Surrogate model to describe temperature field in real-time for hot forging

Surrogate model to describe temperature field in real-time for hot forging


download PDF

Abstract. In the context of certain metallic alloys, the conformity of the product depends on its metallurgical structure. Addressing this, the implementation of a real-time monitoring system to control the evolution of the metallurgical structure and the geometry of the cogging part is proposed. Focusing on the microstructure’s dependence on temperature, this article outlines the requested steps for developing data-driven reduced models for describing the temperature field in the billet. These models use temperature data collected from predictive numerical simulations conducted using FORGE® software. Applying the Proper Orthogonal Decomposition (POD) technique, the images illustrating the temperature field are reconstructed through a 2D matrix-based framework. This matrix, derived from non-discretized elements issued from FORGE®, underwent discretization through an objective method, resulting in a size of 100*100. The utilization of the POD technique in this approach provides a parametric vector description, facilitating rapid image reconstruction through manipulation of vector system parameters. With just two vectors, we can effectively reconstruct the image representing the temperature field.

Hot Forging, Proper Orthogonal Decomposition, Numerical Simulations, Surrogate Model, Real-Time Monitoring System

Published online 4/24/2024, 10 pages
Copyright © 2024 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: MIDAOUI Aya, BAUDOUIN Cyrille, DANGLADE Florence, BIGOT Régis, Surrogate model to describe temperature field in real-time for hot forging, Materials Research Proceedings, Vol. 41, pp 871-880, 2024


The article was published as article 95 of the book Material Forming

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

[1] H. Clemens, S. Mayer, et C. Scheu, « Microstructure and Properties of Engineering Materials », in Neutrons and Synchrotron Radiation in Engineering Materials Science, John Wiley & Sons, Ltd, 2017, p. 1 20.
[2] G. Y. Lai, W. E. Wood, R. A. Clark, V. F. Zackay, et E. R. Parker, « The effect of austenitizing temperature on the microstructure and mechanical properties of as-quenched 4340 steel », Metall Trans, vol. 5, no 7, p. 1663 1670, juill. 1974. 10.1007/BF02646340
[3] R. Douglas et D. Kuhlmann, « Guidelines for precision hot forging with applications », Journal of Materials Processing Technology, vol. 98, no 2, p. 182 188, janv. 2000. 10.1016/S0924-0136(99)00197-1
[4] Z. Allam, E. Becker, C. Baudouin, R. Bigot, et P. Krumpipe, « Forging Process Control: Influence of Key Parameters Variation on Product Specifications Deviations », Procedia Engineering, vol. 81, p. 2524 2529, janv. 2014. 10.1016/j.proeng.2014.10.361
[5] Y. C. Gerstenmaier et G. Wachutka, « Time dependent temperature ®elds calculated using eigenfunctions and eigenvalues of the heat conduction equation », Microelectronics Journal, 2001.
[6] J. Yin, R. Hu, et X. Shu, « Closed-die forging process of copper alloy valve body: finite element simulation and experiments », Journal of Materials Research and Technology, vol. 10, p. 1339 1347, janv. 2021. 10.1016/j.jmrt.2020.12.087
[7] P. Ruane, P. Walsh, et J. Cosgrove, « Development of a digital model and metamodel to improve the performance of an automated manufacturing line », Journal of Manufacturing Systems, vol. 65, p. 538 549, oct. 2022. 10.1016/j.jmsy.2022.10.011
[8] L. Jia, R. Alizadeh, J. Hao, G. Wang, J. K. Allen, et F. Mistree, « A rule-based method for automated surrogate model selection », Advanced Engineering Informatics, vol. 45, p. 101123, août 2020. 10.1016/j.aei.2020.101123
[9] B. Jiang, H. Gong, H. Qin, et M. Zhu, « Attention-LSTM architecture combined with Bayesian hyperparameter optimization for indoor temperature prediction », Building and Environment, vol. 224, p. 109536, oct. 2022. 10.1016/j.buildenv.2022.109536
[10] B. M. de Gooijer, J. Havinga, H. J. M. Geijselaers, et A. H. van den Boogaard, « Evaluation of POD based surrogate models of fields resulting from nonlinear FEM simulations », Advanced Modeling and Simulation in Engineering Sciences, vol. 8, no 1, p. 25, nov. 2021. 10.1186/s40323-021-00210-8
[11] D. Uribe, C. Baudouin, C. Durand, et R. Bigot, « Predictive control for a single-blow cold upsetting using surrogate modeling for a digital twin », Int J Mater Form, vol. 17, no 1, p. 7, déc. 2023. 10.1007/s12289-023-01803-x
[12] W. Yoo, M. J. Clayton, et W. Yan, « ESMUST: EnergyPlus-driven surrogate model for urban surface temperature prediction », Building and Environment, vol. 229, p. 109935, févr. 2023. 10.1016/j.buildenv.2022.109935
[13] Y. Liu, G. Lin, J. Guo, et J. Zhu, « Dynamic prediction of fuel temperature in aircraft fuel tanks based on surrogate », Applied Thermal Engineering, vol. 215, p. 118926, oct. 2022. 10.1016/j.applthermaleng.2022.118926
[14] T. S.A, « Ressources | FORGE® ». (consulté le août 22, 2023).
[15] A. Saha, B. S. Gupta, S. Patidar, et N. Martínez-Villegas, « Spatial distribution based on optimal interpolation techniques and assessment of contamination risk for toxic metals in the surface soil », Journal of South American Earth Sciences, vol. 115, p. 103763, avr. 2022. 10.1016/j.jsames.2022.103763
[16] R. M. Di Biase, A. Marcelli, S. Franceschi, A. Bartolini, et L. Fattorini, « Design-based mapping of plant species presence, association, and richness by nearest-neighbour interpolation », Spatial Statistics, vol. 51, p. 100660, oct. 2022. 10.1016/j.spasta.2022.100660
[17] Y. Liu et G. Yin, « The Delaunay triangulation learner and its ensembles », Computational Statistics & Data Analysis, vol. 152, p. 107030, déc. 2020. 10.1016/j.csda.2020.107030
[18] M. Sun, L. Lan, C.-G. Zhu, et F. Lei, « Cubic spline interpolation with optimal end conditions », Journal of Computational and Applied Mathematics, vol. 425, p. 115039, juin 2023. 10.1016/
[19] O. Vincent et O. Folorunso, « A Descriptive Algorithm for Sobel Image Edge Detection », présenté à InSITE 2009: Informing Science + IT Education Conference, 2009. 10.28945/3351.
[20] S. L. Brunton et J. N. Kutz, « Data Driven Science & Engineering », 2017.