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ISBN print: 978-3-0364-0538-4
ISBN eBook: 978-3-0364-1538-3
DOI book: 10.4028/b-gnDD6p
© 2026 Trans Tech Publications Ltd, All Rights Reserved

Forging and Hot Forming

Edited by
Bernd-Arno Behrens

Forging and Hot Forming

Special topic volume with invited peer-reviewed papers only

Edited by
Bernd-Arno Behrens
Scientific.Net

Copyright © 2026 Trans Tech Publications Ltd, Switzerland

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Volume 1186 of
Materials Science Forum
ISSN print 0255-5476
ISSN web 1662-9752

Full text available online at https://www.scientific.net/book/forging-and-hot-forming/978-3-0364-1538-3

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Materials Science Forum, ISSN: 1662-9752, Vol. 1186, pp -1-0

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

Accepted:
2026-04-09

Online:
2026-04-16

Preface

The original version of this paper is available on https://www.scientific.net/MSF.1186.-1.pdf

Topics covered by this issue include but are not limited to: Development of new and optimization of existing Forging and Hot Forming; Mathematical description and parameterisation of flow, damage and fracture behaviour of new workpiece and tool materials; Mathematical description and parameterisation of friction, heat transfer and wear in forging and rolling; Development of numerical methods, formulations and algorithms for simulation of forging and rolling processes; Validation of simulation models on industrial examples; Computer-aided process control. Furthermore, the results of the Collaborative Research Center 1153 on Tailored Forming are presented.
Leibniz Universität Hannover, IFUM

Table of Contents

Preface

Experimental-Numerical Approach to Determine the Heat Transfer Coefficients for Compound Forging Processes

Norman Mohnfeld, Marie Schute, Simon Peddinghaus, Johanna Uhe, Bernd Arno Behrens

Quasi-Isothermal Forging of Steel-Encapsulated Ti-6AI-4V Billets

Jytte Möckelmann, Julius Peddinghaus, Kai Brunotte, Bernd Arno Behrens

Experimental Characterisation of TRIP Effects of 31CrMoV9 for Hot Forming

Simon Peddinghaus, Norman Mohnfeld, Johanna Uhe, Bernd Arno Behrens

Workpiece-Based Signatures for Machine Diagnostics in Die Forging with Flash

Claudia Glaubitz, Marcel Rothgänger, Eduard Ortlieb, Julius Peddinghaus, Kai Brunotte

ML-Based Prediction of Tool Kinematics in Cold Forging of Tailored Hollow Shafts with Variable Wall Thickness

Alexander Weiss, Mathias Liewald

Process Optimization for Forging an AA6082 Automotive Upper Control Arm

Li Ju Zhou, Lei Gang Wang, Ming Xiao Shi, Angela Daniela La Rosa, Elisabete F. Reia da Costa, Xiang Ma

Experimental Characterisation of Service-Degraded H13 Tool Steel and Numerical Assessment of Die Plasticity Risk in Hot Forging of Nickel-Based Superalloys

Julen Agirre, Unai Ibarretxe, David Abedul, Nagore Otegi, Lander Galdos

Profile Contour and Flatness Control Model for Hot Strip Rolling

Nelson Souto, Nicolas Legrand, Stephane Jimenez

Impact of Separating Agents on the Deformation Regime in Aluminum Roll-Cladding Processes

Felix Muhr, Benedikt Höniger, Philipp Niemietz, Peter Frohn-Sörensen, Bernd Engel, Thomas Bergs

Relating the Cold Rolling Pass Reduction to the Microstructural Evolution and Formability in DP800 Steel

Maximilian Hribsek, Niklas Fehlemann, Irene Biermann, Sebastian Münstermann

Keywords Index

Authors Index

The original version of this eBook is available on https://www.scientific.net/book/forging-and-hot-forming/978-3-0364-1538-3

Materials Science Forum, ISSN: 1662-9752, Vol. 1186, pp 1-11

doi: 10.4028/p-wwLq8G

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

Submitted:
2026-01-15

Revised:
2026-02-27

Accepted:
2026-02-27

Online:
2026-04-16

Experimental-Numerical Approach to Determine the Heat Transfer Coefficients for Compound Forging Processes

The original version of this paper is available on https://www.scientific.net/MSF.1186.1.pdf

Mohnfeld Norman 1,a*, Schute Marie 1, b, Peddinghaus Simon 1,c, Uhe Johanna 1, d and Behrens Bernd-Arno 1,e

1 Leibniz University Hannover, Institute of Forming Technology and Machines (IFUM), An der Universität 2, 30823 Garbsen, Germany

a* mohnfeld@ifum.uni-hannover.de, b  marie.schute@stud.uni-hannover.de, c s.peddinghaus@ifum.uni-hannover.de, duhe@ifum.uni-hannover.de, ebehrens@ifum.uni-hannover.de 

*corresponding author

Keywords: heat transfer coefficient, compound forging, experimental-numerical approach.

Abstract

For the reliable numerical simulation and design of compound forging processes involving dissimilar materials, an accurate representation of thermal boundary conditions is essential. In particular, the heat transfer coefficient (HTC) at the interface of the workpiece and the die strongly influences temperature distribution, material flow, and interfacial integrity. Despite its significance, the HTC is frequently modelled as constant in finite element (FE) simulation due to the lack of experimental data for forging-relevant conditions. Therefore, this study presents an experimentalnumerical methodology for determining load-dependent HTCs representative of compound forging. A specialised test setup was used to reproduce the thermal-mechanical boundary conditions of hot bulk forming, by inducing contact pressures both below and above the flow stress of the workpiece material. Temperature histories were recorded using embedded thermocouples and analysed through an inverse numerical approach based on a one-dimensional (1D) finite element (FE) model. The influence of contact pressure, heating atmosphere, and lubrication on the HTC was systematically investigated for a S235JR specimen temperature of 600∘C. The results demonstrate a pronounced pressure dependency of the HTC, with increasing values at higher contact pressures. Oxide formation and lubrication were shown to significantly affect heat transfer behaviour, particularly while heating under atmospheric conditions. The presented approach provides process-specific HTC data that can substantially improve the predictive capability of numerical simulations for compound forging applications.

Introduction

The original version of this paper is available on https://www.scientific.net/MSF.1186.1.pdf

The reduction of vehicle weight is a key strategy within the automotive sector to lower CO2 emissions. While highly developed lightweight solutions have been established in sheet metal forming, their transfer to bulk metal forming remains comparatively limited [1]. As the total mass of bulk-formed components in vehicles is approximately equivalent to that of sheet metal parts, substantial lightweight potential exists in this domain [2]. Hybrid components made from at least two different materials offer great potential as a new technology for innovative lightweight construction concepts. Using hybrid components allows the advantageous properties of each material to be exploited in a targeted manner, thereby reducing component weight while maintaining the same load capacity. Compound forging enables load-adapted component structures by joining distinct materials within a single forming operation. This facilitates resource-efficient highly productive fabrication of near-netshape hybrid components. During forming, the dissimilar materials are bonded through a combination of mechanical interlocking and metallurgical joining. The process chain consists of the pre-joining of semi-finished parts by form or force fit, followed by targeted inhomogeneous inductive heating to establish material-specific temperatures, and compound forging to a hybrid component [3].
Hybrid components made of aluminium and steel offer an excellent combination of weight reduction through the aluminium on the one hand and increased strength through the steel on the other. The
joining of dissimilar materials such as steel and aluminium presents significant challenges, primarily due to the different forming temperatures and the formation of intermetallic phases (IMP). The thermal conditions at the material interfaces critically influence the forming behaviour and the IMP formation. Heat transfer from the hotter steel to the aluminium may locally exceed the aluminium solidus temperature, causing partial melting and impairing the bond. Also, heat loss of the steel billet may reduce its formability and increase the risk of damage failure. Additionally, continuous heat transfer occurs from the billets to the colder die surfaces [3, 4]. Accurate description of the temperature distribution within the semi-finished parts and tools is therefore essential for a precise process design and simulation. Finite element modelling has proven capable of fulfilling this purpose; however, its accuracy strongly depends on the correct representation of process-specific thermal boundary conditions [5].
A key parameter in modelling thermal interactions is the HTC α at the workpiece-die interface, which depends on time-varying process quantities such as contact pressure ( σN ), surface temperatures ( Tworkpiece ,Tdie  ), and lubrication conditions ( nlubrication  ) [5,6]. Despite its relevance, the HTC is commonly treated as a constant in numerical simulations due to insufficient experimental data [7], thereby limiting predictive accuracy. The experimental characterisation of the HTC is extremely challenging due to the difficult accessibility of the contact area and the control of the test parameters. Experimental determination of the HTC requires temperature measurements near or at the contact interface. Direct approaches, such as tool-surface thermocouples, often interfere with the contact and compromise the measurement, while thermography is not applicable in closed-die conditions [5]. Indirect methods, whereby thermocouples are embedded close to the die surface, are thus commonly employed. By reproducing experimental conditions numerically and iteratively matching the simulated temperature histories to the measured, the HTC can be determined. Fundamental studies using such indirect approaches were presented by Singh et al. [8], Dixit et al. [9], and Volke et al. [10]. Additional contributions include Wen et al. inverse heat conduction analysis for warm sheet forming [6] and Bhattacharya et al.'s characterisation of HTC during press hardening, highlighting the contact pressure as the dominant influence factor [11].
Most of these investigations rely on a test rig developed by Volke et al., involving two heated opposing punches with integrated temperature sensors [10]. However, generating homogeneous initial temperature fields and controlling relevant parameters, such as contact pressure or position of the thermocouples is experimentally demanding. Furthermore, existing studies predominantly address sheet metal forming, whereas bulk forming involves substantially higher contact pressures and a greater thermal mass, leading to slower cooling rates and increased thermal inertia of the workpiece. In processes such as compound forging in closed dies, contact pressures may exceed the material flow stress due to superimposed hydrostatic pressure states.
To this day, no detailed investigation of the HTC under the specific thermal-mechanical conditions of compound forging has been reported. This study presents an extension of the experimental setup by Mohnfeld et al. [12] for recording the HTC of steel for composite forging. In order to prevent the aluminium from melting due to the steel during heating, the HTC was investigated for a steel temperature of 600∘C under varying process boundary conditions. The approach enables the coupled experimental-numerical determination of a load-dependent HTC, providing essential data for accurate modelling and process design.

Methods - Experimental Investigation

The original version of this paper is available on https://www.scientific.net/MSF.1186.1.pdf

The system was mounted in a servo-hydraulic forming simulator (Instron), as shown in Fig. 1. The test set-up consists of three steel plates guided laterally by two columns. The upper plate is fixed to a strain gauge-based load cell (Kistler) and rigidly guided by the outer columns. The columns are fixed against the lower frame of the forming simulator using adjustable feet. The centre plate is mounted in a floating configuration via springs. A U-shaped cavity in the centre plate bears the specimen holder and ensures its alignment between the punches. The lower plate is connected to the hydraulic cylinder. When the cylinder moves towards the force load cell, the springs are compressed, thereby continuously centring the middle plate between the upper and lower plates. This ensures a
simultaneous contact of the upper and lower punches to the specimen. To protect the system from the temperatures typical for hot bulk forming of up to 1,100∘C, cooling channels were added into the plates and connected via hoses, as shown in Fig. 1(a). A continuous water flow through these channels enables a rapid heat extraction from the specimen holder, thereby maintaining reproducible testing conditions and shielding the bearings and columns from thermal loads. The functional principle of the specimen holder (1.2343), the specimen (S235 JR), and the punches (1.2343) is shown schematically in cross-section in Fig. 1(b). The tool steel 1.2343 was chosen because the dies are made from 1.2343 in the subsequent compound forging process, and S235JR is a workpiece material used in the compound forging process. The steel specimen is fixed in the specimen holder, which can be closed in a form-fit manner by rotating the locking mechanism around the z -axis. Openings in both the specimen holder and the lock allow the punches to contact the specimen's end faces. Once the punches establish contact, the specimen is fully enclosed, generating a hydrostatic stress state. This enables the investigation of contact stresses exceeding the flow stress of the specimen material. Each punch has a drilled hole into which a type-K thermocouple (TC1, TC3) is inserted to within 0.9 mm of the punch face, allowing near-surface measurement of the punch temperature during contact heating. An additional thermocouple (TC2) positioned via a lateral bore in the specimen holder measures temperature directly at the specimen surface. Two thermocouples were placed between Ushaped cavity and the cooling channel in the central plate to record its thermal evolution.

[picture]

Fig. 1. (a) Test set-up for determining the HTC under forging-relevant process parameters according to Mohnfeld et al. with extended cooling system, (b) Schematic cross-sectional view of the active components of the experimental setup [12].

Fig. 1. (a) Test set-up for determining the HTC under forging-relevant process parameters according to Mohnfeld et al. with extended cooling system, (b) Schematic cross-sectional view of the active components of the experimental setup [12].

Prior to testing, the specimen holder is equipped with the specimen and heated to the test temperature in a chamber furnace (Nabertherm). The specimen temperature is monitored via the thermocouple in the specimen holder. The transfer time from furnace to test rig remained below 20 s , following the requirement defined in [12]. A combined loading profile was used to ensure a rapid punch-specimen contact and thereby avoid premature cooling of the specimen surface. First, a position-controlled prealignment is carried out; then switches to a load-controlled motion until the target force is reached upon specimen contact. The target force is maintained for 60 s , and then removed. The investigated parameter combinations are listed in Table 1. All tests were performed at a specimen temperature of 600∘C in order to test the integrated cooling system of the test rig and to record the heat transfer for the composite forging of EN AW-6082 and S235JR for the maximum contact zone temperature. The pressure dependency of the HTC was examined using six contact pressures in the range of 50-550 MPa .

The flow stress from S235JR at 600∘C, at a quasi-static strain rate of 0.1 s−1 and a strain of 0.2 , is around 300 MPa , meaning that three pressures below and three above the material's flow stress were investigated [13].

Table 1. Experimental matrix for determining a load-dependent HTC (fully factorial).

Temperature in °C | Load in MPa (kN) | Lubrication | Atmosphere | Repetitions
600 | 50 (35.34) | none ConTraer G300 | Air | 3
150 (106.03)
250 (176.71)
350 (247.40) | LUBRODAL F 3670 | Argon
450 (318.09)
550 (388.77)

To determine the influence of scale formation on the HTC, all parameter combinations were tested both with and without a protective gas atmosphere. Argon 5.0 was used as the protective gas, and the furnace was continuously flooded at a flow rate of 6.6l/min. In hot bulk forming, lubricants are used not only to influence tribological conditions between die and workpiece but also the thermal interaction. The lubricants ConTraer G300 and LUBRODAL F 3670 (Fuchs Lubritech) were evaluated and applied to the punch faces prior to testing. ConTraer G300 was applied with a spray duration of approx. 4 s , whereas LUBRODAL F 3670 was applied using a soaked sponge following a pad-printing approach [14]. Before each test, the punches were cleaned and fresh lubricant was applied. Each parameter combination was repeated three times.

Methods - Numerical Investigation

The original version of this paper is available on https://www.scientific.net/MSF.1186.1.pdf

The determination of HTC was carried out using an iterative inverse optimisation based on the experimentally recorded temperature histories. The developed MATLAB script consists of a onedimensional finite element (1D FE) model and an optimisation module. The basis of the 1D FE model is the discretisation of the test setup, whereby the specimen and the punch are represented as discrete node chains. Heat conduction within the materials is modelled using Fourier's law Eq. 1, while the heat transfer between the two materials is described according to Newton's law of cooling, Eq. 2 [12]. Heat loss through heat radiation is described by the Stefan-Boltzmann law in Eq. 3.

Q˙heat conduction =A·λ·ΔTsQ˙heat transfer =α·A·ΔTQ˙heat radiation =ϵ·σ·AM·ΔT4U˙=Q˙m·cp

(1)(2)(3)(4)

The nomenclature of the energy balances (Eq. 4) for heat conduction, heat transfer, and thermal radiation is illustrated in Fig. 2(a). Fig. 2(b) schematically shows the 1D FE model of the punch, the specimen, and the specimen holder.

[picture]

Fig. 2. (a) Nomenclature of the fundamental equations, (b) Schematic representation of the 1D FE model.

Fig. 2. (a) Nomenclature of the fundamental equations, (b) Schematic representation of the 1D FE model.

To reduce computational effort, only one half is considered, with the plane of symmetry located at the centre of the specimen. The model consists of 500 nodes, where nodes 1−150 represent the specimen and nodes 151-500 correspond to the punch. The energy balance for the punch nodes including the heat radiation is shown in Eq. 5.

U˙x=A·λT, punch ·(Tx−1−Tx)mpunch ·cp,T, punch ·s−A·λT, punch ·(Tx−Tx+1)mpunch ·cp,T, punch ·s−E·σ·AM·(Tx4−TA4)mpunch ·cp,T, punch (5)

The experimental test temperature is initially assigned to the specimen nodes, while the punch nodes are assigned an initial temperature of 25∘C. As a boundary condition, the experimentally measured temperature history from the specimen holder is assigned to node 101 . Nodes 150 and 151 represent the contact nodes, at which the HTC is incorporated into the energy balance, Eq. 6 for node 150.

U˙150=Aspecimen ·λspecimen ·(T149−T150)mspecimen ·cp, specimen ·s−α·Aspecimen ·(T150−T151)mspecimen ·cp, specimen (6)

The position of the thermocouple within the punch is represented by node 160. The temperature history at node 160 is used for comparison with the experimentally recorded temperature curve. The calculation of the time-temperature histories at each node is performed for every iteration using the differential equation solver ode15s. An HTC of 3.000 W/m2 K was selected as the starting value for the HTC iteration. The time step size is adaptively adjusted automatically. As a result, a numerically determined temperature-time history is obtained for every node for a specific HTC value. By comparing the calculated and experimentally measured temperature histories at node 160, the coefficient of determination R2 is calculated and passed to the optimisation module. Based on the R2 value, the fminsearch function within the optimisation module determines a new HTC for the subsequent iteration. Two termination criteria were defined for the optimisation loop: a maximum of 50 iterations or an R2 value greater than 0.98 . Using the optimised HTC, the 1D FE model is solved and the calculated temperature histories are visualised. The thermophysical properties, such as thermal conductivity and specific heat capacity, were defined as temperature-dependent based on data from the JMat-Pro database. The tabular data was approximated to temperature-dependent models using polynomial functions and implemented into the 1D model. The polynomial coefficients used are listed in Table 2. This significantly reduces computational time, as there is no need to interpolate the material data as a function of the current nodal temperature at each time step for every node, but instead only evaluates the polynomial function with the respective nodal temperature.

Table 2. Coefficients of polynomial approximations for describing the thermal material data of 1.2343 and S235JR.

Material | Thermophysical property | x₃³ | x₂² | x₁ | x₀
1.2343 | thermal conductivity in W/(m·K) | 9.23200·10⁻⁷ | -3.60373·10⁻⁴ | 4.39727·10⁻¹ | 437.86345
specific heat capacity in J/(kg·K) | -3.50000·10⁻⁸ | -1.55845·10⁻⁵ | 3.30995·10⁻² | 22.36913
S235JR | thermal conductivity in W/(m·K) | 1.03810·10⁻⁶ | -4.14827·10⁻⁴ | 4.46068·10⁻¹ | 440.61875
specific heat capacity in J/(kg·K) | 5.02716·10⁻⁸ | -6.79118·10⁻⁵ | 1.20086·10⁻⁷ | 47.49684

Results and Discussion

The original version of this paper is available on https://www.scientific.net/MSF.1186.1.pdf

Prior to the experimental series, the influence of the cooling channels on the measured punch temperatures and the temperature evolution within the centre plate was analysed and evaluated. For this purpose, tests were carried out at a specimen temperature of 600∘C both with and without cooling, with ten repetitions approximately every 15 minutes, which represents the testing interval used in prior investigations [12]. In Fig. 3(a), the last five repetitions of the series are compared. The punch temperatures TC1, TC2 and TC3 show no deviations in the maxima. Only a time shift can be seen, as the test series with and without cooling were carried out one after the other and were not completely synchronised. The heating of the centre plate was recorded using thermocouples TC4 and TC5 positioned in front of the bearings, as shown in Fig. 3(b).

[picture]

Fig. 3. (a) Temperature development for the last five repetitions of the test series with and without cooling at 600∘C for TC1 and TC2; (b) Position of thermocouples TC4 and TC5 in the middle plate; (c) Temperature development for the last five repetitions of the test series with and without cooling at 600∘C for TC4 and TC5.

Fig. 3. (a) Temperature development for the last five repetitions of the test series with and without cooling at 600 ∘ C for TC1 and TC2; (b) Position of thermocouples TC4 and TC5 in the middle plate; (c) Temperature development for the last five repetitions of the test series with and without cooling at 600 ∘ C for TC4 and TC5.

In the experiment without cooling, the temperature rose by about 40∘C during the test series, while with cooling, only a partial temperature increase of about 5∘C was measured during the experiment. At a test temperature of 600∘C, this level of heating is considered non-critical. However, the use of cooling ensures constant experimental conditions. The plates are no longer subject to uncontrolled fluctuations in ambient temperature or residual heat in the plates. Instead, reproducible experimental conditions are guaranteed. Therefore, cooling was applied in all subsequent experiments.

The simulated temperature histories obtained from the 1D FE model are exemplarily shown in Fig. 4(a,b) for one experimental series conducted in an argon atmosphere without lubrication at varying contact pressures. Node 101 represents the experimentally recorded temperature measurement from thermocouple TC2 at the lateral surface of the specimen. The temperature decreases continuously from the starting temperature of 600∘C to approx. 550∘C after 50 s . The contact node 150 of the steel specimen reduces by approximately 100∘C within the first 3 s , for a contact pressure of 50 MPa , Fig. 4(a). By increasing the contact pressure to 550 MPa , the temperature at node 150 drops even more sharply within the first 3 s , by approximately 115∘C, Fig. 4(b). An even greater temperature change ΔT occurs at node 151 at the punch contact surface, amounting to approximately 225∘C. The temperature curves for node 160 and thermocouple TC1 in the experiment shown in Fig. 4(a,b) show a high degree of agreement and are almost identical, which is confirmed by the high R2 values of 97.78% and 96.29%.

[picture]

Fig. 4. Calculated temperature profiles from the 1D FE model with comparison to experimental measurements and coefficient of determination for the test series at 600∘C, argon and without lubrication under contact pressures of (a) 50 MPa ; (b) 550 MPa ; (c) Coefficient of determination R2 between experimental and simulated temperature curve for experiment TC1 and node 160 via contact pressure for all tests.

Fig. 4. Calculated temperature profiles from the 1D FE model with comparison to experimental measurements and coefficient of determination for the test series at 600 ∘ C , argon and without lubrication under contact pressures of (a) 50 MPa ; (b) 550 MPa ; (c) Coefficient of determination R 2 between experimental and simulated temperature curve for experiment TC1 and node 160 via contact pressure for all tests.

Across all experiments, an R2 value of ≥95% was achieved, indicating that the experimental temperature curves are described very accurately, see Fig. 4(c). Furthermore, a general trend of decreasing R2 values with increasing contact pressure can be observed. A possible explanation for this behaviour is the increasing plastification of the specimen, which is not taken into account in the 1D model.

The inversely and iteratively determined HTCs for the investigated parameter configurations are plotted as a function of the contact pressure in Fig. 5. In addition to the three repetitions of each parameter combination, the average HTC value of the three data points was calculated and is shown as a line. For the test series conducted under argon, shown in Fig. 5(a-c), all results exhibit a linear trend, independent of the lubrication condition, describing a continuous increase in the HTC with rising contact pressure. An increasing HTC with increasing contact pressure was also observed in the work of Wen et al. for 22 MnB 5 sheet metal up to a maximum contact pressure of 50 MPa [6].

One possible explanation is that, with rising contact pressure, the surface asperities of the softer contact partner are progressively flattened, thereby increasing the real contact area available for heat transfer. The individual data points are located very close to the mean curve, indicating a high reproducibility of the experiments.

[picture]

Fig. 5. Development and comparison of pressure-dependent HTC between air and argon (a) without lubrication; (b) ConTraer G300; (c) LUBRODAL F 3670.

Fig. 5. Development and comparison of pressure-dependent HTC between air and argon (a) without lubrication; (b) ConTraer G300; (c) LUBRODAL F 3670.

The test series carried out in air initially also show a linear increase in HTC with rising contact pressure. At a contact pressure of 250 MPa , the HTC stagnates for the tests without lubrication, Fig. 5(a) and with ConTraer G300, Fig. 5(b). A possible explanation is that, with the onset of plastification, the oxide- and lubricant-covered surface locally ruptures. This reduces the real contact area, so that the HTC no longer increases despite rising contact pressure. Only with a further increase in contact pressure are the ruptured surfaces closed again, causing the HTC to increase once more. In the case of LUBRODAL F 3670, no stagnation is observed in the range between 250 and 350 MPa , see Fig. 5(c). Instead, the slope of the linear increase merely flattens at a contact pressure of 350 MPa . Although the oil-based lubricant evaporates at the applied temperature of 600∘C, it cannot be ruled out that the remaining lubricant layer exhibits higher elasticity compared to ConTraer G300. As a result, the lubricant film may remain intact despite local surface rupture, thus continuing to impede heat transfer. The fact that, regardless of the selected atmosphere, a lower HTC is determined for LUBRODAL F 3670 compared to ConTraer G300 indicates that LUBRODAL F 3670 reduces heat transport even after evaporation of the carrier medium. That LUBRODAL F 3670 exhibits a reduced HTC when the carrier medium is no longer present in the contact zone was also clearly demonstrated in a previous work of the authors during the determination of the HTC for an EN AW-6082 / 1.2343 contact. At 300∘C and contact pressures of up to 200 MPa , a higher HTC was achieved with LUBRODAL F 3670 than with ConTraer G300, which was attributed to the improved wetting of the contact surface by the oil-based carrier medium. At higher contact pressures, however, the HTC decreased rapidly to values below those obtained with ConTraer G300. Due to the increasing pressure, the carrier medium was expelled from the contact zone, leaving only the solid lubricant particles in contact, which reduced heat transport [12]. At 600∘C, the carrier medium evaporates immediately after contact, so that only solid lubricant particles remain in the contact zone.

The influence of oxides in the contact zone is evident from the direct comparison of the HTC curves obtained in air and under argon for all lubrication conditions in Fig. 5(a-c). The HTCs determined under argon are generally higher than the HTC of oxide covered, particularly at contact pressures above 350 MPa . This difference may be attributed to the onset of plastification, since the increase in surface area causes the oxide layers to fracture, thereby reducing the real contact area. At contact pressures below 250 MPa , the specimens are loaded only elastically, so that no increase in surface area occurs and the contact area remains comparable. With the exception of the tests at contact
pressures below 350 MPa without lubrication, the oxide layer in the contact zone generally slows down heat transport, resulting in a reduced HTC.

The formed oxide scale was measured using a confocal microscope, yielding an oxide layer thickness of approximately 80μ m. The scaled specimen is shown in Fig. 6(a). With an increase in application temperature to 900∘C, enhanced oxide formation is to be expected. Bergelt et al. showed in their studies that after only 2 min at 900∘C, an oxide scale thickness of approximately 40μ m had already formed [15]. Due to the slow heating in the chamber furnace of approximately 30−40 min, substantial scale formation must be expected, making investigations with and without argon atmosphere indispensable for studies at higher temperatures.

[picture]

Fig. 6. (a) Cooled and extracted specimens after testing at 600∘C under air without lubrication and 50 MPa with scale formation; end face of the specimen directly after testing at 600∘C, without lubrication, 50 MPa (b) under air and (c) under argon atmosphere.

Fig. 6. (a) Cooled and extracted specimens after testing at 600 ∘ C under air without lubrication and 50 MPa with scale formation; end face of the specimen directly after testing at 600 ∘ C , without lubrication, 50 MPa (b) under air and (c) under argon atmosphere.

In Fig. 6(b,c), the underside of the specimen holder after a test at 50 MPa without lubrication is shown, revealing the end face of the specimen. The specimen in Fig. 6(b) was heated in air, whereas the specimen in Fig. 6(c) was heated under protective gas. It can be observed that even the specimen heated under protective gas exhibits slight oxide formation. This indicates that continuous scale formation already occurs during the transfer from the furnace to the test rig and during the time until the punches come into contact with the specimen surface. This scale formation represents a limitation of the experimental setup and must be taken into account in the interpretation of the results and in further investigations.

Summary and Conclusion

The original version of this paper is available on https://www.scientific.net/MSF.1186.1.pdf

Compound forging offers considerable potential for lightweight design through the integration of dissimilar materials within a single forming operation. However, the numerical design of such processes is strongly dependent on an accurate description of thermal boundary conditions, in particular the HTC at the workpiece-die interface. Due to the lack of experimentally validated data under forging-relevant conditions, the HTC is frequently assumed to be constant in numerical simulations, which limits their predictive capability.

In this study, a dedicated experimental-numerical methodology was developed to determine loaddependent HTCs representative of hot bulk and compound forging processes. A specialised test rig enabled the reproduction of high contact pressures, including pressure levels exceeding the flow stress of the workpiece, while maintaining controlled thermal conditions. Temperature histories were measured close to the contact interface using embedded thermocouples and evaluated by means of an inverse optimisation approach based on a one-dimensional finite element model. This approach allowed the HTC to be iteratively identified by matching simulated and experimental temperature evolutions with high coefficients of determination.

The results demonstrate a clear dependence of the HTC on contact pressure across all investigated conditions. Under a protective argon atmosphere, the HTC increased almost linearly with rising pressure, largely independent of lubrication, indicating an increasing real contact area as surface
asperities were progressively flattened. In contrast, experiments conducted in air revealed deviations from this linear trend at intermediate pressure levels, which were attributed to oxide scale formation and local surface rupture during the onset of plastic deformation. Lubrication was shown to reduce heat transfer in all cases, with oil-based lubricants exhibiting a pronounced insulating effect due to residual solid lubricant particles remaining in the contact zone at elevated temperatures.
Overall, the findings confirm that the HTC in compound forging cannot be represented by a constant value but must instead be described as a function of contact pressure, atmosphere, and lubrication condition. The HTC data generated in this work provides a robust basis for improving the accuracy of finite element simulations of compound forging processes. Their implementation enables more reliable prediction of temperature fields, material flow behaviour, and interfacial phenomena, thereby supporting robust process design and facilitating the industrial application of hybrid forged components.
In future work, experiments will be conducted at temperatures of up to 900∘C in order to determine the HTC for steel under forging-relevant thermal conditions. In addition, controlled heating of the punches by means of heating sleeves will be integrated to further adapt the experimental setup to the boundary conditions of hot bulk forming. To enable the application of the determined heat transfer coefficients in finite element simulations, the obtained findings will be transferred into a model for the mathematical description of the variable-dependent heat transfer coefficient. This will allow heat transfer to be considered as a spatially and temporally dependent quantity in process simulations, thereby not only improving the prediction of the thermal workpiece history but also enabling targeted measures for process control and optimisation.

Acknowledgment

The original version of this paper is available on https://www.scientific.net/MSF.1186.1.pdf

Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Projektnummer (496068488).

References

The original version of this paper is available on https://www.scientific.net/MSF.1186.1.pdf

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A.S. Bhattacharya, K.N. Adhvaryu, A.R. Singh, C. Butcher, K.J. Daun, Influence of the coating on the interfacial heat transfer coefficient in hot stamping of Al-Si coated 22 MnB 5 steel, Int. J. Adv. Manuf. Technol. 137 (2025) 3049-3059. .

N. Mohnfeld, H. Wester, R. Grawe, S. Peddinghaus, J. Uhe, B.-A. Behrens, ExperimentellNumerische Ermittlung des Wärmeübergangskoeffizienten für EN AW-6082: Wärmeübergangskoeffizienten für die Warmmassivumformung, WT Werkstattstechnik 115 (2025) 741-749. .

B.-A. Behrens, J. Uhe, H. Wester, N. Mohnfeld, Development of a test setup for the experimental determination of the heat transfer coefficient for compound forging, in: Proceedings 33rd International Conference on Metallurgy and Materials (METAL 2024), Brno, Czech Republic, 2024, pp. 75-80. .

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The original version of this eBook is available on https://www.scientific.net/book/forging-and-hot-forming/978-3-0364-1538-3

Materials Science Forum, ISSN: 1662-9752, Vol. 1186, pp 13-24

doi: 10.4028/p-c0Lqa2

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

Submitted:
2026-01-19

Revised:
2026-02-16

Accepted:
2026-02-24

Online:
2026-04-16

Quasi-Isothermal Forging of Steel-Encapsulated Ti-6AI-4V Billets

The original version of this paper is available on https://www.scientific.net/MSF.1186.13.pdf

Jytte Möckelmann 1,a*, Julius Peddinghaus 1,b, Kai Brunotte 1,c and Bernd-Arno Behrens 1, d

1 Leibniz University Hannover, Institute of Forming Technology and Machines (IFUM), An der Universität 2, 30823 Garbsen

a* moeckelmann@ifum.uni-hannover.de, b  peddinghaus@ifum.uni-hannover.de, c  brunotte@ifum.uni-hannover.de, d  behrens@ifum.uni-hannover.de

Keywords: titanium, steel, die forging, quasi-isotherm.

Abstract

Isothermal forging is a common method for manufacturing titanium alloys, but it involves complex processes and equipment. The oxidation of titanium leads to the formation of an alpha-case, which in turn promotes increased crack formation. To prevent this, inert gas is typically required. However, by encapsulating the titanium billet (Ti-6Al-4V) in a steel casing made of AISI 316L, a quasi-isothermal process can be achieved without the need for inert gas. This method maintains protection against oxidation while simultaneously reducing cooling. The sealing of the capsules is crucial to ensure that the titanium is effectively enclosed and protected from the surrounding gases. In this study, various encapsulation methods are compared, including rotary friction welding, diffusion bonding, and press-fitting a lid with an interference fit. The investigation involves differing contact conditions between the titanium and steel sleeve, as well as steel wall thicknesses of 2 mm and 4 mm . These factors showed no impact on the material flow or microstructure of the formed components. Encapsulation can prevent the formation of an alpha-case. Intermetallics form between the titanium and the steel capsule, depending on the contact conditions. The use of graphite as a separating agent prevents the formation of them.

Introduction

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Titanium and its alloys find numerous applications in lightweight construction, primarily due to their high specific strength-to-density ratio, with 60% of the density of steel, yet with similar strength [1]. Nevertheless, conventional machining of these alloys is challenging due to high tool wear and insufficient recycling processes for chips, posing ecological and economic issues [2, 3]. Forging offers a viable solution for producing near-net-shape titanium components. This method reduces material waste and allows precise microstructural control through temperature treatment, enabling the adaption of mechanical properties for specific applications.

