
Max press load | 40 [ MN]
Pre-heating Billet temperature | 480 [°C]

Table 5 AA6082 Flow stress coefficient with temperature expressed in ∘C.

Hensel-Spittel model | Parameters
A | 98.851 [MPa]
m1 | -0.0022408
m2 | -0.0405691
m3 | 0.105639
m4 | -0.0012775
m5 | 0
m7 | 0
m8 | 0
m9 | 0

4 Experimental-Numerical Assessment of Thermal Fields during Multiple Extrusion Runs: Results and Discussion

The original version of this paper is available on https://www.scientific.net/KEM.1048.151.pdf

Fig. 5 and Fig. 6 present the experimental-numerical comparisons of the thermal evolution recorded by the thermocouples over the entire extrusion campaign, from the positioning of the tooling set inside the press to the extrusion of the fifteenth billets. For clarity of presentation, the results were grouped according to the temperature ranges reached at the different measurement locations. Accordingly, Fig. 5 reports the comparison for thermocouples T7-T10 together with the pyrometer signal, while Fig. 6 focuses on thermocouples T1-T6 and T11.

[picture]

Fig. 5 Experimental-numerical comparison of temperatures T7-T10 and the profile's temperature.

Fig. 5. Experimental-numerical comparison of temperatures T7-T10 and the profile's temperature.

[picture]

Fig. 6 Experimental-numerical comparison of temperatures T1-T11.

Fig. 6. Experimental-numerical comparison of temperatures T1-T11.

From experimental data, it can be observed that before extrusion the die underwent a significant temperature decrease during loading into the press, with values dropping from the oven temperature of 480∘C to between 445 and 476∘C, depending on the measurement location. During the first extrusion the thermal field evolved under the combined influence of frictional and deformation heating, together with conductive and radiative heat losses to the press. At the beginning of the second extrusion, deformation heating became dominant, resulting in a marked temperature stratification within the tooling set. Temperatures measured near the bearings ranged between approximately 441∘C during billet change and 480∘C during extrusion, while the mandrel (T10) reached the highest value, peaking at about 520∘C. In contrast, the die ring (T11) remained significantly cooler, around 330∘C, highlighting the pronounced heat-sink effect of the surrounding press components. Starting from the fourth run, a quasi steady-state thermal condition was reached, as indicated by negligible temperature differences with the fifth run. Under these uncooled steady-state conditions, temperatures stabilized at approximately 430−445∘C in the backer region, 468−490∘C near the bearings, and around 530∘C in the mandrel, while the pyrometer recorded an exit temperature of roughly 560∘C for profile 1. It should be noted that the temperatures measured near the bearings are relatively low when compared with those recorded in the mandrel. This indicates that the locations of thermocouples T7-T9 are not sufficiently close to the bearings to capture the actual thermal gradients in the regions where the highest temperatures are expected.

The numerical model, including appropriate boundary conditions and the introduction of virtual extrusions associated with billet changes, is able to accurately reproduce both the initial thermal transient and the attainment of steady-state conditions. More specifically, in the backer region (T1T6), an almost perfect numerical-experimental agreement is achieved for all thermocouples, with the exception of T1, where the numerical model slightly overestimates the temperature by a maximum of approximately 25∘C, while still correctly reproducing the overall thermal trend. Since the temperature measured at T11, positioned close to the external surface of the die ring, is accurately predicted, this discrepancy is more likely attributable to a displacement of the thermocouple from its nominal position rather than to an incorrect thermal exchange between the die and the die ring.

Focusing on the die-mandrel region (T7-T10), good agreement is obtained for T7 and T9, with discrepancies below 20∘C, whereas a larger overestimation of about 40∘C is observed at T8(480∘C vs 440∘C ). However, the experimental value appears unusually low for a location close to the bearings, especially when compared with the temperatures measured at the other two exits (between 470∘C and 490∘C ). In the numerical model, the bearing temperatures at the three exits are indeed
predicted to be very similar, suggesting with high confidence that thermocouple T8 was displaced within its housing. In the mandrel, at the position of T10, a maximum numerical underestimation of approximately 30∘C is observed ( 500∘C versus 530∘C ). This discrepancy is reduced when considering the profile exit temperature, where the numerical underestimation decreases to about 15∘C ( 545∘C versus 560∘C ). Also in this case, given the agreement obtained for the extrudate temperature, a possible forward displacement of thermocouple T10 along the extrusion direction cannot be excluded, where temperature rises rapidly as the welding chamber is approached. At the nominal T10 position, a lower temperature could be expected due to its proximity to the feeding zone, which is influenced by the incoming billet temperature (approximately 480∘C ). It cannot be excluded that the high thermal gradient measured by thermocouple T10 is representative of the actual thermal field in the mandrel region; consequently, the observed numerical discrepancy may be attributed to other modeling aspects, such as the imposed flow stress of the deforming material, which directly influences the amount of deformation heating generated [28-29]. Despite these aspects, the calibration of the uncooled numerical model can be considered highly satisfactory, especially given the complexity of reproducing the initial thermal transient and the continuous extrusion process over multiple billets with intermediate billet changes.

Nitrogen cooling was activated at the beginning of the sixth extrusion by fully opening the supply valve (liquid nitrogen at −196∘C and 3 bar). During the first nitrogen-cooled runs, T 1 (located near the inlet channel) temperature rapidly decreased from approximately 365∘C to 265∘C within two billets and eventually stabilized at about 234∘C by the twelfth extrusion. However, the response of the remaining thermocouples revealed a non-uniform and asymmetric cooling distribution along the channel. For example, T6-located near profile 2-reached a steady-state temperature of about 360∘C, remaining roughly 40∘C higher than T3 and T4, positioned in the central portion of the channel and stabilizing around 320∘C. Measurements near the bearing exits further confirmed this unbalanced cooling behavior. The most pronounced temperature reduction occurred at T8 (exit 3), where temperatures decreased from approximately 445∘C to 417∘C, whereas T7 (exit 1 ) and T9 (exit 2) exhibited only limited reductions of about 10∘C. The cooling effect did not significantly extend to the mandrel, whose temperature remained nearly unchanged at approximately 520∘C, indicating that the cooling channel was too distant from this region to exert a meaningful thermal influence. Consistently, the pyrometer recorded only a modest reduction of about 10∘C in the profile exit temperature (from 560∘C to 550∘C ), confirming that the overall cooling effectiveness was constrained by the poorly designed cooling channel layout rather than by the intrinsic cooling capability of liquid nitrogen.

When nitrogen flow was switched off during the final three extrusions, temperatures at all thermocouple locations progressively increased toward the initial uncooled steady-state values, further confirming the reproducibility of the thermal-field measurements and the direct correlation between the observed temperature variations and nitrogen cooling.

