Combined material model to predict flow curves of cold forging raw materials having high strain hardening exponent

Combined material model to predict flow curves of cold forging raw materials having high strain hardening exponent


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Abstract. In order to increase the accuracy of cold forging simulations, flow curves obtained by experimental compression tests are used instead of the material models existing in the software library. The parameters of Ludwik material model were determined with respect to the constructed experimental flow curves at different temperatures and strain rates. Then, the flow curves were defined into the software by using these parameters. While Ludwik model can represent the material flow curve with high accuracy at low plastic strain values, the error rate between the experimental flow curve and the Ludwik model increases at high plastic strain values. Voce material models were known to predict the flow curve of materials with high strain hardening exponents more accurately, especially at high temperature and strain values. In this study, the performance of Ludwik material model was compared to four Voce material models given in the literature and a more accurate combined material model was defined for each flow curve at different temperature and strain rates for 42CrMoS4 material. All experimental flow curves were predicted with a minimum R2 of 0.99 and the lowest mean absolute error value with the new combined material model.

Metal Forming, Voce Material Model, Flow Curve Prediction, Finite Element Method

Published online 4/19/2023, 8 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: ZEREN Doğuş, KOCATÜRK Fatih, TOPARLI M. Burak, Combined material model to predict flow curves of cold forging raw materials having high strain hardening exponent, Materials Research Proceedings, Vol. 28, pp 1503-1510, 2023


The article was published as article 162 of the book Material Forming

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[1] H. Hollomon, Tensile deformation, Aime Trans. 12 (1945) 1 -22.
[2] D.C. Ludwigson, Modified stress-strain relation for FCC metals and alloys, Metall. Trans. 2 (1971) 2825-2828.
[3] P. Ludwik, Elemente der Technologischen Mechanik, Springer, 1909.
[4] H.W. Swift, Plastic instability under plane stress, J. Mech. Phys. Solids 1 (1952) 1-18.
[5] Voce, The relationship between stress and strain from homogenous deformation, J. Inst. Met. 74 (1948) 537-562.
[6] J.H. Guo, S.D. Zhao, G.H. Yan, Z.B. Wang, Novel flow stress model of AA 4343 aluminium alloy under high temperature deformation, Mater. Sci. Technol. 29 (2013) 197-203.
[7] D.T. Nguyen, A new constitutive model for AZ31B magnesium alloy sheet deformed at elevated temperatures and various strain rates, High. Temp. Mater. Process. 33 (2014) 499-508.
[8] U. Rotpai, T. Arlai, S. Nusen, P. Juijerm, Novel flow stress prediction and work hardening behavior of aluminium alloy AA7075 at room and elevated temperatures, J. Alloy. Compd. 891 (2021) 162013.
[9] J. Cao, F. Li, W. Ma, D. Li, K. Wang, J. Ren, H. Nie, W. Dang, Constitutive equation for describing true stress -strain curves over a large range of strains, Philos. Mag. Lett. 100 (2020) 476-485.
[10] F. Kocatürk, M.B. Toparli, B. Tanrıkulu, S. Yurtdaş, D. Zeren, C. Kılıçaslan, Flow curve prediction of cold forging steel by artificial neural network model, 24th International Conference on Material Forming, 2021.
[11] T. Aydın, F. Kocatürk, D. Zeren, A Model to Construct and Predict Flow Curve of Materials from Compression Test Results with Machine Learning Models Using Python, Key Eng. Mater. 926 (2022) 2022-2030.
[12] ASTM E9-19, Standard Test Methods of Compression Testing of Metallic Materials at Room Temperature, ASTM International, West Conshohocken, PA, 2019.