Forging titanium alloys presents challenges due to their high reactivity with ambient gases. At elevated temperatures, titanium reacts particularly with oxygen [4]. This reaction leads to the formation of a brittle alpha-case near the surface. The alpha-case consists of a layer enriched with the α-phase due to the alpha-stabilising effect of the oxygen, which can compromise the material's integrity [5]. Hence, reducing its formation is essential during processing. For instance, protective gases such as argon can be used to mitigate this issue. However, this involves increased demands on the peripheral process technology.

The titanium alloy Ti-6Al-4V alloy, which is an (α+β)-alloy, undergoes a phase transformation at around Tβ=995∘C[2]. The hexagonal close-packed α-phase is more brittle compared to the more ductile body-centred cubic β-phase. In conventional forging, the contact between the billet and die leads to cooling and potential phase transformation. The forming temperature is crucial as it affects the yield stress and attainable true plastic strain. The cooling of the workpiece surface due to the contact with the die results in severe deformation inhomogeneities [6]. To precisely control the forming temperature and distribution, allowing forging above the β-transus-temperature Tβ, isothermal forging is used. This method aligns the die temperature with the billet, ensuring consistent temperature distribution. The die materials used in isothermal processes must withstand significant thermomechanical stresses. Nickel and molybdenum-based alloys are often employed for this
purpose, but they require the use of protective gases to prevent sublimation losses [7]. Overall, isothermal forging results in more complex equipment and additional energy for precise temperature control of the dies.

Encapsulation of titanium aims to prevent its reaction with oxygen. It also serves as a heat storage mechanism, which slows titanium's cooling process. Various approaches have been explored. Zhang et al. used steel foils around a TiAl billet with a thickness of 0.1 mm[8]. This method reduces temperature loss during upsetting. However, the removal of these foils is not discussed. Forged components with more complex geometries can present process difficulties. A mandrel would likely tear the foil, allowing oxygen to react with the titanium. As a result, this approach is less suitable for more complex geometries. In a direct powder forging process by Careau et al. Ti-6Al-4V powder is encapsulated in 1.3 mm thick 316L stainless steel. This encapsulation cracked, causing oxidation of the titanium surface [9]. They describe the easy decapsulation due to the intermetallics between titanium and the steel capsule. The brittle intermetallic phases crack during the second forging step. Excess material in the flash region is then cut, so that the stainless steel casing can be removed [9].

Preliminary Research.

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Previous studies focused on forming Ti-6Al-4V encapsulated in stainless steel with a capsule thickness of 4 mm [10]. The capsule, as detailed in Siring et al., serves to maintain the precise temperature conditions necessary for titanium forming, while preventing interaction with reactive atmospheric gases [11]. This approach facilitates a quasi-isothermal forming process, which eliminates the intricate procedural and technological demands typically associated with fully isothermal methods. Consequently, the encapsulation technique significantly optimises material efficiency relative to conventional machining techniques and also reduces the extent of subsequent machining processes required. Graphite proved effective as it enhanced material flow [10]. However, the capsule design did not completely prevent oxygen from reaching the titanium because the steel partially thinned out and ruptured. In the area between the fully pressed-in lid and the steel casing, a separation occurred. During the forming process, titanium flowed into this unprotected area and became oxidised. In a subsequent study, die geometries were numerically designed to produce the identical titanium core with different steel wall encapsulations of 2 mm and 4 mm despite varying material flow of the capsule [12]. In the experimental validation cracks appeared in the 2 mm steel capsule. The extent to which this leads to a reaction between titanium and oxygen has not yet been investigated.

Research Gap.

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There is a research gap concerning the effects of encapsulation wall thickness and joining methods on the deformation behaviour and microstructure of titanium components. Specifically, it is not wellunderstood how different wall thicknesses ( 2 mm and 4 mm ) affect material flow and microstructure, potentially leading to defects. Additionally, the effectiveness of various capsule sealing methods in preventing unintended openings and protecting the titanium core from oxygen exposure, to avoid the formation of an alpha case, requires investigation. Furthermore, the formation of intermetallic compound (IMC) at the interface between the titanium billet and the steel capsule is also not well understood during this process.

The hypotheses of this study propose that the steel capsule's wall thickness significantly impacts the deformation behaviour of titanium. Moreover, the effectiveness of different sealing methods, such as diffusion bonding, friction welding, and press fitting, in preventing oxidation will be assessed. Additionally, the formation of IMCs will be examined based on the contact conditions between the titanium billet and the steel capsule. Since the hypothesis is that the decapsulation of the titanium core is significantly influenced by the IMC understanding this is essential for subsequent work. To investigate the effects of the encapsulation strategy (including diffusion bonding, rotary friction welding, and press fitting) and capsule wall thickness on the microstructure, microscopic examinations will be conducted on the encapsulated titanium components. Following the forging trials, the components will be sectioned, undergo microscopic analysis, and be characterised using hardness measurements.

Materials and Methods

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The capsules are fabricated from AISI 316L stainless steel, with the dimensions of the components visible in Fig 1. Capsule wall thicknesses of 2 mm and 4 mm have already been utilised in previous studies; however, the microstructure and the formation of an alpha case were not investigated [12]. The illustration presents the titanium semi-finished product from TiA−6Al−4 V with an interference fit and a clearance fit (Fig. 1a), capsules with a wall thickness of 2 mm (Fig. 1b) and 4 mm (Fig. 1c), and the semi-finished products for rotary friction welding with a wall thickness of 2 mm (Fig. 1d) and 4 mm (Fig. 1e). All components were manufactured using machining. The capsule is designed to be thicker at the upper and lower sections to increase thermal capacity and therefore reduce temperature loss of the titanium core. These areas experience the longest contact times with the dies and are subject to high mechanical stress due to the mandrel.

[picture]

Fig. 1. Dimensions of the semi-finished products used: Ti-6Al-4V semi-finished product with an interference fit and a clearance fit (a), capsules with a wall thickness of 2 mm( b) and 4 mm(c), and the semi-finished products for friction welding with a wall thickness of 2 mm( d) and 4 mm(e).

Fig. 1. Dimensions of the semi-finished products used: Ti-6Al-4V semi-finished product with an interference fit and a clearance fit (a), capsules with a wall thickness of 2 m m ( b ) and 4 m m ( c ) , and the semi-finished products for friction welding with a wall thickness of 2 m m ( d ) and 4 m m ( e ) .

Both the wall thickness and the joining technique of the steel capsules are varied while keeping the dimensions of the titanium core constant. Regarding the diffusion bonding process (DB), An et al. demonstrated that the joining of AISI 316L can be improved by using a nickel foil [13]. Accordingly, a nickel foil ( h+ s Präzisionsfolien GmbH, Vohenstrauß, Germany) with a thickness of 0.1 mm and a purity of over 99.2% is placed in the lid groove. The capsules were preheated to 300 ∘C to enable insertion of the titanium core and the lid, both of which have an interference fit after cooling. This process is carried out using a hydraulic hand press PJ20H (AC Hydraulic a/S, Viborg, Denmark). Due to the existing fit between the lid and the capsule, an airtight seal of the titanium core is achieved. The DB process then takes place using the sinter press DSP 507 (Dr. Fritsch, Fellbach, Germany). The machine heats the material using direct electrical current (DC) through a process known as Joule heating. During the process, the temperature is measured with a METIS M3 pyrometer (SensorTherm GmbH, Steinbach, Germany) and used to regulate and control the temperature. The set emission coefficient is 0.66 . The temperature measurement takes place just below the lid, as this position reflects the temperature relevant for the bonding process (see Fig. 2).

The pyrometer is positioned using a built-in laser that corresponds to the measurement point. A low vacuum ( 20 mbar ambient pressure) is maintained during diffusion bonding to reduce oxidation of the capsule. In preliminary tests, deformation of the capsule and the titanium core occurred at the minimum adjustable pressure of 73.8 MPa at temperatures exceeding 700∘C. Since the diffusion process is temperature-controlled, the temperature is set to a maximum of 700∘C to prevent deformation of the semi-finished products while maximising diffusion. The heating rate is 40 K/min and the holding time at the bonding temperature is 1 h .

For the encapsulation of the titanium semi-finished product via rotary friction welding (RFW), the titanium semi-finished product is first pressed into the steel capsule as previously described. Between the titanium billet and the capsule, there is a gap above the lower section, which has an interference fit to ensure the fixation of the titanium within the capsule during the welding. After the insertion of the titanium, the capsule is then sealed with the lid using the friction welding machine Genius Plus R30 (KUKA, Augsburg, Germany). The steel lid component is secured to the rotating spindle side using three clamping jaws. The capsule, with the titanium pressed inside, is clamped on the translating slide side. The friction parameters were previously determined iteratively to ensure bonding between the capsule shell and the lid. At the start of the process, the still-open capsule is pressed against the lid rotating at 3,000rpm with a force of 30 kN during the friction phase. Following the completion of the 2 mm friction path, the upsetting phase begins, in which the rotation is stopped and an upsetting force of 46 kN is applied for 5 s . After the samples have cooled, they are cut to size and the flash is removed (see Fig 2).

For coating the titanium core, the separating agent graphite SC 42 B (Dr. Fritsch, Fellbach, Germany) is applied. Then, a waiting period of 5 min is set to allow the solvent to evaporate. Following this, the titanium core is pressed in again as previously described. The resulting sample is named GP.

[picture]

Fig. 2. Plan and procedure for different encapsulation approaches and forging experiments.

Fig. 2. Plan and procedure for different encapsulation approaches and forging experiments.

After encapsulating the capsulated titanium billets are heated for 1 h at 1,150∘C in a convection oven to ensure the temperature is uniformly reached across the entire cross-section of the samples. The hydraulic press Schirmer + Plate is used for the forging experiments. The dies made from X38CrMoV5-3 (AISI H11) were designed in a prior study for the different wall thicknesses to produce the same titanium core [12]. For this purpose, the flash gap after numerical design for the capsule with a 2 mm wall thickness should accordingly be 2 mm , and for the 4 mm thick steel capsule, 3 mm . The forming speed is set at 30 mm/s for all samples. This speed is used to ensure minimal cooling of the titanium core [12].

For each variation of the experiment, five parts are forged. For further examination, one sample from each is cut using a wet abrasive cutter. The microstructure and material flow are examined using light microscopy on cross-sections etched with Kroll's reagent for 45 s. Subsequently, the samples were treated for 90 s in a V2A pickling bath at 65∘C. Microhardness tests are conducted at HV 0.05 in accordance with DIN EN ISO 6507-1 with QNESS 10A+ (QATM, Mammelzen, Germany) [14]. To achieve a dense positioning of measurement points along a line while ensuring the distances between the hardness indentations, the measurement points are staggered in their arrangement.

Results and Discussion

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Investigation of encapsulated titanium billets.

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Initially, the joining zones in the encapsulated titanium billets were examined before forging to ensure proper connection (see Fig. 3). The cross-sections of the diffusion-bonded capsules (see Fig. 3 a, b) show that the nickel foil did not improve the joining. This could be attributed to the insufficient contact between the joining partners. No bonding occurred in the horizontal section of the groove, which can be attributed to the existing interference fit. In the vertical areas between the lid and the sleeve, both capsules demonstrated partial joining. There is no visible IMC between the titanium billet and the capsule. Previous experiments observed this layer at temperatures above 700∘C, these parameters led to deformation of the capsule. The titanium core's microstructure indicates a row-like alignment in the vertical direction (see pink framed detail image) due to its manufacturing process. No grain coarsening occurred in the titanium core, unlike in the friction-welded samples exposed to increased heat input for a shorter time. Overall, the micrographs suggest that joining between the capsule shell and lid occurred, but not at the expected location in the upper groove area. In future experiments, the nickel foil could be introduced vertically to enhance bonding. The sleeve's thickness in this area should also be adjusted appropriately.

The RFW of the capsule with its corresponding lid successfully created a joint. The cross-sections of the RFW samples show variations in flash formation depending on the wall thickness of the steel capsule (see Fig. 3c,d ). With a capsule wall thickness of 2 mm , the desired swelling of the sleeve is evident, and the detailed view reveals the intermixing of the two joining partners. At the same friction force, a longer friction duration of an additional 5 s occurred with the thicker wall. This resulted in grain coarsening around the joining zone due to the extended heat input and hence local overheating. The grain refinement observed during RFW near the joining zone can be attributed to frictional heating and severe plastic deformation [15]. The titanium structure still displays its initial row-like grain alignment, resulting from its manufacturing process. However, in the upper titanium region near the joining zone, a change in the structure is visible, with grain coarsening present (exemplary seen in violet framed detail image). This is due to the temperature input during the RFW process. Additionally, an intermediate layer is sometimes visible, attributed to IMC, which appear to be localised (see yellow detail image). Due to the RFW, the capsule has bulged, and the interference fit has loosened. The stress from RFW likely caused slight deformation of the capsule. Notably, in the 4 mm capsule, a different gap width is observed when comparing both sides.

Both joining processes result in a bond between the capsule shell and the lid. The formation of an alpha-case could not be detected in the edge region. The bond of the RFW capsules appears more consistent compared to the DB capsules. Additionally, the process times are significantly shorter. Overall, both methods effectively achieve the sealing of the titanium billets.

[picture]

Fig. 3. Overview micrographs of the joined capsules: (a,b) diffusion bonded and (c,d) friction welded, with wall thicknesses of 2 mm(a,c) and 4 mm( b, d). Detailed micrographs assigned to the positions in the overview image with coloured frames.

Fig. 3. Overview micrographs of the joined capsules: ( a , b ) diffusion bonded and ( c , d ) friction welded, with wall thicknesses of 2 m m ( a , c ) and 4 m m ( b , d ) . Detailed micrographs assigned to the positions in the overview image with coloured frames.

Photographs of the forged components.

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During the heating of some GP capsules, the lids separated from the sleeves. This was likely due to gases within the capsule expanding. Residual solvents from the graphite coating that did not fully evaporate could also be a cause. During heating in the furnace, these solvent vapours may have evaporated and expanded, pushing open the lid. To verify this, the graphitised titanium core could be exposed to elevated temperatures in the furnace to ensure complete removal of any remaining solvent residues. Overall, the capsules remain intact after forming (see Fig. 4), with the exception of the RFW capsule with a 2 mm wall thickness. The welded RFW capsules are designed with tapered edges to aid removal from the adapter. Damage in the form of tearing was observed in the thinner areas (b). As a result, the titanium in these areas is not protected against oxidation. However, with a wall thickness of 4 mm , no such cracks are present. An exemplary component of the joined capsules is shown, where the joining zone of the friction-welded sample was subjected to different stresses: one facing upwards during forging (e), experiencing more stress, and one with reduced stress placed with the joining zone at the bottom of the die (b). Sample (e) exhibits separation between the lid and sleeve all around the mandrel imprint. Titanium with distinctive discolouration appears, explained by tensile stresses from the mandrel indentation. The discolouration indicates the oxidation of the titanium. No additional cracks or external differences were observed in the capsules. To ensure complete encapsulation, the joining zone of friction-welded samples should be positioned in the less-stressed lower die. Due to the tapering for adapter removal, the 2 mm thick RFW capsule is unsuitable for forming titanium without atmospheric contact.

[picture]

Fig. 4. Photographs of the forged components with a wall thickness of 2 mm (a-c) and 4 mm (d-f). The capsules are sealed differently: (a, d) DB, (b, e) RFW, and (c, f) GP.

Fig. 4. Photographs of the forged components with a wall thickness of 2 mm (a-c) and 4 mm (d-f). The capsules are sealed differently: (a, d) DB, (b, e) RFW, and (c, f) GP.

Microscopic examination.

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Using the cross-sections, the material flow of the encapsulated titanium components can be observed (see Fig. 5). Coloured squares in the figure indicate the positions of the detailed images (see Fig. 6). In the DB and GP capsules, the sleeve has detached from the groove, but the capsule remains sealed via the outer groove area, with titanium flowing into the gap. In the RFW samples, particularly 4RFW (Fig. 5e), the capsule has closed further due to pressure. However, a gap is visible in the joining zone of the 2RFW sample. It is important to note that this is a cross-section, and despite the rotational symmetry of the components, local differences in material flow and phases may exist. For instance, uncertainty in handling and position can affect flash formation and material distribution across the section. A further phase can be seen between the titanium and steel capsule, which literature identifies as IMC: TiFe, NiTi2, and complex oxides [16]. Overall, material flow within the same capsule thicknesses is comparable. The dies were designed in preliminary work to yield the same titanium core despite varying steel thicknesses. This is not achieved here, likely due to an inaccurate setting of the reversal point. The capsules protect the titanium core from the surrounding atmosphere during heating and forming, as they remain intact and there are no cracks.

[picture]

Fig. 5. Overview micrographs of the cross-sections of the forged components with a wall thickness of 2 mm (a-c) and 4 mm (d-f). The capsules are sealed differently: (a, d) DB, (b, e) RFW, and (c, f) GP.

Fig. 5. Overview micrographs of the cross-sections of the forged components with a wall thickness of 2 mm (a-c) and 4 mm (d-f). The capsules are sealed differently: (a, d) DB, (b, e) RFW, and (c, f) GP.

To analyse the forged components in detail, detailed images of the cross-sections are investigated, see Fig. 6. In specimen 2DB (a), the nickel foil is visible in the former groove area of the lid. After forming, the casing has separated from the lid's groove, with some titanium flowing into this area. A further intermediate layer is visible between the titanium and the nickel foil, resulting from their reaction. The titanium presents no alpha-case, indicating successful oxidation prevention. Specimen 2RFW (b) shows a distinct layer of intermetallic phases in the mandrel area, away from the friction welding zone. The thicker IMC compared to other samples may be due to the rotation of the titanium core during RFW, leading to early IMC formation. The IMC is riddled with cracks and pores (see Fig. 6 b), attributed to the phase's brittleness especially of TiFe and Fe−Cr−Ti [17]. The pores may result from diffusion processes linked to the Kirkendall effect [17]. This level of IMC formation is undesirable since it requires removal from the forged titanium, increasing process complexity and material waste. A potential solution in this variant could be the use of a separating agent to reduce IMC formation. Specimen 2GP (c) indicates IMC formation only in the lower area where the titanium core and capsule are in contact with each other. Graphite as a separating agent prevents further IMC formation. It is possible that the graphite application was insufficient in those areas. Overall, compared to specimen 2DB, there is a more even thinning of the steel capsule, likely due to reduced friction between the capsule and the titanium core through graphite lubrication. The detailed image reveals no alpha-case, indicating effective oxidation prevention. It also shows that high deformation rates can be achieved without the titanium cracking. Furthermore, the joining between the lid and housing is visible. The simple pressing of the titanium ensures isolation from the surrounding atmosphere with the chosen geometry. Specimen 4DB (d) exhibits a pronounced IMC along the entire interface between titanium and steel. This could be caused by IMC formation occurring during the diffusion process for capsule joining in this area. The IMC develops distinct regions and is interspersed with cracks. Consequently, upon capsule removal, separation between capsule and titanium core, as observed by Careau et al., is anticipated in the flash region [9]. Specimen 4RFW (e) also displays a pronounced IMC layer, which is more uniform compared to 2RFW. This may result from locally varied deformation during friction welding and the resulting different contact conditions. The detailed image shows the separation between titanium and IMC, as well as emerging gaps. In this area, a clear separation between IMC and titanium is visible, desirable for decapsulation to minimise IMC removal. An alpha-case is not observed in this specimen either. Specimen 4GP (f), like 2GP (c), exhibits reduced IMC formation. In the GP samples, an IMC formed exclusively between the capsule base and the titanium. This can be attributed to the prolonged contact during heating. During this period, the applied graphite spray does not effectively prevent the formation of the IMC. However, in all other areas that come into contact during forging, the graphite spray is sufficient to prevent the intermediate layer. The detailed image clearly illustrates the material flow without crack formation. Overall, encapsulation prevents alpha-case formation in all specimens, and IMC formation depends on contact conditions and can be utilised for later decapsulation.

[picture]

Fig. 6. Detailed micrographs of the cross-sections of the forged components with a wall thickness of 2 mm (a-c) and 4 mm (d-f). The capsules are sealed differently: (a, d) DB, (b, e) RFW, and (c, f) GP. The coloured border indicates the corresponding position marked in Figure 5.

Fig. 6. Detailed micrographs of the cross-sections of the forged components with a wall thickness of 2 mm (a-c) and 4 mm (d-f). The capsules are sealed differently: (a, d) DB, (b, e) RFW, and (c, f) GP. The coloured border indicates the corresponding position marked in Figure 5.

Hardness Investigation.

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To further investigate the mechanical properties of the forged component, hardness tests are conducted. These tests are performed at the positions marked in Figure 7a for the sample variations. As expected, the hardness profiles reveal that AISI 316L has a lower hardness compared to Ti-6Al4 V (see Fig. 7b, c). According to previous investigations, the hardness for Ti-6Al-4V is around 350 HV0. 05 [10], as in this study. When measuring from the impact point of the mandrel to the bottom of the capsule, there is an increase in hardness in the base area (see Fig. 7b). This is attributed to the intermetallic phases present. Within the titanium core, the hardness fluctuates due to varying phases, true plastic strains and hence, dislocation densities. Also, when measuring from the flash area into the titanium core, the formation of IMC at the DB samples is visible externally. Overall, fluctuations are observed in the titanium core. No significant difference can be discerned between different wall thicknesses and thus different temperature distributions during forging, or their impact on hardness.

[picture]

Fig. 7. a) Schematic illustration of hardness measurement positions, b) hardness measurements from mandrel area to capsule base, c) hardness measurements from flash area into the interior of the titanium.

Fig. 7. a) Schematic illustration of hardness measurement positions, b) hardness measurements from mandrel area to capsule base, c) hardness measurements from flash area into the interior of the titanium.

To investigate the IMC, Fig. 8 illustrates an example of an IMC and the corresponding hardness profile. Generally, different layers can be observed. Starting from the steel, a darker layer is visible, which has increased hardness compared to the steel. The next layer features a structure of light and dark grains. These various phases likely have different hardness levels, which would explain the drop in hardness at position 4 . As the titanium is approached, the hardness continues to decrease. Overall, the IMCs that have formed exhibit varying characteristics depending on the sample preparation. It is expected that the IMC will fracture at the point of highest hardness. According to this study, this will occur at the interface between titanium and the IMC, and consequently, after decapsulation, the IMC will already be separated from the titanium component. This is crucial for efficient decapsulation to minimise the necessary post-processing. This should be investigated in subsequent decapsulation trials.

[picture]

Fig. 8. a) Detailed positioning of hardness measurements for sample 2DB,b) results of the hardness measurements.

Fig. 8. a) Detailed positioning of hardness measurements for sample 2DB,b) results of the hardness measurements.

Summary and Outlook

The original version of this paper is available on https://www.scientific.net/MSF.1186.13.pdf

In conclusion, the die forging of titanium billets encapsulated in steel was conducted, investigating an approach for isothermal forging conditions. The focus was placed on different encapsulation methods, the varying fit between titanium and steel sleeves, and different steel wall thicknesses to influence heat distribution within the titanium billet during forging. Encapsulating the titanium billets prevents pronounced oxide layers and the formation of an alpha-case, as seen in nonencapsulated forging. The selected lid geometry in these experiments provides protection against ambient gases. A 2 mm thick steel wall encapsulation is sufficient to isolate the titanium from ambient gases. As no beneficial effects on material flow and microstructure are observed with thicker encapsulation, it is advisable to avoid the thicker encapsulation for material and energy efficiency. Depending on the contact conditions, different thicknesses of IMCs are formed, which can with the exception of the capsule base be prevented by introducing graphite as a separating agent, resulting in a somewhat more uniform material flow.

The different formation of IMC allows for a comparison of the decapsulation process with and without IMC in future studies. Further investigations will examine alpha-beta forging, where temperature distribution is even more critical. Thus, more significant differences are expected with different capsule wall thicknesses. Additionally, other separating agents like glass coating or alumina particles can be used to prevent IMC formation in the lower capsule area.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/MSF.1186.13.pdf

Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - project number 461918196.

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The original version of this paper is available on https://www.scientific.net/MSF.1186.13.pdf

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The original version of this eBook is available on https://www.scientific.net/book/forging-and-hot-forming/978-3-0364-1538-3

Materials Science Forum, ISSN: 1662-9752, Vol. 1186, pp 25-36

doi: 10.4028/p-OanM4P

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

Submitted:
2026-01-19

Revised:
2026-03-01

Accepted:
2026-03-02

Online:
2026-04-16

Experimental Characterisation of TRIP Effects of 31CrMoV9 for Hot Forming

The original version of this paper is available on https://www.scientific.net/MSF.1186.25.pdf

Peddinghaus Simon 1,a*, Mohnfeld Norman 1, b, Uhe Johanna 1,c, Behrens Bernd-Arno 1, d

1 Leibniz University Hannover, Institute of Forming Technology and Machines, An der Universität 2, 30823 Garbsen, Germany

a* s.peddinghaus@ifum.uni-hannover.de, b  mohnfeld@ifum.uni-hannover.de, c  uhe@ifum.uni-hannover.de, d  behrens@ifum.uni-hannover.de

*corresponding author

Keywords: hot forming, TRIP, material characterisation.

Abstract

During the hot bulk forming of long parts, inhomogeneous distributions of deformations and temperatures occur. The gradients of these distributions lead to complex, overlaying residual stresses, which can cause critical geometric deviations and mechanical failures. Common finite element (FE)-simulations for designing a process are in principle capable to predict the thermal, mechanical and metallurgical effects, but require extended material models. Thereby, the total strain increment can be described through the partial strain components of the elastic, plastic, thermal transformation related and transformation plasticity strain. To allow the numerical prediction of the distortion of long hot formed parts, an experimental characterisation of the transformation-induced plasticity (TRIP) and backflow effects is presented for the steel 31CrMoV9. Time temperature transformation (TTT) and continuous cooling transformation (CCT) diagrams are determined with JMatPro and verified by means of microstructure analysis and hardness measurements. Based on these diagrams, the transformation plasticity is investigated through dilatometric tests whereby tensile and compressive loads are applied during the phase transformation. The martensite phase transformation showed the highest amounts of TRIP of 1.55% strain under tensile loads and 1.32% strain for compressive loads, whilst the bainite transformation exhibited lower strains of 0.7% in the tensile load case and 0.74% in the compression load case, but a high tensile backflow strain of 0.56 %. For pearlite the beginning of the phase transformation was delayed and its duration extended due to the induced loads.

Introduction

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To manufacture high-performance parts with complex geometries through hot bulk forming, high forces are applied at elevated temperatures to achieve high degrees of plastic deformation [1]. Thereby numerous varying effects occur within the workpiece itself, like phase transformations, or at the interface between the workpiece and the die, such as heat transfer and friction. For steel, during thermo-mechanical processing multiple interrelated phenomena occur. Their interactions can cause defects as for example critical distortions [2]. In hot bulk forming processes, these phenomena develop unsteady in terms of time and location. As the distribution of the aforementioned effects is particularly inhomogeneous in geometrically complex or long parts with uneven mass distributions, they are vulnerable to distortion, which can lead to scrap production and is costly especially for big parts [3]. FE simulations allow to predict and prevent such critical phenomena through an adjusted process design. In numerical simulations, the additive strain composition method as shown in equation 1 , can be applied, whereby the five strains: elastic (εijel), plastic (εijpl), thermal (εijth), transformation related (tr) and transformation plastic (tp), are considered separately. The partial strains are determined separately from the current thermal, metallurgical and mechanical conditions, added up and then applied as total strain increment [4].

dεijtotal=dεijel+dεijpl+dεijth+dεijtr+dεijtp(1)

The individual strains require extended material models determined either through thermodynamical computation (e.g. through JMatPro) or an experimental characterisation. The elastic and plastic strains can be derived from well-known characterisation tests such as compression or tensile tests. The thermal and transformation related strains are caused by volumetric changes, either of the heat expansion or phase transformations, such as from austenite to ferrite, pearlite or martensite and can be determined using conventional dilatometric tests. When such phase transformations are overlayed by other mechanically or thermically induced stresses, the so-called transformation-induced plasticity leads to transformation plastic strains and isotropic transformation stresses in dependence of the applied mechanical loads. When the overlaying stresses are released during the transformation, a part of the TRIP strain can form back due to the so-called backflow effect [5]. This effect is particularly relevant for hot bulk forming processes, as they show inhomogeneously distributed and changing stresses [6].

The experimental characterisation of the transformation plastic strain however, presents a challenging task. On the one hand, in contrast to the other partial strains, no widely established norms or standards exist regarding the experimental method or the equipment. On the other hand, the approaches of current research require extensive prior knowledge of the time and temperature dependent phase transformations. For this, continuous cooling transformation (CCT) and time temperature transformation (TTT) diagrams have to be determined either numerically or experimentally.

Additionally, in terms of modelling, for the elastic, plastic, thermal and the transformation related strains, established models such as the flow curve approach of Hensel Spittel [7], are available and corresponding material parameters for a wide range of materials can be found either in databases or the results of numerous research work such as [8] or [9]. However, for the transformation plastic strain, there is neither a standardised experimental approach, nor sufficient corresponding material data available through data bases or in literature.

In the state of the art, to experimentally characterise the transformation induced plasticity under the thermomechanical conditions of hot forging, TTT- diagrams are used to identify cooling routes to achieve full transformations of the individual phases. These transformation processes are then overlayed with mechanical stresses through compression or tension of the specimen, as demonstrated for example by Lakhdar et al. [10] and Holzweissig et al. [11]. The backflow effect is investigated through additional tests, where the applied stresses are removed during the transformation [12]. The corresponding TRIP and backflow coefficients can be determined from the experimental data either by means of an analytical approach as in [13] or numerically iteratively through the comparison of experimental and numerical results [3]. For this, for example Suhr et al. in [14] as well as Behrens et al. in [3] implemented the TRIP equations into commercial FE software and validated their approaches by recreating the strain behaviour of the experimental tests.

For hot forging processes of a 42 CrMo 4 steel, Behrens showed that the complete incremental strain can be modelled in detail based on an extensive material characterisation which allowed to predict the distortion of a hot bulk formed workpiece [6]. However, this approach is not applied widely, because it requires very detailed material data, which requires specialised testing equipment and a large amount of testing and is not available in the literature. Although the fundamental phase transformations to pearlite, bainite and martensite occur for most steels, the time and temperature dependent transformation behaviour strongly varies in dependence of the alloying elements [15]. Therefore, findings and models with regards to the TRIP and backflow behaviour are not directly transferable and require separate characterisations.

The steel 31CrMoV9 is usually applied for manufacturing heavily loaded components through hot forging as a high strength can be reached after heat treatment. Additionally, it can be nitrided deeply to achieve a higher wear resistance [16]. As severe residual stresses are induced during the nitration process, a detailed knowledge of the occurring stresses and strains is essential for an optimised development of products made of 31CrMoV9 [17]. However, little to no experimental data can be found about the transformation-induced plasticity and the backflow effects of 31CrMoV9.

Therefore, this paper presents an investigation of the TRIP behaviour of the steel alloy 31 CrMoV 9 in a quenched and tempered state. The results shall also allow to derive TRIP coefficients for following numerical simulations. First, a material dataset is created numerically using JMatPro as a basis to define heating-cooling routes for the phase transformation to pearlite, bainite and martensite. After the cooling routes are verified through stress free tests and microstructure analysis with hardness measurements, the experimental setup of Behrens et al. is applied to investigate the TRIP and backflow effects for varying loads [12].

Material and Methods

The original version of this paper is available on https://www.scientific.net/MSF.1186.25.pdf

The experimental determination of TTT and CCT-diagrams requires a great amount of costly and time-consuming tests whilst the resulting diagrams are only applicable for specific process conditions. To reduce the test effort, the TTT and CCT-diagrams were calculated with the material simulation software JMatPro based on the application for hot forming and on the chemical composition determined through optical emission spectrometry, as shown in table 1.

Table 1. Chemical composition of the investigated steel 31CrMoV9 (in wt% ).

Steel grade | C | Si | Mn | P | S | Cu | Cr | Ni | Al | Mo | V | Ti | Nb
31CrMoV9 | 0.329 | 0.272 | 0.632 | 0.009 | 0.002 | 0.127 | 2.48 | 0.123 | 0.025 | 0.19 | 0.143 | 0.012 | 0.004

The TTT and CCT-diagrams were calculated for an austenisation at 1,200∘C to recreate the conditions of hot bulk forming [1] as illustrated in Fig. 1. The CCT-diagram in Fig 1 a) shows that, for example a pearlite transformation can be expected when the temperature has not dropped from 1,200∘C to under approximately 530∘C within roughly 500 s after the forming process. As after hot forming, especially of large parts, a cooling process can take considerable longer and has an inhomogeneous temperature distribution, each of the three phase transformations is investigated [1].

[picture]

Fig. 1. a) TTT and b) CCT-diagrams of 31CrMoV9 determined using JMatPro with the transformation areas of Bainite (B), Pearlite (P) and Martensite (M) from Austenite(A).

Fig. 1. a) TTT and b) CCT-diagrams of 31 C r M o V 9 determined using JMatPro with the transformation areas of Bainite (B), Pearlite (P) and Martensite (M) from Austenite(A).

In addition to the TTT and CCT-diagrams, flow curves were determined with JMatPro. As illustrated in Fig. 2, the applied axial loads 100 MPa of Behrens et al. do not exceed the flow stresses of the base material at 450∘C but at 650∘C until a plastic deformation of 0.02 [12]. As the base material shows overall lower flow stresses than the individual phases at the corresponding temperatures, it can be excluded, that an unwanted plastic deformation occurs after the phase transformation which would overlay the TRIP effects.

[picture]

Fig. 2. Flow curves of 31CrMoV9 determined with JMATPro for the base material as well as for of bainite and pearlite.

Fig. 2. Flow curves of 31 C r M o V 9 determined with JMATPro for the base material as well as for of bainite and pearlite.

The experimental investigation of the TRIP and backflow effects, was carried out with a quenching and deformation dilatometer DIL 805 A/D+T (TA instruments). The specimens were taken from billets for a hot forming process, which were hot rolled and then quenched and tempered. As shown in Fig. 3, the specimen geometry has a tapered testing area in the middle with a diameter of 5 mm and a length of 10 mm . The specimen is clamped by threads at each end to allow the application of tensile as well as compressive loads through a hydraulic cylinder at the moveable specimen holder. Changes of the length are measured using quartz pushing rods at the edges of the tapered testing area. The temperature is controlled with a thermocouple type K, which is welded on the surface at the middle of the specimen.

[picture]

Fig. 3. Schematic experimental test setup with the detailed geometry of the test specimen.

Fig. 3. Schematic experimental test setup with the detailed geometry of the test specimen.

During the experiment, the specimen is heated inductively in a vacuum atmosphere to an austenisation temperature of 1,200∘C with a rate of 10 K/s, held for 120 s to homogenise the temperature distribution and then cooled down at controlled rates with helium gas from nozzles in the induction coil. As the cooling of forging.