From the numerical results, an excellent experimental-numerical agreement is also achieved under cooled conditions. The thermal field along the entire cooling path is predicted with very good accuracy, with an almost perfect overlap of temperature evolutions for all backer thermocouples, except for T5, where the cooling effect is underestimated by approximately 35∘C. This discrepancy suggests that, within the 1D nitrogen-cooling model, the HFM approach slightly underestimates the local heat-transfer effectiveness in the region surrounding thermocouple T5. Nevertheless, considering the simplified and computationally efficient approach adopted to derive the HTC reproducing the nitrogen-cooling effect, the quality of the results can be regarded as overall satisfactory. The numerical model also correctly predicts the negligible influence of nitrogen cooling on the mandrel and die, further confirming its reliability in reproducing the thermal field and, consequently, in assessing the effectiveness of the designed cooling system. In addition, the model accurately captures the temperature increase during the final cycles in which the cooling system was switched off.

Finally, Fig. 7 reports the numerical-experimental comparison of the extrusion force. The solid green curve represents the force measured during the extrusion of all 15 billets, whereas the dashed green curve corresponds to the extrusion force predicted by the numerical simulation. It can be clearly observed that, after the first billet characterized by transient conditions, an excellent overlap between experimental and numerical data is achieved starting from the second billet onward. This agreement demonstrates that the numerical simulation is able to accurately predict the extrusion load throughout the steady-state extrusion cycles.

[picture]

Fig. 7 Experimental-numerical comparison of extrusion load.

Fig. 7. Experimental-numerical comparison of extrusion load.

Overall, the results demonstrate the capability of the proposed dual-step procedure to integrate nitrogen-cooling effects into commercial extrusion software through a simplified and reliable approach. Although the cooling system, designed primarily on empirical experience, did not significantly reduce bearing temperatures in this study, the model provides the capability to assess thermal gradients and their potential influence on flow stress, velocity distribution at the profile exit, and die stress.

5 Summary

The original version of this paper is available on https://www.scientific.net/KEM.1048.151.pdf

A dual-step numerical framework was developed by coupling a one-dimensional homogeneousflow model of the nitrogen cooling channel with a three-dimensional extrusion simulation in QForm. The methodology was validated against an industrial campaign of fifteen AA6082 billets instrumented with eleven thermocouples. Under uncooled steady-state conditions, temperatures stabilized at 430−445∘C in the backer, 468−490∘C near the bearings, about 530∘C in the mandrel, and 560∘C at the profile exit. Numerical predictions generally differed by less than 20∘C, with a maximum deviation of about 30∘C, which can be considered highly satisfactory given the complexity of reproducing the initial thermal transient and the continuous extrusion of multiple billets. The extrusion force was accurately reproduced from the second billet onward, showing excellent agreement during steady-state operation.

When liquid nitrogen cooling was activated, the temperature near the channel inlet decreased from 365∘C to 234∘C, while downstream locations stabilized between 320∘C and 360∘C. The maximum reduction at the bearing exits was about 28∘C, and the profile exit temperature decreased by approximately 10∘C. The model reproduced both the magnitude and spatial distribution of these variations, as well as the temperature recovery after cooling deactivation.

These results demonstrated that the proposed dual-step procedure is able to reproduce the transient thermal evolution over multiple billets, exploiting the computational efficiency of a 1D cooling model coupled with an industrial 3D extrusion simulation environment. Overall, this approach enables the simultaneous evaluation of thermal gradients induced by the cooling channels and their interaction
with key technological aspects of the extrusion process, including aluminum flow, deformation heating, extrusion load, and die thermal response. In this way, the strengths of advanced cooling models are combined with the comprehensive process description provided by commercial extrusion software.

Acknowledgements

The original version of this paper is available on https://www.scientific.net/KEM.1048.151.pdf

This work was supported by PNRR-M4C2INV1.5, NextGenerationEU-Avviso 3277/2021 ECS_00000033 ECOSISTER-spk 3

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The original version of this paper is available on https://www.scientific.net/KEM.1048.151.pdf

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1048, pp 165-177

doi: 10.4028/p-wC1Lah

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-20

Revised:
2026-02-12

Accepted:
2026-02-27

Online:
2026-04-14

Numerical Data-Driven Modelling of Modified Samanta Process for Cold Extrusion of Gears

The original version of this paper is available on https://www.scientific.net/KEM.1048.165.pdf

Tahsin Deliktas 1,a*, Marcel Görz 1, b, Adrian Schenek 1,c, Marco Speth 1, d, Mathias Liewald 1,e

1 University of Stuttgart, Institute for Metal Forming Technology (IFU), Holzgartenstraße 17, 70174 Stuttgart, Germany

a tahsin.deliktas@ifu.uni-stuttgart.de, b  marcel.goerz@ifu.uni-stuttgart.de, c  adrian.schenek@ifu.uni-stuttgart.de, d  marco.speth@ifu.uni-stuttgart.de, e  mathias.liewald@ifu.uni-stuttgart.de

(*corresponding author)

Keywords: Metal Forming, Cold Forging, Gears, Simulation, Machine Learning

Abstract

The Guided Material Flow (GMF) process is an advanced variant of the Samanta process designed for the net shape cold extrusion of gears. The GMF process employs a modified die geometry to control material flow and significantly reduce maximum tool loads, effectively overcoming traditional process limitations. Key advantages include enhanced tooth tip strength and a reduction in face end deformations, which are characteristic defects in the conventional Samanta process. Minimising these deformations reduces the requirement for subsequent machining and enhances overall material efficiency. A numerical dataset was generated to train and validate data driven surrogate models, facilitating rapid process analysis without the computational cost of continuous Finite Element Analysis (FEA). The models developed in this paper enable the precise prediction of critical process outputs, including maximum punch force, die filling behaviour, material utilisation and strain hardening at the tooth tip. This paper details the numerical data acquisition, the specific training and validation methodologies of the machine learning models and demonstrates their capability to accurately predict complex process outcomes when varying the geometry of the die active surface in the GMF process.

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1048.165.pdf

The demand for high performance gears for industrial applications continues to drive the development of resource-efficient manufacturing processes. Cold forging, and cold extrusion in particular, offers significant advantages over conventional machining, including high material utilisation, superior surface quality and increased strength of component due to strain hardening [1]. For this reason, numerical and experimental investigations into the prediction of material flow and tool loads have been essential for enhancing process reliability in gear forging [2-7]. Within this field, the Samanta process represents a special cold extrusion process for gear manufacturing. Its characteristic operating principle consists of extruding full or hollow blanks partially into a splined die without ejection. After the punch is retracted, a subsequent blank is inserted, which then presses the previously partially formed blank completely through the splined die to obtain the finished gear [8-10]. However, the industrial application of the conventional Samanta process is limited by several technological challenges. In particular, high tool loads, limited gear accuracy and characteristic material deformations at the face ends often necessitate subsequent machining, thereby reducing overall material efficiency [8, 11]. In order to address these challenges, a modified variant of the Samanta process, the so-called Guided Material Flow (GMF) process, was developed and previously investigated by the authors [12, 13]. The GMF approach fundamentally differs from the conventional Samanta process through a redesigned active die surface, which significantly alters the material flow and the resulting stress state within the deformation zone. As illustrated in Fig. 1, this modified die geometry is characterised by three primary features: 1. an elongated forming zone, 2. a taper of the tooth cavity, and 3. an undercut of the cavity at the tooth tip area. The elongated forming zone distributes deformation work to reduce peak punch forces and improve gear precision. Although such
elongation may compromise tooth filling, the tapered cavity geometry generates tangential compressive stresses to prevent material flow in a radial inward direction. Furthermore, the undercut compensates for increased friction and enhances strain hardening through pronounced local plastic deformation.