Based on the TTT-diagrams, individual cooling routes are defined for the diffusion-based phase transformations to pearlite, bainite and the diffusionless transformation to martensite as shown in Fig. 4. For the phase transformation to pearlite, a temperature of 650∘C is held for 1,700 s. For the bainite transformation, a temperature of 450∘C with a holding time of 250 s is chosen. In order to minimise other transformation processes, the highest possible stable cooling rate of the machine of 50 K/s is chosen for the martensite transformation and for the cooling from the austenisation to the individual holding temperatures of the other phase transformations.

To characterise the TRIP effect, each of the three phase transformations was superimposed with mechanical stresses as illustrated in Fig. 4. As in the experiments of [3] and [12], axial loads of 1,964 N , corresponding to 100 MPa , are applied throughout the complete transformations as compression or tension. As this exceeds the flow stress at 650∘C as shown in Fig. 2, for the pearlite transformation, additional tests were carried out with 982 N , corresponding to 50 MPa .

To avoid plastic deformation before the martensite transformation, the loads were induced once a temperature of 450∘C was reached. In additional tests to investigate the backflow effects, the loads are removed after approximately the half of the time of the transformation based on the TTT-diagrams from JMatPro. For each cooling route and load case, the tests were repeated three times.

[picture]

Fig. 4. Test procedure with investigated temperatures and axial loads.

Fig. 4. Test procedure with investigated temperatures and axial loads.

To verify the cooling route for each of the phase transformation, the microstructure was characterised. Therefore, specimens of the stress free tests were cut from the middle of the parts, embedded, grinded, and polished to 0.5μ m and then etched with HNO3. Additionally, for each cooling route, hardness measurements (HV0.5) were carried out.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/MSF.1186.25.pdf

The microstructures resulting from the cooling routes for the different phase transformations are compared in Fig. 5. In the microstructure of the specimens which were held at 650∘C in Fig. 5 a), a pure and homogenic pearlite structure can be observed, with a hardness of 221 HV0.5 to 274 HV0.5. The microstructure of the cooling route at 450∘C mostly consists of bainite, which is recognisable as bright blue structure with an average hardness of 466 HV0.5. However, as shown in Fig. 5 c ), small portions of a different brown-colored structure can be noticed. The cooling route directly to room temperature with a cooling rate of 50 K/s results in a fine martensitic structure with an average hardness of 610 HV0.5 as shown in Fig. 5 c ). In the microstructures of the cooling route for bainite and for martensite, bright stripes in the longitudinal direction of the specimen can be seen. As the hardness is the highest in these stripes with approximately 700 HV0.5, a possible explanation for these could be, that the stripes consist of carbide precipitations resulting from the prior hot rolling of the specimens. The microstructure analysis with the hardness measurements demonstrates that the chosen cooling routes lead to the targeted phase transformation and therefore can be considered suitable for the following experimental investigations, although the influence of the presumed carbides and also the uncertainty in the bainite has to be taken into consideration for the discussion of the experimental results.

[picture]

Fig. 5. Microstructure analysis in the testing area of the specimens of the stress free tests: a) pearlite transformation, b) bainite transformation and c) martensite transformation.

Fig. 5. Microstructure analysis in the testing area of the specimens of the stress free tests: a) pearlite transformation, b) bainite transformation and c) martensite transformation.

The TRIP and the backflow strains are determined similar to work of [12] with the middle curve of the three repetitions of each parameter configuration. The range between the minimum and maximum curve of each investigated parameter configuration are visualised in the analysis diagrams as surfaces around the corresponding curve to emphasise the deviations between the repetitions.

Thereby, the TRIP strain is determined from the difference of the length change of the constantly loaded curve and the stress free curve at the end of the transformation. For the backflow strain, the difference between the constantly and the dynamically loaded curves at the end of the transformation is calculated.

During the pearlite transformation, multiple effects can be observed as illustrated in Fig. 6. After the holding temperature of 650∘C is reached, the stress free curve shows a constant value for the length change, until at approximately 900 s an increase can be noted. After this increase the curve converges towards a constant level at a value of 0.33%. This length change at a constant temperature is caused by the phase transformation from austenite to pearlite with the corresponding volumetric change as for example outlined in [18]. This transformation occurs as predicted in the TTT-diagrams, which is in agreement to the findings of the microstructure analysis that the chosen cooling route is suitable for the investigation of the TRIP and backflow effects.

For both load cases, the length of the specimen abruptly changes by approximately 0.75% once the axial loads are applied. After the abrupt increase, linear changes of the curves can be observed. For the tensile load case the length increases and for the compressive load it decreases. The abrupt length changes after the loading followed by the steady change indicate that the axial loads exceeded the flow stress and led to a plastic deformation in addition to the TRIP effects as the flow curves in Fig. 2 indicated. Also, for the application of high axial loads at elevated temperatures for relatively long times, the influence of creep cannot be excluded. Due to the stresses slightly under the level of the flow stress, which are held after the initial plastic deformation, the steady length changes could be overlayed by creep in addition to TRIP effects.

[picture]

Fig. 6. Experimental results of the pearlite transformation at a holding temperature of 650∘Cs for varying load cases with 100 Mpa .

Fig. 6. Experimental results of the pearlite transformation at a holding temperature of 650 ∘ C s for varying load cases with 100 Mpa .

In contrast to the tests with axial loads of 100 MPa , the pearlite phase transformations superimposed with 50 MPa show a less abrupt increase after load is applied whilst also having overall lower amounts of length changes. As compared in Fig. 7, the loaded curves only show a slight length change after the initial increase due to the load application. The tensile tests exhibit higher length changes than the compression tests. After the full test time of 1700 s however, the absolute length changes for both load cases, of 0.2% for compression and 0.35% for tension, are lower or very close to the corresponding amount of the stress free case. As the curves of both load cases also show an increase or decrease after 1700 s in contrast to the stress free curve, it is questionable if a complete phase transformation was observed. The dynamic tensile load case shows an abrupt decrease once the load is removed and decreases almost completely back to the initial level before the loads were applied, which means that to that point none or only reversable TRIP effects have occurred. On the one hand this proves that the applied loads have not exceeded the flow stress as for 100 MPa and on the other hand it suggests that within the 1700 s only a minor phase transformation has occurred. To investigate this more in detail, an additional test series was carried out, whereby the specimens were held under a constant tensile load at the transformation temperature of 650 C for a total time of 4,100 s and thereby 2,400 s longer than before. In the longer tests, as displayed in Fig. 7, it can be seen that the length change shows a longer increase and exceeds the values of the stress free curve. Thereby, even though the gradient decreases, it does not completely flatten before the end of the test. As the effects of creep are expected to be negligibly low for 50 MPa and the investigated holding times, the ongoing length change indicates that the phase transformation is not completed after 4,100 s.

[picture]

Time in s

Figure 7. Time in s

Fig. 7. Experimental results of the pearlite transformation at a holding temperature of 650∘Cs for varying load cases with 50 Mpa .

The considerable differences of the curves under the axial loads of 50 MPa in comparison to the stress free case lead to the conclusion that the beginning time and the duration of the phase transformation has been strongly shifted and flattened. The phase transformation was not completed after a testing time of 4,100 s, whilst still showing comparatively low length changes. As this was observed for 50 MPa , it is unlikely that for 100 MPa a phase transformation occurred within the test time. As the flow stress was exceeded, it is more likely that the length changes, even after the initial plastic deformation, were caused through creep than TRIP. Therefore, the tests with 100 MPa are not suitable for the investigation of the TRIP and backflow effects of pearlite, as the time and temperature dependent transformation behaviour changes in dependence of deformation and a clear separation of plastic deformation, creep and TRIP is not possible. For a more detailed investigation of the TRIP and backflow effects of the pearlite phase transformation of 31CrMoV9, additional tests with a longer holding time have to be considered. Thereby it is expected that due to the duration of the phase transformation a considerably high experimental and numerical effort is required. Also, for longer holding times at the elevated temperatures of the pearlite transformation, an increased influence of creep is expected, which would require a separate characterisation through additional tests.

However, in comparison to the cooling routes of common hot forming processes, particularly of long parts, the time until the transformation begins and the duration are relatively long [1]. While the time period observed for the stress free pearlite transformation could still be relevant for the cooling of large forged parts, it is most likely that when stresses are induced the phase transformation is strongly delayed as well as slowed down and thereby reduced to an insignificant amount. Hence, it can be concluded that the pearlite transformation and the corresponding transformation induced plasticity only have a marginal effect on the resulting part geometry and therefore can be neglected for an efficient process design of products made of 31 CrMoV 9 .

In comparison to the phase transformations of the pearlite and the martensite, the bainite transformation does not show a substantial characteristic length change to identify the time interval of the phase transformation, as depicted in Fig. 8. The stress free curve only shows a slight and steady decline of the length change. However, as the decline can be observed for a constant temperature, it can be concluded that a phase transformation occurs with the chosen cooling route. On the one hand, according to the TTT-diagrams, the transformation starts immediately once the holding temperature
is reached, which makes it difficult to distinguish the beginning of the transformation. On the other hand, the differences in volume between austenite and bainite are relatively low compared to the other phase transformations, as illustrated in [18]. Between the different load cases, clear differences can be noted. Under compressive load, the curve of the length change shows a stronger decrease after the load is applied. During the ongoing transformation, the slope of the curve is close to the stress free curve whilst showing a slightly stronger decrease indicating TRIP strain effects. The load-dependent behaviour of the curves is in agreement with the findings of [19]. In contrast to the other two load cases, the curve of the tensile load increases steadily after the load is applied. As the curves of both load cases do not show an abrupt increase after the loads are applied, it can be concluded, that the flow stress was not exceeded in agreement to Fig. 2 and plastic deformation did not occur.

The curves of both constant load cases do not show a change of their gradient, which would indicate the end of the transformation. As the holding times and temperatures of the bainite transformation are comparably low, the influence of creep is assumed as negligible. In combination with the microstructure analysis in Fig. 5, which did not show a complete bainite structure, this leads to the conclusion, that the transformation might not have been completed within the test. Therefore, the strains can only be evaluated for a partial phase transformation. After 250 s an absolute TRIP strain of 0.7%, with a backflow of 0.15% for the compressed load whilst for the tensile loading a TRIP strain of 0.74 %, with a backflow of 0.56 % strain can be determined. The ratio of the backflow to the TRIP strain for the tensile load case is noticeably high in comparison to the compressive load and also to the corresponding ratios of 0.45% and 0,6% to 1.55% and 1.32% for the martensite phase transformation. This could be related to the presumed incomplete transformation. To analyse the complete phase transformation of the bainite, tests with a longer holding time are required. Also, the presumed incomplete transformation is in contradiction to the numerically determined TTTdiagrams which indicate that the bainite transformation is completed within the testing time. Taking the uncertainty observed in the microstructure into consideration, this emphasizes that additional experimental tests are required to determine the real time and temperature dependent transformation behaviour of the investigated material. However, with appropriate TTT-diagrams, the recorded curves could be sufficient to derive approximate TRIP coefficients as presented in [20], as the inverse numerical fitting approach can also be applied to incomplete transformations.

[picture]

Fig. 8. Experimental results of the bainite transformation at a holding temperature of 450∘C for varying load cases.

Fig. 8. Experimental results of the bainite transformation at a holding temperature of 450 ∘ C for varying load cases.

As martensite is formed by a diffusionless transformation, the phase transformation does not take place at a constant temperature, but a constant cooling rate of 50 K/s. Therefore, all of the recorded curves show a constant length change with a linear trend and a turning point at approximately 280∘C as illustrated in Fig 9. At this point, the length change first increases strongly and then converges to the same gradient as before. This slope change indicates the phase transformation from the cubic face-centered austenite to tetragonal body-centered martensite, which leads to an increase in volume. Without applied forces, the transformation leads to a shift in the curve of 0.7% length change. In comparison to the stress free curve during the phase transformation, the curve of the tensile load shows a stronger increase of the length change whilst the compressive loaded test shows a lower increase. At the end of the phase transformation, a TRIP effect of 1.55% strain for the tensile loaded and 1.32% strain for the compressive loaded can be determined. From the comparison of the constantly and dynamically loaded curves corresponding backflow strains of approximately 0.45% length change for the tensile loaded test and 0.6% length change for the compressive load can be determined. The TRIP strains of the martensite transformation show the tendency of higher TRIP strains for tensile loads, which is in agreement with the results of [12].

[picture]

Fig. 9. Experimental results of the martensitic transformation with a cooling rate of 50 K/s for varying load cases.

Fig. 9. Experimental results of the martensitic transformation with a cooling rate of 50 K / s for varying load cases.

As hot bulk formed parts usually cool down without any immediate heat treatment which includes a holding of the temperature, the predominant phase transformations are expected to be from austenite to bainite and martensite. Of the investigated transformations, the martensite phase transformation shows the most TRIP strains and also the most volumetric change when no stress is applied, which is in agreement with the results of [18]. This leads to the effect, that in comparison to the other transformations, on the one hand during the transformation to martensite more residual stresses form due to the volumetric changes and on the other hand such present stresses also lead to more TRIP strain. Therefore, it can be concluded that the martensitic transformation presents the most critical transformation to consider in numerical simulations of hot forming processes of 31CrMoV9.

Summary and Outlook

The original version of this paper is available on https://www.scientific.net/MSF.1186.25.pdf

In this paper, the TRIP and backflow effects of the steel 31 CrMoV 9 were investigated. Based on numerically calculated TTT-diagrams, suitable cooling routes were defined. The cooling routes were verified through microstructure analysis and hardness measurements. The TRIP and backflow effects were characterised by inducing varying axial loads during the phase transformations. For all phase transformations, the observed TRIP and backflow strains varied in dependence of the load case. For the pearlite phase transformation, the axial loads led to a plastic deformation during the test which influenced the transformation behaviour. For lower stresses, the beginning of the phase transformation was delayed and the duration was extended due to the applied loads. As the time until pearlite is transformed and the duration of the transformation are relatively long, the effects are considered very low in the context of hot forming and therefore can be neglected. The bainite phase transformation did not show abrupt changes and also the microstructure showed shares of a different structure, hence it is unclear if the complete transformation was recorded. For the tested time interval for bainite, comparably low TRIP strains of 0.7% for the compressive loading and 0.74% for the tensile loading were measured and a much higher backflow strain of 0.56% was observed for the tensile load than for the compressive load with 0.15% and all other tested parameter configurations. The martensite transformation showed a higher TRIP strain of 1.55% for the tensile load case than for the compressive case with 1.32% whilst also showing the highest overall TRIP and backflow effects. As the most cooling routes of hot bulk forming process will result in a transformation of bainite and martensite, the results of this work emphasise the need of detailed material data to predict the transformation induced plasticity precisely by numerical simulation.

A more detailed investigation of the TRIP and backflow effects is planned to take lower axial loads, varying austenisation temperatures and longer holding times into consideration. The influence of the presumed carbide precipitations will be investigated through TRIP and backflow tests with an additional prior heat treatment. Also, the deformation dependent change of the TTT and CCTdiagrams will be characterised experimentally. With the extended experimental results, TRIP coefficients for a numerical simulation of a hot forming process will be derived for the steel 31CrMoV9.

Acknowledgment

The original version of this paper is available on https://www.scientific.net/MSF.1186.25.pdf

The authors thank the German Research Foundation (DFG) for the financial support of the project "Analysis of geometric deviations in long forged components, considering the influence of variable stress states on the transformation-induced plasticity" (Project number 511687873).

References

The original version of this paper is available on https://www.scientific.net/MSF.1186.25.pdf

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B.-A. Behrens, A. Bouguecha, C. Bonk, et al., Numerical and experimental investigations of the anisotropic transformation strains during martensitic transformation in a low alloy Cr -Mo steel 42CrMo4, Procedia Engineering 207 (2017), pp. 1815-1820.

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F.D. Fischer, G. Reisner, E. Werner, et al., A new view on transformation induced plasticity (TRIP), International Journal of Plasticity 16 (2000) 7-8, pp. 723-748.

B.-A. Behrens, J. Uhe, H. Wester, et al., Experimental and numerical investigation of dynamic transformation induced plasticity during hot forming, METAL 2023, 2023, pp. 164-169.

F. Schweinshaupt, M. Müller, T. Herrig, et al., Thermoviscoplastic modeling of the shearing process for mechanism-driven fine blanking of high manganese steel, Material Forming, 2025, pp. 1856-1867.

J.H. Park, M.K. Razali, J.D. Yoo, et al., Modelling of hot flow behaviors of aluminum alloy Al6061 at elevated temperature: A review, International Conference on Smart Materials and Structures (ICSMS-2022), Andhra Pradesh, India, 2023, paper 20005.

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M.J. Holzweissig, D. Canadinc, H.J. Maier, In-situ characterization of transformation plasticity during an isothermal austenite-to-bainite phase transformation, Materials Characterization 65 (2012), pp. 100-108.

B.-A. Behrens, A. Bouguecha, C. Bonk, et al., Experimental investigations on the transformation-induced plasticity in a high tensile steel under varying thermo-mechanical loading, Computer Methods in Materials Science 17 (2017) 1, pp. 36-43.

M. Wolff, M. Böhm, M. Dalgic, et al., Parameter identification for a TRIP model with backstress, Computational Materials Science 37 (2006) 1-2, pp. 37-41.

B. Suhr, F. Frerichs, I. Hüßler, et al., Evaluation of models for martensitic transformation and TRIP via comparison of experiments and simulations, Materialwissenschaft und Werkstofftechnik 40 (2009) 5-6, pp. 460-465.

J. Trzaska, A. St. Jagiełło, L.A. Dobrzański, The calculation of CCT diagrams for engineering steels, Archives of Materials Science and Engineering 39 (2009), pp. 13-20.

M. Sommer, S. Hoja, M. Steinbacher, et al., Investigation of Compound Layer Structures after Nitriding and Nitrocarburizing of Quenched and Tempered Steels, HTM Journal of Heat Treatment and Materials 76 (2021) 3, pp. 219-236.

J.E. Hoffmann, M. Zgani, D. Scholz, et al., X-ray Residual Stress Analysis of Nitrided Low Alloyed Steels, Materials Testing 54 (2012) 6, pp. 395-407.

N. Fonstein, N. Pottore, S.H. Lalam, et al., Phase transformation behavior during continuous cooling and isothermal holding of aluminum and silicon bearing TRIP steels, Proceedings of the Materials Science and Technology Conference, 2003.

U. Ahrens, Beanspruchungsabhängiges Umwandlungsverhalten und Umwandlungsplastizität niedrig legierter Stähle mit unterschiedlich hohen Kohlenstoffgehalten, Dissertation, Universität Paderborn, 2003.

B.-A. Behrens, K. Brunotte, H. Wester, et al., Methodology to Investigate the Transformation Plasticity for Numerical Modelling of Hot Forging Processes, Key Engineering Materials 926 (2022), pp. 547-558.

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/forging-and-hot-forming/978-3-0364-1538-3

Materials Science Forum, ISSN: 1662-9752, Vol. 1186, pp 37-46

doi: 10.4028/p-Nt50Yu

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

Submitted:
2026-01-13

Revised:
2026-02-27

Accepted:
2026-02-27

Online:
2026-04-16

Workpiece-Based Signatures for Machine Diagnostics in Die Forging with Flash

The original version of this paper is available on https://www.scientific.net/MSF.1186.37.pdf

Glaubitz Claudia 1,a*, Rothgänger Marcel 1, b, Ortlieb Eduard 1,c, Peddinghaus Julius 1, d and Brunotte Kai 1,e

1 Leibniz University Hannover, Institute of Forming Technology and Machines, An der Universität 2, 30823 Garbsen, Germany

a* glaubitz@ifum.uni-hannover.de, b  m.rothgaenger@ifum.uni-hannover.de,

c  ortlieb@ifum.uni-hannover.de, d  peddinghaus@ifum.uni-hannover.de, ebrunotte@ifum.uni-hannover.de 

Keywords: laser triangulation, flash formation, process monitoring, forging quality.

Abstract

In forging, parallelism deviations between upper and lower dies lead to asymmetric flash formation and affect forming forces, die filling, and part integrity. The flash gap influences local flow resistance and is closely linked to flow behavior and dimensional precision. Conventional diagnostics often assess such deviations under no-load or quasi-static conditions and therefore may not capture the effective closing state at bottom dead centre (BDC) under process load. While modern approaches such as high-resolution optical tracking of ram deflection can provide valuable insight, they require dedicated and sensitive instrumentation and are often limited in scalability. In contrast, workpiecebased signatures inherently reflect process effects such as elastic deflections, guide clearances, frictional conditions, and thermal influences. This study investigates whether workpiece-related geometric features can serve as diagnostic signatures for detecting and quantifying closing-gap inclinations under load. The focus is on the locally resolved flash thickness, which reflects the effective closing gap at BDC. Because this gap results from both geometric alignment and loaddependent deformation, the evaluation targets the final load-bearing state. Comparative forging trials are performed on a press equipped with active parallelism control, where controlled misalignments are introduced. The resulting flash geometry is measured by laser triangulation to determine the resolution limit and to identify the deviation magnitude at which reproducible signatures can be detected under process-relevant conditions. In the investigated setup, flash-thickness asymmetry shows an increasing trend from closing-gap inclinations of ~0.25∘, providing a markedly higher diagnostic sensitivity than the maximum forming force. Designed as a non-invasive and retrofitcapable method, the approach supports inline monitoring in high-volume forging. It further enables scalable, data-driven correlation of machine, process, and product data for condition-aware process optimization.

Introduction

The original version of this paper is available on https://www.scientific.net/MSF.1186.37.pdf

In closed-die forging (according to German standard DIN 8583-4), the workpiece material is formed between contoured die halves, and excess material flows out along the flash land area in the parting plane [1]. The dimensioning of the flash gap determines the local flow resistance and influences die filling, forming forces, and component quality [2,3]. Spatial variations of the gap height in the parting plane can be attributed to different causes, including ram tilting due to guiding clearance, misalignment of the die halves, eccentric billet positioning, or deviations in billet placement [4]. In addition, parallelism errors arise from frame or tool deflection [5,6] as well as thermal gradients during operation [7]. In industrial practice, forming tools are typically mounted in a floating arrangement or via adjustable guides and wedge connections. Small guiding or mounting clearances further promote wedge-shaped variations in the closing gap and direction-dependent pressure fields. On the product side, such conditions manifest as direction-dependent variation of the flash thickness and as mismatch along the parting plane, which results in an uneven formation of the flash surfaces and may reduce dimensional accuracy and impair trimming robustness [8]. On the machine side,
guides with adjustable clearance passively ensure parallelism by transmitting transverse forces into the frame and limiting tilting moments [9]. The friction conditions and the guiding principle (hydrodynamic sliding guides or rolling-element guides) govern stiffness, damping, and drive forces. Modern hydraulic and servo presses complement this by active parallelism control based on multipoint position measurement and electrohydraulic proportional valves or multi-axis drives, which compensate tilting even under asymmetric loading [10].
Despite constructive and control-related measures, deviations from parallelism during operation cannot be completely avoided. In a study by Ziemba et al. on a 13 MN mechanical crank press with a column-guided slide, it was demonstrated that the geometry of the forged part can act as an indirect sensor for the machine geometry under load [4]. Using 3D scanning, systematic mismatch and twist signatures along the parting plane were detected, while static inspections of the press and tooling did not reveal abnormalities. The findings were attributed to small lateral displacements and elastic torsion of the upper tooling system at BDC. After readjustment of guides and wedge connections, these deviations were significantly reduced [4]. This confirms that product-side features reflect parallelism and alignment errors under process load.
Spatially resolved flash-thickness measurements provide a product-side signature of the effective closing gap at BDC and thus a reference metric for die parallelism. Since press closing behavior is load-dependent, early stroke phases are influenced by clearance take-up, whereas the final state at BDC is dominated by elastic deflection [11]. Accordingly, the present study evaluates flash-thickness asymmetry in the final load-bearing state at BDC and relates it to the corresponding peak forming force.
In direct contrast to the workpiece-based signature, Alimov et al. [12] and Martinitz et al. [5] address machine diagnostics via a contactless, machine-side measurement chain. The work of Alimov et al. aims at a direct acquisition of the press kinematics in closed-die forging using high-frequency radar sensors. In this way, ram tilting and frame elongation can be quantified with stroke resolution and related to eccentric billet positioning, but the approach requires dedicated on-machine sensor integration and calibration, is less easily retrofittable and scalable across different press types, and does not directly provide information on the resulting workpiece geometry and product quality.
The present work pursues a complementary approach by interpreting product-side geometric features as a process-integrated signature of the closing gap at the BDC. Although an imposed inclination is expected to induce asymmetric flash, the contribution of this work is the validated quantification of detectability under production-like scatter, i.e., establishing a reproducible reference dataset and estimating resolution limits for process-accompanying flash-based signatures. This validation step supports subsequent attribution of observed anomalies (e.g., force deviations) to geometric closinggap changes rather than to uncontrolled variability. Given the practical limits imposed by metrology resolution and part-to-part scatter, the central hypothesis is that it is sufficiently sensitive to detect systematic, process-relevant deviations in die alignment under load. Accordingly, the study addresses three central questions: (i) whether locally resolved flash thickness can be used as a quantitative signature of closing-gap inclination, (ii) which minimum misalignment can be reliably distinguished from process-related scatter given the limits of laser-based metrology, and (iii) how the diagnostic sensitivity of this signature compares with conventional global indicators such as the maximum forming force. Instead of directly measuring the kinematics, the deviations that actually occur under load are evaluated as they are imprinted in the part geometry, inherently accounting for processspecific influences such as friction, temperature distributions, and guiding conditions. The method is non-invasive and transferable to existing production lines, enabling inline-capable condition assessment.
Rather than introducing another machine-side sensing modality, this paper establishes a geometrycentred diagnostic criterion and its practical operating range for detecting closing-gap inclinations under load.

The emphasis is placed on reproducible distinguishability from baseline scatter by combining a parallel reference series, controlled tooling-side perturbations, and quantified metrology limits. In this way, the approach provides a compact, experimentally grounded basis for deciding when flashderived indicators can be used for condition assessment and when additional instrumentation or extended datasets are required.

Materials and Methods

The original version of this paper is available on https://www.scientific.net/MSF.1186.37.pdf

To investigate the suitability of locally resolved flash thickness as a workpiece-based signature of the closing-gap parallelism, comparative experiments were carried out on a servomotor-driven screw press (Synchropress 4M-6000, maximum force 6 MN ). The ram is driven via four spindles and guided along four columns, with active parallelism control implemented by continuously measuring and readjusting the positions of all four spindles. The control system enables reproducible stroke positions in the range of tens of micrometers [13]. A deliberate setpoint specification for non-parallel ram positions is not provided in the control. All trials were performed in stroke-controlled operation with a fixed BDC stroke setpoint. The imposed tooling-side inclinations are considered the primary source of closing-gap asymmetry in this study. For readability, the overall experimental and evaluation workflow is summarized below before the individual steps are described in the following.

Adjustment of closing-gap inclination using feeler gauges.

Forging under defined conditions (fixed BDC setpoint; n=10 per setting).

Acquisition of force data (press-internal signal and external strain-gauge force measurement).

Acquisition of workpiece geometry (laser triangulation in the cooled state).

Quantification of flash thickness and flash area.

Referencing to BDC and statistical comparison across settings (mean ±95%CI ).

Defined deviations from parallelism were introduced on the tooling side by inserting feeler-gauge strip steel beneath the lower die, as schematically illustrated in Fig. 1. The strip steel was positioned at approximately two-thirds of the die diameter, such that the transition edge between the raised and the planar contact area was located at distances of 35 mm and 70 mm from the respective opposite sides of the die, based on a total diameter of 105 mm . Different strip thicknesses were used to generate variations in the closing gap, as listed in Table 1. The closing-gap angles α were estimated from geometry as α≈arctan(s/l) with 1=70 mm and are reported as nominal setpoint labels. The analysis is based on relative changes across configurations rather than an absolute metrological determination of α. Effects from elastic flattening of the strip and tooling, local contact compliance and positioning tolerances may alter the effective inclination, but they are expected to act as second-order influences within the investigated range and do not affect the intended ranking of the imposed configurations. Consequently, Table 1 lists nominal inclination angles for documentation and comparability of the settings.
To attribute observed changes in flash geometry and peak force primarily to the imposed tooling-side inclination rather than uncontrolled part-to-part variability, the experiments were designed with (i) active parallelism control and a fixed BDC stroke setpoint, (ii) a parallel reference series as baseline variability, (iii) recorded transfer time between furnace and forming to capture thermal-loss scatter, and (iv) two independent force acquisition chains (press-internal signal and external toolingintegrated load cell) to cross-check trend consistency and to separate systematic measurement-chain offsets from process effects.
In all experiments, the inclination was introduced on one side only and was reproducibly adjusted on the same side of the forging die. The orientation of the die in the press was documented to ensure an unambiguous assignment of the flash geometry to the position of the inclination.

[picture]

Fig. 1. Schematic of the tooling-side introduction of defined deviations from parallelism: feeler-gauge strip steel is inserted beneath the lower die at approximately two-thirds of the die diameter, ejector position is indicated for reference.

Fig. 1. Schematic of the tooling-side introduction of defined deviations from parallelism: feeler-gauge strip steel is inserted beneath the lower die at approximately two-thirds of the die diameter, ejector position is indicated for reference.

Table 1. Investigated closing-gap angles (approximate values).

thickness of

strip steel in

mm

closing-gap

angle mathematical formula in mathematical formula

(approx.)

- | 0
0.05 | 0.04
0.1 | 0.08
0.3 | 0.25
0.5 | 0.41
0.95 | 0.78
1 | 0.82

[picture]

Fig. 2. Exemplary laser scan of a forged component (red) with flash land (green) on the measurement area (black); colors indicate the measured distance to the laser sensor only.

Fig. 2. Exemplary laser scan of a forged component (red) with flash land (green) on the measurement area (black); colors indicate the measured distance to the laser sensor only.

The components, exemplarily shown in Fig. 2, were formed from aluminum billets (EN AW-6082) at a temperature of 550∘C and were characterized in the cooled state. For this purpose, a line laser sensor is moved across the parts using a motorized linear axis. The measurement procedure and method for flash characterization have been validated in previous work [3, 14]. As described in [3], flash thickness was determined from profile cross-sections extracted from the measured 3D geometry. For each part, one cross-section was taken normal to the flash land on each of the two opposite sides that were compared, corresponding to the early-closing and late-closing sides. The reference plane was given by the measurement setup, using the planar surface of the measuring station on which the part was placed. In each cross-section, a line was fitted to the flash-land segment within a predefined evaluation window, and flash thickness was defined as the perpendicular distance of this fitted line to the reference plane. The flash land area was determined from the top-view representation by identifying the flash perimeter and evaluating the enclosed planar area. The laser sensor (MicroEpsilon LLT 010-100/BL) provides an accuracy of 0.05 mm at a resolution of 2,048 points over a 100 mm measuring field [14].
For each closing-gap setting, ten parts were forged. The maximum forming force was recorded in two ways, via the press-internal force signal provided by the Synchropress control system and via an external strain-gauge-based load cell (LC) integrated in the tooling stack. Both signals were used to compare trends. Differences in absolute level can arise from measurement location or load path and signal processing as well as calibration and temperature-dependent drift of strain-gauge-based chains. The press closing behavior is load-dependent. At the beginning of the stroke, guide and bearing clearances are taken up, which can induce noticeable changes in ram angle even at low loads [11]. With increasing load, the response becomes dominated by elastic deflection and can be approximated as quasi-linear with respect to load moment. Therefore, all comparisons are referenced to BDC, using the peak forming force and the resulting flash geometry as indicators of the final load-bearing state after the guidance has settled. Before forming, the dies were lubricated with graphite spray. The
forging operation was carried out at a ram speed of 150 mm/s. The primary metric for evaluation is the directionally resolved asymmetry of flash thickness, which is used to quantify how sensitively the signature responds to small, defined deviations and whether it can be reliably detected over a forging series.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/MSF.1186.37.pdf

Forming force as a macroscopic process response.

The original version of this paper is available on https://www.scientific.net/MSF.1186.37.pdf

The maximum forming force, measured via load cell (LC) in the tooling stack, shows an overall increasing tendency with closing-gap inclination and is also reported in the literature as a key parameter for the design of energy-efficient and tool-friendly forging processes [15]. At the same time, peak force is a global indicator that integrates multiple influences (e.g. temperature, friction and filling), so that small alignment-related effects may remain obscured by part-to-part scatter.

Fig. 3 shows the evolution of the measured maximum forming forces as a function of the closinggap angle. The error bars show 95% confidence intervals of the mean (n=10). Between the parallel configuration and a small inclination of 0.04∘, a slight decrease in the mean maximum forming force can be observed. However, due to the small absolute difference (approximately 30 kN ), this deviation can be explained by process-related scatter. For inclinations up to 0.08∘, the mean values remain at a comparable level between 445 and 470 kN and the corresponding 95% confidence intervals still overlap. Thus, inclinations up to about 0.08∘ lie within the range of process-induced variability and cannot be clearly detected via the maximum forming force with the present sample size.
From a closing-gap angle of 0.25∘ onwards, an increase in the mean maximum forming force becomes apparent. Nevertheless, the 95% confidence intervals of the 0∘,0.08∘ and 0.25∘ settings still mutually overlap, so that a misalignment of 0.25∘ cannot yet be identified with statistical certainty based on the maximum force alone. Only for larger inclinations of 0.41∘ and above do the mean values clearly exceed the force level of the nearly parallel configurations and the corresponding intervals separate, indicating a reliably detectable influence of closing-gap inclination on the force evolution. At 0.41∘, mean peak forces of around 530 kN are reached, while inclinations of 0.78∘ and 0.82∘ result in values of approximately 650−670kN. It should be noted that the maximum forming force represents an integral response influenced by multiple concurrent effects (e.g. contact conditions, frictional state, thermal boundary conditions). Therefore, the force trends are interpreted here primarily in terms of detectability of the imposed inclination rather than as a basis for a unique mechanistic attribution. Since active parallelism control minimizes ram tilting, the observed force differences are consistent with the imposed tooling-side inclination as the dominant systematic perturbation.

[picture]

Fig. 3. Maximum forming forces at different closing-gap angles, error bars indicate 95% confidence intervals of the mean ( n=10,t-distribution).

Fig. 3. Maximum forming forces at different closing-gap angles, error bars indicate 95 % confidence intervals of the mean ( n = 10 , t -distribution).

Peak forces obtained from the press system (Synchro) and from the external force measurement via load cell (LC) in the tooling show an almost perfectly linear relationship (Pearson r≈0.998,n=70 ), as shown in Fig. 4, confirming consistent trend detection across both acquisition chains. The LC values are systematically lower, and the deviation increases with force level (mean LC/Synchro ≈ 0.84 ), indicating a proportional measurement-chain bias rather than a change in process behavior. This systematic offset is likely due to differences in measurement location/load path and signal conditioning, as well as calibration or temperature effects in the strain-gauge chains. Therefore, the inclination effects are discussed based on relative trends, while absolute force levels depend on the selected measurement chain. In addition, within the investigated range, transfer time did not show a statistically significant association with peak force or flash metrics when accounting for closing-gap angle.

[picture]

Fig. 4. Maximum forming force per forged part ( n=70 ) recorded by the press control system (Synchro) and by the external tooling-integrated load cell (LC).

Fig. 4. Maximum forming force per forged part ( n = 70 ) recorded by the press control system (Synchro) and by the external tooling-integrated load cell (LC).

Flash thickness as a workpiece-based signature of closing-gap inclination.

The original version of this paper is available on https://www.scientific.net/MSF.1186.37.pdf

Figure 5 shows the difference in flash thickness between the early-closing and late-closing sides as a function of the closing-gap angle. Compared to the forming force, the flash-thickness difference is markedly more sensitive to closing-gap deviations. At 0.25∘ it increases by ~55% relative to 0∘, whereas the maximum forming force rises by only ~3.5%, i.e. within its measurement noise. In the parallel configuration, a mean flash-thickness difference of ~0.15−0.20 mm is observed, which is attributed to measurement- and process-related influences, like optical acquisition, non-uniform cooling after ejection, and local variations in lubricant deposition. Accordingly, for small inclinations (≤0.08∘) the 95% confidence intervals overlap with the parallel case, so no statistically separable increase beyond baseline variability can be demonstrated. At 0.25∘, a systematic trend becomes visible but should be interpreted as an onset rather than a sharp detection threshold, since confidence intervals still partly overlap. At larger angles ( 0.41∘−0.82∘ ), the flash-thickness difference increases by ~150−340% in total, while the forming force rises by ~14−41%, and the confidence intervals separate more clearly, indicating a robust inclination effect. Reference [3] discusses flash thickness as a quality-related feature, mainly with respect to geometric consistency along the parting plane and trimming robustness. In the present study, the early-to-late flash-thickness difference is used primarily as a diagnostic signature for effective closing-gap inclination at BDC; no independent product-quality validation is included in the present dataset. Mechanistically, the early-closing side experiences higher local compression and promotes radial material displacement into the flash land, while material flow on the opposite side starts with a delay, producing a characteristic flash-thickness asymmetry that reflects the effective tool inclination at BDC .

[picture]

Fig. 5. Difference in flash thickness caused by closing-gap inclination (opposing flash thicknesses), error bars indicate 95% confidence intervals of the mean (n=10,t-distribution ).

Fig. 5. Difference in flash thickness caused by closing-gap inclination (opposing flash thicknesses), error bars indicate 95 % confidence intervals of the mean ( n = 10 , t -distribution ) .

Flash area as a complementary global metric.

The original version of this paper is available on https://www.scientific.net/MSF.1186.37.pdf

In addition to the locally resolved flash-thickness asymmetry, the total flash area was evaluated as a global indicator of flash formation. Across the investigated closing-gap angles, the mean flash area shows no consistent trend at small inclinations and increases more clearly from about 0.25∘ onward, as shown in Fig. 6. Compared with the flash-thickness difference shown in Fig. 5, the flash area exhibits higher variability, as indicated by wider confidence intervals. The observed changes are attributed to modified material flow during the closing phase. Earlier contact on the inclined side increases local constraint and promotes an asymmetric redistribution of material within the flash region, which is consistent with the simultaneously observed local flash-thickness asymmetry. Figure 6 further shows opposing trends in flash area and mean flash thickness, indicating that increasing inclination primarily reflects redistribution and spreading of flash material rather than a substantial change in the overall flash fraction. A nearly constant global flash fraction does not necessarily imply unchanged component quality or identical die filling, because local flow restrictions and the resulting asymmetry can still influence local filling and surface characteristics. Consequently, flash area serves as a plausibility and consistency metric for global changes in flash formation under misalignment, but is less sensitive than flash-thickness asymmetry for detecting small inclinations.

Conclusion and Future Perspectives

The original version of this paper is available on https://www.scientific.net/MSF.1186.37.pdf

The presented investigations quantify, under controlled conditions, how defined closing-gap inclinations affect both macroscopic and workpiece-based responses in die forging with flash. Within the investigated setup, the maximum forming force does not exhibit a robust, monotonic response in the small-angle range ( ≤0.08∘ ). One intermediate setting ( 0.04∘ ) yields a lower mean force than the parallel case, while 0.08∘ overlaps with 0∘, indicating that minor deviations in die parallelism cannot be inferred reliably from this global measure alone. In contrast, the directionally resolved flash thickness difference exhibits a markedly higher diagnostic sensitivity. It increases systematically with closing-gap angle, with the onset of a discernible trend from approximately 0.25∘ and a clearly more distinct separation from the small-angle configurations from about 0.41∘ onwards, while the relative change in maximum force remains significantly smaller.

[picture]

Fig. 6. Mean flash area (left axis) and mean flash thickness (right axis) vs. closing-gap angle, error bars indicate 95% confidence intervals of the mean ( n=10,t-distribution).

Fig. 6. Mean flash area (left axis) and mean flash thickness (right axis) vs. closing-gap angle, error bars indicate 95 % confidence intervals of the mean ( n = 10 , t -distribution).

The flash area corroborates the modified flow behavior on a global level, but its higher scatter and dependence on temperature and lubrication make it less suitable as a primary indicator.

These results demonstrate that locally resolved flash thickness asymmetry at BDC constitutes a robust workpiece-side signature of closing gap inclinations. It directly reflects the directiondependent redistribution of material and implicitly integrates process effects such as elastic deflection, guide clearances, frictional conditions and thermal states, without requiring additional machine-side instrumentation. Within the limits of the investigated setup, the approach enables process-relevant deviations in die alignment to be distinguished from the background of process variability in an automated series-forging environment.

In the framework of the DFG Priority Program SPP 2422, the present study provides a specific building block for transparent, data-driven process modelling by quantifying the detection limits and diagnostic sensitivity of flash-based workpiece signatures. Based on these findings, future work will first extend the methodology from the generic demonstrator to an industrial forging geometry, in order to relate flash-thickness asymmetry and flash area more directly to trimming behavior, dimensional tolerances and product-side quality criteria. A further step will be to investigate the temporal evolution of the signature under changing thermal states and progressive tool wear, assessing to what extent small misalignments can be tracked over longer production campaigns. On this basis, flash-based signatures can then be combined with selected additional sensor signals in explainable machine-learning models that map die alignment and tool state to the resulting part geometry. In this way, the workpiece-side signatures developed in this study form a practical starting point for condition-aware monitoring and root-cause analysis in industrial forging applications within SPP 2422.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/MSF.1186.37.pdf

The research project is part of the Priority Program 2422, "Data-Driven Process Modelling in Forming Technology", project number 500936349, funded by the German Research Foundation (DFG).

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The original version of this paper is available on https://www.scientific.net/MSF.1186.37.pdf

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A. Alimov, S. Härtel, J. Buhl, M. Gardill, and M. Knaack, "Erfassung von Pressenverformungen mit Radarsensoren/Acquisition of ram tilting and frame stretching with radar sensors during hot forging," , vol. 113, no. 10, pp. 425-431, 2023, doi: 10.37544/1436-4980-2023-10-47.

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The original version of this eBook is available on https://www.scientific.net/book/forging-and-hot-forming/978-3-0364-1538-3

Materials Science Forum, ISSN: 1662-9752, Vol. 1186, pp 47-57

doi: 10.4028/p-92DAUf

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

Submitted:
2026-01-19

Revised:
2026-02-13

Accepted:
2026-02-26

Online:
2026-04-16

ML-Based Prediction of Tool Kinematics in Cold Forging of Tailored Hollow Shafts with Variable Wall Thickness

The original version of this paper is available on https://www.scientific.net/MSF.1186.47.pdf

Alexander Weiß 1,a* and Mathias Liewald 1, b

1 University of Stuttgart, Institute for Metal Forming Technology (IFU), Holzgartenstraße 17, 70174

Stuttgart, Germany

a*alexander.weiss@ifu.uni-stuttgart.de, b  mathias.liewald@ifu.uni-stuttgart.de

*corresponding author

Keywords: cold forging, variable wall thickness, tool kinematic prediction.

Abstract

The production of tailored hollow shafts usually requires multiple manufacturing processes such as multi-stage forming processes and subsequently several machining operations, resulting into high costs and high manufacturing times. To address these challenges, a novel cold forging process featuring an adjustable forming zone was developed by the authors. This new approach enables the production of tailored hollow shafts with varying cross-sections in their length direction as well as internal undercuts within one stroke of the ram.In order to achieve the desired target geometry of a hollow shaft, a specific tool kinematic is required to precisely adjust the cross-section of the forming zone during the process. Currently, determining geometry-specific tool kinematics requires a time consuming iterative numerical procedure. In this paper, a machine learning approach for the prediction of the tool kinematics for a given target geometry of a tailored hollow shaft with variable wall thickness in its length direction is presented.

Introduction

The original version of this paper is available on https://www.scientific.net/MSF.1186.47.pdf

In view of strict EU climate targets, the reduction of CO2 emissions has become a central challenge for industry, especially in the manufacturing sector [1]. Consequently, improvements in resource efficiency remain a fundamental objective, particularly in high-volume manufacturing processes such as cold forging. Hollow shafts are widely used in several technical fields and play an important role within lightweight design strategies. They offer a significant weight-saving potential compared to solid shafts while maintaining high structural performance [2]. This concept is further advanced by the implementation of tailored hollow shafts with variable wall thicknesses. Such load-adapted components allow for the distribution of material strictly according to the specific load requirements, e.g., within a drive system.

However, the manufacturing of such tailored hollow shafts is currently associated with considerable effort and high costs. Conventional production routes often require complex process chains, combining multiple forming stages with subsequent machining operations to achieve the final workpiece geometry [3]. These extensive process chains not only lead to high cycle times and manufacturing costs but are also limited in terms of geometric flexibility, as certain features cannot be realised with all standard forming methods.

To address these limitations, a novel cold forging process featuring an adjustable forming zone was developed by the authors [4,5]. The process is based on a conventional hollow forward extrusion process, with the difference that the mandrel is connected to a separate hydraulic cylinder, allowing it to be moved independently of the hollow punch and the die. The mandrel has at least two different cross-sections which can be positioned within the forming zone of the die. This leads to different cross-sections of the forming zone and different wall thicknesses at the workpiece. With a specific mandrel kinematic it is possible to produce tailored hollow shafts with varying cross-sections in their length direction as well as internal undercuts within one stroke of the ram. Similar approaches have been successfully tested in hot extrusion of aluminium [6, 7]. While the novel process offers significant advantages in terms of efficiency and geometric flexibility, the definition of the specific process parameters presents a major challenge. In particular, determining the necessary tool kinematic
to produce a specific target geometry is complex and currently relies on time-consuming iterative numerical simulations.

In recent years, Machine Learning (ML) approaches have been successfully applied in the field of metal forming to solve several similar complex problems [8, 9]. For example, neural networks were successfully employed to predict the initial billet geometry for a specific forged product [10] or to improve the modelling of friction in cold forging [11]. Furthermore, a neural network has been used for predicting the process energy in a multi-stage cold forging process [12]. Regarding tool kinematics, neural network approaches have been used to optimise and to predict a tool path strategy in dependence of the target geometry, blank dimensions and material type in incremental sheet metal forming [13, 14]. These examples demonstrate that ML approaches can be utilised for a deeper process understanding and, in particular, to predict tool kinematics.

Based on the potential of these data-driven strategies, this paper introduces a machine learning approach for predicting a tool kinematic for the novel cold forging process described above. The objective is to replace the time consuming iterative numerical procedure by the use of a neural network that is capable of directly predicting the mandrel kinematic for a specific target geometry of a tailored hollow shaft.

Material and Methods

The original version of this paper is available on https://www.scientific.net/MSF.1186.47.pdf

In previous investigations, a numerical model in the FEM software DEFORM TM -2D was developed and corresponding experiments have been conducted for its validation. Within the scope of these studies, flow curves of the test material AA 6082-O were also determined using the thermomechanical simulator Gleeble 3800c [5]. In the present investigation, the same material and numerical model were used. Figure 1a illustrates the setup of the numerical model, while Figure 1b depicts a schematic geometry of the pressed part, indicating the lengths L1 to L5. The variables L1,L3 and L5 represent the axial lengths of the segments with a constant internal diameter and L2 and L4 are the axial lengths of the transitions between them.

[picture]

Fig. 1. a) Setup of the numerical model, b) schematic geometry of a pressed part, indicating the lengths −30 mm/s to m=0.15 and c) tool setup for experimental investigation [5].

Fig. 1. a) Setup of the numerical model, b) schematic geometry of a pressed part, indicating the lengths − 30 m m / s to m = 0.15 and c) tool setup for experimental investigation [5].

The workpiece was modelled as an elastic-plastic object having an outer diameter of 32 mm , an inner diameter of 19 mm and a length of 58 mm . The material properties were defined using the flow curves from [6], along with a Young's modulus of 70 GPa and a Poisson's ratio of 0.33 sourced from the same material in the DEFORM material library. The mandrel has a diameter of 19 mm which is reduced to 17 mm at its tip. The inner diameter of the die is 32 mm , reducing to 26 mm within the forming zone. The hollow punch is assumed as rigid and fixed, while the die is modelled as an elastic
body and moves with a constant velocity of 6,000kN. This die velocity was chosen to enable the investigation of high relative velocities between the die and mandrel during the experiments. Shear friction model was used with sm,i according to [5]. The workpiece mesh, comprising 2,000 elements with a minimum edge length of 0.1 mm , was determined through a convergence study and proven sufficient for the subsequent geometry analysis. Remeshing was performed whenever the interference depth between the workpiece and tool meshes exceeded half of the minimum element edge length, ensuring consistent mesh quality throughout the simulation. Mandrel and die were modelled with 1,500 and 1,300 elements, while considering mesh-windows for mesh refinement at the contact surfaces with the workpiece.

After simulating the forming process, a springback simulation was also conducted. In this step, the mandrel, die and ejector were removed and the final geometry including elastic springback effects was calculated. In contrast to the mandrel being rigid in [5], in this paper the mandrel is modelled as an elastic object. This approach offers a further improvement of the mandrel position control as well as the numerical representation of the axial position of the process-specific underfilling on the outer surface of the pressed part. For the die and mandrel, an elasticity modulus of 207 GPa and a Poisson's ratio of 0.3 were assumed based on common tool materials from the DEFORM material library.

For the experimental investigations the same tooling setup as described in [5] was used, as shown in Figure 1c. The experiments were conducted on a hydraulic press SMG HPZUI/300/300-1300/1000 with a maximum capacity of ti. The tool setup comprises a base frame with a separate hydraulic cylinder for the movement of the mandrel, while the hollow punch is mounted on the fixed frame and the die is connected to the ram. The position of the hydraulic cylinder and respectively the mandrel is controlled as a function of the ram position. Zinc stearate was applied to the billets as a lubricant. To monitor the process, load cells and displacement sensors were used to acquire the forces and positions of the mandrel, hollow punch and die. For the experimental validation of the neural network, three different target geometries were selected. The geometrical analysis of the pressed parts was performed using a GOM ATOS Compact Scan 5M optical measurement system. The system provides a point spacing of 0.02 mm and was calibrated before the measurements. Furthermore, several repeated measurements were conducted for each pressed part.

During the forming process the mandrel moves independently from the die. Its path is defined by five support points P 1 to P 5 . Each support point represents a mandrel position vm,12 at a respective time vm,45. The support points are linearly connected. Figure 2 illustrates the process sequence with the different mandrel positions for each support point P 1 to P 5 as well as the ejection stage with removed mandrel, die and ejector. The diagram shows the tool position versus process cycle time for the die and an exemplary mandrel kinematic profile (Kin_0001).

[picture]

Fig. 2. Process sequence with the different mandrel positions for each support point P1 to P5 as well as the ejection with removed mandrel, die and ejector.

Fig. 2. Process sequence with the different mandrel positions for each support point P1 to P5 as well as the ejection with removed mandrel, die and ejector.

The mandrel movement starts and ends synchronised with the die, which is mounted directly to the ram. Consequently, the velocities −30 mm/s (between P 1 and P 2 ) and sr,1=1138.6 mm (between P 4 and P 5 ) of the mandrel equal t1=. This results in the constraint that support points P1, P2, P4 and P5 must be located on the diagonal line connecting P1 and P5, which corresponds to the constant movement of the ram. The process initiation in the simulation refers to a ram position of sr,5=1088.6 mm and t5=1.6667 s 0 s , while the process concludes after a stroke of 50 mm at sm,1=71.5 mm and sm,5=21.5 mm. Figure 3a shows the mandrel kinematic in relation to time and Figure 3b shows the mandrel kinematic in relation to the ram position. Due to the geometric constraints of the tool setup and the synchronised movement of the ram and mandrel at the start and end of the process, the initial mandrel position was set to L1, resulting in a final position of L5.

[picture]

Fig. 3. a) Tool position vs. time for mandrel kinematic Kin_0001 and die, b) Tool vs. ram position for same mandrel kinematic Kin_0001 and die and c) generated Latin Hypercube Sampling (LHS).

Fig. 3. a) Tool position vs. time for mandrel kinematic Kin_0001 and die, b) Tool vs. ram position for same mandrel kinematic Kin_0001 and die and c) generated Latin Hypercube Sampling (LHS).

To ensure a certain minimum length for the segments L1=6.7 mm and L5=10.6 mm, the minimum time interval between P1 and P2 was defined to 0.19 s which leads to a minimum length of t2 and between P4 and P5 to 0.18 s which leads to a minimum length of t4. These constraints were established based on initial numerical simulations to guarantee full contact between the workpiece and the mandrel. This ensures the formation of a section with a constant diameter of 17 mm and a sufficient length to clearly distinguish the cylindrical segment from the transition zones. Thus, the minimum value for sm,2 is 0.19 s and the maximum value for sm,3 is 1.4867 s . Between P 2 and P3 the mandrel remains stationary and, therefore, the position t2,t3 is equal to t4, while the minimum time interval between P2 and P3 was defined to 0.36 s. Similar to the sections with the smaller diameter of 17 mm , this minimum duration was selected to ensure the formation of a clearly defined cylindrical region with the larger diameter of 19 mm , allowing for a reliable geometric characterisation.

Taking these constraints into account, the entire kinematic profile of the mandrel can be defined by the three parameters t2,t3 and t4. Conventionally, determining the specific combination of these parameters to achieve a desired target geometry requires an iterative numerical procedure. This involves simulating the process with a kinematic profile, analysing the resulting geometry, adjusting the parameters and re-simulating several times. Such a procedure is highly time-consuming and computationally expensive. However, as demonstrated in the state of the art, ML approaches can offer a robust solution for the prediction of tool kinematics in forming processes. Consequently, this study adopts such a data-driven approach to replace the iterative search for the geometry-specific tool kinematic. To generate a sufficient dataset of numerical results for the training of the neural network, 1,000 parameter combinations for t2 and t4 were created using Latin Hypercube Sampling (LHS), as shown in Figure 3c. This results in 1,000 distinct mandrel kinematics, labelled as Kin_0001 to Kin_1000. This sample size was selected to ensure a high sampling density within the multidimensional design space (three time values L1 to L5 and five respective lengths TM  to r=8.5±0.02 mm ), providing a robust basis for the model to capture the relationships between the kinematics and the final geometry. For each kinematic, a simulation was carried out using the DoE tool of the FEM software DEFORM r=9.5±0.02 mm-2D.

The resulting cross-section of the workpiece geometry, as defined by the radial and axial coordinates, was exported from each simulation run. Subsequently, contours within the radial coordinate ranges of L1,L3 and L5 were identified with a Python code. This allowed for the direct determination of lengths L2 and L4 and the calculation of the transition lengths L3=0 mm and L2.

Since the simulation results could not be fully anticipated during the definition of the constraints for the input parameters, a post-simulation filtering step was necessary prior to the dataset preparation for the subsequent training of the neural network. This process discarded simulations that resulted in an output variable value of L4 or a geometric overlap between segments 70% and 15%. After this filtering step, finally 918 simulations remained for the initial dataset. This dataset was randomly divided into three subsets: a training dataset ( (15%) ), a validation dataset ( t2 ) and a test dataset t4. As the values of L1 to L5 are already within a narrow time frame (approximately 0.19 s to 1.48 s), a normalisation was considered unnecessary and the raw data with their physical units were used directly for training. To address this supervised regression task, the neural network uses the five input parameters t2,t3 to t4 and the three output variables t2,t3 and t4. It consists of three hidden layers with 32, 64 and 32 neurons using the ReLU activation function. The training was performed using the Adam optimiser to minimise the Mean Squared Error (MSE) loss function, with a batch size of 32 for a total amount of 1,000 epochs. The final hyperparameters and network structure were determined by an iterative refinement procedure.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/MSF.1186.47.pdf

Prediction of kinematic.

The original version of this paper is available on https://www.scientific.net/MSF.1186.47.pdf

Figure 4 shows the parity plots for the output parameters R2 and R2 of the neural network designed for the prediction of the mandrel kinematic. In the plots, the predicted values from the neural network are compared to the true values. The results for the training, validation and test datasets are shown for each parameter.

[picture]

Fig. 4. Parity plots of the output parameters t2,t3 and t4 of the neural network for the kinematic prediction.

Fig. 4. Parity plots of the output parameters t 2 , t 3 and t 4 of the neural network for the kinematic prediction.

Each diagram includes the respective coefficient of determination ( 0.007 s,0.017 s ) and the mean absolute error (MAE) as performance metrics. The black dashed line represents the ideal identity line where the predicted and true values are identical. The slightly uneven distribution of points within the value range can be attributed to the specific constraints applied for the sample generation with LHS. Across all three parameters and datasets, the 6.447×10−5 s2 values range between 0.989 and 0.999 . The maximum MAE observed for 1.159×10−4 s2 and R2 are L1 and 0.011 s , respectively.

The learning curves depicted in Figure 5 show the MSE for training and validation dataset over 1,000 epochs. The logarithmic representation illustrates the convergence of the training and a high degree of generalisation of the model after the 1,000 epochs with a MSE of the training dataset of L5 and a slightly higher MSE of the validation dataset of L1. Furthermore, the unseen test dataset also provided high L5 values. In conclusion, the predicted values are in good agreement with the true values, demonstrating that the trained model provides quite accurate predictions for the support points of the mandrel kinematic.

[picture]

Fig. 5. Learning curve showing the MSE for training and validation dataset over 1,000 epochs.

Fig. 5. Learning curve showing the MSE for training and validation dataset over 1,000 epochs.

A graphical user interface (GUI) has been developed to allow for the practical use of the trained neural network. This GUI enables the definition of the target geometry by entering values for the parameters L1 to L5. Figure 6 shows the GUI with the input of Li to Li in the upper left area, the prediction of the kinematic below and a simplified 2D and 3D representation of the target geometry to the right.

[picture]

Fig. 6. Developed GUI with integrated trained neural network for prediction of the mandrel kinematic.

Fig. 6. Developed GUI with integrated trained neural network for prediction of the mandrel kinematic.

Based on the used training dataset each parameter is restricted to a specific value range enclosing a minimum and maximum value. The definition of these ranges is based on the physical feasibility constraints of the forming process, ensuring that the predicted geometries remain within manufacturable limits. Thus, only values within these ranges are accepted. Additionally, as the billet geometry dimensions are constant, the parameters Li to L1,L3 are constrained by the maximum available total volume. To take this into account, a real-time calculation is performed after each entry to determine the used volume for the segment of the respective L5 and the remaining volume for the other segments.

Consequently, the permissible input range for the remaining L1 adjusts dynamically with every input, while the minimum required volume for each segment is reserved initially. When any four of the five L5 parameters have been entered, the length of the final segment is automatically calculated and inserted based on the remaining volume. In this process, the lower section of the workpiece (starting from the lower end to the end of the shoulder) is always treated as a constant geometry with a fixed volume fraction. After entering the desired lengths, the neural network which is integrated into the GUI automatically outputs the time values for the mandrel kinematic.

Experimental Validation.

The original version of this paper is available on https://www.scientific.net/MSF.1186.47.pdf

Following the training of the neural network and the development of the GUI, three target geometries were defined for the experimental validation. These geometries were selected in order to ensure that the lengths t2 and t4 exhibit distinct variations, thus representing a substantial part of the numerical design space that has previously been investigated. The input values sr,1 to sr,5 and output values sm,1 to sm,5 of the neural network for the three target geometries are depicted in Figure 7a. The corresponding calculated ram positions 270∘ to 270∘ and mandrel positions L1 to L5 of the support points P1 to P5 are depicted in Figure 7b.

b)

Fig. 7. a) Input and output values of the neural network for the prediction of the mandrel kinematic and b) ram and mandrel position of the support points.

For each geometry the predicted kinematic was entered into the control system of the hydraulic cylinder to define the movement of the mandrel during the ram stroke and the parts were pressed. Subsequently, the pressed parts were cut into Li segments to measure the inner and outer geometry using the GOM ATOS Compact Scan 5M optical measurement system. From the acquired 3D measurement data, 2D cross-sections were exported. The simplified 2D cross-sections from the kinematic prediction GUI with linear connection of the main points and photos of the cut L5 segment of the pressed parts for the kinematics 1 to 3 are shown in Figure 8a-c.

[picture]

Fig. 8. Simplified 2D cross-section from GUI and cut L3 segment of the pressed part for a) kinematic 1, b) kinematic 2 and c) kinematic 3.

Fig. 8. Simplified 2D cross-section from GUI and cut L 3 segment of the pressed part for a) kinematic 1, b) kinematic 2 and c) kinematic 3.

The segment lengths were then determined from the measured 2D cross-sections following the same procedure used for the analysis of the numerically obtained geometries. Table 1 presents the comparison between the target and measured values for L5 to L3 for the three test kinematics 1,2 and 3. Additionally, the absolute errors (AE) for each 1,0.04 mm and mean absolute errors (MAE) are provided. An analysis of the deviations from the target values reveals a maximum AE of 0.60 mm for L4 in kinematic 1, 0.67 mm for L4 in kinematic 2 and 0.89 mm for L1 in kinematic 3. Regarding the minimum deviations, the results show a minimum AE of 0.02 mm for L5 in kinematic −30 mm/s for −4 mm/s in kinematic 2 and 0.21 mm for −17 mm/s in kinematic 3. The MAE for kinematics 1 to 3 are 0.31 mm , 0.30 mm and 0.47 mm .

Table 1. Comparison of target and measured values of the variables L1 to L5 for three test kinematics, including absolute errors and mean absolute errors (in mm).

Length | Test kinematic 1 | Test kinematic 2 | Test kinematic 3
| Target | Measured | AE | Target | Measured | AE | Target | Measured | AE
L₁ | 12.00 | 12.30 | 0.30 | 34.30 | 33.75 | 0.55 | 9.30 | 9.54 | 0.24
L₂ | 7.60 | 7.74 | 0.14 | 7.70 | 7.87 | 0.17 | 7.50 | 7.79 | 0.29
L₃ | 25.70 | 25.72 | 0.02 | 11.70 | 12.37 | 0.67 | 9.10 | 9.81 | 0.71
L₄ | 14.30 | 14.78 | 0.48 | 11.37 | 11.41 | 0.04 | 16.50 | 16.71 | 0.21
L₅ | 23.83 | 23.24 | 0.59 | 15.40 | 15.33 | 0.07 | 38.22 | 37.33 | 0.89
| MAE | 0.31 | MAE | 0.30 | MAE | 0.47

The results show that by using the predicted kinematics, geometries with an MAE of the lengths −30 mm/s to TM  of less than 0.47 mm were obtained across the three tested variants. These observed deviations can be attributed to several factors. The numerical model was validated before with one specific kinematic. Afterwards the simulations with the LHS were conducted with the valid FEM model. Though, there are still uncertainties remaining such as friction variation due to, e.g., inhomogeneous lubrication or deviations of the idealised elastic properties of tools and workpiece and the real properties. In addition, the rather low ram velocity of t2,t3 was necessary to ensure that the additional hydraulic cylinder could achieve its required relative velocity with respect to the ram. However, this velocity is at the lower end of the possible range for the used hydraulic press. For this reason, the velocity was not held constant and varied significantly throughout the entire process. Likewise, the pressure build-up of the hydraulic press was considerably delayed at such low ram velocities, which led to even more drops in velocity. In fact, the experimentally determined ram velocity varied in a range from t4 to R2 which can be seen in the velocity plot over the
ram position for the mandrel and the die in Figure 9a, although a value of mathematical formula was set in the control unit of the hydraulic press. These rather low and inhomogeneous ram velocities resulted in a deviation of the numerically calculated and experimentally obtained strain rate within the process.

The predicted time values contain some inaccuracies as well. These inaccuracies are amplified when converting the predicted time values into the mandrel's control positions, as they are multiplied by the ram velocity (factor 30). Furthermore, the numerical model uses a piecewise linear kinematic profile with sharp transitions at the five support points. In reality, however, the hydraulic system executes these movements with smooth, continuous transitions. Finally, delays within the hydraulic control system led to deviations between the ideal and the actual experimentally obtained kinematic profile. Figure 9b shows the position of the die and the ideal and experimentally obtained positions of the mandrel on the left ordinate as well as the deviation between the two on the right ordinate. All values are depicted in relation to the ram position in accordance with Figure 3b. Direct comparison of Figure 9a with Figure 9b shows that the difference between the ideal and experimental mandrel positions is greatest in the transition regions during acceleration and deceleration. During these phases, deviations of up to 0.5 mm are observed.

Despite these deviations induced by the machine, the results demonstrate that the developed model can be used as an effective predictive tool. Based on a target geometry, the required kinematics can be determined immediately, eliminating the need for several initial trial-and-error simulations. However, machine-dependent adjustments are still necessary and, unfortunately, cannot always be compensated for. In the future, replacing the hydraulic system with a mechanical actuation system would enable a more precise control of the mandrel movement, thereby minimising machine-induced deviations.

[picture]

Fig. 9. a) Velocity of the mandrel and the die in relation to the ram position and b) positions of the die and the ideal and experimentally obtained positions of the mandrel as well as the deviation between the ideal and experimentally obtained positions of the mandrel position in relation to the ram position.

Fig. 9. a) Velocity of the mandrel and the die in relation to the ram position and b) positions of the die and the ideal and experimentally obtained positions of the mandrel as well as the deviation between the ideal and experimentally obtained positions of the mandrel position in relation to the ram position.

Conclusion and Outlook

The original version of this paper is available on https://www.scientific.net/MSF.1186.47.pdf

In this contribution, an approach for a ML-based prediction of tool kinematics in cold forging of tailored hollow shafts with variable wall thickness is presented. Based on a numerical model of the cold forging process, a kinematic variation was conducted using the DoE tool in the FEM software DEFORM mathematical formula-2D. A total of 1,000 kinematic variants were created with a LHS, taking specific process boundaries into account. The 2D cross-sections of the numerically calculated workpiece geometries were analysed to determine specific segment lengths of the workpiece. Using these geometric values alongside the corresponding time values which define the mandrel kinematic, a dataset for the training of a neural network was created. Instead of iteratively testing different mandrel kinematics to achieve a certain part geometry, this neural network enables the prediction of the required mandrel kinematic based on a specific target geometry. For the prediction of the three time values superscript reference and mathematical formula for the mandrel kinematic, superscript number values between 0.989 and 0.999 were obtained with a maximum MAE ranging from 0.007 s to 0.017 s .

Subsequently, the trained model was integrated into a GUI which allows a user to enter a target geometry of a part and provides the predictions for the mandrel kinematic. For three selected target geometries the respective mandrel kinematics were predicted and used in an experimental setup to validate the prediction. The pressed parts were optically measured and the results were compared to the predicted geometry. For the three target geometries a MAE of all characteristic lengths ranging from 0.30 mm to 0.47 mm was observed. Consequently, the developed model significantly reduces the need for several trial-and-error simulations. However, potential for optimisation remains, particularly regarding machine-induced deviations.

Future works could focus on reducing the remaining inaccuracies to optimise the prediction quality. Thus, replacing the hydraulic system with a mechanical actuation system is suggested to enable a more precise control of the mandrel movement, thereby minimising machine-induced deviations. Finally, the model could be extended to cover a wider range of geometric variants. Although the current model is limited to the specific 'thick-thin-thick' wall thickness profile, the methodology presented in this paper can be adapted to other geometric profiles.

Acknowledgment

The original version of this paper is available on https://www.scientific.net/MSF.1186.47.pdf

The research project "Development of a cold forging process for manufacturing hollow parts with variable wall thicknesses" (fund number ZF4012808LP9) in cooperation with the company LSMechanik GmbH was funded by the Federal Ministry of Economic Affairs and Energy through the German Federation of Industrial Research Associations (AiF) as part of the Central Innovation Programme for SMEs (ZIM) based on a decision of the German Bundestag. The authors would like to thank the German Federation of Industrial Research Associations for the financial support and the project partner LS-Mechanik GmbH for the collaboration.

Supported by:

Federal Ministry for Economic Affairs and Energy
on the basis of a decision by the German Bundestag

ZIM
Zentrales Innovationsprogramm Mittelstand
Zentrales
Innovation Mittelstand

References

The original version of this paper is available on https://www.scientific.net/MSF.1186.47.pdf

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Materials Science Forum, ISSN: 1662-9752, Vol. 1186, pp 59-72

doi: 10.4028/p-ir99uX

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

Submitted:
2026-01-20

Revised:
2026-02-17

Accepted:
2026-02-26

Online:
2026-04-16

Process Optimization for Forging an AA6082 Automotive Upper Control Arm

The original version of this paper is available on https://www.scientific.net/MSF.1186.59.pdf

Liju Zhou 1,a, Leigang Wang 1, b, Mingxiao Shi 2,c, Angela Daniela La Rosa 3, d, Elisabete F. Reia da Costa 4,e and Xiang Ma 4,f*

1 School of Materials Science and Engineering, Jiangsu University, 212013 Zhenjiang, China

2 School of Materials Science and Engineering, Jiangsu University of Science and Technology, 212003 Zhenjiang, China

3 Department of Manufacturing and Civil Engineering, Norwegian University of Science and Technology, 2815 Gjøvik, Norway

4 SINTEF Industry, POB 124 Blindern, 0314 Oslo, Norway

e  elisabete.f.r.costa@sintef.no, f  xiang.ma@sintef.no

Keywords: 6082 aluminum alloy, automotive control arm, rolling, forging, die design, finite element analysis, response surface methodology.

Abstract

This study examines the forging process of an aluminum upper control arm for automotive applications. To address the geometric complexity and forming challenges, a multi-step forging route is developed, consisting of roll forging, two-stage bending, pre-forging and final forging. Finite element analysis (FEA) using DEFORM-3D software is employed to optimize key forming process parameters in the pre-forging stage. The response surface methodology (RSM), combined with the Box-Behnken design, is utilized to construct predictive models and identify optimal parameter combinations. A successful forged upper control arm is subsequently produced using these optimized forming parameters. The findings demonstrate that integrating FEA with statistical process optimization strengthens the predictive accuracy of the process design and supports defect-free forging of AA6082 upper control arms.