[picture]

Fig. 1. Modifications of active die surface in the GMF process compared to the Samanta process

Fig. 1. Modifications of active die surface in the GMF process compared to the Samanta process

Previous studies on the GMF process demonstrated that the modified die geometry effectively reduces tool loads while improving material flow in the tooth cavity. In these investigations, the finite element model was successfully validated against experimental cold extrusion tests using novel GMF die active surfaces, where numerical results showed excellent agreement with physical test data [12]. Even though these die active surfaces have since been iteratively modified and further optimised, the initial validation remains the scientific foundation for the identified correlations between die geometry and process outputs.

However, there is a highly non-linear correlation between changes to the active die surface and the process outputs. The complex interdependencies between the various geometric parameters and process outputs, such as punch force and die filling, require a more sophisticated approach for optimisation, as the effectiveness of surrogate models for such data-driven analyses has already been demonstrated by Schenek [14] for sheet metal stamping processes. Relying on a full factorial design of experiments (DOE) based on 3D Finite Element Analysis (FEA) simulations is prohibitively timeconsuming and cost-intensive, which prevents a comprehensive exploration of the design space.

Addressing these computational limitations, this paper presents a data driven surrogate modelling approach. By using Machine Learning algorithms and integrating data normalisation, which accounts for the scaling laws of gear geometry based on gear module m, a precise predictive model is developed. The python-based framework enables the rapid prediction of process loads, strain hardening at the tooth tip, tooth filling and material utilisation, thereby providing a foundation for automated optimisation of the GMF process.

Numerical Data Acquisition

The original version of this paper is available on https://www.scientific.net/KEM.1048.165.pdf

The generation of a high-quality dataset of a manufacturing technology stands for a fundamental step for developing corresponding data-driven models. This chapter details the methodology used to acquire the numerical data for the GMF process. It covers the revision of the tool geometry, the definition of the numerical boundary conditions in DEFORM-3D TM  and the systematic exploration of the parameter space. By combining advanced FEA with a structured sampling strategy, a comprehensive database is established to capture the complex interactions between tool design and process outputs.

Tool Design and Process Setup. The design of the die in the GMF process has been established in previous studies [12, 13]. In this work, the model was refined by revising the shoulder geometry. While earlier investigations utilised linear, quadratic and cubic functions to realise shoulder geometries, it was found that the cubic variant offered no significant advantages. Consequently, the
shoulder geometry in the current model is defined by a single radius Rs. For the subsequent data processing and model training, this geometric parameter is transformed into a standardised factor, hereafter referred to as the R-factor. This factor is defined as the scaled curvature of the shoulder:

R− factor =1Rs·1000(1)

This mathematical transformation serves two primary purposes. First, using the inverse of the radius eliminates the numerical singularity of a linear shoulder geometry ( Rs→∞ ) by representing it as a curvature of zero. This enables a continuous parameter space where the sign determines the curvature direction. As Fig. 2b illustrates, a positive R-factor defines a concave shoulder design, while a negative value results in a convex geometry.

[picture]

Fig. 2. GMF process, (a) process setup in DEFORM 3D™; (b) side view; (c) top view on die cavity.

Fig. 2. GMF process, (a) process setup in DEFORM 3D™; (b) side view; (c) top view on die cavity.

Second, the scaling factor of 1.000 aligns the magnitude of the curvature values with the other geometric parameters, ensuring balanced feature weighting for the data-driven models. Beyond the shoulder geometry, the study investigates additional geometric variables shown in Fig. 2b and Fig. 2c. These include the axial length of the forming zone L together with the taper angle of the cavity β. Furthermore, the transition radii leading to the shoulder and the calibration zone, denoted as R1 and R2, as well as the magnitude of the undercut U, constitute the key variables for this investigation.

Validation of Numerical Simplifications. The numerical process mapping is shown in Fig. 2a. This model is based on the Samanta principle, which inherently operates without the need for ejectors. Although the physical process typically involves extrusion in a stacked package, the numerical setup was simplified to the direct extrusion of a single workpiece. Both numerical setups are shown in Fig. 3a. The simulation of the stacked process variant requires the modelling of three consecutive strokes. This results in the complete forming of two workpieces, while a third remains partially formed within the die cavity. To ensure a valid steady-state analysis, the evaluation of the stacked simulation is performed specifically on the second workpiece in sequence. Furthermore, all simulations in this study were performed using a half-tooth segment of the respective gear geometry to exploit symmetry and reduce calculation time. For this numerical comparative analysis, a gear with a module of m=1 mm and a number of teeth z=39 was selected, using the material 16 MnCrS 5 (modelled as ideally plastic). In all simulation variants, the workpieces were defined with a constant height of 25 mm . The GMF die for this comparison was defined with a random geometric configuration of the functional surface ( L=10 mm,U=0.2 mm, etc.). The extended punch force
curve and the resulting larger stroke for the GMF process in Fig. 3b result from the elongated forming zone, which represents an intended effect to lower the maximum punch force.

[picture]

Fig. 3. Numerical analysis of single and stacked process, (a) numerical setups; (b) punch force calculation.

Fig. 3. Numerical analysis of single and stacked process, (a) numerical setups; (b) punch force calculation.

The reduction of the model complexity to a single-workpiece setup was chosen to mitigate computational costs and contact-related instabilities associated with modelling multiple interacting workpieces, following a methodological simplification previously utilised by Kiener et al. [7]. To verify these assumptions, comparative simulations between the single-workpiece model and the fullpackage process were performed using the same numerical setup as described in the subsequent sections. It was shown that this approach does not affect the maximum punch force (see Fig. 3b), the material flow within the cavity or the stress-strain state within the forming zone during stationary process phases.

The model simplification primarily affects the face end deformations, as compared in Fig. 4 for the Samanta and GMF processes using a single workpiece (WP) and stacked workpieces setups. In the Samanta process, the differences between the two setups are minimal. The face end deformation in the single workpiece variant amounts by 0.43 mm higher, while the tooth filling remains nearly identical. Consequently, after machining the defective workpiece volume, both Samanta variants result in an almost identical net gear height Δh.

[picture]

Fig. 4. Numerical evaluation of net gear height: Comparison between single and stacked workpieces (WP) for the Samanta and GMF processes.

Fig. 4. Numerical evaluation of net gear height: Comparison between single and stacked workpieces (WP) for the Samanta and GMF processes.

The GMF process using the single workpiece model shows that the deformations at the bottom of the workpiece are slightly more pronounced compared to the stacked workpiece model. This is due to the lack of axial counter-pressure in the single workpiece variant, which is otherwise provided by the partially formed workpiece already present in the die. Consequently, this absence of resistance results also in a different characteristic of the tooth filling, as seen in the detail view in Fig. 4. In
contrast, deformation and mold filling on the top side of the workpiece remain nearly identical in both variants. After the numerical removal of underfilled material sections, a minor difference in net gear height is observed, decreasing from 17.6 mm (stacked WP) to 17.2 mm (single WP). Due to this small deviation, the simplification of the process sequence for the GMF process is considered valid and is therefore utilised for the further investigations.