Introduction

The original version of this paper is available on https://www.scientific.net/MSF.1186.59.pdf

The control arm (including upper and lower arms) is one of the most critical components in a vehicle's chassis suspension system, responsible for both guiding and transmitting loads. It transfers various forces and moments acting on the wheels through the suspension system to the vehicle's frame or body, thereby ensuring stable and safe driving performance. Given the complex service conditions, control arms must exhibit high strength, stiffness, impact toughness and fatigue resistance [1]. With the growing emphasis on lightweight technologies, there has been increasing research and industrial interest in replacing steel with aluminum alloys for control arm manufacturing [2]. Among various forming methods, forging stands out for its ability to produce components with superior mechanical properties. In particular, 6082 aluminum alloy (AA6082), an Al−Mg−Si series alloy, has emerged as one of the most preferred alloys for forged control arms due to its high specific strength, excellent hot workability and strong corrosion resistance [3-5].

Compared with steel, aluminum alloys show several distinct forging characteristics: (1) a narrow deformation temperature window [6-7]; (2) pronounced strain-rate sensitivity. Numerous studies on the hot-deformation behavior of aluminum alloys show strain rate sensitivity [8-10]. Thus, forging equipment with a faster and steadier working speed should be selected, for example, a mechanical press; and (3) a strong tendency to adhere to die surfaces [11-12]. In terms of heat treatment, wrought aluminum alloys typically undergo annealing (including homogenization annealing, recrystallization annealing, and stress-relief annealing), retrogression treatment, quenching (or solution treatment), and aging. Specially, Al−Mg−Si series alloys exhibit a pronounced delayed-aging (stagnation) effect: after quenching, the material inevitably experiences a period of natural room-temperature holding before artificial aging, during which premature clustering reduces the subsequent age-hardening
response [13]. The forging trial in this work reduces the interval between quenching and artificial aging to retain the hardenability of the 6082 alloy. It is noted that the complex geometry of control arm component, combined with the narrow forging temperature range and intricate flow behavior of aluminum alloys, may pose significant challenges. If the forming process is not properly designed, various defects may occur in the forged parts. Hot forming process analysis and shape control are achieved primarily through finite element (FE) simulation [14-16].

In this study, by integrating deformation theory with FE modelling, a comprehensive forging strategy is developed. FE software, DEFORM-3D, is employed to optimize key forming process parameters, aiming to enhance the quality and reliability of the forged component. Particularly, the response surface methodology [17] and the Box-Behnken design approach [18] are used to assist in optimizing the pre-forging stage. Trial forging is conducted based on the simulated results.

SSbD (Safe and Sustainable by Design) Guided Analysis of Forming Process

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The SSbD principle used in [19] is closely followed in analyzing and designing the forming process. The forging geometry of the upper control-arm examined in this study is shown in Fig. 1. The component has an overall U-shaped geometry. Except for the two nearly cylindrical bushing-boss ends, the remaining sections form an asymmetric I-shaped cross-section perpendicular to the bending axis. The central web is thin, and the ribs along the arm edges include geometrically complex features, such as protruding mounting ears.

[picture]

Fig. 1. The geometry of the upper control arm.

Fig. 1. The geometry of the upper control arm.

[picture]

Fig. 2. Process flow chart of the control arm forming.

Fig. 2. Process flow chart of the control arm forming.

A structural analysis of the forging reveals key challenges associated with its forming process:
(1). The significant variation in cross-section along the bending axis requires precise control of material flow during forming.
(2). The large regions located at the two bushing bosses, the protruding mounting ears, and the ball joint boss area pose a risk of incomplete die filling.
(3). During the formation of the I-shaped cross-section, the component is particularly susceptible to the development of folding defects, which may compromise structural integrity.

The control arm forging is a bent, long-axis forging. To enhance material utilization and ensure the required forging quality, a multi-stage forming route is necessary to transform the initially simple bar billet into the final component geometry. Accordingly, a process scheme comprising billet preparation, pre-forging, and final forging is proposed. The billet-preparation stage consists of roll-forging followed by bending. The forging process flow is illustrated in Fig. 2. Especially, roll forging is employed to facilitate the redistribution of billet volume along its longitudinal axis, thereby significantly enhancing material utilization during the billet-preparation stage. The control arm exhibits localized geometric complexity, and direct single-step forging after billet preparation would likely cause folding, incomplete die filling, or excessive deformation. To mitigate these issues, an intermediate pre-forging operation is introduced between billet preparation and the final forging stage. Similar multi-step forming strategy on long shaft forgings is employed in [20].

The FE Model

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The FE model is created by first constructing the billet and die geometries for each forming stage using SolidWorks, after which the models are exported in STL format and imported into DEFORM3D for simulation. Key process parameters for roll forging and bending are assigned based on established production experience (Table 1). To accurately represent the thermo-mechanical evolution occurring in actual manufacturing, the final billet geometry obtained at the end of each forming step is used directly as the initial geometry for the subsequent step in the FE analysis.

Table 1. The process parameter setting in FE simulation of roll forging and bending.

Process step | Process parameter | Value
Roll forging | Billet temperature (°C) | 490
Die temperature (°C) | 200
Rolling speed (r/min) | 50
Bending | Die temperature (°C) | 20
Forming speed (mm/s) | 100

Because deformation in the roll-forging and bending stages is modest, the billet mesh is limited to approximately 60,000 elements to improve computational efficiency. A volume-compensation procedure is also applied. In contrast, the pre-forging stage involves more complex material flow and larger local deformation gradients; therefore, the mesh is refined to 160,000 elements with a mesh size ratio of 5 to ensure adequate resolution of the deformation field. Mesh-convergence behavior is also examined by comparing results obtained from two mesh levels: the current mesh, and a mesh with element size increased by roughly one-third. Both solutions converge while the current mesh setting shows a slight improvement. Proper remeshing is applied during the forming process.

The constitutive model of AA6082 is chosen from the material database of DEFORM-3D. A shear friction model, which is widely adopted for bulk metal forming simulations, is employed throughout the analysis [21]. To account for billet bite-in during roll forging, a higher friction factor of 0.7 is specified at the roll entry surface, while a value of 0.4 is used for bending operation. Thermal boundary conditions are also included. The interfacial thermal contact conductivity between billet and dies is set to 1100 W/(m2· K), and the convective heat-transfer coefficient between the billet surface and the surrounding air is set to 20 W/(m2· K).

FE Results of Roll-Forging and Bending

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The details of the simulated roll forging and bending can be found in [22]. The optimized rollforging die design is performed by FEM. The FE results indicate that increasing the initial billet temperature leads to reductions in both the maximum equivalent stress and the peak forming load, with an optimal initial temperature range of Tp≥470∘C. Variations in die temperature exert only a
minor influence on the overall billet temperature; however, higher die temperatures noticeably reduce the thermal gradient between the billet surface and core. Increasing the roll-forging speed results in higher maximum equivalent stress and forming load, while also causing a significant increase in the minimum billet temperature during processing. Favorable formability is achieved when the friction factor lies within the range 0.4−0.55 for roll-forging only. The simulation results demonstrate that employing a two-pass roll-forging sequence followed by two-station bending produces a preform that closely approximates the desired pre-forged geometry, with uniform stress distribution and a favorable metal flow pattern. This helps establish a robust foundation for the subsequent pre-forging and final-forging stages of the control-arm manufacturing process.

Optimization of Control Arm Pre-Forging Process Parameters Based on RSM

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RSM design

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The RS design in this study is developed using the Design-Expert software package. Among the various design methodologies available in the software, the Box-Behnken Design (BBD) and the Central Composite Design are the most widely employed. BBD is particularly suitable for systems involving two to five factors, where each factor is restricted to three discrete levels, making it advantageous for response-surface modeling scenarios in which the parameter ranges are relatively moderate. For these reasons, the BBD methodology is selected for the present study.

The die load during the pre-forging stage ( Y1 ) and the average maximum equivalent stress ( Y2 ) are chosen as the primary response outputs. Four process parameters are selected as design variables: billet temperature ( X1 ), die preheating temperature ( X2 ), upper-die velocity ( X3 ), and the friction factor at the die-billet interface (X4). A four-factor, three-level BBD scheme is generated in Design-Expert, and the selected levels for each design variable are summarized in Table 2. As recommended in [23], the billet preheating temperature range for AA6082 is chosen as 450−490∘C : a narrow range with an upper limit of 490∘C to avoid approaching the melting point. The die preheating temperature range (200-300 ∘C ), around 200∘C lower than the billet temperature, follows the recommendation in [23] for a closed die forging using mechanical press. A 2500-ton mechanical press is used, and the forging velocity is set within the medium-to-high range ( 50−150 mm/s ) to limit billet cooling and reduce die contact time [24]. The friction factor range of 0.2-0.4 is chosen as low and medium friction occurs between die and material [25].

Table 2. Design variables and level setting.

Variable | No. | Level range
billet preheating temperature (°C) | X₁ | 450 | 470 | 490
die preheating temperature (°C) | X₂ | 200 | 250 | 300
upper-die velocity (mm·s⁻¹) | X₃ | 50 | 100 | 150
friction factor | X₄ | 0.2 | 0.3 | 0.4

Simulation models are constructed sequentially according to the design scheme, and the simulation outputs are shown in Table 3.

Table 3. The test and results of BBD .

No. | X₁ (°C) | X₂ (°C) | X₃ (mm/s) | X₄ | Y₁ (kN) | Y₂ (MPa)
1 | 470 | 200 | 50 | 0.3 | 6895 | 71.51
2 | 470 | 250 | 150 | 0.2 | 6306 | 75.94
3 | 470 | 250 | 100 | 0.3 | 6982 | 73.27
4 | 470 | 300 | 100 | 0.4 | 7001 | 73.86
5 | 470 | 250 | 100 | 0.3 | 6982 | 73.27
6 | 470 | 300 | 100 | 0.2 | 5903 | 71.48
7 | 450 | 250 | 150 | 0.3 | 7133 | 77.46
8 | 490 | 300 | 100 | 0.3 | 6179 | 68.39
9 | 470 | 250 | 50 | 0.2 | 6216 | 69.67
10 | 450 | 200 | 100 | 0.3 | 7273 | 75.48
11 | 470 | 250 | 100 | 0.3 | 6982 | 73.27
12 | 450 | 300 | 100 | 0.3 | 7347 | 75.08
13 | 450 | 250 | 50 | 0.3 | 7532 | 72.93
14 | 450 | 250 | 100 | 0.4 | 7782 | 74.11
15 | 470 | 200 | 100 | 0.4 | 7090 | 73.01
16 | 470 | 300 | 150 | 0.3 | 6470 | 75.80
17 | 470 | 250 | 100 | 0.3 | 6982 | 73.27
18 | 490 | 250 | 150 | 0.3 | 6593 | 73.66
19 | 470 | 250 | 100 | 0.3 | 6982 | 73.27
20 | 470 | 250 | 150 | 0.4 | 7373 | 75.56
21 | 470 | 200 | 100 | 0.2 | 6161 | 73.80
22 | 490 | 250 | 100 | 0.4 | 6949 | 69.48
23 | 470 | 200 | 150 | 0.3 | 6901 | 75.19
24 | 490 | 250 | 100 | 0.2 | 5848 | 71.57
25 | 490 | 250 | 50 | 0.3 | 6410 | 67.40
26 | 490 | 200 | 100 | 0.3 | 6391 | 71.10
27 | 470 | 300 | 50 | 0.3 | 6789 | 67.21
28 | 470 | 250 | 50 | 0.4 | 7115 | 68.04
29 | 450 | 250 | 100 | 0.2 | 6853 | 75.14

Establishment and verification of the RS model

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The RS model approximates the relationship between the response value and design variables using polynomial expressions. A second-order polynomial is selected because forging responses exhibit nonlinear behavior and variable interactions, and this is the highest order fully supported by RSM theory. Thus, the real response value Y is described as:

Y=β0+∑i=1kβiXi+∑i=1kβiXi2+∑i≠jβijXiXj+ε(1)

Where: β0,βI,βj,βij : polynomial coefficients; Xi,Xj : design variables; ε : the approximation error of the objective function.

Least-squares regression is applied using Design-Expert software to fit the relationships between the design variables and the responses. The polynomial expressions of Y1 and Y2 are given as:

Y1=24345.653−82.393X1+75.198X2−59.479X3+3214.012X4−0.0717X1X2+0.1466X1X3+21.529X1X4−0.033X2X3+8.451X2X4+8.413X3X4+0.006X12−0.085X22−0.018X32−18778X42(2)

Y2=86.53203−0.022164X1−0.161708X2−0.283564X3−13.6701X4−0.000579X1X2+0.000433X1X3−0.132921X1X4+0.000491X2X3+0.158372X2X4+0.062578X3X4(3)

[picture]

Fig. 3. Comparison between predicted and simulated values Y1 (a) and Y2 (b).

Fig. 3. Comparison between predicted and simulated values Y 1 (a) and Y 2 (b).

To ensure the model's reliability, predicted values by Eqs (2) and (3) are plotted in Fig. 3 against the corresponding simulated values in Table 3. The 45∘ line in Fig. 3 represents zero-error conditions, while the color gradient (blue for early runs and red for later runs) indicates the sequence of simulation cases. The predicted values align closely with the simulated results, with most points lying near the zero-error line indicating high predictive accuracy.

To assess the reliability of the experimental results and the credibility of the mathematical models, significance testing is performed. Common analytical approaches include analysis of variance (ANOVA) and coefficient-of-determination-based evaluations. ANOVA is applied in this work to evaluate the statistical significance of the regression model and to determine the sensitivity and relative importance of each process parameter, rather than overall model reliability. The ANOVA results for the die load and the average maximum equivalent stress are summarized in Table 4 and Table 5, respectively. The F-value reflects the degree of influence exerted by each factor on the corresponding response: a larger first-order F-value indicates a stronger effect. The P-value signifies the statistical significance of the model: values of P<0.01 (denoted by **) indicate a highly significant effect, values in the range 0.01≤P<0.05 (denoted by *) indicate a significant effect, and P≥0.05 suggests limited significance, with increased model error.

Table 4. Analysis of variance of pre-forging forming load.

Source | Sum of Squares | df | Mean Square | F-value | P-value |
Model | 6.33×10¹² | 14.00 | 4.52×10¹¹ | 48.49 | < 0.0001 | **
X₁ | 2.57×10¹² | 1.00 | 2.57×10¹² | 275.45 | <0.0001 |
X₂ | 8.69×10¹⁰ | 1.00 | 8.69×10¹⁰ | 9.32 | 0.01 |
X₃ | 2.76×10⁹ | 1.00 | 2.766×10⁹ | 0.30 | 0.60 |
X₄ | 3.02×10¹² | 1.00 | 3.02×10¹² | 324.52 | < 0.0001 |
X₁X₂ | 2.05×10¹⁰ | 1.00 | 2.05×10¹⁰ | 2.20 | 0.16 |
X₁X₃ | 8.48×10¹⁰ | 1.00 | 8.48×10¹⁰ | 9.10 | 0.01 |
X₁X₄ | 7.426×10⁹ | 1.00 | 7.426×10⁹ | 0.80 | 0.39 |
X₂X₃ | 2.64×10¹⁰ | 1.00 | 2.64×10¹⁰ | 2.84 | 0.11 |
X₂X₄ | 7.146×10⁹ | 1.00 | 7.146×10⁹ | 0.77 | 0.40 |
X₃X₄ | 7.086×10⁹ | 1.00 | 7.086×10⁹ | 0.76 | 0.40 |
X₁² | 3.716×10⁹ | 1.00 | 3.716×10⁹ | 0.40 | 0.54 |
X₂² | 2.93×10¹¹ | 1.00 | 2.93×10¹¹ | 31.45 | < 0.0001 |
X₃² | 1.35×10¹⁰ | 1.00 | 1.35×10¹⁰ | 1.45 | 0.25 |
X₄² | 2.29×10¹¹ | 1.00 | 2.29×10¹¹ | 24.55 | 0.00 |
Residual | 1.30×10¹¹ | 14.00 | 9.326×10⁹ |  |  |
Lack of fit | 1.30×10¹¹ | 10.00 | 1.30×10¹⁰ |  |  |
Cor total | 6.46×10¹² | 28.00 |  |  |  |

Table 5. Variance analysis of the average maximum effective stress.

Source | Sum of Squares | df | Mean Square | F-value | P-value |
Model | 199.4 | 10 | 19.94 | 39.16 | <0.0001 | **
X₁ | 68.15 | 1 | 68.15 | 133.85 | <0.0001 |
X₂ | 5.7 | 1 | 5.7 | 11.19 | 0.0036 |
X₃ | 113.21 | 1 | 113.21 | 222.34 | <0.0001 |
X₄ | 1.05 | 1 | 1.05 | 2.05 | 0.169 |
X₁X₂ | 1.34 | 1 | 1.34 | 2.63 | 0.122 |
X₁X₃ | 0.7497 | 1 | 0.7497 | 1.47 | 0.2407 |
X₁X₄ | 0.2827 | 1 | 0.2827 | 0.5552 | 0.4658 |
X₂X₃ | 6.03 | 1 | 6.03 | 11.84 | 0.0029 |
X₂X₄ | 2.51 | 1 | 2.51 | 4.93 | 0.0395 |
X₃X₄ | 0.3916 | 1 | 0.3916 | 0.7691 | 0.392 |
Residual | 9.16 | 18 | 0.5092 |  |  |
Lack of fit | 9.16 | 14 | 0.6546 |  |  |
Cor total | 208.57 | 28 |  |  |  |

As shown in Table 4, the regression model for the pre-forging die load exhibits a P-value <0.0001, demonstrating a highly significant influence and indicating strong agreement between the design variables and the response. Thus, the response-surface model provides sufficient fidelity for subsequent optimization. A comparison of F-values reveals that the relative influence of the design variables on the pre-forging load, from greatest to least, is: friction factor, billet temperature, die preheating temperature, and upper-die velocity.

The overall reliability and goodness-of-fit of the response surface models is assessed using the coefficient of determination ( R2 ) and the adjusted coefficient of determination Radj2·R2 assumes a value within the range [0,1]. Values approaching unity indicate a high degree of agreement between the model predictions and the observed results. Similarly, Radj2 also lies within [0,1] and accounts for the number of model terms; values closer to 1 signify strong predictive capability and minimal deviation between the predicted and actual responses. As summarized in Table 6, the two response-surface models explain 97.98% and 95.61% of the variance in their respective responses, demonstrating excellent model fidelity and confirming that the proposed regression models provide an accurate representation of the experimental trends.

Table 6. Error analysis of regression equations.

Fitting variable | R² | R²adj
Y₁ | 0.9798 | 0.9596
Y₂ | 0.9561 | 0.9316

Analysis of influencing factors in the RS model

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Fig. 4 illustrates the response surfaces describing the effects of two-factor interactions among the four process variables (billet temperature ( X1 ), die-preheating temperature ( X2 ), upper-die velocity (X3), and friction factor (X4)) on the pre-forging die load (Y1).

[picture]

Fig. 4. Response surface of different influencing factors on pre-forging die load.

Fig. 4. Response surface of different influencing factors on pre-forging die load.

The response surfaces in Fig. 4 reveal that the influence of different two-factor interactions on the pre-forging die load varies considerably. The interaction between billet temperature and die-preheating temperature shows that increasing billet temperature consistently reduces the die load, with the effect becoming more pronounced when die-preheating temperature is elevated (Fig. 4(a)). When billet temperature interacts with upper-die velocity, billet temperature again dominates the load response, whereas changes in die velocity produce only secondary effects (Fig. 4(b)). A much stronger interaction effect is observed between billet temperature and friction factor (Fig. 4(c)), where the die load changes sharply with friction level, especially at lower billet temperatures, highlighting friction as one of the most influential process variables. Interactions involving die-preheating temperature, whether paired with billet temperature, upper-die velocity, or friction factor (Fig. 4 (a), (d), (e)), show moderate but consistent effects on die load, reinforcing its role as a secondary factor. Moreover, variations in upper-die velocity produce the smallest changes in die load across all interaction surfaces (Fig. 4 (b), (d), (f)), indicating limited sensitivity to this parameter compared with temperature or friction.

The combined trends in Fig. 4 demonstrate that the dominant factors affecting pre-forging die load are the friction factor and billet temperature, followed by die-preheating temperature, while upper-die velocity has the weakest influence.

The response surfaces in Fig. 5 show that the average maximum equivalent stress Y2 is jointly influenced by billet and die-preheating temperatures, upper-die velocity, and friction factor, with varying degrees of sensitivity. Higher billet temperature and die preheating temperature reduce the thermal gradients and maintain the billet at elevated temperature enhancing material flow (Fig. 5 (a), (e)). The interaction between billet temperature and upper-die velocity indicates that lower die-closing speeds lead to lower stress, particularly at higher billet temperatures (Fig. 5 (b)). Friction factor plays only a minor role when billet temperature is low, but improved lubrication (lower friction factor) further reduces peak stress at elevated temperatures or in combination with high die-preheating temperature (Fig. 5 (c), (e), (f)). Interactions involving die-preheating temperature and upper-die velocity produce only modest stress variations, confirming their secondary influence (Fig. 5 (d)).

The analysis indicates that upper-die velocity has the strongest effect on the equivalent stress, followed by billet temperature and die-preheating temperature, whereas the friction factor exerts only a limited influence on this response.

The optimization module in Design-Expert is employed to evaluate the fitted polynomial response-surface models. In this study, the optimization objective is to minimize both the pre-forging die load and the average maximum equivalent stress, thereby identifying the optimal combination of process parameters. The optimization is restricted to the prescribed parameter ranges to ensure the validity of the solution.

Using this approach, Design-Expert recommends the following optimal parameter set: a billet temperature of 490∘C, a die-preheating temperature of 300∘C, an upper-die velocity of 50 mm/s, and a friction factor of 0.26 .

Numerical simulations performed using this optimized parameter combination are compared with the predictions and the results are summarized in Table 7. The deviation between the predicted and simulated values of the pre-forging load is 1.98%, while the error for the average maximum equivalent stress is only 0.04%. These small deviations demonstrate that the optimized response-surface model provides a reliable and accurate prediction of both die load and maximum equivalent stress during the pre-forging process.

[picture]

Fig. 5. Response surface of influencing factors on the average maximum effective stress of preforging.

Fig. 5. Response surface of influencing factors on the average maximum effective stress of preforging.

Table 7. Predicted and simulated values for the optimization of process parameters.

X₁ (°C) | X₂ (°C) | X₃ (mm/s) | X₄ | Prediction | Simulation
|  |  |  | Y₁ (kN) | Y₂ (MPa) | Y₁ (kN) | Y₂ (MPa)
490 | 300 | 50 | 0.26 | 5843 | 64.41 | 5961 | 64.38

Simulated results of the pre-forging process

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Based on the optimized pre-forging process parameters described above, a thermo-mechanical coupled analysis is conducted for the pre-forging under the optimal conditions. Fig. 6 shows the equivalent-stress distribution during deformation, with the main volume region names marked in Fig. 6(c) and (d). As the upper die descends, the large material volumes at the bushing bosses and joint boss contact the lower die first and begin to fill the cavity (Fig. 6(a)), producing noticeably higher equivalent stress than the still-free regions. During this early stage, the forming load increases slowly
(Fig. 7). When the arm sections subsequently contact the die (Fig. 6(b)), deformation intensifies and the load begins to rise more noticeably. As the die continues to close, the cavity becomes mostly filled. Only the deeper regions, such as the bushing-boss pockets, joint-boss area, mounting-ear zones, and the inner ribs, remain partially unfilled at this stage (Fig. 6(c)). Excess material is then pushed toward the flash through the flash-bridge region, where the smallest height produces the highest equivalent stress and greatest flow resistance, helping ensure complete die filling. Once flash forms and restricts metal flow, the forming load rises rapidly. At die closure, the billet fully fills the cavity and exhibits a relatively uniform stress distribution without excessive local concentration (Fig. 6(d)). By the end of pre-forging, the die load reaches 5961 kN and cavity filling is satisfactory.

[picture]

Fig. 6. Evolution of effective stress distributions during pre-forging process.

Fig. 6. Evolution of effective stress distributions during pre-forging process.

Fig. 8 shows that maximum equivalent stress from the simulated pre-forging process stays near or below the average value of 64 MPa in the early and middle stages, then rises in the later stage. Under the optimized parameters, it remains below 105 MPa , indicating stable and uniform deformation.

Although this work focuses on pre-forging, the final forging step is also simulated with the same process parameter setup. These forming process parameters are implemented in practice and a successful forged upper control arm is produced (Fig. 9). Key geometric features, such as the central axial distance between the joint boss and the bushing boss, are measured and compared with the simulation results, showing a deviation of 3%. This confirms that the simulation accurately predicts the final forging outcome.

[picture]

Fig. 7. Pre-forging load-time curves.

Fig. 7. Pre-forging load-time curves.

[picture]

Fig. 8. Scatter plot of maximum effective stress in simulated pre-forging process.

Fig. 8. Scatter plot of maximum effective stress in simulated pre-forging process.

[picture]

Fig. 9. Forged upper control arm.

Fig. 9. Forged upper control arm.

The material model used in this study is the default DEFORM-3D library model and does not include microstructural parameters. The constitutive model and microstructural parameters of 6082 aluminum alloy can be obtained through physical experiments in future work. This would improve the accuracy of the simulations and enable analysis and control of the forging process from both macroscopic and microscopic perspectives.

Due to limitations in design capability, a two-stage free-bending process is adopted in the current bending stage. In the future, new dies may be developed to achieve one-step bending, thereby improving forging efficiency. In addition, the control-arm forging examined in this work exhibits relatively large flash. Exploring more optimized billet-preparation processes may help improve material utilization.

Conclusion

The original version of this paper is available on https://www.scientific.net/MSF.1186.59.pdf

This work examines the pre-forging process of an aluminum alloy upper control arm for automotive applications. FE simulation is combined with the response surface methodology to optimize key forming process parameters in the pre-forging stage. The main findings are summarized as follows:
(1) The pre-forging die load and the average maximum equivalent stress are used as the primary optimization objectives. Four key process parameters (billet temperature, die-preheating temperature, upper-die velocity, and friction factor) are designated as design variables, and a Box-Behnken Design framework is constructed to develop the response surface models.
(2) The accuracy and statistical significance of the BBD models are verified, and the individual effects of each design variable on the objectives are analyzed. A multi-objective optimization is subsequently performed, identifying the following optimal parameter combination: billet temperature of 490∘C, die-preheating temperature of 300∘C, upper-die velocity of 50 mm/s, and friction factor of 0.26 .
(3) A thermo-mechanical coupled FE analysis is conducted using the optimized process parameters to evaluate the pre-forging behavior. FE simulation yields a pre-forging load of 5961 kN and an average maximum equivalent stress of 64.4 MPa . The results show stable forming behavior, with controlled metal flow and adequate cavity filling. A successful forged upper control arm is subsequently produced using these optimized forming parameters.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/MSF.1186.59.pdf

This work is partially supported by the National Natural Science Foundation of China (Grant No. 51775249), SFI Future Aluminium Structures (FAST) funded by the Research Council of Norway (Grant No. 360119), the COGNIMAN project (Grant Agreement No. 101058477) funded by the Horizon Europe research and innovation program of the European Union, and the POLYKAP project funded by SINTEF SEP program.

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The original version of this paper is available on https://www.scientific.net/MSF.1186.59.pdf

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Materials Science Forum, ISSN: 1662-9752, Vol. 1186, pp 73-84

doi: 10.4028/p-C6AnYu

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

Submitted:
2026-01-21

Revised:
2026-02-25

Accepted:
2026-02-25

Online:
2026-04-16

Experimental Characterisation of Service-Degraded H13 Tool Steel and Numerical Assessment of Die Plasticity Risk in Hot Forging of Nickel-Based Superalloys

The original version of this paper is available on https://www.scientific.net/MSF.1186.73.pdf

Julen Agirre 1,a*, Unai Ibarretxe 1, b, David Abedul 1,c, Nagore Otegi 1, d and Lander Galdos 1,e

¹Mechanics and Industrial Production Department, Mondragon Unibertsitatea, Loramendi 4, 20500 Mondragon, Spain

a*jagirreb@mondragon.edu, b  uibarretxe@mondragon.edu, c  dabedul@mondragon.edu, d  notegi@mondragon.edu, elgaldos@mondragon.edu 

Keywords: die plasticity risk, hot forging, nickel-based superalloys, thermal degradation.

Abstract

Hot forging of nickel-based superalloys involves severe thermo-mechanical loading of the forming dies due to the high strength of these materials, even at elevated temperatures. Under industrial production conditions, forging dies are subjected to repeated heating-cooling cycles, which progressively degrade the mechanical properties of the tool steel and increase the risk of die plastic deformation. Reliable assessment of die performance therefore requires material characterisation that accounts for both temperature effects and service-induced degradation. In this work, an H13 tool steel used in an industrial hot forging application (nickel-based superalloy case study), was experimentally and numerically investigated in both its raw and service-degraded conditions. Hardness measurements, microstructural analysis, uniaxial compression tests, and quasi-static tensile tests were carried out from room temperature up to 600∘C. An artificial degrading heat treatment was applied to reproduce the mechanical state of the most degraded die regions, and the resulting data were used to quantify the temperature-dependent reduction in yield strength with service exposure. Finite element simulations of the industrial forging process were then carried out using deformable dies to evaluate temperature evolution and stress levels in critical die regions. The risk of die plastification was assessed by comparing simulated von Mises stresses with the experimentally determined temperature-dependent yield strengths for the raw and degraded conditions. The results show a significant reduction in yield strength due to both increasing temperature and service-induced degradation, leading to a substantially higher risk of die plastic deformation under production conditions. The study underlines the importance of incorporating degraded material properties into tool design and process assessment, and motivates improved cooling systems to enhance tool life and process stability.

Introduction

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Hot forging under severe thermo-mechanical loading conditions, such as those found during the forming of nickel-based superalloys, poses significant challenges for forging die materials. Nickelbased superalloys are widely employed in advanced sectors such as aerospace and energy generation due to their exceptional strength and creep resistance at elevated temperatures. However, their high flow stress, even at forging temperatures, results in elevated contact pressures and substantial thermal exposure of the dies. During industrial hot forging, dies are subjected to repeated heating-cooling cycles, with local temperatures reaching up to 600∘C[1,2]. These conditions progressively degrade the mechanical properties of the tool steel, reducing its yield strength and, consequently, increasing the risk of die plastification [3,4].

Conventional tool design approaches often rely on the mechanical properties of tool steels in the raw, as-heat-treated state. Such methods do not account for service-induced degradation, which may lead to an overestimation of tool performance and non-conservative design. A more realistic understanding of the degraded mechanical behaviour of tool steels after industrial production is
therefore essential to improve tool life prediction and enable the development of more robust tool design methodologies [4,5].

Although the temperature-dependent softening of H13 tool steel has been extensively reported in the literature [6-8], experimental studies directly linking service-induced degradation of industrial forging dies with temperature-dependent mechanical characterisation and numerical stress assessment remain limited. In particular, the combined investigation of a real service-degraded tool, controlled reproduction of the degraded material state, and thermo-mechanically coupled numerical analysis for plastification risk assessment has received comparatively little attention. The novelty of the present work lies in this integrated approach, bridging industrial degradation, laboratory characterisation, and numerical evaluation under representative forging conditions.

In this study, the mechanical properties and the microstructure of a H13 tool steel employed in an industrial hot forging application were analysed in both its raw and service-degraded states. First, the raw steel was characterised through quasi-static tensile tests carried out from room temperature up to 600∘C. In the second stage, a forging die that had been used in serial industrial production was examined to evaluate service-induced degradation. Micro-hardness measurements were carried out across the die cross-section to assess property gradients from the surface to the core. Cylindrical specimens were then extracted from three regions (near the surface, mid-depth, and the core), and uniaxial compression tests were performed at room temperature to analyse the yield strength gradient across the die cross-section.

In addition, based on the hardness reduction observed in the most degraded zone of the industrial die, a controlled degrading heat treatment was applied to the raw H13 steel to reproduce comparable mechanical properties [9,10]. Tensile specimens were subsequently extracted from the degraded material, and quasi-static tensile tests were performed from room temperature up to 600∘C, enabling experimental determination of the temperature-dependent yield strength of the degraded H13 tool steel.

Finally, thermo-mechanically coupled finite element simulations of an industrial hot forging process were carried out using deformable dies [11,12], with the die material model calibrated based on the experimental data obtained in this work. The stress state in the forging die was analysed for both the raw and degraded material conditions, and the risk of plastification was assessed through comparison between simulated stress levels and experimentally determined yield strengths. This approach provides insight into the influence of service-induced degradation on die performance under representative industrial forging conditions.

Experimental Procedure

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An industrial forging die manufactured from H 13 tool steel and previously employed in hot forging production was employed for the experimental analysis (Fig.1a). The exact number of forging cycles performed with the analysed die prior to specimen extraction was not provided by the industrial partner. However, the die had been used under regular industrial production conditions and exhibited clear evidence of service-induced thermal degradation in regions close to the contact interface. A 10 mm thick slice was extracted from the cross-section of the die by wire electrical discharge machining (EDM) to enable detailed experimental characterisation (Fig.1b).

[picture]

Fig. 1. a) H13 hot forging die employed for the study, and b) slice cut from the die with wire EDM for experimental characterisation.

Fig. 1. a) H13 hot forging die employed for the study, and b) slice cut from the die with wire EDM for experimental characterisation.

First, a hardness analysis of the die cross-section was carried out to evaluate the mechanical property gradients induced by service conditions. For this purpose, micro-Vickers hardness tests (HV0.5) were performed along three directions: longitudinal, radial-oblique, and radial-horizontal, as illustrated in Fig.2a. The hardness measurements were programmed and performed automatically using a Vickers QATM Qness 60A+ hardness testing machine. Indentations were spaced at intervals of 1 mm , allowing a detailed mapping of the hardness distribution across the die cross-section.

[picture]

Fig. 2. a) Hardness test directions and microstructural analysis zones, and b) zones from which uniaxial compression samples were cut with wire EDM.

Fig. 2. a) Hardness test directions and microstructural analysis zones, and b) zones from which uniaxial compression samples were cut with wire EDM.

Optical microstructural analysis was subsequently conducted on the tool steel in two representative conditions: the as-heat-treated state at the base of the die, which was not affected by productionrelated degradation, and the degraded zone located at the die edge (Fig.2a). Standard metallographic sample preparation procedures were employed, followed by chemical etching using Nital. The microstructures were examined using a Leica DM i8C optical microscope to identify possible microstructural changes associated with service-induced degradation.

Cylindrical uniaxial compression specimens with dimensions ∅5×10 mm were extracted by wire EDM from four different locations across the die slice, ranging from the base region (non-affected zone) to the die edge (most affected zone), as shown in Fig.2b. Quasi-static uniaxial compression tests were performed at room temperature using an Instron 4206 universal testing machine to analyse the degradation of yield strength across the die cross-section resulting from industrial service (Fig.3). Zinc stearate lubricant was applied to the specimen-tool interfaces to minimise friction and reduce excessive barrelling. The force during the tests was acquired with the load cell integrated to the compression machine, and the displacement with a linear variable displacement transformer (LVDT) attached to the upper tool.

[picture]

Fig. 3. Uniaxial compression test at room temperature conducted in the Instron 4206 universal machine.

Fig. 3. Uniaxial compression test at room temperature conducted in the Instron 4206 universal machine.

To determine the temperature-dependent yield strength of the H 13 tool steel in its initial, nondegraded state, tensile specimens were extracted by wire EDM from the base of the die (Fig.4). This
zone was representative of the raw, as-heat-treated material condition, corresponding to forging dies at the beginning of production (Fig.4). Quasi-static tensile tests were carried out at room temperature, 300∘C, and 600∘C using the Instron 4206 universal testing machine. Prior to testing, the specimens were prepared for temperature and strain measurements. One side of each specimen was coated with a heat-resistant, high-emissivity paint to enable accurate temperature monitoring using an infrared (IR) thermal camera, while the opposite side was coated with a heat-resistant stochastic speckle pattern for strain measurement by Digital Image Correlation (DIC) (Fig.5a). Specimens were heated using an in-house developed Joule heating device, providing stable and uniform temperature control within the deformation zone, which was continuously monitored using the IR camera (Fig.5b). Strain in the gauge length was evaluated using GOM Correlate DIC software, employing a virtual extensometer.

[picture]

Fig. 4. Tensile samples extracted from the base of the die, representative of the raw, as-heat-treated material condition.

Fig. 4. Tensile samples extracted from the base of the die, representative of the raw, as-heat-treated material condition.

Based on the hardness values measured in the most degraded zone of the die cross-section, corresponding to the die edge, an artificial degrading heat treatment was applied to tensile specimens extracted from the non-affected zone of the die. The heat treatment consisted of exposure at 650∘C for 10 h in an electrical furnace, with the aim of reproducing mechanical properties comparable to those observed at the degraded die edge. This approach was adopted due to the insufficient material volume available at the die edge for direct extraction of tensile specimens.

After the tensile specimens were degraded using the aforementioned heat treatment, quasi-static tensile tests were performed at room temperature, 300∘C, and 600∘C to determine the temperaturedependent yield strength of the H13 tool steel in the degraded condition. The same experimental setup and Joule heating device as those used for the raw material tests were also employed in this case (Fig.5b).

[picture]

Fig. 5. a) Preparation of the tensile specimen for high-temperature testing, showing the highemissivity coating for temperature measurement (left) and the stochastic speckle pattern for DIC strain measurement (right); and b) tensile testing setup with the in-house developed Joule heating device, the IR camera, and the DIC camera in the opposite side.

Fig. 5. a) Preparation of the tensile specimen for high-temperature testing, showing the highemissivity coating for temperature measurement (left) and the stochastic speckle pattern for DIC strain measurement (right); and b) tensile testing setup with the in-house developed Joule heating device, the IR camera, and the DIC camera in the opposite side.

This experimental campaign enabled an extensive characterisation of the H 13 tool steel in both its raw and degraded conditions, covering the full range of temperatures and material states observed under real industrial hot forging conditions. The combination of hardness measurements, microstructural analysis, and mechanical testing provided a comprehensive understanding of the evolution of mechanical behaviour across the die cross-section and with increasing temperature.

It should be noted that the mechanical characterisation performed in this study is limited to quasistatic uniaxial tensile and compression tests. In industrial forging operations, dies are subjected to multiaxial stress states and repeated thermo-mechanical cycles. Therefore, the yield strength values obtained here provide a conservative basis for assessing plastification risk, but they do not capture cyclic damage accumulation, fatigue effects, or stress-state sensitivity. The results should thus be interpreted as indicative of static plastification likelihood rather than a full representation of longterm die degradation mechanisms.

Results and Discussion

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In this section the experimental and numerical results obtained from the characterisation of the H13 tool steel in both its raw and service-degraded conditions are presented. To begin with, Fig. 6 presents the Vickers hardness distribution across the die cross-section from the edge to the base, measured along the longitudinal, radial-oblique, and radial-horizontal directions. From the base of the die up to a distance of approximately 80 mm from the edge, the hardness remains relatively constant, with values in the range of 500−550 HV0.5. This indicates that, in this region, the material is not affected by the cyclic heating-cooling experienced during production and retains its raw mechanical properties.

Beyond this distance, starting at approximately 80 mm from the edge, the hardness decreases progressively towards the die edge, reaching minimum values of around 225 HV 0.5 at the surface. A similar hardness evolution is observed along the longitudinal, radial-oblique, and radial-horizontal directions, although slightly lower hardness values are measured in the radial-oblique direction.

[picture]

Fig. 6. Hardness evolution in the cross-section of the die from the edge to the base, in the horizontal, radial-oblique and radial-horizontal directions.

Fig. 6. Hardness evolution in the cross-section of the die from the edge to the base, in the horizontal, radial-oblique and radial-horizontal directions.

The microstructures of the non-affected zone at the base of the die and the service-degraded zone at the die edge are shown in Fig.7a and Fig.7b, respectively. The microstructure observed in the nonaffected zone at the base of the die (Fig.7a) is characteristic of H13 tool steel in the as-heat-treated condition. The microstructure consists of a tempered martensitic matrix with a relatively uniform distribution of fine carbides, indicating that the original quenching and tempering treatment has been preserved in this region.

In contrast, the microstructure observed at the die edge (Fig.7b), corresponding to the most servicedegraded zone, exhibits clear signs of thermal degradation. The microstructure appears more homogeneous and less defined, with evidence of carbide coarsening and a reduction in microstructural contrast compared to the base material. This microstructural evolution is indicative of over-tempering caused by prolonged exposure to elevated temperatures and repeated heatingcooling cycles during hot forging. Such changes are consistent with the significant reduction in hardness measured near the die edge.

[picture]

Fig. 7. a) The microstructures of the non-affected zone at the base of the die; and b) the servicedegraded zone at the die edge.

Fig. 7. a) The microstructures of the non-affected zone at the base of the die; and b) the servicedegraded zone at the die edge.

Fig. 8 shows the engineering stress-strain curves obtained from uniaxial compression tests performed at room temperature on specimens extracted from four different locations across the die cross-section, ranging from the base material (zone 4) to the die edge (zone 1), as indicated in Fig.2b. A clear degradation of mechanical properties is observed as the extraction location approaches the die edge.

Hardness measurements previously showed that the service-affected zone extends up to approximately 80 mm from the die edge. Accordingly, the uniaxial compression specimens extracted from zones 1,2 , and 3, located at distances of approximately 10 mm,20 mm, and 40 mm from the edge, respectively, fall within this affected region. In contrast, specimens extracted from the base material (zone 4) are located outside the affected zone.

A noticeable scatter in the mechanical response is observed among specimens extracted from the same zone, particularly in the affected regions. This variability can be attributed to slight differences in the actual distance from the die edge, as specimens located closer to the edge are exposed to higher thermal and mechanical loads and therefore exhibit more pronounced degradation. Conversely, specimens extracted from the base material (zone 4) exhibit the highest mechanical strength and significantly lower scatter, with behaviour comparable to that of the original, as-heat-treated material. These results are in good agreement with the hardness measurements and further corroborate the observed mechanical property gradients across the die cross-section.

[picture]

Fig. 8. Engineering stress-strain curves obtained from uniaxial compression tests at room temperature for specimens extracted from different locations across the die cross-section.

Fig. 8. Engineering stress-strain curves obtained from uniaxial compression tests at room temperature for specimens extracted from different locations across the die cross-section.

As described previously, to reproduce the mechanical properties observed in the degraded state at the die edge, an artificial degrading heat treatment of 10 h at 650∘C was applied to tensile specimens extracted from the base material in the raw condition. The hardness obtained after the degrading heat treatment was 288 HV0.5 ( ± 7.3), which is very close to the hardness measured at the edge of the industrial die.

Fig. 9 shows the microstructure of the H13 tool steel after the artificial degrading heat treatment. The microstructure exhibits strong similarities to that observed in the service-degraded zone at the die edge, characterised by a tempered martensitic matrix with reduced microstructural contrast and evidence of carbide coarsening. This close correspondence between hardness values and microstructural features confirms that the artificially degraded tensile specimens are representative of the service-degraded state of the industrial die and can therefore be reliably used to characterise the tensile properties of the tool steel after production.

[picture]

Fig. 9. Optical microstructure of the H13 tool steel in the artificially degraded condition after heat treatment at 650∘C for 10 h .

Fig. 9. Optical microstructure of the H13 tool steel in the artificially degraded condition after heat treatment at 650 ∘ C for 10 h .

Fig. 10 presents the engineering stress-strain curves obtained from tensile tests conducted at room temperature, 300∘C, and 600∘C on H 13 tool steel specimens in both the raw and artificially degraded conditions. Two tests were performed for each temperature and material state, demonstrating good repeatability of the results. The corresponding yield strength values are summarised in Fig.11. For
the raw material, the yield strengths were 1320 MPa at room temperature, 885 MPa at 300∘C, and 694 MPa at 600∘C. In contrast, the artificially degraded material exhibited significantly lower yield strength values of 653MPa,500MPa, and 254 MPa at the same respective temperatures. In both conditions, a clear temperature dependence of the mechanical response is observed, with progressively decreasing stress levels at higher testing temperatures. Additionally, the degraded material shows an increased elongation capacity compared to the raw condition, consistent with its reduced strength and thermally softened state.

[picture]

Fig. 10. Engineering stress-strain curves of the H13 steel in the raw and degraded conditions at room temperature, 300∘C and 600∘C.

Fig. 10. Engineering stress-strain curves of the H13 steel in the raw and degraded conditions at room temperature, 300 ∘ C and 600 ∘ C .

The direct comparison of the yield strength evolution with temperature for the raw and degraded material conditions reveals a significant combined effect of thermal exposure and service-induced degradation (Fig.11). While the raw H13 tool steel already exhibits a significant reduction in yield strength with increasing temperature, this effect is amplified in the degraded condition. At all investigated temperatures, the degraded material shows markedly lower yield strength values than the raw state, with the difference becoming particularly critical at the highest tested temperature ( 600∘C ). This reduction in yield strength implies that, due to service-degradation, the forging die becomes increasingly prone to plastic deformation under the high contact stresses imposed by hot forging of nickel-based superalloys.

[picture]

Fig. 11. Comparison of the yield strength of H 13 tool steel in the raw and degraded conditions at room temperature, 300∘C and 600∘C.

Fig. 11. Comparison of the yield strength of H 13 tool steel in the raw and degraded conditions at room temperature, 300 ∘ C and 600 ∘ C .

Finally, a numerical study was conducted to assess the temperature evolution and stress levels reached in the analysed die during an industrial hot forging process. A thermo-mechanically coupled 2D finite element simulation of the forging process was carried out using the FORGE® simulation software. A full forging sequence of a real nickel-based superalloy component was simulated on a mechanical press. The billet material was modelled using a viscoplastic Hensel-Spittel constitutive model calibrated for Inconel 625, enabling accurate representation of its temperature- and strain-ratedependent flow behaviour during forging.

The dies were modelled as deformable bodies using a purely elastic material model with a Young's modulus of 210 GPa . Plastic deformation of the dies was not directly simulated. Instead, the risk of die plastification was assessed by comparing the calculated von Mises equivalent stresses with the experimentally determined temperature-dependent yield strengths of the H13 tool steel in both the raw and service-degraded conditions.

The simulation was fully thermo-mechanically coupled. The initial billet temperature was set to 1150∘C, while the initial die temperatures were assumed to represent steady-state production conditions, with 250∘C for the upper die and 200∘C for the lower die. It should be noted that die temperatures were not experimentally measured during production. The simulated temperature evolution therefore represents an indicative assessment based on the assumed steady-state initial die temperatures and thermal boundary conditions. Consequently, the numerical results should be interpreted as qualitative rather than fully predictive.

Heat transfer between billet and die was modelled using a heat transfer coefficient of 10,000 W/m2 K. Contact conditions were defined using a Coulomb-limited Tresca friction model, with a Coulomb's friction coefficient of 0.15 and a Tresca's constant 0.3 . The die mesh consisted of a general element size of 5 mm , refined to 1.25 mm in contact regions to improve stress resolution (Fig.12a). A single forging stroke was simulated, and intermediate stages of the stroke are presented to illustrate the evolution of temperature (Fig.12a) and stress fields (Fig.12b and Fig.12c).

Fig.12a illustrates the temperature distribution in the die at different stages of the forging stroke. Localised temperature increases are observed in regions close to the contact interface, with maximum temperatures reaching up to 400∘C at the die edge by the end of the process. As demonstrated by the experimental results presented in this work, the yield strength of H 13 tool steel decreases significantly with increasing temperature, an effect that is further intensified in the service-degraded condition.

Regarding the stress state, Fig.12b and Fig.12c present the von Mises equivalent stress distribution in the die at different stages of the forging sequence. Fig.12b and Fig.12c correspond to simulations assuming the die material in the raw state and in the degraded state, respectively. Localised regions near the die edge are subjected to high stress levels, reaching values of up to approximately 1000 MPa. These stress levels are critical, as the combination of elevated temperatures and high contact stresses in these regions significantly increases the likelihood of local plastic deformation of the die during production. This risk becomes particularly severe when the degraded material condition is considered, since a large portion of the die experiences stress levels exceeding the yield strength of the tool steel in the degraded state.

[picture]

Fig. 12. a) Temperature distribution in the die at different stages of the forging sequence. von Mises equivalent stress distribution in the die at different stages of the forging sequence, b) assuming the die material in the raw state, and c) in the degraded state.

Fig. 12. a) Temperature distribution in the die at different stages of the forging sequence. von Mises equivalent stress distribution in the die at different stages of the forging sequence, b) assuming the die material in the raw state, and c) in the degraded state.

These results indicate that thermal exposure during hot forging significantly contributes to the degradation of die mechanical properties. Elevated temperatures reduce the yield strength of the tool steel, increasing the likelihood of die plastification under prolonged production conditions. From an industrial perspective, appropriate control of die thermal loading is therefore important for maintaining tool performance and dimensional stability. The present findings show that neglecting service-induced degradation in material modelling may lead to a non-conservative assessment of die behaviour, emphasising the need to incorporate temperature-dependent and degradation-sensitive properties into tool design.

It should be noted that, in the numerical simulations, the dies were modelled as purely elastic bodies and plastic deformation was not directly simulated. The risk of plastification was assessed by comparing the simulated von Mises stresses with the experimentally determined temperaturedependent yield strengths of the material. Accordingly, the numerical results should be interpreted as indicative of plastification likelihood rather than fully predictive of permanent die deformation.

In addition, die temperatures during industrial production were not experimentally monitored. The simulated temperature fields are therefore based on assumed steady-state initial conditions and prescribed thermal boundary parameters, and should be regarded as representative estimates of the thermal loading experienced during forging. Furthermore, while the experimentally observed material
degradation results from repeated thermo-mechanical cycling during long-term industrial service, the simulation represents a single forging stroke under steady-state conditions. The numerical results thus provide a representative stress and temperature state for an individual forging cycle, whereas the experimentally characterised degradation reflects the cumulative effects of prolonged service exposure.

Conclusion

The original version of this paper is available on https://www.scientific.net/MSF.1186.73.pdf

This study investigated the mechanical degradation of H13 tool steel under industrial hot forging conditions through combined experimental characterisation and numerical stress analysis. Based on the results obtained, the following conclusions can be drawn:

A comprehensive experimental characterisation of H13 tool steel in both raw and servicedegraded conditions was performed, covering the temperature range relevant for industrial hot forging (room temperature to 600∘C ).

Hardness measurements and microstructural analysis of an industrial forging die revealed a pronounced degradation of mechanical properties within approximately 80 mm from the die edge, consistent with thermal exposure during production.

Uniaxial compression testing confirmed a significant reduction in yield strength in regions closer to the die edge, in agreement with the measured hardness gradients and observed microstructural changes.

Quasi-static tensile testing demonstrated a strong temperature dependence of yield strength for H13 steel, with the degradation effect becoming particularly severe at elevated temperatures representative of forging conditions.

An artificial degrading heat treatment successfully reproduced the mechanical and microstructural state of the service-degraded die edge, enabling controlled evaluation of temperature-dependent material behaviour.

Thermo-mechanically coupled finite element simulations of a representative forging stroke showed that high von Mises stress levels are reached in critical die regions, particularly near the contact interface. Comparison of simulated stress levels with experimentally determined yield strengths indicates an increased risk of die plastification in the degraded condition.

The results indicate that neglecting service-induced degradation in material modelling may lead to non-conservative assessment of die performance under industrial forging conditions.

The findings highlight the importance of incorporating temperature-dependent and degradation-sensitive material properties into numerical tool design methodologies and support improved consideration of thermal loading in tool life assessment.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/MSF.1186.73.pdf

The authors would like to acknowledge the financial support of SMART Eureka, the Spanish Government (CDTI) and the Basque Government (Hazitek) through the OPTFORGE project (F20045241). Special thanks to ULMA Forja S. Coop. for the financial support and the valuable technical collaboration.

References

The original version of this paper is available on https://www.scientific.net/MSF.1186.73.pdf

Kim DH, Lee HC, Kim BM, Kim KH. Estimation of die service life against plastic deformation and wear during hot forging processes. J Mater Process Technol 2005;166:372-80. https://doi.org/10.1016/j.jmatprotec.2004.07.103.

Li C-D, Yang H-Y, Dong B-X, Chen D-L, Shu S-L, Qiu F, et al. Thermal fatigue failure mechanisms and enhancement strategies of die steel. J Mater Res Technol 2025;38:4567-99. .

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Emamverdian AA, Sun Y, Chunping C. Deformation and wear in a H21 (3Cr2W8V) steel die during hot forging: simulation, mechanical properties, and microstructural evolution. J Mater Res Technol 2021;15:268-77. .

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Yuan M, Cao Y, Karamchedu S, Hosseini S, Yao Y, Berglund J, et al. Characteristics of a modified H13 hot-work tool steel fabricated by means of laser beam powder bed fusion. Mater Sci Eng A 2022;831:142322. .

Feng H, Wang HJ, Li HB, Jiao WC, Zhu HC, Zhang SC, et al. Softening Behavior and Hardness Prediction of H13 Hot-Work Die Steel during Isothermal Tempering. Steel Res Int 2024;95:111. . 202300251.

Lin M, Zhao X, Han L, Liu Q, Gu J. Microstructural Evolution and Carbide Precipitation in a Heat-Treated H13 Hot Work Mold Steel. Metallogr Microstruct Anal 2016;5:520-7. .

Ning A, Guo H, Chen X, Wang M. Precipitation behaviors and strengthening of carbides in H13 steel during annealing. Mater Trans 2015;56:581-6. .

Chenot J, Bouchard P, Fourment L, Lasne P, Sim- PLN. Numerical Simulation and Optimization of the Forging Process To cite this version : Numerical Simulation and Optimization of the Forging Process 2012.

António CC, Castro CF, Sousa LC. Optimization of metal forming processes. Comput Struct 2004;82:1425-33. .

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/forging-and-hot-forming/978-3-0364-1538-3

Materials Science Forum, ISSN: 1662-9752, Vol. 1186, pp 85-96

doi: 10.4028/p-EIN5Ft

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

Submitted:
2026-01-14

Revised:
2026-02-13

Accepted:
2026-02-24

Online:
2026-04-16

Profile Contour and Flatness Control Model for Hot Strip Rolling

The original version of this paper is available on https://www.scientific.net/MSF.1186.85.pdf

Nelson Souto 1,a*, Nicolas Legrand 2, b and Stephane Jimenez 3,c

¹ArcelorMittal Global R&D Maizieres, Voie Romaine BP 30320, 57283 Maizières-lès-Metz, France

2 ArcelorMittal Global R&D East Chicago, 3001 E. Columbus Drive, IN 46312, USA

3 ArcelorMittal Global R&D Software Factory, F-13776 Fos-sur-Mer, France

a*nelson.souto@arcelormittal.com, b  nicolas.legrand2@arcelormittal.com, c stephane.jimenez@arcelormittal.com

Keywords: hot rolling, strip crown, flatness control.

Abstract

A full Profile Contour and Flatness Control (PCFC) model prototype has been developed for hot finishing mills. This model prototype accounts for several physical based sub-models calculating the different contributions to the roll gap profile and allows for offline predictions in both preset and recalculation modes. To evaluate the PCFC model developed, an exhaustive comparison analysis between its calculations, the ones coming from the plant model and measures at the finishing mill exit has been carried out. An industrial mill database composed of different rolling campaign types was applied for this purpose and both (i) strip crown and flatness indicators as well as (ii) full strip profiles results have been used for the comparisons. Encouraging results were obtained from this performance assessment since the PFCF model developed leads to similar behavior compared to the existing plant's model (from an industrial supplier). As a result, the PCFC model developed shows high potential for online implementation in hot strip mills.

Introduction

The original version of this paper is available on https://www.scientific.net/MSF.1186.85.pdf

In hot rolling, Profile Contour and Flatness Control (PCFC) models are crucial not only to respect the tolerance of the final strip crown and flatness but also for rolling stability to maintain a good strip shape in inter-stands to avoid cobles, rolling incidents and increase mill productivity. These PCFC models consist of complex systems that must account for (i) any mechanical actuator applied on the finishing mill stands, (ii) several sub-models to calculate the different contributions to the roll gap profile and strip shape, (iii) preset strategies as well as (iv) adaptation contribution [1]. In this way, the performance of those models depends on the actuators technologies applied on the rolls and finishing stands, like work-roll bending [2], continuous variable crown [3,4], smart crown [5] or work roll shifting [6], but also on the quality itself of the PCFC model that presets these actuators to reach targeted exit strip crown and flatness from costumer requirements [7].

Most part of the PCFC systems currently deployed on hot strip mills are based on physical submodels to predict roll gap profile and strip shape at several hot finishing stands [8-11]. More recent developments on PCFC systems tend to be focused on applying machine learning (ML) approaches. However, part of these ML approaches may struggle with accuracy and reliability as they usually rely only on data driven learning and fail to incorporate process specific knowledge [12]. To address this type of limitation, physics-informed ML models integrating engineering insights have also been proposed by some authors by deriving process knowledge through finite element analysis [12], mechanical principles [13] or physical based formulations [14,15] to enhance predictions and ensure better consistency with real-world process dynamics. However, a large number of these ML approaches are mainly focused on the prediction of the hot-rolled strip crown for the last stand by often using theoretical strip crown [16,17] or roll gap [18,19] values of the previous stands as initial input data. Nevertheless, a full PCFC system must predict roll gap contribution and consequent strip crown not only for the last stand but for all the finishing stands in order to preset mill actuators accordingly. It must be kept in mind that, for industrial online application, a PCFC system must be designed to determine effective mill actuators preset simultaneously for all the finishing stands and at every strip to be rolled by delivering fast calculations.

Still, it was demonstrated that ML model approaches may outperform the physical based models for hot strip mills running low number of different strip grades, limited strip width ranges or specific rolling schedules [15,20]. In fact, several studies have proved that ML approaches have significant potential in this field but there are still problems such as unstable high data quality and data standardization, poor adaptability to the actual production environment with continuous working conditions changes, insufficient model interpretability and decision-making trust for process engineers that must be addressed to make possible a full industrial application [21]. Then, some progress still needs to be done to have this type of solutions running online for real-time control of the rolling process over all the hot finishing stands and for a large production mix.

From an industrial point of view, commercial PCFC models running today on hot strip mills are essentially black-box supplier's solutions not allowing for tailor-made modifications. On top of that, these supplier's solutions are sometimes showing limitations on delivering reliable mill actuators preset particularly for most recent strip grades.

Thus, the main goal of this work is to develop an effective PCFC model prototype for hot finishing mills i) based on physical models knowledge and ii) that will enable to anticipate tighter tolerances in strip flatness and profile for 3rd  generation Advanced High Strength Steels (AHSS) and especially, for electrical steel grades that require very uniform and flat profiles along the coils. A first performance assessment of this PCFC model prototype is also addressed here by using industrial data coming from a hot strip mill with 7 finishing stands (F1-F7), all equipped with bending and shifting actuators as well as continuous variable crown (CVC) rolls.

PCFC Model Description

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The PCFC model developed allows for fast computations for an entire rolling campaign ( ≈0.2 sec per strip) and for 2 simulation modes which rely on different applications, for instance, recalculation and prospection. The recalculation mode is used to predict strip crown and flatness by giving mill actuators values as input data for all the finishing stands, while the prospection mode determines the mill actuators data set for each stand that would lead to a desired exit strip crown and flatness targets, at the exit of the finishing mill, by minimizing rolling instabilities on the inter-stands. Several submodels calculating the different contributions to the roll gap profile, namely, (i) work-roll and backup roll wear, (ii) work-roll thermal crown, (iii) strip profile and (iv) stand models are included in the PCFC model prototype (Figure 1).

[picture]

Fig. 1. Different models needed to calculate strip crown and flatness shape at each hot finishing mill stand.

Fig. 1. Different models needed to calculate strip crown and flatness shape at each hot finishing mill stand.

Stand model.

The original version of this paper is available on https://www.scientific.net/MSF.1186.85.pdf

The stand model corresponds to a semi-analytical formulation that allows for reliable work-roll deformation calculations by computing the equilibrium state of a vertical stack of rolls [22]. This model depends on initial strip shape, rolls geometry (variable profile like CVC or smart crown, chamfers, necks), strip and roll material properties, mill actuators as well as bending and rolling
forces. Its modelling is based on the axial rolls' discretization for half of the stand by using the following theoretical formulations [23]:

Timoshenko beam theory for calculation of roll bending

Hertz contact theory for arc length contact calculation

Hitchcock theory for determining the longitudinal distribution of the rolling force over the roll-bite

Boussinesq theory and Berger analytical solution to calculate roll flattening due to work-roll / strip or backup roll / work-roll contacts
A complete stand model calculation is launched at every new strip and then, this calculation is linearized around the working point of the strip by the following equation,

bc=MLam· FLam+MAct· FAct+ICT· bvCT+ICA· bvCA+T(1)

where 𝐛c stands for the deformed roll gap profile at each contact node between strip and work-roll, 𝐅Lam is a vector describing the rolling force distribution, 𝐅Act is the vector containing the values of all mechanical actuators equipping the stand while 𝐛vCT and 𝐛vCA are vectors containing the off-load crown distribution at each node (grinding, wear and thermal crown contributions) for the work and backup rolls, respectively. In addition, 𝐈,𝐓,𝐌Lam and 𝐌Act are tensors depending on the number of strip nodes and/or stand actuators.

This linearization approach is needed to enable the coupling of the stand model with the strip model, however, it is also needed to know the sensitivity of the on-load crown to the off-load workroll crown distribution. Therefore, the initial linearization given by Eq. (1) is thus adapted to,

bc=MLam· FLam+MAct· FAct+MvidT· Bva+T(2)

where, 𝐌vidT  is a tensor for the off-load work-roll crown contribution and 𝐁va corresponds to the theoretical off-load crown adaptation amplitude considering the full work-roll barrel length. This linearized equation is then used along with a scalar strip model formulated to determine strip crown and flatness values at the 3 usual positions from the strip edge ( 25,40 and 100 mm from edge). In this scalar approach, the effect of the rolling force heterogeneity over the strip width is neglected. Although the strip model is defined in a scalar approach, full strip crown and flatness profiles are anyway obtained through a polynomial extrapolation between the strip centre, the 3 crown indicators positions (C25, C40 and C100) and extreme strip edge.

Strip model.

The original version of this paper is available on https://www.scientific.net/MSF.1186.85.pdf

The strip model developed to run the PCFC calculations consists of a scalar formulation which can be written as [24],

bh=bch+η(ε−fla)(3)

with,

ε=BH+FLA−bch(4)

and where, b and B stand respectively for exit and entry strip crown, fla and FLA stand respectively for exit and entry flatness, h and H correspond respectively to exit and entry thickness at strip center, bc is the roll bite exit crown in affinity with entry strip crown, ε is the general deformation and η is the crown heredity coefficient which depends on the strip grade features. In this formulation, the exit flatness (fla) is defined as,

fla=ξε(5)

where parameter ξ is a coefficient of sensitivity to flatness.
In order to link both strip and stand models, Eq. (2) must be simplified to assume a scalar form as well. This equation is then re-written in a scalar way as,

bch=φ+θ+βh+Th=α+Th(6)

and then, stand model given by Eq. (6) and strip model given by Eq. (3) can be coupled as

bh=α+Th+η(ε− fla )(7)

where the term α is commonly called as intrinsic on-load crown ratio. By using this coupled equation, strip crown and flatness calculations are done at the 3 common crown indicator positions (C25, C40 and C100, which correspond to strip crown calculations respectively at 25,40 and 100 mm from strip edge). As a result, this strip model accounts for 6 tuning coefficients to adjust the flatness sensitivity coefficient ξ,

ξi=Ai(wh)Bi, with i=1 to 3(8)

where, w is the strip width, index i=1 to 3 stands respectively for the crown indicator positions C25, C40 and C100 and both Ai and Bi are the strip model tuning parameters for each one of these indicator positions. Although this strip model is defined in a scalar mode, full strip crown and flatness profiles are obtained through a polynomial extrapolation between the strip center, the 3 crown indicators positions (C25, C40 and C100) and the extreme strip edge.

Roll thermal model.

The original version of this paper is available on https://www.scientific.net/MSF.1186.85.pdf

The roll thermal crown model applied is a classic finite difference axisymmetric ( r,z ) model to calculate 2D roll temperature distribution based on averaged heat transfer over the roll circumference. This 12 axisymmetric thermal model calculates an input heat flux ( φ ) based on a semi-empiric function which accounts for the roll-bite and cooling thermal exchanges over the roll surface. This model considers refined meshing over the radial direction and includes roll necks modelling as shown in Figure 2. On top of that, this thermal model also accounts for work-roll shifting and different thermal properties for roll core and shell.

[picture]

Fig. 2. One half work-roll mesh of PCFC 's thermal model.

Fig. 2. One half work-roll mesh of PCFC 's thermal model.

The heat exchanges along the roll barrel length are defined considering 2 distinct zones (see Figure 2), a zone A in direct contact with the strip and a zone B with no contact with the strip. The heat flux Φ for roll section in contact with the strip (zone A) is calculated by,

Φ=Hstrip lrbvR(Tstrip −T)+2πR.Hcool AvR(T−Tcool )(9)

while the heat flux Φ for roll sections without strip contact (zone B) is calculated as,

Φ=2πR·Hcool B(T−Tcool )(10)

where Lrb is the roll-bite contact length, Hstrip  is the roll-bite heat coefficient, vr and R are the roll speed and roll radius, Hcool A and Hcool B are the coolant heat coefficients for zones A and B and, Tcool  and Tstrip  are respectively the coolant and strip temperatures. From this formulation, heat flux dissipated by friction on roll-bite is neglected. However, higher roll-bite contact length or roll speed leads automatically to increased entry heat flux.

The roll thermal crown is calculated in this model by considering plane stress assumption [25],

uR=2αRollR∫0Rr.ΔT(r).∂r(11)

where uR is the roll radial displacement due to thermal expansion, αRoll  is the roll thermal dilatation coefficient and ΔT is the local roll temperature variation from its initial temperature.

Work-roll wear model.

The original version of this paper is available on https://www.scientific.net/MSF.1186.85.pdf

Roll wear phenomenon may involve simultaneously several mechanisms like abrasion, adhesion, chemical degradation, thermal and mechanical fatigue. The individual characterization and quantification of these mechanisms still remains very difficult, and so, most part of the wear models consist of contact-mechanic based equations assuming simplified relations between both material and process parameters [26]. The wear model adopted in the PCFC model prototype is also a contactmechanic based equation. This roll wear equation is derived from Archard principles which stipulate that wear (volume loss) is proportional to the normal force by the sliding length [27]. This work-roll wear equation is based on a slicing method by equally dividing the work-roll in a certain number of slices over the roll barrel direction and can be defined as,

WWR=kF·Lw[1+axn](12)

where, WWR is the work-roll wear for one strip of the rolling campaign, F is the rolling force, w is the strip width, L is the rolling length, k is a wear factor tuning coefficient, a is a tuning coefficient of preferential wear at the edges of the roll wear profile, n is an exponent coefficient and x is the relative position along the strip width. Both k and a tuning coefficients can be defined as function of the finishing stand, work-roll type and strip grade family (further details can be found in [26]). This model accounts for work-roll shifting but assumes constant pressure along strip width.

Backup roll wear model.

The original version of this paper is available on https://www.scientific.net/MSF.1186.85.pdf

Similarly to the work-roll wear modelling, the backup roll wear is also formulated by a contactmechanic equation. This equation assumes a simplified relation based on the cumulated rolling length of the backup roll and is written as,

WBUR=kBUR·LBUR[1−bxm](13)

where, WBUR  is the backup roll wear, LBUR  is the cumulated rolling length seen by the backup roll, kBUR  and b are backup wear tuning coefficients, m is an exponent coefficient and x is the relative position along the backup roll barrel width. The wear tuning coefficient kBUR  can be defined as function of the mill stand and the backup roll types.

PCFC Model Calibration

The original version of this paper is available on https://www.scientific.net/MSF.1186.85.pdf

An automatic optimization methodology was deployed to calibrate the tuning coefficients of the PCFC sub-models. This methodology corresponds to an inverse optimization approach whose main goal is to seek for a set of model coefficients leading to the smallest difference between measurements
and model calculations [26,28]. To do this, the implemented calibration methodology combines an optimization algorithm, responsible for updating the unknow model coefficients, with a cost function, responsible for defining the gap between experimental and calculated data. During the optimization, the set of coefficients is iteratively updated with the purpose of minimizing the cost function value. Figure 3 summarizes the inverse optimization process framework developed to calibrate the tuning coefficients of the PCFC sub-models.

[picture]

Fig. 3. Optimization framework used to calibrate PCFC wear and thermal crown sub-models [8].

Fig. 3. Optimization framework used to calibrate PCFC wear and thermal crown sub-models [8].

In this automatic optimization methodology, the cost function plays a crucial role on the model calibration since the optimization process is guided by its minimization, so a suitable definition of this function is required. Here, the cost function ( Fcost  ) used for calibrating the PCFC sub-models is defined by,

Fcost =∑i=jnt1npn∑i=1npn(δi,jRef−δi,jCalcwabs,j)2(14)

where nt is the number of experiments (from different rolling campaigns or different stands), npn is the number of points of each experiment, δi,jRef  and δi,jCalc  correspond respectively to the reference and calculated values for each point i belonging to the experiment j and wabs,j is a weighting factor that must be normalized according to the different units or scales of the experiment j. A constrained optimization approach is applied by defining empirical upper and lower bound values for each one of the tuning coefficients subjected to calibration.

Roll wear model tuning.