Regarding the comparison between the Samanta and newly developed GMF processes, the differences in net gear height observed in this study are primarily a result of the non-optimised, exemplary die configuration selected for the GMF process.

Numerical Setup in DEFORM-3D ™ . Numerical simulations were performed using the commercial FE software DEFORM-3D ™ . To improve computational efficiency while maintaining high calculation accuracy, the GMF process was modelled as hollow forward extrusion (as shown in Fig. 2a). This configuration reduces the workpiece volume without affecting the material flow in the splined forming zone, punch force or the stress-strain state in the forming zone compared to a full workpiece. Due to the symmetry of the gear geometry, the simulation was limited to a half-tooth segment. The initial height of the workpiece section was 25 mm . The case-hardening steel 16 MnCrS 5 was selected as the workpiece material for this investigation. To accurately represent the material behavior, flow curves experimentally determined via the Gleeble 3800C thermomechanical simulator at strain rates of 0.1,1 and 10 s−1 for temperatures of 20,100,200,300 and 400∘C were implemented into the FE software. The workpiece was modelled using an ideal plastic material law to reduce calculation time. Since tool loads are directly linked to the punch force, an analysis of elastic die expansion will be conducted in future studies focusing on load optimised GMF die geometries. The kinematics were defined by a constant punch and mandrel velocity of 15 mm/s.

Boundary conditions at the tool-workpiece interfaces were defined using the shear friction model with a coefficient of m=0.12. The heat transfer coefficient was set to 5000 W/(m2 K) to account for the thermal exchange between all components during the process. Both the friction and heat transfer coefficients represent established default values for the realistic representation of cold extrusion processes. These parameters provide a reliable basis for the current numerical investigation, yet experimental validation and specific adjustment of these values are planned for future research.

The discretization was performed using an adaptive meshing strategy to account for the varying workpiece segment volumes resulting from the different tooth numbers z of 16,43 and 88 . This strategy ensured a consistent minimum element length of 0.08 mm for all geometries by adjusting the total element count. Specifically, the workpiece segment for the gear with z=16 had a total of 195.000 mesh elements, while the segments for z=43 and z=88 comprised 105.000 and 95.000 elements respectively. Local precision was enhanced by implementing a mesh window with a scaling factor of 0.1 in the toothing area while a global size ratio of 4 was maintained across all simulations. This approach was chosen to accurately model the material flow inside the tooth cavity while maintaining high computational efficiency. The complete set of simulation parameters is displayed in Table 1.

Regarding computational effort, simulations were performed on a workstation with an Intel Core i7 14700 KF and 32 GB RAM. Average calculation times ranged from 4 to 6 hours while generating nearly 8 gigabytes of data per run. For the dataset of 450 simulations, this resulted in approximately 2250 hours of processing and 3.5 terabytes of storage. These demands highlight the necessity of the surrogate model for rapid and resource efficient optimisation.

Table 1. Simulation parameters for numerical Design of Experiment study.

Simulation parameter | Specification / Value
Software | Deform 3D™ - Version 14.0
Workpiece material | 16MnCrS5
Workpiece material model | Ideally plastic
Mesh type | Tetrahedral mesh
Minimum element size of mesh | 0.08 mm
Workpiece dimensions | D = 45 mm, d = 30 mm, h = 25 mm
Initial process temperature | 20 °C
Friction model | Shear friction model, m= 0.12
Tool material model | Ideally rigid
Punch velocity | 15 mm/s
Simulation type | Lagrangian incremental with heat transfer
Solver | Iterative Solver: Conjugate gradient
Iteration method | Direct iteration
Solution step definition | 0.024 mm/step

Design of Experiments (DoE) Setup. A constrained Latin Hypercube Sampling (LHS) approach was employed to generate a representative and space-filling set of design parameters for the forming zone geometry. LHS was selected over factorial sampling methods to efficiently and uniformly cover the high dimensional space with a manageable number of simulations. The considered parameter space comprises the forming zone length L, the inlet radius R1, the outlet radius R2, the shoulder radius Rs, the undercut U and the cavity taper angle β (see Fig. 2b and Fig. 2c). The corresponding parameter ranges used for the sampling procedure are shown in Table 2.

Table 2. Parameter intervals used for Latin Hypercube Sampling with geometrical constraints of die design.

Parameter | Symbol | Unit | Minimum | Maximum
Forming zone length | L | mm | 2.5 | 15
Inlet radius | R₁ | mm | 0.1 | 70
Outlet radius | R₂ | mm | 0.2 | 60
Shoulder curvature | R-factor | − | −220.0 | 220.0
Undercut | U | − | 0.1 | 0.8
Cavity taper angle | β | ° | −5.5 | 5.5

Valid parameter boundaries were iteratively determined within the CAD environment to account for geometric constraints and ensure that all sampled configurations represent physically feasible die designs. The boundaries of the permissible parameter combinations were defined by envelopes derived from the iteratively determined dataset. Due to these constraints, the parameter ranges could not be evenly divided, but a modified LHS procedure was employed to achieve a quasi-uniform coverage of the design space. For this purpose, each parameter range was subdivided into intervals corresponding to the number of samples and the median value of each interval was selected and randomly permuted across samples. This approach ensured that all regions of the admissible parameter space were represented as evenly as possible while respecting geometric constraints. Dependent parameters such as the forming zone radius, the inlet radius and the undercut were assigned randomly within their allowable ranges, but always constrained to remain geometrically and technically feasible according to the CAD based dependency tables.

In addition to the forming zone geometry, the design of the tooth flank geometry was also considered. The design of experiments was performed separately for three different gear configurations:

Gear 1: z=88,m=0.5 mm

Gear 2: z=43,m=1.0 mm and

Gear 3: z=16,m=2.5 mm

For each gear variant, an independent LHS was conducted and 150 design variants were generated and subsequently simulated. So, in total, 450 3D simulations were set up and performed.

Data extraction. The extraction of data from the numerical DoE focused on key process and geometric variables critical to the performance of the cold forging process and the gear quality. The evaluation targeted the punch force and the effective strain distribution at the tooth tip as an indicator for local strain hardening. Additionally, the die filling of the tooth and the resulting net gear height (see Fig. 4) were extracted as decisive parameters for the geometric feasibility and material efficiency of the cold forging process.

The extraction of results from the numerical DoE was executed through the DEFORM TM  Application Programming Interface (API), which enabled a Python-based automated data acquisition. By utilising this approach, the punch force was acquired through the export of force-stroke data for each simulation variant. Parallel to the punch force data, local process variables were captured using a point tracking configuration. By positioning ten tracking points equidistantly at the workpiece midheight, it was possible to record the radial displacement and effective strain along the gearing throughout the forming process. Regarding the strain analysis, the maximum value of effective strain recorded at the tooth tip tracking point was identified and utilised as the primary output for the subsequent training. The tooth underfilling was quantified by calculating the maximum radial displacement of the tracking point at the tooth tip from the defined tip circle diameter after the forming process was completed.

In order to evaluate available material efficiency, the final geometries were exported as '.GEOfiles' and processed using a customised Python script. As illustrated in Fig. 5a, the analysis begins by extracting a point cloud along the symmetry plane of the gear tooth. For a robust evaluation across various geometries, the nodal data is transformed into a cylindrical coordinate system ( r,z ), allowing for a two-dimensional projection of the profile (see Fig. 5b).

[picture]

Fig. 5. Evaluation of net gear height: (a) ‘.GEO-point’ cloud of gear wheel segment in cartesian coordinates gained from simulation; (b) 2D point cloud projected onto the r-z plane of the cylindrical coordinate system.

Fig. 5. Evaluation of net gear height: (a) ‘.GEO-point’ cloud of gear wheel segment in cartesian coordinates gained from simulation; (b) 2D point cloud projected onto the r-z plane of the cylindrical coordinate system.

The assessment of die filling is performed at the maximum radial extension, which for the all gear configurations corresponds to a tip diameter of r=22.5 mm. Within a defined tolerance of 0.05 mm , the algorithm identifies all nodes at this reference radius. Any radial displacement below this threshold is classified as underfilling. This way, the net gear height Δh is determined by performing a continuity check on the data points to identify connected segments of points within the defined tolerance, from which the geometric extrema zmin  and zmax  are extracted. An additional condition
ensures that if the detected zmax  value exceeds the maximum height of deformation at the inner gear diameter (red cross in Fig. 5b), zmax  is restricted to this inner height.

Data Processing and Gear-based Scaling

The original version of this paper is available on https://www.scientific.net/KEM.1048.165.pdf

These steps are crucial to improve the numerical convergence of the machine learning algorithms and to ensure that the model accurately captures the influence of variations in the active die surface on the characteristics of the GMF process. The raw data extracted from the FE simulations requires systematic preparation to ensure high predictive accuracy and robust model performance. This section details the data processing pipeline and the scaling based on gear parameters applied to the input features and target variables, ensuring that the specific geometric properties of the gear teeth are appropriately represented in the model.

Gear-based Scaling. Numerical stability and scale-independence are achieved through a structured normalisation of the simulation data. This procedure ensures that the machine learning model prioritises geometric proportions over absolute dimensions. For this purpose, it was necessary to normalise the linear geometric parameters of the die's active surface by dividing them by the module m to generate dimensionless ratios. The shoulder radius used for designing linear, convex or concave shoulders, were normalized by the previously defined R-factor in Eq. 1. Normalising these geometric input parameters ensures that the data-based model evaluates the tools shape rather than its absolute size. Additionally, a gear-based scaling of the maximum punch force Fmax by the product of the tooth number z and the squared module m2, as shown in Eq. 2, was necessary:

Fmax,n=Fmaxz·m2(2)

Scaling of the punch force is required to account for the varying projected deformation areas associated with different gear geometries, which fundamentally determine the resulting process load. In order to make the results comparable across different gear sizes, two scaling effects must be considered. First, the module m determines the size of an individual tooth; since the cross-sectional area grows quadratically as the module increases, a quadratic adjustment ( m2 ) is required. Secondly, the number of teeth z specifies the number of these individual sections that are formed simultaneously. By dividing the punch force data by z·m2, the value is effectively reduced to a specific load per unit area. This transformation allows the machine learning model to recognise that the underlying physics remains consistent, regardless of whether a small or large gear is produced. Thus, all three gear sizes can be calculated within the same model.

Surrogate Modelling Methodology

The original version of this paper is available on https://www.scientific.net/KEM.1048.165.pdf

A data-driven modeling methodology was developed to map the complex interdependencies between the normalised tool geometry and the resulting forging parameters. A hybrid modeling approach was implemented, which combines a broad dataset from a Design of Experiments (DoE) with additional specific numerical reference cases (such as z=60,m=2 mm ), hereafter referred to as anchor data points. This hybrid strategy ensures that the surrogate models benefit from the wide-ranging exploration of the design space while maintaining strict consistency at critical reference geometries and scale boundaries. Consequently, the methodology enables the reliable prediction of gear geometries with tip circle diameters that extend beyond the da=45 mm constraint targeted for experimental validation in this study.

Model Generation and Training Strategy. The training process was conducted using the AutoGluon framework, employing an Automated Machine Learning (AutoML) strategy. To prioritise predictive performance, the 'best_quality' preset was selected, with a fixed training time of 3600 seconds assigned per model. To maximise predictive stability, the methodology utilises a Stacked Ensemble approach, training diverse model families, including Gradient Boosted Decision Trees (XGBoost, CatBoost, LightGBM) and Deep Neural Networks, in parallel.

A central challenge in surrogate modeling for forging processes, especially for gear forging, is the reliable prediction across different scales, particularly when varying the module m and or tooth number z. While the normalised geometric parameters often remain within the interpolated range, the resulting process parameters, such as the maximum punch force, scale quadratically with the module. The training dataset was augmented with the aforementioned anchor data points to prevent poor extrapolation behavior across different size scales of the gear and to ensure consistency at the boundaries of the design space. These anchor points consist of additional numerical simulations for two distinct reference tool geometries based on tooth numbers z=30 and z=60. For each group, a randomised but fixed geometric configuration was selected to serve as a baseline. These configurations are summarised in Table 3, with the parameters specified for a reference module of 1.0 mm .

By scaling these fixed geometries across three discrete modules ( 0.5 mm,1.0 mm, and 2.0 mm ), the surrogate models are trained to distinguish between shape-dependent effects and pure sizedependent scaling laws. This approach ensures that the training data covers a significantly broader range of gear configurations ( z and m ) with tip circle diameters extending far beyond the 45 mm constraint of the primary DoE. An oversampling strategy was applied to these anchor points for the training of all surrogate models, including maximum punch force, tooth underfilling, effective strain on the tooth tip, and net gear height. This prioritization ensures the accurate learning of the scaling laws across the design space, effectively creating a hybrid model that combines broad-range DoE data with precise numerical references for scale transition.

Table 3. Geometric configuration for the anchor data points at reference module of 1 mm

Parameter | Symbol | Unit | Configuration z = 60 | Configuration z = 30
Forming zone length | L | mm | 10.373 | 9.83
Inlet radius | R₁ | mm | 1.0 | 1.0
Outlet radius | R₂ | mm | 1.0 | 9.57
Shoulder radius | RS | mm | 35.0 | 100.0
Scaled shoulder Radius | R-factor | − | -28.57 (convex) | -10.0 (convex/linear)
Undercut | U | − | 0.08 | 0.8
Cavity taper angle | β | ° | -5.0 | 1.18

Model Validation and Performance. The reliability of the surrogate models was ensured through a two-stage validation process:

First, an internal 10-fold cross-validation (bagging) was employed during the training phase. In this procedure, the dataset is partitioned into ten distinct subsets, where each subset serves as a validation set once while the remaining nine subsets are used for training. This iterative process allows for an "out-of-fold" assessment of model stability across the entire dataset. The final prediction is an aggregate of these ten models, which significantly enhances robustness and provides a cross-validated performance metric that is less sensitive to individual outliers.