The original version of this paper is available on https://www.scientific.net/MSF.1186.85.pdf

In the case of the work-roll wear model, the optimization approach to tune the work-roll wear in the 7 hot finishing stands was split in 2 steps. Firstly, tune work-roll wear centreline amplitude (wear factor coefficient k ) at all the stands using a reference database composed by work-roll wear profiles for 20 rolling campaigns. Secondly, once wear amplitude was calibrated, tune the work-roll wear shape across the full roll width (tuning coefficient a ) considering the successive strip widths and roll shifting positions along the rolling campaigns. Figure 4 shows work-roll wear model calibration results obtained by applying the 2 steps tuning approach for the last 2 finishing stands (F6 and F7).

[picture]

Fig. 4. Work-roll wear a) centreline amplitude for all campaigns and b) profile at end of campaign 11 predicted by tuned wear model (PCFC wear) and compared with reference data coming from the ArcelorMittal plant (called as Plant data).

Fig. 4. Work-roll wear a) centreline amplitude for all campaigns and b) profile at end of campaign 11 predicted by tuned wear model (PCFC wear) and compared with reference data coming from the ArcelorMittal plant (called as Plant data).

Roll thermal model tuning.

The original version of this paper is available on https://www.scientific.net/MSF.1186.85.pdf

For roll thermal model tuning, it is recommended to use both roll temperature and thermal crown profiles as reference data [28]. Nevertheless, only thermal crown profiles coming from the plant were available. Thus, the automatic tuning approach was applied to find the optimum parameters set only minimizing the gap between mill's thermal crown profiles and respective calculated profiles coming from the PCFC thermal model. Thermal crown profiles were calibrated for all the 7 finishing stands by using a reference rolling campaign and by tuning the heat transfer coefficients Hstrip Hcool A and Hcool B, respectively, for each one of the stands. Figure 5 shows the tuned PCFC thermal crown results obtained for all stands at the end of the rolling campaign.

[picture]

Fig. 5. Work-roll thermal crown profiles for stands a) F1-F4 and b) F5-F7 coming from tuned PCFC thermal model and reference data provided by the ArcelorMittal plant (called as Plant data).

Fig. 5. Work-roll thermal crown profiles for stands a) F1-F4 and b) F5-F7 coming from tuned PCFC thermal model and reference data provided by the ArcelorMittal plant (called as Plant data).

Strip model tuning.

The original version of this paper is available on https://www.scientific.net/MSF.1186.85.pdf

No calibration has been applied to fine tune the strip model coefficients. In this way, all the calculations were carried out with a standard set of parameters. These standard parameters are the same for all strip grade families. So, possible behaviour differences between strip families are not distinguished here on the strip model calculations.

PCFC Performance Analysis

The original version of this paper is available on https://www.scientific.net/MSF.1186.85.pdf

The performance analysis of the developed PCFC model is carried out using industrial hot strip rolling data coming from an ArcelorMittal plant. This plant is equipped with seven 4-high stands (F1F7) with individual roll bending and roll CVC-shifting actuators. A multi-function gauge system at the finishing mill exit measures strip crown profile and flatness for all the rolled strips.

After PCFC sub-models calibration, a comparison study between PCFC calculations, plant's supplier model predictions and real measurements was carried out by using a mill database composed by a dozen of schedules coming from different rolling campaign types (total of 1650 rolled strips). Both PCFC prototype and plant supplier's model calculations were carried out in recalculation mode and with no adaptation contribution to enable an unbiased performance assessment between both PCFC solutions.

PCFC prototype calculations were compared with exit finishing mill measures (exit stand F7) for all strip crown indicators C25, C40 and C100 and for the strip flatness indicator Flat100. However, only C40 and Flat100 predictions from PCFC prototype and PCFC plant's supplier model could be compared since strip indicators C25 and C100 outputs were not available in the supplier solution running at the mill.

Figure 6 summarizes the comparative performance between PCFC prototype and plant's PCFC supplier model with respect to strip crown C40 and flatness Flat100 for the different rolling campaign types investigated. Encouraging results were obtained from this performance assessment since PCFC prototype tends to lead to similar behavior compared to the plant's PCFC supplier model:

Strip crown C40 gap ( △C40 ) between measures and models was in average ≈10μ m for PCFC supplier model and ≈16μ m for the developed PCFC prototype. So, PCFC prototype C40 strip crown predictions tend to be, in average, a bit less accurate than the ones coming from the supplier PCFC solution ( ≈6μ m gap) when compared to the exit mill measures.

Strip crown C40 predictions obtained by the PCFC prototype model were similar to the ones from the plants PCFC supplier model for harder rolling campaigns ( D,E,H ) but less accurate for softer rolling campaigns ( B,C,G ). In the case of high carbon (A) and thin material (F) rolling campaigns, C40 strip crown predictions from PCFC prototype were below ≈10μ m (total gap average between measures and PCFC supplier model predictions).

Strip flatness Flat100 gap ( Δ Flat100) between measures and models was in average ≈45 I-Units for PCFC supplier model and ≈13 I-Units for the developed PCFC prototype. So, strip flatness was better predicted by the PCFC prototype since the calculations were in average ≈32 I-Units more accurate than the ones from the PCFC supplier's solution.

[picture]

Fig. 6. Statistics for strip crown C40 and flatness Flat100 results obtained by the PCFC prototype model developed and plant supplier's PCFC model (called as Plant PCFC) for the different rolling campaign types evaluated. No supplier PCFC predictions could be extracted for campaign types A and F but mill measures were available.

Fig. 6. Statistics for strip crown C40 and flatness Flat100 results obtained by the PCFC prototype model developed and plant supplier's PCFC model (called as Plant PCFC) for the different rolling campaign types evaluated. No supplier PCFC predictions could be extracted for campaign types A and F but mill measures were available.

Additional observations were also obtained from the detailed analysis of the measured and calculated strip crown and flatness indicators (C25, C40, C100 and Flat100) all along the rolling campaigns investigated. Figure 7 shows the measured and calculated strip crown and flatness indicators over one of the rolling campaigns of type D (composed by hard grades). From these results, it can be stressed out that:

PCFC prototype calculations tend to follow the same trend than strip crown measures over most of the rolling campaign revealing that this PCFC prototype seems able to well capture the coil-to-coil strip crown variations.

PCFC prototype strip flatness (Flat100) predictions were significantly better than the ones coming from plant's PCFC model. Actually, plants' PCFC supplier model seems not able to give satisfying flatness predictions at the finishing mill exit and so, a correction by coil-to-coil regulation is usually applied for last stand.

[picture]

Fig. 7. Strip crown a) C25, b) C40, c) C100 and flatness d) Flat100 indicators measured at the mill stand F7 exit and calculated by both PCFC models for a rolling campaign type D. Suppliers' PCFC model results (Plant PCFC) only available for C40 and Flat100 indicators.

Fig. 7. Strip crown a) C25, b) C40, c) C100 and flatness d) Flat100 indicators measured at the mill stand F7 exit and calculated by both PCFC models for a rolling campaign type D. Suppliers' PCFC model results (Plant PCFC) only available for C40 and Flat100 indicators.

To complement the performance analysis, full strip profiles obtained by the developed PCFC model prototype and plants' PCFC supplier model for some strips of one of the rolling campaigns have also been compared with the ones coming from the measures at the exit of the finishing mill. These calculated strip profiles, shown in Figure 8, reveal that the PCFC prototype model seems able to deliver satisfying prediction of the full strip profile shape when reliable calculation of strip crown C40 is reached (case of strip 30). However, strip profile predictions from plant's PCFC solution still tend to outperform the ones from the PCFC prototype because this last one is not yet distinguishing behaviour differences between strip families on the strip model calculations.

[picture]

Fig. 8. Strip crown profiles measured and calculated by both PCFC models at the exit of F7 stand for different strips of a rolling campaign type H .

Fig. 8. Strip crown profiles measured and calculated by both PCFC models at the exit of F7 stand for different strips of a rolling campaign type H .

Conclusion

The original version of this paper is available on https://www.scientific.net/MSF.1186.85.pdf

A full PCFC model prototype including several physical based sub-models calculating the different contributions to the roll gap was developed with the purpose of predicting strip crown and flatness profile for hot finishing mills. In addition, an inverse parameters identification approach was also developed and applied to effectively tune the unknow parameters of the sub-models of this PCFC prototype.

A performance analysis of the PCFC model prototype was then carried out by comparing its predictions against plant's PCFC supplier model calculations and mill measures for an industrial mill database composed of a dozen of schedules coming from different rolling campaign types. Strip crown and flatness indicators (C25, C40, C100 and Flat100) as well as full strip profiles results were compared by running both PCFC prototype and plant's models in recalculation mode and without adaptation contribution to allow a fair comparison between the calculated results.

The overall performance study carried out led to encouraging results since the developed PCFC model prototype tends to give strip crown and flatness predictions similar to the ones obtained by the plant's PCFC model (coming from an industrial supplier).

Moreover, all the PCFC prototype calculations were carried out using the same set of strip model parameters for the different rolling campaign types analyzed. So, PCFC prototype predictions can still be improved in the future by tuning specific strip model parameters sets for different strip families defined by the plant (distinguishing strip model behavior for harder and softer grades). The PCFC prototype performance assessment caried out here led to very promising results and confirms that this developed PCFC solution shows high potential for an online industrial implementation on Hot Finishing Mills.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/MSF.1186.85.pdf

The authors would like to acknowledge the hot rolling process team from the ArcelorMittal plant by providing the industrial database and measurements used in this study.

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The original version of this paper is available on https://www.scientific.net/MSF.1186.85.pdf

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The original version of this eBook is available on https://www.scientific.net/book/forging-and-hot-forming/978-3-0364-1538-3

Materials Science Forum, ISSN: 1662-9752, Vol. 1186, pp 97-107

doi: 10.4028/p-6UdHbQ

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

Submitted:
2025-12-19

Revised:
2026-02-14

Accepted:
2026-02-25

Online:
2026-04-16

Impact of Separating Agents on the Deformation Regime in Aluminum Roll-Cladding Processes

The original version of this paper is available on https://www.scientific.net/MSF.1186.97.pdf

Muhr Felix 1,a*, Höniger Benedikt 2,b, Niemietz Philipp 1,c, Frohn-Sörensen Peter 2, d, Engel Bernd 2,e and Bergs Thomas 1,f

1 Manufacturing Technology Institute MTI of RWTH Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany

2 Forming Technology Siegen, Institute of Production Technologies, University of Siegen, Breite Straße 11, 57076 Siegen, Germany

a*f.muhr@mti.rwth-aachen.de, b benedikt.hoeniger@student.uni-siegen.de, c  p.niemietz@mti.rwth-aachen.de, d  peter.frohn@uni-siegen.de, ebernd.engel@uni-siegen.de,  ft.bergs@mti.rwth-aachen.de

Keywords: partial roll-cladding, battery cooling, heat exchanger, e-mobility, BEV, aluminum.

Abstract

Application fields and requirements for roll-cladded cooling plates are continuously rising. Especially as part of the thermal management systems in battery electric vehicles (BEV), the share of roll-cladded cooling plates is growing. A deeper understanding of the deformation regime in the roll bite is needed to completely fulfill the high quality, performance and cost requirements of the automotive industry. Whereas most cause-effect relationships in the roll-cladding process have been scientifically evaluated, the influence of separating agents on the deformation regime in partial rollcladding has not yet been investigated. To examine this relationship, an experimental set up is created and trials are conducted on a laboratory size roll-cladding mill. Two different aluminum alloy blanks are joined together under temperature by roll-cladding without the application of strip tensions and with different separating agent patterns. The results show: Firstly, there is a correlation between the materials' relative flow stress difference and their relative deformation. Secondly, the separating agents' areal share over the blank width significantly impacts the deformation regime in the roll bite. Thirdly, in areas with separating agent there is a correlation between the surface elongation of the bottom blank and the elongation of the contact interface between the blanks, which governs the later cooling channel tolerances. To use the results in the industrial application, the impact of so far neglected parameters such as strip tensions have to be considered in future research.

Introduction

The original version of this paper is available on https://www.scientific.net/MSF.1186.97.pdf

Political and social awareness have led and will further lead to a significant increase in the worldwide share of produced BEV compared to the share of internal combustion engine vehicles (ICEV) [1]. To be able to participate in future market growth, it is crucial for the original equipment manufacturers (OEMs) and their supplier base to decrease the cost and increase the performance of BEV cars by using innovative solutions [2, 3]. Example given, the range of a BEV is nowadays on average significantly lower than the range of an ICEV. Additionally the BEV production costs are roughly 45% higher than the ICEV costs. [4] The resulting ever-rising customer demand for an improved range performance, cost and safety of BEV increases the importance of the battery and its thermal management system. Today's state-of-the-art lithium-ion batteries require a constant and homogenously distributed cell-temperature between 20∘C and 40∘C. Any negligence of this requirement can lead to safety risks, such as the self-ignition of the battery, or a significant reduction of the BEV's performance respectively it's lifespan. There are different types of battery cooling or heating technologies used in today's BEVs. One method is the indirect cooling by a cooling plate, that is placed either above, below or between the battery cells. A cooling fluid, flowing in channels, is extracting the heat from the cells. [5] The process of roll-cladding has been used for decades to produce similar evaporator plates in the household industry, e.g. in refrigerators. Nowadays, the first BEVs in series feature use roll-cladded cooling plates as part of the thermal management system. [6]

Compared to other technologies the use of cooling plates in BEVs is evaluated as generally suitable [7]. Alongside with the roll-cladding [8] there are different processes to produce cooling plates, such as the brazing technology [9]. This paper focusses on the partial roll-cladding process. Automotive requirements regarding cooling performance, geometry, permanent bonding of the layers and cost make it inevitable to deepen the understanding of the partial roll-cladding process for aluminum cooling plates.

State of the Art

The original version of this paper is available on https://www.scientific.net/MSF.1186.97.pdf

To produce aluminum cooling plates, two sheets are joined together by rolling under pressure, temperature and surface area enlargement in the partial hot roll-cladding process. In the area of the later cooling channel pattern a separating agent is applied to locally avoid joining of the sheets. These areas are inflated in an internal high pressure forming process after roll-cladding and subsequently form the channels guiding the cooling fluid. [8] As only parts of the blanks are roll-cladded, the process is referred to as partial roll-cladding.

[picture]

Fig. 1. Partial roll-cladding serial process for production of cooling plates.

Fig. 1. Partial roll-cladding serial process for production of cooling plates.

Fig. 1 shows the serial process of continuous partial roll-cladding in an overview. Two strips are uncoiled (1) and brushed (2) on the bottom side of the top strip and on the top side of the bottom strip with steel brushes to remove oxides and generate a particular surface roughness supporting the subsequent mechanical and chemical joining mechanisms. Thereafter, a graphite separating agent is applied on the top side of the bottom strip with a screen-printing process ( 3 ) before both strips are heated in a furnace (4). In the roll bite the two brushed and heated strips are joined together under high forces (rolling force in combination with strip tension). Usually, a quarto rolling mill (5) with one pair of work rolls and one pair of backup rolls is used. To control the hardening effect occurring during the rolling process, the joined strip is heated again to recrystallize and then quenched with water to set the final material parameters (6). After cutting the blanks from the coil (7), the areas with separating agent are being inflated (9) in an internal high pressure forming process before they receive their final shape on a contour cutting press. Finally, the connecting points to the thermal management system of the vehicle's battery assembly are joined on the plate. For serial production continuous processes are often used, but partial roll-cladding of single blank packages is as well state-of-the-art. In that case, the preparation steps need to be performed separately and often in a manual process, however strip tensions cannot be applied.

Schmidtchen (2017) describes the bonding mechanisms governing the cladding process in three steps: Initially during the cladding process the roughened surfaces of the two strips are being further opened, which exposes metallically bare surfaces on both sides of the contact interface. These surfaces are not yet affected by oxides or other environmental interactions. Adsorption processes in the boundary surfaces initiate a first bonding before diffusion processes strengthen the bonding between the materials. One important parameter for technically reliable bonding is a pronounced thickness reduction of the materials. For cold roll-cladding the minimum thickness reduction/plastic deformation is defined at around 30−70%, whereas in hot roll-cladding a sufficient bond can already be created at thickness reductions/plastic deformations starting from 20%. [10]

Industrial practice shows that often thickness reductions higher than 20% are used in hot-rollcladding to ensure sufficient bonding strength between the materials. Melzner and Hirt (2014) have observed that when using different material alloys, severe differences in thickness reduction can occur
during rolling, because the softer material is being deformed stronger than the harder material. They were able to show that a temperature difference between both incoming materials can compensate for this effect. [11] The effect of different reductions when using materials with different flow stresses (like aluminum and copper) was also described in Bauer and Schadt (2017), supporting the conclusion that the softer material has a higher reduction than the harder one. This leads to relative velocities and thus frictional stresses between the two cladding partners. An increase in the incoming materials' thickness difference increases the relative velocities between the cladding partners. [12] Melzner and Hirt (2017) stated that it is important for a strong bond that the relative velocities between the two materials during the cladding process are minimized, otherwise the bond can break up due to high shear stresses during rolling. Methods are described to minimize the difference in thickness reduction between the top and the bottom material by e.g. varying the roll-diameter, the incoming strip thicknesses and the flow stress differences between the materials. [13]

In contrast to the referenced literature, that focuses on the impact of temperature and roll bite geometry differences on the bonding quality, the focus of this paper lies on the influence of the separating agent on the elongation of the channel pattern. An impact on the deformation regime is expected. Considering the high level of the minimum required thickness reduction threshold, evidently even small reduction differences in the bottom blank can significantly change the geometry, especially the length variation, of the channel pattern applied on the bottom blank. For the battery cooling plate alongside the requirement of sufficient bonding, a homogeneous cooling performance is needed, which highlights the importance of correctly formed cooling channels.

In summary, significant research has been underwent on most process-influencing parameters regarding the complex, thermo-mechanically coupled process of roll-cladding. However, the influence of the separating agent and its areal shape on the complex deformation behavior in the roll bite is yet to be examined to fully utilize the technology's potential.

Materials and Methods

The original version of this paper is available on https://www.scientific.net/MSF.1186.97.pdf

Experimental setup.

The original version of this paper is available on https://www.scientific.net/MSF.1186.97.pdf

To investigate the influence of presence of separating agent on the thickness reduction of the cladding partners, a laboratory size quarto rolling mill (see Fig. 2 b)) is used at RWTH Aachen University. The maximum rolling force of the mill is Fmax=2.800kN. Work rolls with a diameter of DWR= 150 mm are used to roll-clad two materials in a blank size of l0=450 mm length and b0=150 mm width. For the top blank a soft EN-AW-1050-O and for the bottom blank a harder EN-AW-3003-H14 aluminum alloy is used. The blanks are manually cut to shape from coil material, straightened and brushed on the contact sides of both blanks. Subsequently, the separating agent is manually applied on the brushed surface of the bottom blank via screen printing technology. To ensure that both blanks are reproducibly aligned on top of each other, they are spot welded together over the width of the plate on one side. The incoming thickness of both blanks is h0, top =h0, bottom =2 mm. To heat up the plates a chamber furnace (see Fig. 2 a)) is used in which both blanks are heated for 5 minutes at a furnace temperature of TFurnace =430∘C, before they are transported manually to the roll bite. A temperature difference of 30∘C during transport has been verified by contact temperature measurements. Consequently, the roll bite temperature is assumed as TRollbite =400∘C. A total number of nprint =8 blank packages are roll-cladded with a separating agent applied between the blanks, a reference amount of nref =3 is rolled without separating agent as a control group. The targeted thickness reduction of the blank package is calculated to approximately −50% by (Eq. 1):

εh, package,target =h1−h0 h0*100%≈−50%(1)

[picture]

Fig. 2. Experimental setup for rolling trials with a) chamber furnace, b) laboratory size rolling mill and c) detail view of roll bite with process parameters and sample specifications.

Fig. 2. Experimental setup for rolling trials with a) chamber furnace, b) laboratory size rolling mill and c) detail view of roll bite with process parameters and sample specifications.

To investigate not only the influence of the presence of separating agent, but also the influence of the areal share and shape, a special screenprint pattern, shown in Fig. 3 b), is introduced. To support a sufficiently homogenous deformation regime in the roll bite, the space between the different sectors is designed to be at least in the size of the contact length of roll and material. Based on Kopp and Wiegels (1999) this length is defined according to the elementary plasticity theory to approximately 12,5 mm by (Eq. 2) [14]:

ld=DWR2*Δ h=DWR2*( h0, top +h0, bottom −h1, top −h1, bottom ).(2)

Due to the time and distance needed to close the mill to the desired gap, there is extra space before sector A held up for the mill to close, highlighted in dark grey in Fig. 3. The same applies for the end of the plate. These front and rear trim sections are scrap and not used for evaluation. Between the dark grey areas different separating agent sectors are designed to represent different areal shares of separating agent, different symmetries and gradients. The areal share of the separating agent varies from 0% in sector E to 100% in sector F . In sector D , a short separating agent area is followed by a short, bonded area and a short separating area again to evaluate the influence of the length of the patterns. The pattern in sector C is the only asymmetrical pattern intended to create different elongations over the width of the blanks. Patterns in sectors B & A are geometries that are typically used in the automotive cooling plate industry. All sectors are the same length and width.

[picture]

Fig. 3. a) Blank package before rolling with b) separating agent pattern (black) on bottom blank and c) etched mesh on outer surfaces.

Fig. 3. a) Blank package before rolling with b) separating agent pattern (black) on bottom blank and c) etched mesh on outer surfaces.

The creation of the test pattern was based on the hypothesis that varying areal shares of separating agents over the rolling width must provoke alterations in the integral frictional force through altered local frictional behaviors in areas with and without separating agent. The share of separating agent over the width is determined in CAD in 1 mm steps and quantified by (Eq. 3):

xagent =Aagent Atotal_area *100%(3)

Evaluation method.

The original version of this paper is available on https://www.scientific.net/MSF.1186.97.pdf

In this paper, etching of a raster pattern intended for optical strain evaluation is used to make the surface layer deformations of the top side of the top blank and the bottom side of the bottom blank visible. All 22 blanks ( (nprint +nref )*2=22 ) are manually etched on the outsides with a pattern, shown in Fig. 3 c), using an electrolyte DE20 from Östling Marking Systems. Because of the high deformations, the contrast of the points is not sufficient to allow a photo optical measurement of the deformations. For this reason, the distance of the points after rolling is measured with a caliper and set in relation to the distance before rolling. Additionally, reference holes are drilled through both blanks together prior to rolling to localize the start and end point of the separating agent pattern after rolling. The positions of the reference points are shown in Fig. 3.

Before and after the roll-cladding process, thickness measurements of all blanks by micrometer screw are conducted at 10 equidistant measuring points along the blank length with 30 mm space from each blank's edge. To evaluate the start and end point of the different separating agent sectors, a non-destructive thickness measurement technique is used, which is capable to detect the single layer thickness in areas with separating agent as well as the total thickness in areas without separating agent. At points where the thickness transitions from single to double layer, the start or end point of a sector is defined. These points are marked on the outer surface. The length of the sector is measured with a caliper afterwards and is set in relation to the sector length before rolling to determine the elongation of each pattern. This method is adapted from Kleiner et al. [15].

A significant difference in reduction and elongation of the top vs. the bottom blank is anticipated due to differences in the flow stresses. To align all measurements after rolling on the original separating agent pattern, a reference scale of 0 to 100 is defined between the two reference holes on both blanks, see Fig. 3. The distance between the reference holes after rolling is divided into 100 equisized segments for each blank. All further measurements are linked to these reference points rather than to the absolute position on the blank.

Experimental constraints lead to elongation deviations compared to the target elongation εh,package,target . The total thickness reduction of the blank packages i is defined as εh,package,i . A normalization factor is required to make the absolute thickness reductions between the blank packages comparable. The normalization factor for each blank package i is defined in (Eq. 4) as:

Xnorm ,i=εh, package , target εh, package ,i(4)

with the thickness reduction being defined in (Eq. 5) as:

εh, package ,i=h1,i−h0,i h0,i*100%(5)

For the incoming thickness h0,i the sum of bottom and top blank incoming thickness (averaged over each blank) is considered. For h1,i the averaged sum of the total package outgoing thickness in areas without separating agent is considered. The total length elongation of each blank package is calculated as in (Eq. 6):

εl,i=l1,i−l0,il0,i*100%(6)

As the blank width is assumed to be constant ( εb=0 ), the normalized elongation is obtained according to (Eq. 7):

εl,norm,i=l1,i−l0,il0,i*xnorm,i*100%(7)

For each blank the normalized elongation is calculated in (Eq. 8) and (Eq. 9) as:

εl, top ,i=l1, top ,i−l0, top ,il0, top ,i*xnorm ,i*100%,εl, bottom ,i=l1, bottom ,i−l0, bottom ,il0, bottom ,i*xnorm ,i*100%.

(8)(9)

If e.g. εl, top ,i=100% the strip length has been doubled during the rolling process. The length l1 is either taken from the distance measurement between the etched mesh or the distance between start and end of a separating agent sector. The above-mentioned formulas are based on Kopp and Wiegels (1999). [14]

Results and Discussion

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During the experiment it was observed that all 11 plates had a saber like deformation in the direction of rolling as shown in Fig. 4, meaning that the blank edges formed a curve instead of a linear elongation. This can be explained by slightly different roll gap settings along the width causing one side to be elongated more than the other side. Another reason for the saber is that the blanks were rolled without strip tensions supporting a straight strip control.

[picture]

Fig. 4. Comparison of ideal with real sample shape after rolling.

Fig. 4. Comparison of ideal with real sample shape after rolling.

Total elongation difference test pattern vs. reference samples.

The original version of this paper is available on https://www.scientific.net/MSF.1186.97.pdf

Fig. 5 shows the total normalized elongation separately for the bottom and top blank surface based on the length difference of the etched points from reference point 0 to 100 (see chapter evaluation method). Fig. 5 a) shows the results of group nprint  with separating agent whereas Fig. 5 b) shows results for the reference group nref  without separating agent. As anticipated, because of flow stress differences in both cases the surface of the harder bottom material (EN-AW-3003-H14) always elongates less than the surface of the softer top material (EN-AW-1050-0). If separating agent is present between the blanks it can be stated that the bottom material elongates even less than in the case where no separating agent is present. The surface of the bottom material sees an elongation of roughly 85% with separating agent (left box in Fig. 5 a)) and roughly 97% without separating agent (left box in Fig. 5 b)). The surface of the softer top blank material on the other hand elongates more (roughly 138 %) when separating agent is present (right box in Fig. 5 a)) and less (roughly 123 %) when no separating agent is present (right box in Fig. 5 b)). The observed result is the inverse for the two materials. The effect that the top blank generally elongates more than the bottom blank can also be seen in Fig. 5 a) and b). It is visible in Fig. 5 c) that the top material is always longer than the bottom material.

[picture]

Fig. 5. a) Total normalized elongation of top and bottom blank with and b) without separating agent and c) example for elongation differences between top and bottom blank at rear trim section.

Fig. 5. a) Total normalized elongation of top and bottom blank with and b) without separating agent and c) example for elongation differences between top and bottom blank at rear trim section.

Local elongation difference top vs. bottom material.

The original version of this paper is available on https://www.scientific.net/MSF.1186.97.pdf

Analyzing the local normalized surface elongations in reference to the separating agent pattern, the above-mentioned phenomenon is confirmed. Fig. 6 shows the local normalized surface elongation of both top (orange, full line) and bottom (blue, dashed line) blank referenced on the separating agent pattern. It is qualitatively visible that in areas where separating agent is present, the hard bottom material elongates less, and the soft top material elongates more. In the areas without separating agent the inverse is visible. In addition, evidently in sector E both elongations differ by just roughly 17% points while in sector F the elongations are, at maximum, nearly 106%-points apart from each other. The standard deviation of each measurement is indicated by vertical error bars where the highest standard deviation is observed in sector F . In addition to that, the elongation is more pronounced for the top material in sector F, which is correlated to the observed saber-like edge deformation. A thickness measurement has been conducted to support the theory of the influence the separating agent has on the deformation.

[picture]

Fig. 6. Local elongation of bottom (3003) and top (1050) blanks with separating agent on middle measurement line.

Fig. 6. Local elongation of bottom (3003) and top (1050) blanks with separating agent on middle measurement line.

Local elongation difference in asymmetrical separating agent areas.

The original version of this paper is available on https://www.scientific.net/MSF.1186.97.pdf

Contrary to Fig. 6, Fig. 7 shows the normalized elongation only of the harder bottom material. The dashed blue line shows the elongation on the left and the full blue line the elongation on the right side. It can be stated that both have the same qualitative progression. There are two areas that need to be highlighted though. One is sector C, the other one is sector F. In sector C (see Fig. 7 b)), which is the only asymmetrical separating agent area of the screenprint, it is visible that when there is more separating agent on the left compared to the right side, the first-mentioned is elongated less. If there is more separating agent on the right side than on the left side, the effect switches to the opposite. This again proves the above discovered observation. In sector F the left side is elongated less than the right side, which agrees to the observed saber phenomenon.

[picture]

Fig. 7. a) Local elongation of left and right measurement line of bottom (3003) blank and b) detail view sector C.

Fig. 7. a) Local elongation of left and right measurement line of bottom (3003) blank and b) detail view sector C.

Correlation between separating agent areal share and elongation.

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In Fig. 8 the normalized elongation on the middle measurement line over the areal share of separating agent over the width is plotted separately for the bottom and the top blank. The dashed blue line shows a linear regression curve fitting the points. It is visible that for the bottom material there is a strong negative correlation between elongation and share of separating agent, whereas for the top blank there is a positive correlation. This fits the above-mentioned results, underlining that the higher the share of separating agent over the width gets, the smaller the elongation gets for the hard bottom and the higher it gets for the soft top material.

[picture]

Fig. 8. Correlation between separating agent areal share and a) bottom blank (3003) surface elongation and b) top blank (1050) surface elongation.

Fig. 8. Correlation between separating agent areal share and a) bottom blank (3003) surface elongation and b) top blank (1050) surface elongation.

Correlation of surface elongation and elongation of contact area between blanks.

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Up to now, the focus of this paper was to evaluate the surface elongation of the outside surfaces of the blank package. For the application of the roll-cladding technology in the BEV-industry, however, it is of high importance how the separating pattern is being elongated in the interface between the two blanks, as this governs the later geometrical tolerances of the cooling channel pattern. In this chapter the elongation of the pattern is evaluated by locating the start and end point of each pattern and measuring the elongated distance (see chapter evaluation method). Fig. 9 shows the averaged normalized elongations between the outer surface of the bottom (blue) and surface of the top material (red) as well as the contact area (grey) elongation between top and bottom blank. In the fully bonded area (sector E) it is visible that the surface of the hard bottom material is elongated by 97%, the contact area is elongated by 107% and the surface of the soft top material is elongated by 123%. The bond between the materials in the contact area explains this gradient. In sector F, where separating agent is applied, evidently the contact area is elongated in the same way as the surface of the hard bottom material by 62% whereas the soft top material is elongated by 168%. Thus, it is concluded that the elongation of the separating agent area in sector F can be derived from the surface elongation of the harder bottom blank material.

[picture]

Fig. 9. Elongation of bottom blank surface (3003), contact area and top blank surface (1050) in a) sector E and b ) sector F .

Fig. 9. Elongation of bottom blank surface (3003), contact area and top blank surface (1050) in a) sector E and b ) sector F .

Restrictions of investigation.

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The experiments have been conducted on a laboratory size rolling mill in a discontinuous process and without application of strip tensions. This will presumably have a strong impact on the deformation regime. The roll diameters were by factor 2.5 smaller than in a serial production line. Taking Melzner and Hirt (2017) into consideration it can be expected that the roll diameter has a high influence on the different elongations of the top and the bottom blank [13]. The measuring methods used were partially manual. Despite best possible diligence, deviations in repeatability cannot be completely ruled out. In particular visually difficult to identify areas on some of the etched grids had to be detected manually. Finally, the elongations were only measured in three layers, which are the outer surface of the top blank, the interface area and the outer surface of the bottom blank. With the current experimental set up it is not possible to fully visualize the deformations that occur between these layers, whereas validated simulation models might provide valuable insights.

Summary and Outlook

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The present paper focused on the influence of a separating agent on the deformation regime in the roll gap of a roll-cladding process of EN-AW-3003-H14 and EN-AW-1050-O. The focus lied on the elongation of different separating agent patterns as this is decisive for the cooling channels tolerances of cooling plates that are produced with this process and used in battery electric vehicles. Under the defined experimental conditions, three main conclusions are drawn:

The materials' relative deformation in the roll bite correlates with its relative flow stress: The softer material tends to be elongated more, because the flow stress is approximately 40% lower than the flow stress of the harder material at 400∘C at the given deformation (see Fig. 5, 6 and 9).

The harder material roughly elongates 36% less when separating agent is applied (sector E vs. sector F ), whereas the softer material roughly elongates 36% more when separating agent is applied (sector E vs. sector F) (see Fig. 9). This not only has an impact on the final cooling channel pattern geometry, but can also increase relative movements between the layers, resulting in inner stress or in the worst case in a delamination of the bond that could have fatal consequences (see Fig. 5-9).

The surface elongation of the harder bottom material represents the elongation of the contact interface between both materials if separating agent is applied with a decent accuracy. If no separating agent is applied, a deviation is observed. This is an important finding since the elongation of the interface is hard to measure directly and determines the geometrical tolerances of the later cooling channel pattern of the cooling plate (see Fig. 9).
Table 1 shows the qualitative relations of the thickness as well as the thickness reduction comparing areas with separating agent to areas without separating agent.

Table 1. Qualitative relations of thickness, thickness reduction and elongation of bottom (3003) and top (1050) blank with and without separating agent.

|  | with separating agent |  | without separating agent
Thickness | Bottom blank | Thickness + | bidirectional arrow | Thickness -
Top blank | Thickness - | bidirectional arrow | Thickness +
Thickness Reduction | Bottom blank | Thickness Reduction - | bidirectional arrow | Thickness Reduction +
Top blank | Thickness Reduction + | bidirectional arrow | Thickness Reduction -
Elongation | Bottom blank | Elongation - | bidirectional arrow | Elongation +
Top blank | Elongation + | bidirectional arrow | Elongation -

Future research should continue to investigate the influence of the separating agent layer on the deformation regime in the roll bite. Especially, it is important to conduct further experiments under conditions closer to serial production and not on a laboratory size mill. The application of strip tension and larger work roll diameters will have an influence on the relationships described. Furthermore, it is important to set up a simulation model to deepen the understanding of the local deformation governing the pass through of the roll bite. Current simulation and AI methods, if trained with real production data, can open new possibilities in process development and should be used in further research [16].

References

The original version of this paper is available on https://www.scientific.net/MSF.1186.97.pdf

D. Harrison and C. Ludwig, "Electric Vehicle Battery Supply Chain Analysis: How Battery Demand and Production are Reshaping the Automotive Industry," ABB Technik, 2021.