Second, the predictive performance was rigorously evaluated by comparing the final ensemble outputs against an independent validation set consisting of 90 unseen data set ( 20% of the total data set). In contrast to the augmented training data, this validation set consists of unique, non-duplicated FE-simulations to ensure an unbiased assessment of the generalization capabilities. It includes the randomised geometric variants from the initial DoE as well as the specific anchor point configurations for tooth numbers z=30 and z=60 at modules of 0.5 mm,1.0 mm, and 2.0 mm . By including these diverse scales in the independent test set, the model performance was explicitly assessed under both interpolation and extrapolation conditions. The results confirm that the integration of physical scaling laws and anchor points enables the model to maintain high accuracy even at the edges of the design space. As shown in Table 4, the methodology demonstrates excellent accuracy across all target parameters.

Table 4. Predictive performance and error metrics of the surrogate models for the target process parameters

Target parameter | Symbol | R² score (Training) | R² score (Test) | RMSE
Maximum punch force (scaled) | Fmax,n | 0.9402 | 0.9367 | 0.1827
Tooth underfilling | Δr | 0.9935 | 0.9836 | 0.0259
Effective strain on tooth tip | εtip | 0.9778 | 0.9646 | 0.1879
Net gear height | Δh | 0.9751 | 0.9491 | 3.7028

The high consistency between the cross-validated training scores and the independent test scores (R2>0.93) confirms that the models successfully captured the underlying physical relationships without overfitting. For the geometry-related outputs, such as tooth underfilling ( Δr ) and net gear height ( Δh ), the training and test performances show nearly identical R2 values, with scores of 0.9836 and 0.9491 on the test set, respectively. This indicates that the stacked ensemble of both models reached its maximum predictive stability for these continuous geometric correlations.

The small difference in accuracy for the maximum punch force ( Fmax, n ) delivers a deliberate result of the oversampling strategy. By giving the anchor points more weight during training, the model is forced to follow the physical laws of gear scaling (or module scaling). This prevents the model from "smoothing out" important peaks in the data just to get a better statistical average. Although this leads to a slightly lower global R2 score, it ensures that the model remains physically accurate and reliable even for very large or very small gear sizes at the edges of the design space.

As shown in Fig. 6 and Fig. 7, the feature importance analysis identifies the forming zone length (L) as the most significant factor across all four surrogate models.

[picture]

Fig. 6. Evaluation of feature importance and model performance of the surrogate models for punch force and underfilling prediction

Fig. 6. Evaluation of feature importance and model performance of the surrogate models for punch force and underfilling prediction

The dominant influence of the forming zone length L on tooth underfilling and effective strain demonstrates that this axial dimension is the main driver for material flow and geometric accuracy. The R-factor, which defines the curvature of the extrusion shoulder, is the second most important feature for net gear height and underfilling. This reflects how sensitive the material flow and the subsequent cavity filling are to the geometry of the transition zone where the radial material displacement occurs.

In contrast, the cavity taper angle has almost no impact on the process outputs within the tested range. This suggests that the primary dimensions of the forming zone are far more important for the forging results than the slight tapering angle of the tooth cavity. The scatter plots in Fig. 6 and Fig. 7 show that the validation data points lie very close to the ideal diagonal line. Combined with the low RMSE values, this confirms that the developed methodology is a reliable and fast replacement for expensive FE-simulations in the design of dies for the GMF process.

[picture]

Fig. 7. Evaluation of feature importance and model performance of the surrogate models for strain and net gear height prediction

Fig. 7. Evaluation of feature importance and model performance of the surrogate models for strain and net gear height prediction

Conclusion and Outlook

The original version of this paper is available on https://www.scientific.net/KEM.1048.165.pdf

In the present study, a hybrid surrogate modeling approach was developed to predict the process parameters and geometric outcomes of the Guided Material Flow (GMF) process. The core of this methodology lies in the integration of a broad-range numerical Design of Experiments (DoE) with targeted, oversampled anchor points. These anchor points significantly extend the model's operational range by incorporating gear configurations with pitch and tip circle diameters that considerably exceed the initial 45 mm constraint of the primary numerical DoE. This strategy effectively overcomes the inherent extrapolation limitations of conventional meta-models and ensures physical consistency across a wide spectrum of gear dimensions.

The developed models provide a profound understanding of the GMF process, enabling the user to systematically influence and control forging results according to specific requirements, such as the reduction of maximum punch force or the enhancement of material utilization. The high predictive accuracy (R2>0.93) and the sensitivities identified through feature importance analysis demonstrate that the surrogate models reliably capture the complex non-linear correlations of this cold forging operation.

Future research will focus on the development of a multi-objective optimization framework based on these validated surrogate models. By using a weighted objective function, the system will allow users to find the best tool geometry for a given task. Specifically, this framework will be used to identify tool designs that minimise tool loads and maximise filling quality. This development represents a significant advancement toward the cold forging of gears with lower tool stress and, consequently, higher dimensional accuracy of the forged components.

Acknowledgments

The original version of this paper is available on https://www.scientific.net/KEM.1048.165.pdf

The authors would like to thank the Deutsche Forschungsgemeinschaft (DFG) for the financial support of the research project "Cold forging of gears with targeted reduction of friction surfaces Guided Material Flow (GMF) process" under the project number LI 1556/80-1.
D) EG

Deutsche
Forschungsgemeinschaft

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The original version of this paper is available on https://www.scientific.net/KEM.1048.165.pdf

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Keywords Index

#

6060 Aluminum Alloy; Liquid Nitrogen Cooling; Nanoindentation; Tensile Testing; Creep Behavior Mechanical Evaluation of 6060 Aluminum Alloy under Elevated Extrusion Ram Speed Using Liquid Nitrogen Die Cooling

7000 Series Aluminum Alloys Effect of Chemical Composition on Hot Extrudability and Tribological Behavior of 7000 Series Aluminum Alloys

A

Aa6082 Transient Thermal Analysis of Multiple Extrusion Runs with Nitrogen Cooling by Means of Qform Code

Advanced Numerical Simulation Transient Thermal Analysis of Multiple Extrusion Runs with Nitrogen Cooling by Means of Qform Code

Aluminium Charge Weld Evolution in Profile Extrusion: Variability Across Billets and Multi-Profile Designs

Aluminum Alloys Comparative Characterization through Torsion Tests of Primary and Secondary Aluminum Alloys for the Extrusion Process

Aluminum Extrusion An Experimental Investigation of the Effect of Compression Calibration on the Ductility of AA6061 Extrusions; Development of a System for Ultrasonic Die Oscillations during Extrusion of Aluminum Hollow Profiles

C

Charge Weld Charge Weld Evolution in Profile Extrusion: Variability Across Billets and Multi-Profile Designs

Charge Welds Detection of Charge Welds in Lateral Angular Co-Extrusion Using Non-Destructive Testing

Cold Extrusion; Adjustable Dies; Dimensional Accuracy; Adaptive Tooling; Ai Surrogate Compensating Property Fluctuations in Cold Extrusion Using Adaptive Dies

Cold Forging Calibrating a Lode-Parameter-Dependent Damage Evolution Equation to Cold Extrusion Experiments; Numerical Data-Driven Modelling of Modified Samanta Process for Cold Extrusion of Gears

D

Defect Charge Weld Evolution in Profile Extrusion: Variability Across Billets and Multi-Profile Designs

Die Design Lightweight Battery Housings from Thin-Walled, Large-Scale Aluminum Profiles by Hot Extrusion

Ductile Damage Calibrating a Lode-Parameter-Dependent Damage Evolution Equation to Cold Extrusion Experiments

Ductility An Experimental Investigation of the Effect of Compression Calibration on the Ductility of AA6061 Extrusions

E

Element Adhesion Effect of Chemical Composition on Hot Extrudability and Tribological Behavior of 7000 Series Aluminum Alloys

Ev Lightweight Battery Housings from Thin-Walled, Large-Scale Aluminum Profiles by Hot Extrusion

Extrudability Effect of Chemical Composition on Hot Extrudability and Tribological Behavior of 7000 Series Aluminum Alloys

Extrusion Comparative Characterization through Torsion Tests of Primary and Secondary Aluminum Alloys for the Extrusion Process; Transient Thermal Analysis of Multiple Extrusion Runs with Nitrogen Cooling by Means of Qform Code

F

Fea Charge Weld Evolution in Profile Extrusion: Variability Across Billets and Multi-Profile Designs

Fem Lightweight Battery Housings from Thin-Walled, Large-Scale Aluminum Profiles by Hot Extrusion; Development of a System for Ultrasonic Die Oscillations during Extrusion of Aluminum Hollow Profiles

Flow Stress Modeling Comparative Characterization through Torsion Tests of Primary and Secondary Aluminum Alloys for the Extrusion Process

G

Galling Effect of Chemical Composition on Hot Extrudability and Tribological Behavior of 7000 Series Aluminum Alloys

Gears Numerical Data-Driven Modelling of Modified Samanta Process for Cold Extrusion of Gears

H

High-frequency Die Oscillation Development of a System for Ultrasonic Die Oscillations during Extrusion of Aluminum Hollow Profiles

Hollow Profile Development of a System for Ultrasonic Die Oscillations during Extrusion of Aluminum Hollow Profiles

Hot Extrusion Effect of Chemical Composition on Hot Extrudability and Tribological Behavior of 7000 Series Aluminum Alloys

L

Lateral Angular Co-extrusion Detection of Charge Welds in Lateral Angular Co-Extrusion Using Non-Destructive Testing

Local Heating Using Tungsten Inert Gas Welding Equipment to Locally Heat Tubes and Achieve Inhomogeneous Thickness in Tube Sinking Processes

Lode-parameter Calibrating a Lode-Parameter-Dependent Damage Evolution Equation to Cold Extrusion Experiments

M

Machine Learning Numerical Data-Driven Modelling of Modified Samanta Process for Cold Extrusion of Gears

Mechanical Calibration An Experimental Investigation of the Effect of Compression Calibration on the Ductility of AA6061 Extrusions

Mechanical Properties An Experimental Investigation of the Effect of Compression Calibration on the Ductility of AA6061 Extrusions

Metal Forming Numerical Data-Driven Modelling of Modified Samanta Process for Cold Extrusion of Gears

N

Nitrogen Cooling Transient Thermal Analysis of Multiple Extrusion Runs with Nitrogen Cooling by Means of Qform Code

Non-destructive Testing Detection of Charge Welds in Lateral Angular Co-Extrusion Using Non-Destructive Testing

P

Parameter Identification Calibrating a Lode-Parameter-Dependent Damage Evolution Equation to Cold Extrusion Experiments

Process Control Lightweight Battery Housings from Thin-Walled, Large-Scale Aluminum Profiles by Hot Extrusion

Process Sustainability; Friction Stir Extrusion; Finite Element Analysis; Process Optimization Numerical Investigation of Round Profile Production: A FEM-Based Comparison between Friction Stir Extrusion and Conventional Extrusion

Process Window Using Tungsten Inert Gas Welding Equipment to Locally Heat Tubes and Achieve Inhomogeneous Thickness in Tube Sinking Processes

Q

Qform Charge Weld Evolution in Profile Extrusion: Variability Across Billets and Multi-Profile Designs

Quality Charge Weld Evolution in Profile Extrusion: Variability Across Billets and Multi-Profile Designs

S

Secondary Alloys Comparative Characterization through Torsion Tests of Primary and Secondary Aluminum Alloys for the Extrusion Process

Simulation Charge Weld Evolution in Profile Extrusion: Variability Across Billets and Multi-Profile Designs; Numerical Data-Driven Modelling of Modified Samanta Process for Cold Extrusion of Gears

Size Scaling Lightweight Battery Housings from Thin-Walled, Large-Scale Aluminum Profiles by Hot Extrusion

T

Torsion Tests Comparative Characterization through Torsion Tests of Primary and Secondary Aluminum Alloys for the Extrusion Process

Transverse Weld Charge Weld Evolution in Profile Extrusion: Variability Across Billets and Multi-Profile Designs

Tube Sinking Using Tungsten Inert Gas Welding Equipment to Locally Heat Tubes and Achieve Inhomogeneous Thickness in Tube Sinking Processes

V

Variable Wall Thickness Using Tungsten Inert Gas Welding Equipment to Locally Heat Tubes and Achieve Inhomogeneous Thickness in Tube Sinking Processes

Authors Index

A

Acerbi, C. Numerical Investigation of Round Profile Production: A FEM-Based Comparison between Friction Stir Extrusion and Conventional Extrusion

B

Barton, S. Detection of Charge Welds in Lateral Angular Co-Extrusion Using Non-Destructive Testing

Besong, L. Using Tungsten Inert Gas Welding Equipment to Locally Heat Tubes and Achieve Inhomogeneous Thickness in Tube Sinking Processes

Biba, N. Charge Weld Evolution in Profile Extrusion: Variability Across Billets and Multi-Profile Designs

Bieker, P. Lightweight Battery Housings from Thin-Walled, Large-Scale Aluminum Profiles by Hot Extrusion

Bocchi, S. Numerical Investigation of Round Profile Production: A FEM-Based Comparison between Friction Stir Extrusion and Conventional Extrusion

Buhl, J. Using Tungsten Inert Gas Welding Equipment to Locally Heat Tubes and Achieve Inhomogeneous Thickness in Tube Sinking Processes

D

Deliktas, T. Numerical Data-Driven Modelling of Modified Samanta Process for Cold Extrusion of Gears

Dohda, K. Effect of Chemical Composition on Hot Extrudability and Tribological Behavior of 7000 Series Aluminum Alloys

Donati, L. Comparative Characterization through Torsion Tests of Primary and Secondary Aluminum Alloys for the Extrusion Process; Transient Thermal Analysis of Multiple Extrusion Runs with Nitrogen Cooling by Means of Qform Code