Martin Wietschel et al., "Batterien für Elektroautos: Faktencheck und Handlungsbedarf - Ein Update (Policy Brief 01 / 2025)," 2025.

A. Kampker and H. H. Heimes, Eds., Elektromobilität: Grundlagen einer Fortschrittstechnologie, 3rd ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 2024. [Online]. Available: .

A. König, L. Nicoletti, D. Schröder, S. Wolff, A. Waclaw, and M. Lienkamp, "An Overview of Parameter and Cost for Battery Electric Vehicles," WEVJ, vol. 12, 2021, doi: .

R. Korthauer, Ed., Handbuch Lithium-Ionen-Batterien, 1st ed. Berlin Heidelberg: Springer Berlin Heidelberg; Imprint: Springer Vieweg, 2013.

L. Deng, Y. Li, P. Xu, Z. Chen, W. Zhou, and B. Li, "Fabrication and thermal performance of a novel roll-bond flat thermosyphon," Applied Thermal Engineering, vol. 181, 2020, doi: 10.1016/j.applthermaleng.2020.115959.

S. Arora, "Selection of thermal management system for modular battery packs of electric vehicles: A review of existing and emerging technologies," Journal of Power Sources, vol. 400, pp. 621-640, 2018, doi: 10.1016/j.jpowsour.2018.08.020.

S. Kasuba, "Aluminium roll bond - A single innovatie circuit for air conditioner, car radiator, refigerator and water heater," International Journal & Magazine of Engineering, Technology, Management and Research, vol. 1, S. 11-16, 2014.

O. Arsenyeva, L. Tovazhnyanskyy, P. Kapustenko, J. J. Klemeš, and P. S. Varbanov, "Review of Developments in Plate Heat Exchanger Heat Transfer Enhancement for Single-Phase Applications in Process Industries," Energies, vol. 16, no. 13, 2023, doi: 10.3390/en16134976.

M. Schmidtchen, "Mehrskalige Modellierung des Walzplattierens und des Walzens von Werkstoffverbunden," Habilitationsschrift, Fakultät für Werkstoffwissenschaften und Technologie, Technische Universität Bergakademie Freiberg, Freiberg, 2017.

A. Melzner and G. Hirt, "Roll Bonding of Two Materials Using Temperature to Compensate the Material Strength Difference," AMR, 966-967, pp. 471-480, 2014, doi: 10.4028/www.scientific.net/AMR.966-967.471.

H. G. Bauer and W. Schadt, Walzen von Flachprodukten, 1st ed. Berlin, Heidelberg: Springer, 2017. [Online]. Available: .

A. Melzner and G. Hirt, "Investigations on the elongation differences of aluminum in two layer roll bonding," 2017, doi: 10.1063/1.5008228.

R. Kopp and H. Wiegels, Einführung in die Umformtechnik, 1st ed. Aachen: Verlag Mainz, 1999.

M. Kleiner et al., "Fertigung innovativer IHU-Stahlbauteile auf Basis neuartiger, partiell plattierter Blechhalbzeuge: Forschungsbericht P 565," 2006.

M. Liewald et al., "Perspectives on data-driven models and its potentials in metal forming and blanking technologies," Prod. Eng. Res. Devel., vol. 16, pp. 607-625, 2022, doi: .

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/forging-and-hot-forming/978-3-0364-1538-3

Materials Science Forum, ISSN: 1662-9752, Vol. 1186, pp 109-118

doi: 10.4028/p-0wN0lJ

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

Submitted:
2025-12-22

Revised:
2026-02-13

Accepted:
2026-02-24

Online:
2026-04-16

Relating the Cold Rolling Pass Reduction to the Microstructural Evolution and Formability in DP800 Steel

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Maximilian Hribsek 1,a*, Niklas Fehlemann 1, b, Irene Biermann 1,c, Sebastian Münstermann 1, d

1 Institute of Metal Forming, RWTH Aachen University

a* maximilian.hribsek@ibf.rwth-aachen.de, b  niklas.fehlemann@ibf.rwth-aachen.de, c  irene.biermann@ibf.rwth-aachen.de, d  sebastian.muenstermann@ibf.rwth-aachen.de

Keywords: dual phase steel, non-proportional loading path, formability, process simulation.

Abstract

The influence of cold rolling pass schedules on the microstructural evolution, mechanical response and stress state of a DP800 base material was investigated. A micro-alloyed S355 steel with ferritic-pearlitic microstructure was subjected to identical total thickness reductions using different numbers of pass reductions. The mechanical behavior was characterized by uniaxial tensile tests while microstructural features were analyzed using Electron Backscatter Diffraction (EBSD) and light optical microscopy, with grain morphology quantified by elliptical approximations. All investigations are carried out on the deformed ferritic-pearlitic microstructure, before the final intercritical annealing to produce the final dual phase microstructure. Finite element simulations of the flat rolling process were conducted to evaluate the evolution of non-proportional stress states in terms of stress triaxiality and Lode angle parameter. The results show that varying the number of passes leads to subtle but systematic differences in strength and ductility together with pronounced grain elongation and strongly banded pearlite morphologies that challenge ellipsoidal grain representations. While the overall stress-state trajectories remain similar, increasing the number of passes results in smoother stress evolution with reduced stress peaks. These findings highlight the non-trivial role of pass scheduling in shaping deformation-induced microstructures prior to annealing.

Introduction

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To understand how modern steels can be optimally processed, it is essential to quantify how each individual processing step influences the evolving microstructure and thus the resulting material properties. In dual-phase steels, even at constant chemical composition, the sequence of hot rolling, cooling, cold rolling and intercritical annealing provides numerous parameters for tailoring microstructure and mechanical behavior through controlled thermomechanical processing. Previous work by Fehlemann et al. demonstrated that the number of pass reductions during cold rolling strongly affects both the ductile damage induced during forming and the impact toughness of the final DP steel [1,2]. While those studies primarily compared different processing strategies based on the final material state at the end of the process chain after the intercritical annealing step, the present work focuses on the cold-rolled DP base material with its ferritic-pearlitic microstructure, prior to intercritical annealing. In particular, microstructural and mechanically induced differences that arise during cold rolling are investigated, while the subsequent intercritical annealing step is deliberately excluded, since it was already part of the earlier studies.

To capture those mechanical differences, which are characterized by a non-proportional loading path, it is essential to understand the underlying stress states. Local stress states are described using two scalar parameters: the stress triaxiality η and the Lode angle parameter θ¯, as defined in Eq. 1 and 2. The stress triaxiality is defined as the ratio of the hydrostatic stress σm to the equivalent von Mises stress σvM and serves as a measure of the relative contribution of volumetric to deviatoric stresses. Negative values of η correspond to compressive stress states, whereas positive values indicate tensile stress states, which are known to promote ductile damage initiation [3].

η=σmσvM(1)

While the stress triaxiality provides information on the tendency for damage initiation, the Lode angle parameter supplies complementary information on the shape of the stress state. It is related to the third invariant of the deviatoric stress tensor ξ[4].

θ¯=1−2πarccosξ(2)

In this work, both experimental and numerical approaches are employed to figure out the mechanisms responsible for the differences in damage evolution observed at the end of the processing chain. Uniaxial tensile tests are used to characterize the global formability of the material, microstructural features are quantified using pairplots derived from EBSD and light optical microscopy data. Finally, finite element simulations are performed to evaluate the stress states experienced by the sheet during the rolling process.

Materials & Methods

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The methodology presented in the following section follows the procedure previously described by Fehlemann et al. [1]. The material investigated in this study serves as the base material for the production of a DP800 steel. Chemically, it corresponds to a micro-alloyed S355 steel, with its composition summarized in Table 1. The material was initially hot rolled on a universal two-high rolling mill (Buehler). Starting from an initial thickness of 140 mm , it was reduced to 3 mm over 18 passes. The rolling process begun once the material reached a temperature of 1200∘C. After hot rolling, the sheet was cooled inside the furnace for 24 hours to ensure the formation of a ferriticpearlitic microstructure. To achieve the surface quality required for subsequent cold rolling, the sheets were than sandblasted. Cold rolling was performed on a four-high mill to a final thickness of 1.5 mm , using three different pass schedules. The first schedule involved an initial reduction of Δh1,1=1 mm, followed by a second reduction of Δh1,2=0.5 mm, resulting in a total of two passes. The non-uniform two-pass rolling schedule was intentionally selected to maximize the reduction per pass while minimizing the number of passes. High single-pass reductions are known to promote pronounced through-thickness gradients in residual stress and microstructure, whereas multiple small reductions tend to deform more homogeneity [5]. Thus, the remaining two schedules applied constant pass reductions of Δh2=0.3 mm and Δh3=0.15 mm, corresponding to five and ten passes, respectively. From the final cold-rolled sheet, tensile specimens were prepared as well as additional metallographic samples for the microstructural characterization.

Table 1. Chemical composition of the initial S355 steel.

Element | C | Si | Mn | Cu | Al | Mo | Ni | Cr | V | Nb | Ti | Co
Wt.% | 0.1 | 0.4 | 1.8 | 0.05 | 0.03 | 0.02 | 0.05 | 0.2 | ≤0.005 | 0.04 | 0.02 | 0.005

Tensile tests were carried out on a ZwickRoell Z100 universal testing machine to determine the materials flow curves at a constant strain rate of 0.001 s−1. For the Inverse Pole Figure (IPF) maps, EBSD measurements were performed on metallographic samples that had been ground, polished and subsequently electropolished using a QUETCH 1000 electrolytic polishing and etching machine using the producers K1 electrolyte to erode the surface. The measurements were taken with an accelerating Voltage of 20 kV and a current of 0.14 nA , while mapping a field of 300×225μ m2 and a step size of 0.5μ m. The examined surfaces correspond to the RD (rolling direction) × ND (normal direction) plane. EBSD measurements were conducted using a focused ion beam scanning electron microscope, Helios 5 Hydra UX, manufactured by Thermo Fisher Scientific, equipped with an EDAX detector. Post-processing was carried out using the EDAX OIM Analysis 8TM software, where only ferrite was evaluated by filtering out all phases that do not exhibit a body-centered cubic crystal structure. Minor losses of bcc ferrite located between cementite lamellae were accepted, as they are not relevant for the subsequent microstructural evaluation. To identify and quantify the pearlite phase,
light optical microscopy was employed. Samples were polished and etched with 2 % Nital, which renders ferrite bright and pearlite dark. For each specimen, images of the RD×ND plane were recorded at a magnification of x750 using a digital VHX-1000D light microscope (Keyence Corporation), ensuring a statistically sufficient number of grains. Image processing was conducted using the OpenCV Python library in accordance to Jin et al. [6]. Following noise reduction, pearlite islands and bands were detected through contour recognition, and their shapes were approximated using elliptical fitting. In cases where pearlite bands extended across the entire image, a watershed algorithm was applied to segment the bands into individual regions. The resulting processed images enabled a reliable determination of the ferrite and pearlite phase fractions.

[picture]

Fig. 1. Schematic illustration of the finite element model used for flat rolling.

Fig. 1. Schematic illustration of the finite element model used for flat rolling.

In addition to the experimental processing, three-dimensional finite element (FE) simulations were conducted using Abaqus/Standard 2022 to analyze the stress conditions that arise during flat rolling, in a similar fashion as in Mahmoudi et al. and Wang et al. [7,8]. Figure 1 illustrates a representative stress distribution obtained from the simulations. Stress triaxiality and Lode angle parameter were evaluated after each deformation increment to characterize the local stress state throughout the process. Thermal effects were not considered, as the temperatures reached during cold rolling remain far below levels at which microstructural evolution or significant changes in the flow behavior would occur [9]. To initiate contact between the workpiece and the rollers, a rigid pusher was introduced, allowing the sheet to remain stationary without prescribing a velocity [7]. Contact interactions were modelled using a tangential penalty formulation with a friction coefficient of 0.3 , while normal contact was enforced through a "hard" contact overclosure rule. For the five- and ten-pass configurations [10,11], the C3D8R element type was selected, which is a hexagonal 8-node linear brick with reduced integration [7,12]. This element type was chosen due to its efficiency, as it requires the least computational time. However, for the two-pass configuration, this element type was not suitable because the height reduction per pass was too large, causing excessive mesh distortion. To address this issue, the C3D10 element type was used instead [13]. This is a tetragonal 10 -node quadratic element that allows for the application of remeshing rules, effectively resolving the distortion problem. For computational efficiency, the model was represented as a quarter-symmetry configuration by mirroring the geometry along the normal and transverse directions, while retaining realistic initial dimensions. [14] The simulations were carried out in a sequential manner, with each step representing a single rolling pass. After each pass, the resulting geometry was imported into the subsequent analysis, and the corresponding stress field was applied as a predefined field. This stepwise modelling strategy improved numerical stability and facilitated error detection between passes [15]. Moreover, it enabled accurate representation of the alternating rolling direction inherent to the laboratory rolling setup, which differs from industrial mills with fixed alignment.

Results

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[picture]

Fig. 2. Evolution of stress triaxiality and Lode parameter over the equivalent plastic strain for a.) two passes, b.) five passes and c.) ten passes.

Fig. 2. Evolution of stress triaxiality and Lode parameter over the equivalent plastic strain for a.) two passes, b.) five passes and c.) ten passes.

Stress state analysis.

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To capture the load paths experienced by the material during cold flat rolling, the elemental values of the entire finite element mesh were extracted and averaged over the sheet thickness and width. Figure 2 illustrates the resulting evolution of the non-proportional stress state as a function of the equivalent plastic strain for different numbers of passes. The solid line represents the evolution of the stress triaxiality, while the dashed line shows the corresponding Lode angle parameter. Three characteristic stress states are particularly noteworthy, all of which are located within the plane-stress region of the stress triaxiality - Lode angle parameter space. At a stress triaxiality of 1/3 and a Lode angle parameter of 1 , the material is in uniaxial tension. When both the stress triaxiality and Lode angle parameter reach zero, the stress state corresponds to pure torsion. At a stress triaxiality of −1/3 and a Lode angle parameter of -1 , the material is in uniaxial compression. For more detailed explanations of stress-state definitions and their implications, see [3].
The stress-state evolution exhibits similar gradients across all rolling conditions. In all cases, the stress response begins with an instantaneous rise into the uniaxial tension regime. This initial peak may stem from the biting conditions as the material enters the roll gap. However, it is unclear whether this behaviour reflects a numerical artefact related to the contact formulation or an actual physical phenomenon. Therefore, this peak should be interpreted with caution, and the subsequent analysis focuses primarily on the stress evolution following the first pass. Each pass can be identified by a brief increase in stress triaxiality at its onset, followed by another peak at its completion. With an increasing number of passes, the peaks in stress triaxiality become less pronounced, resulting in a smoother overall stress evolution. After the initial peak, the triaxiality transitions into the compressive regime, slightly below zero, while the Lode angle parameter remains just above zero. Only in the five-pass configuration the triaxiality briefly returns to positive values after the third pass, reaching a maximum of 0.075 . The lowest triaxiality is observed in the two-pass case, reaching -0.141 at the end of the second pass, immediately before peaking again. Variations in the Lode angle parameter are similarly miner, without providing additional insight. A notable point is the accumulated plastic strain: five and ten passes yield comparable values of 0.866 and 0.836 , respectively, whereas the twopass simulation reaches 1.09 . The minor difference between five and ten passes suggests a potential trend, but, considering that the overall degree of deformation is identical in all three cases, this appears unlikely.

Tensile Properties.

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Tensile tests were performed to characterize the mechanical properties of the material. Because the influence of cold rolling was the primary focus, Figure 3 distinguishes between the hot-rolled and furnace cooled condition (left side), representing the state prior to cold rolling, and the cold rolled conditions with various numbers of pass reductions (right side). As no thermal treatments were applied between hot rolling and subsequent cold rolling, all observed differences in the stress-strain curves must have been induced due to mechanical deformation during rolling. The microstructure remains ferritic-pearlitic in all conditions. Consequently, variations in the curves for the cold rolled samples reflect differences in work hardening behaviour associated with the respective pass reductions. These work hardening effects explain not only the differences between the various cold rolled conditions but also their deviation from the hot rolled reference. The hot rolled material exhibits minimal scatter in yield and ultimate tensile strength, whereas the fracture strain shows considerable variability, indicating greater sensitivity of the ductility to local microstructural or geometric heterogeneities in the undeformed state When evaluated on a relative basis, a comparable scatter is also observed for the cold-rolled conditions. The yield strength reaches its maximum after two passes at approximately 825 MPa , whereas after ten passes it is about 30 MPa lower. Additionally, the fracture strain decreases by roughly 25% when the number of passes increases from two to ten. Although these differences appear relatively small, they nonetheless suggest a trend that may indicate subtle microstructural modifications associated with varying the number of rolling passes.

[picture]

Fig. 3. Uniaxial tensile tests before (left) and after (right) cold rolling with different passes.

Fig. 3. Uniaxial tensile tests before (left) and after (right) cold rolling with different passes.

[picture]

Fig. 4. Pairplot of the ferrite phase with corresponding EBSD-IPF.

Fig. 4. Pairplot of the ferrite phase with corresponding EBSD-IPF.

Characterizing the microstructure.

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To analyse the microstructural features, the samples are examined using EBSD maps and light optical microscopy. For quantification, the grains are approximated as ellipses. This approach provides a simplified geometric representation of grain shape and allows for direct comparison of different microstructures using pairplots, as shown in Figure 4 for ferrite and Figure 5 for pearlite. From there, qualitative parameters were chosen for this characterization, which are the grain area, the aspect ratio, defined as the ratio of the major to minor axis of the ellipse, and the grain slope, which describes the angle between the major axis and the x -axis of the corresponding image.

These pairplots can be divided into three regions: the upper-right corner, the diagonal, and the lower-left corner. In the uppe r-right corner, images of the individual pole figures (for ferrite) and etched surface images (for pearlite) are included to indicate the origin of the displayed information. Along the diagonal, kernel density estimates (KDEs) illustrate the distributions of the three grain descriptors, while the lower-left panels show the relationships between these variables. From the KDEs, it can be seen that both the grain area and the aspect ratio follow a lognormal-type distribution across all three material configurations. The distributions are very similar, with mean grain areas
ranging from 2.67 to 2.95μ m2 for two and ten passes, respectively. The mean aspect ratios lie between 4.47 and 5.66, reflecting a pronounced elongation of the ferrite grains. The grain slope consistently approaches 90∘, corresponding to the rolling direction. This trend indicates that as grains align more closely with the rolling direction, their aspect ratios increase, resulting in a greater elongation along that direction.

[picture]

Fig. 5. Pairplot of the perlite phase with corresponding light optical images.

Fig. 5. Pairplot of the perlite phase with corresponding light optical images.

Comparable trends are observed for the pearlite phase, as shown in Figure 5. The grain areas are considerably larger than those in ferrite, with mean values ranging from 48.77μ m2 for two passes, 66.16μ m2 for five passes, up to 75.23μ m2 for ten passes. This is likely due to pearlite's tendency to form banded structures, which are not easily represented by simple ellipses. Supporting this interpretation, the aspect ratios also increase, reflecting the elongated morphology of these bands.

The final microstructural feature to consider is the volume fraction of the two phases. Despite minor variations in the measured values, the pearlite fraction can be regarded as constant across all three samples due to the absence of thermal treatments. The pearlite volume fraction was determined by binarizing the light optical microscopy images, yielding values between 11.5% and 13%. These results are in good agreement with the pearlite content reported in the literature.

Discussion

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The results demonstrate that varying the number of pass reductions alone leads to non-trivial differences in the resulting materials properties. Moreover, an evaluation of the new methods used to capture the process-structure relationship highlights promising possibilities for future investigations. While the tensile tests already indicate subtle variations in the mechanical properties, the subsequent analyses were intended to confirm these differences and to provide insight into their underlying origins. With regard to future work, the microstructural observations are particularly relevant in the context of constructing representative volume elements (RVEs) for the evaluation of damage accumulation during the cold rolling process. In contrast to previous studies, such as in Fehlemann et al. [1], the present work focuses on the deformation-induced microstructure prior to intercritical annealing, where cold rolling already leads to markedly increased grain aspect ratios. As this state has not yet undergone recrystallization driven relaxation and phase transformation, the resulting microstructure is considerably more elongated than in post-annealed conditions, to a point where the perlite grains often exceed the images boundaries. This observation suggests that grains should no
longer be represented solely as idealized ellipsoids. Instead, banded microstructural features should be explicitly considered, as they are expected to play a crucial role in accurately capturing the materials response and damage evolution [16,17]. In addition, statistical characterization approaches such as n -point correlation functions offer a promising alternative for capturing complex spatial correlations and anisotropic microstructural features beyond idealized grain geometries [18]. As discussed above, this work also employed new methods to address limitations encountered in previous studies. One such issue concerned the accuracy of EBSD imaging when polishing with a conventional OPS-polisher, as earlier investigations reported measurement uncertainties when distinguishing between different phases. To mitigate this, an electro-polishing technique was applied to improve phase contrast and overall image quality. While this approach significantly enhanced image clarity, it also led to excessive surface erosion, resulting in increased surface roughness and the appearance of blanks and bulges in the images. Consequently, this effect may have influenced the grain size evaluation and it can therefore be assumed to contribute to the comparatively small measured ferrite grain areas relative to pearlite, even though ferrite does not exhibit a banded morphology. This fact also hindered the construction of RVEs, which will be addressed in future studies.
A key aspect of this study was the investigation of the stress states occurring during the rolling process and their influence on both damage accumulation and microstructural evolution. Previous studies have shown that an increasing number of rolling passes leads to an enhanced damage tolerance in dual-phase steels, while simultaneously affecting the development of deformation induced microstructural features such as grain elongation, phase banding, and strain partitioning. Based on these findings, it can be assumed, although this still needs to be confirmed by future investigations, that the amount of accumulated damage and the resulting microstructural state already differ during the cold rolling process when different pass schedules are applied. An evaluation of the stress state analysis suggests that this behavior may be related to the evolution of stress peaks during rolling. With an increasing number of passes, the stress peaks at entry and exit of each rolling pass become less pronounced, while the overall stress progression becomes more stable. This stabilization may result in reduced stress fluctuations within the material, thereby lowering the likelihood of damage initiation during the rolling process. When comparing the obtained stress states with those reported by Wang et al. for caliber rolling [7], it can be observed that the loading conditions remain comparatively constant over the duration of deformation. This indicates that, for further analyses, it may be sufficient to investigate different areas of the sheet, for example by comparing the stress responses at the surface and in the core. Such an approach could help to identify local differences in deformation and damage-relevant stress states. Furthermore, the influence of different mesh types on the calculated stress states requires additional investigation to determine whether observed differences in plastic deformation originate from numerical effects or from actual variations in deformation induced by different pass schedules, even if the total degree of deformation remains constant. In addition, the measured stress states provide an opportunity to experimentally reproduce the flat rolling process using scaled model experiments, which would allow for a faster and more efficient evaluation of different processing strategies. Prior to such investigations, experimental validation and a comparison of damage evolution between the rolling process and the model experiments will be necessary. A key challenge in this context will be the identification of suitable loading conditions. In accordance with the framework proposed by Bai Wierzbicki [3], loading paths between uniaxial tension, compression, and torsion represent an obvious choice. However, for stress states characterized by negative stress triaxiality combined with a positive Lode angle parameter, the realization of appropriate loading conditions may be challenging. A detailed discussion of these scenarios, however, goes beyond the scope of this work.

Summary

The original version of this paper is available on https://www.scientific.net/MSF.1186.109.pdf

In this study, the influence of different cold rolling pass schedules on the mechanical response, microstructure, and stress state evolution of a micro-alloyed S355 steel was investigated. The material serves as a base for DP800 steel production and was subjected to identical total thickness reductions
but varying numbers of rolling passes. Experimental rolling tests and mechanical characterization were combined with finite element simulations to establish links between processing conditions, resulting microstructural features, and deformation-induced stress states. Tensile testing revealed that variations in pass schedules lead to measurable, albeit subtle, differences in yield strength, fracture strain and work hardening behavior, despite identical overall deformation and the absence of intermediate heat treatments. Microstructural analysis using EBSD and light optical microscopy revealed the expected pronounced grain elongation aligned with the rolling direction. While ferrite grains can still be reasonably approximated by elongated ellipses, the pearlite phase is characterized by very high aspect ratios and a pronounced banded morphology, which challenges the common assumption of ellipsoidal grain representations. Finite element simulations of the flat rolling process provided detailed insight into the evolution of stress triaxiality and Lode angle parameter throughout deformation. Although the overall stress-state trajectories were similar for all pass schedules, increasing the number of passes led to a smoother stress evolution with reduced stress peaks at pass entry and exit. This stabilization of the stress state suggests a potential reduction in damage initiation during rolling, offering a possible explanation for the enhanced damage tolerance reported for multipass rolling in dual-phase steels. Overall, the results demonstrate that the number of rolling passes, even at constant total reduction, influences both microstructural morphology and local stress states in a non-trivial manner. The combined experimental-numerical methodology provides a robust framework for future investigations, particularly for the development of representative volume elements and material models that account for deformation-induced microstructures prior to intercritical annealing.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/MSF.1186.109.pdf

This research was funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation; Projectnumber 278868966 - TRR 188; Damage Controlled Forming Processes, subproject A08).

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The original version of this paper is available on https://www.scientific.net/MSF.1186.109.pdf

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Keywords Index

#

6082 Aluminum Alloy Process Optimization for Forging an AA6082 Automotive Upper Control Arm

A

Aluminum Impact of Separating Agents on the Deformation Regime in Aluminum Roll-Cladding Processes

Automotive Control Arm Process Optimization for Forging an AA6082 Automotive Upper Control Arm

B

Battery Cooling Impact of Separating Agents on the Deformation Regime in Aluminum Roll-Cladding Processes

Bev Impact of Separating Agents on the Deformation Regime in Aluminum Roll-Cladding Processes

C

Cold Forging ML-Based Prediction of Tool Kinematics in Cold Forging of Tailored Hollow Shafts with Variable Wall Thickness

Compound Forging Experimental-Numerical Approach to Determine the Heat Transfer Coefficients for Compound Forging Processes

D

Die Design Process Optimization for Forging an AA6082 Automotive Upper Control Arm

Die Forging Quasi-Isothermal Forging of Steel-Encapsulated Ti-6AI-4V Billets

Die Plasticity Risk Experimental Characterisation of Service-Degraded H13 Tool Steel and Numerical Assessment of Die Plasticity Risk in Hot Forging of Nickel-Based Superalloys

Dual Phase Steel Relating the Cold Rolling Pass Reduction to the Microstructural Evolution and Formability in DP800 Steel

E

E-mobility Impact of Separating Agents on the Deformation Regime in Aluminum Roll-Cladding Processes

Experimental-numerical Approach Experimental-Numerical Approach to Determine the Heat Transfer Coefficients for Compound Forging Processes

F

Finite Element Analysis Process Optimization for Forging an AA6082 Automotive Upper Control Arm

Flash Formation Workpiece-Based Signatures for Machine Diagnostics in Die Forging with Flash

Flatness Control Profile Contour and Flatness Control Model for Hot Strip Rolling

Forging Process Optimization for Forging an AA6082 Automotive Upper Control Arm

Forging Quality Workpiece-Based Signatures for Machine Diagnostics in Die Forging with Flash

Formability Relating the Cold Rolling Pass Reduction to the Microstructural Evolution and Formability in DP800 Steel

H

Heat Exchanger Impact of Separating Agents on the Deformation Regime in Aluminum Roll-Cladding Processes

Heat Transfer Coefficient Experimental-Numerical Approach to Determine the Heat Transfer Coefficients for Compound Forging Processes

Hot Forging Experimental Characterisation of Service-Degraded H13 Tool Steel and Numerical Assessment of Die Plasticity Risk in Hot Forging of Nickel-Based Superalloys

Hot Forming Experimental Characterisation of TRIP Effects of 31CrMoV9 for Hot Forming

Hot Rolling Profile Contour and Flatness Control Model for Hot Strip Rolling

L

Laser Triangulation Workpiece-Based Signatures for Machine Diagnostics in Die Forging with Flash

M

Material Characterisation Experimental Characterisation of TRIP Effects of 31CrMoV9 for Hot Forming

N

Nickel-based Superalloys Experimental Characterisation of Service-Degraded H13 Tool Steel and Numerical Assessment of Die Plasticity Risk in Hot Forging of Nickel-Based Superalloys

Non-proportional Loading Path Relating the Cold Rolling Pass Reduction to the Microstructural Evolution and Formability in DP800 Steel

P

Partial Roll-cladding Impact of Separating Agents on the Deformation Regime in Aluminum Roll-Cladding Processes

Process Monitoring Workpiece-Based Signatures for Machine Diagnostics in Die Forging with Flash

Process Simulation Relating the Cold Rolling Pass Reduction to the Microstructural Evolution and Formability in DP800 Steel

Q

Quasi-isotherm Quasi-Isothermal Forging of Steel-Encapsulated Ti-6AI-4V Billets

R

Response Surface Methodology Process Optimization for Forging an AA6082 Automotive Upper Control Arm

Rolling Process Optimization for Forging an AA6082 Automotive Upper Control Arm

S

Steel Quasi-Isothermal Forging of Steel-Encapsulated Ti-6AI-4V Billets

Strip Crown Profile Contour and Flatness Control Model for Hot Strip Rolling

T

Thermal Degradation Experimental Characterisation of Service-Degraded H13 Tool Steel and Numerical Assessment of Die Plasticity Risk in Hot Forging of Nickel-Based Superalloys

Titanium Quasi-Isothermal Forging of Steel-Encapsulated Ti-6AI-4V Billets

Tool Kinematic Prediction ML-Based Prediction of Tool Kinematics in Cold Forging of Tailored Hollow Shafts with Variable Wall Thickness

Trip Experimental Characterisation of TRIP Effects of 31CrMoV9 for Hot Forming

V

Variable Wall Thickness ML-Based Prediction of Tool Kinematics in Cold Forging of Tailored Hollow Shafts with Variable Wall Thickness

Authors Index

A

Abedul, D. Experimental Characterisation of Service-Degraded H13 Tool Steel and Numerical Assessment of Die Plasticity Risk in Hot Forging of Nickel-Based Superalloys

Agirre, J. Experimental Characterisation of Service-Degraded H13 Tool Steel and Numerical Assessment of Die Plasticity Risk in Hot Forging of Nickel-Based Superalloys

B

Behrens, B. Experimental-Numerical Approach to Determine the Heat Transfer Coefficients for Compound Forging Processes; Quasi-Isothermal Forging of Steel-Encapsulated Ti-6AI-4V Billets; Experimental Characterisation of TRIP Effects of 31CrMoV9 for Hot Forming

Bergs, T. Impact of Separating Agents on the Deformation Regime in Aluminum Roll-Cladding Processes

Biermann, I. Relating the Cold Rolling Pass Reduction to the Microstructural Evolution and Formability in DP800 Steel

Brunotte, K. Quasi-Isothermal Forging of Steel-Encapsulated Ti-6AI-4V Billets; Workpiece-Based Signatures for Machine Diagnostics in Die Forging with Flash

C

Costa, E. Process Optimization for Forging an AA6082 Automotive Upper Control Arm

E

Engel, B. Impact of Separating Agents on the Deformation Regime in Aluminum Roll-Cladding Processes

F

Fehlemann, N. Relating the Cold Rolling Pass Reduction to the Microstructural Evolution and Formability in DP800 Steel

Frohn-Sörensen, P. Impact of Separating Agents on the Deformation Regime in Aluminum Roll-Cladding Processes

G

Galdos, L. Experimental Characterisation of Service-Degraded H13 Tool Steel and Numerical Assessment of Die Plasticity Risk in Hot Forging of Nickel-Based Superalloys

Glaubitz, C. Workpiece-Based Signatures for Machine Diagnostics in Die Forging with Flash

H

Hribsek, M. Relating the Cold Rolling Pass Reduction to the Microstructural Evolution and Formability in DP800 Steel

Höniger, B. Impact of Separating Agents on the Deformation Regime in Aluminum Roll-Cladding Processes

I

Ibarretxe, U. Experimental Characterisation of Service-Degraded H13 Tool Steel and Numerical Assessment of Die Plasticity Risk in Hot Forging of Nickel-Based Superalloys

J

Jimenez, S. Profile Contour and Flatness Control Model for Hot Strip Rolling

L

Legrand, N. Profile Contour and Flatness Control Model for Hot Strip Rolling

Liewald, M. ML-Based Prediction of Tool Kinematics in Cold Forging of Tailored Hollow Shafts with Variable Wall Thickness

M

Ma, X. Process Optimization for Forging an AA6082 Automotive Upper Control Arm

Mohnfeld, N. Experimental-Numerical Approach to Determine the Heat Transfer Coefficients for Compound Forging Processes; Experimental Characterisation of TRIP Effects of 31CrMoV9 for Hot Forming

Muhr, F. Impact of Separating Agents on the Deformation Regime in Aluminum Roll-Cladding Processes

Möckelmann, J. Quasi-Isothermal Forging of Steel-Encapsulated Ti-6AI-4V Billets

Münstermann, S. Relating the Cold Rolling Pass Reduction to the Microstructural Evolution and Formability in DP800 Steel

N

Niemietz, P. Impact of Separating Agents on the Deformation Regime in Aluminum Roll-Cladding Processes

O

Ortlieb, E. Workpiece-Based Signatures for Machine Diagnostics in Die Forging with Flash

Otegi, N. Experimental Characterisation of Service-Degraded H13 Tool Steel and Numerical Assessment of Die Plasticity Risk in Hot Forging of Nickel-Based Superalloys

P

Peddinghaus, J. Quasi-Isothermal Forging of Steel-Encapsulated Ti-6AI-4V Billets; Workpiece-Based Signatures for Machine Diagnostics in Die Forging with Flash

Peddinghaus, S. Experimental-Numerical Approach to Determine the Heat Transfer Coefficients for Compound Forging Processes; Experimental Characterisation of TRIP Effects of 31CrMoV9 for Hot Forming

R

Rosa, A. Process Optimization for Forging an AA6082 Automotive Upper Control Arm

Rothgänger, M. Workpiece-Based Signatures for Machine Diagnostics in Die Forging with Flash

S

Schute, M. Experimental-Numerical Approach to Determine the Heat Transfer Coefficients for Compound Forging Processes

Shi, M. Process Optimization for Forging an AA6082 Automotive Upper Control Arm

Souto, N. Profile Contour and Flatness Control Model for Hot Strip Rolling

U

Uhe, J. Experimental-Numerical Approach to Determine the Heat Transfer Coefficients for Compound Forging Processes; Experimental Characterisation of TRIP Effects of 31CrMoV9 for Hot Forming

W

Wang, L. Process Optimization for Forging an AA6082 Automotive Upper Control Arm

Weiss, A. ML-Based Prediction of Tool Kinematics in Cold Forging of Tailored Hollow Shafts with Variable Wall Thickness

Z

Zhou, L. Process Optimization for Forging an AA6082 Automotive Upper Control Arm