Donato, S. Comparative Characterization through Torsion Tests of Primary and Secondary Aluminum Alloys for the Extrusion Process; Transient Thermal Analysis of Multiple Extrusion Runs with Nitrogen Cooling by Means of Qform Code

F

Felci, M. Numerical Investigation of Round Profile Production: A FEM-Based Comparison between Friction Stir Extrusion and Conventional Extrusion

Funazuka, T. Effect of Chemical Composition on Hot Extrudability and Tribological Behavior of 7000 Series Aluminum Alloys

G

Gebhard, J. Lightweight Battery Housings from Thin-Walled, Large-Scale Aluminum Profiles by Hot Extrusion

Giarmas, E. Mechanical Evaluation of 6060 Aluminum Alloy under Elevated Extrusion Ram Speed Using Liquid Nitrogen Die Cooling

Gitschel, R. Calibrating a Lode-Parameter-Dependent Damage Evolution Equation to Cold Extrusion Experiments

Groche, P. Compensating Property Fluctuations in Cold Extrusion Using Adaptive Dies

Görz, M. Numerical Data-Driven Modelling of Modified Samanta Process for Cold Extrusion of Gears

K

Kloppenborg, T. Lightweight Battery Housings from Thin-Walled, Large-Scale Aluminum Profiles by Hot Extrusion

Klose, C. Detection of Charge Welds in Lateral Angular Co-Extrusion Using Non-Destructive Testing

Klusemann, B. Numerical Investigation of Round Profile Production: A FEM-Based Comparison between Friction Stir Extrusion and Conventional Extrusion

Kniazkin, I. Charge Weld Evolution in Profile Extrusion: Variability Across Billets and Multi-Profile Designs

Korkolis, Y. Lightweight Battery Housings from Thin-Walled, Large-Scale Aluminum Profiles by Hot Extrusion; Calibrating a Lode-Parameter-Dependent Damage Evolution Equation to Cold Extrusion Experiments

Kulakov, I. Charge Weld Evolution in Profile Extrusion: Variability Across Billets and Multi-Profile Designs

L

Laengst, N. Development of a System for Ultrasonic Die Oscillations during Extrusion of Aluminum Hollow Profiles

Lai, N. Comparative Characterization through Torsion Tests of Primary and Secondary Aluminum Alloys for the Extrusion Process; Transient Thermal Analysis of Multiple Extrusion Runs with Nitrogen Cooling by Means of Qform Code

Liewald, M. Numerical Data-Driven Modelling of Modified Samanta Process for Cold Extrusion of Gears

Lutey, A. Numerical Investigation of Round Profile Production: A FEM-Based Comparison between Friction Stir Extrusion and Conventional Extrusion

M

Maier, H. Detection of Charge Welds in Lateral Angular Co-Extrusion Using Non-Destructive Testing

Maier, J. Development of a System for Ultrasonic Die Oscillations during Extrusion of Aluminum Hollow Profiles

Merklinger, V. Development of a System for Ultrasonic Die Oscillations during Extrusion of Aluminum Hollow Profiles

Mohnfeld, N. Detection of Charge Welds in Lateral Angular Co-Extrusion Using Non-Destructive Testing

Mueller, S. Development of a System for Ultrasonic Die Oscillations during Extrusion of Aluminum Hollow Profiles

N

Negendank, M. Development of a System for Ultrasonic Die Oscillations during Extrusion of Aluminum Hollow Profiles

Negozio, M. Numerical Investigation of Round Profile Production: A FEM-Based Comparison between Friction Stir Extrusion and Conventional Extrusion; Comparative Characterization through Torsion Tests of Primary and Secondary Aluminum Alloys for the Extrusion Process; Transient Thermal Analysis of Multiple Extrusion Runs with Nitrogen Cooling by Means of Qform Code

Ngernbamrung, S. Effect of Chemical Composition on Hot Extrudability and Tribological Behavior of 7000 Series Aluminum Alloys

O

Oshima, J. Effect of Chemical Composition on Hot Extrudability and Tribological Behavior of 7000 Series Aluminum Alloys

P

Pelaccia, R. Comparative Characterization through Torsion Tests of Primary and Secondary Aluminum Alloys for the Extrusion Process; Transient Thermal Analysis of Multiple Extrusion Runs with Nitrogen Cooling by Means of Qform Code

R

Rana, H. Numerical Investigation of Round Profile Production: A FEM-Based Comparison between Friction Stir Extrusion and Conventional Extrusion

Reggiani, B. Comparative Characterization through Torsion Tests of Primary and Secondary Aluminum Alloys for the Extrusion Process; Transient Thermal Analysis of Multiple Extrusion Runs with Nitrogen Cooling by Means of Qform Code

S

Schenek, A. Numerical Data-Driven Modelling of Modified Samanta Process for Cold Extrusion of Gears

Schulz, O. Lightweight Battery Housings from Thin-Walled, Large-Scale Aluminum Profiles by Hot Extrusion

Schäfke, F. Detection of Charge Welds in Lateral Angular Co-Extrusion Using Non-Destructive Testing

Selvaggio, A. Lightweight Battery Housings from Thin-Walled, Large-Scale Aluminum Profiles by Hot Extrusion

Shiratori, T. Effect of Chemical Composition on Hot Extrudability and Tribological Behavior of 7000 Series Aluminum Alloys

Siedbürger, C. Compensating Property Fluctuations in Cold Extrusion Using Adaptive Dies

Speth, M. Numerical Data-Driven Modelling of Modified Samanta Process for Cold Extrusion of Gears

Suhuddin, U. Numerical Investigation of Round Profile Production: A FEM-Based Comparison between Friction Stir Extrusion and Conventional Extrusion

T

Tekkaya, A. Calibrating a Lode-Parameter-Dependent Damage Evolution Equation to Cold Extrusion Experiments

Thomas, T. Development of a System for Ultrasonic Die Oscillations during Extrusion of Aluminum Hollow Profiles

Tsongas, K. Mechanical Evaluation of 6060 Aluminum Alloy under Elevated Extrusion Ram Speed Using Liquid Nitrogen Die Cooling

Tzetzis, D. Mechanical Evaluation of 6060 Aluminum Alloy under Elevated Extrusion Ram Speed Using Liquid Nitrogen Die Cooling

Tzimtzimis, E. Mechanical Evaluation of 6060 Aluminum Alloy under Elevated Extrusion Ram Speed Using Liquid Nitrogen Die Cooling

U

Uhe, J. Detection of Charge Welds in Lateral Angular Co-Extrusion Using Non-Destructive Testing

Uzun, M. An Experimental Investigation of the Effect of Compression Calibration on the Ductility of AA6061 Extrusions

V

Verschinin, A. Detection of Charge Welds in Lateral Angular Co-Extrusion Using Non-Destructive Testing

W

Welo, T. An Experimental Investigation of the Effect of Compression Calibration on the Ductility of AA6061 Extrusions

Wurth, S. Lightweight Battery Housings from Thin-Walled, Large-Scale Aluminum Profiles by Hot Extrusion