Fast prediction of the material displacement in open die forging using neural networks

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Fast prediction of the material displacement in open die forging using neural networks

JAGTAP Nikhil Vijay, REINISCH Niklas, BAILLY David

Abstract. This paper presents a data-driven approach to predict the material displacement in open die forging using neural networks. Training data for different process parameters and workpiece geometries is generated using finite element simulations. A neural network architecture is designed that takes the process parameters and the coordinates of a point in the geometry as inputs and outputs the displacement of that point after the deformation. This is systematically implemented for open die forging, using relevant process information. The neural network model is trained and tested on various FEA-simulations for different process parameters and shows good accuracy and generalization. The model is also able to simulate multiple strokes of a single pass in a fast and efficient way. It is demonstrated how the neural network model can enable building a digital material shadow of open die forging processes. The advantages and limitations of the approach are then further discussed.

Keywords
Open Die Forging, Neural Networks, Data-Driven Modeling, FEA

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: JAGTAP Nikhil Vijay, REINISCH Niklas, BAILLY David, Fast prediction of the material displacement in open die forging using neural networks, Materials Research Proceedings, Vol. 41, pp 2299-2308, 2024

DOI: https://doi.org/10.21741/9781644903131-253

The article was published as article 253 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.

References
[1] F. Becker, P. Bibow, M. Dalibor, A. Gannouni, V. Hahn, C. Hopmann, M. Jarke, I. Koren, M. Kröger, J. Lipp, J. Maibaum, J. Michael, B. Rumpe, P. Sapel, N. Schäfer, G. J. Schmitz, G. Schuh, and A. Wortmann, “A Conceptual Model for Digital Shadows in Industry and Its Application,” in Conceptual Modeling: 40th International Conference, ER 2021, Virtual Event, October 18–21, 2021, Proceedings, 2021, pp. 271–281.
[2] J. Yanagimoto, D. Banabic, M. Banu, and L. Madej, “Simulation of metal forming – Visualization of invisible phenomena in the digital era,” CIRP Annals, vol. 71, no. 2, pp. 599–622, 2022, https://doi.org/10.1016/j.cirp.2022.05.007
[3] D. Recker, M. Franzke, and G. Hirt, “Fast models for online-optimization during open die forging,” CIRP Annals, vol. 60, no. 1, pp. 295–298, 2011, https://doi.org/10.1016/j.cirp.2011.03.142.
[4] E. Siemer, “Qualitätsoptimierende Prozeßsteuerung des Reckschmiedens,” 1987.
[5] C. M. Sellars, “Modelling microstructural development during hot rolling,” Materials Science and Technology, vol. 6, no. 11, pp. 1072–1081, 1990, https://doi.org/10.1179/mst.1990.6.11.1072
[6] A. Tomlinson, and J. Stringer, “Spread and elongation in flat tool forging,” Journal of the Iron and Steel Institute, 1959.
[7] D. Rosenstock, D. Recker, G. Hirt, K. J. Steingießer, R. Rech, B. Gehrmann, and R. Lamm, “Application of a Fast Calculation Model for the Process Monitoring of Open Die Forging Processes,” KEM, 554-557, pp. 248–263, 2013, https://doi.org/10.4028/www.scientific.net/KEM.554-557.248
[8] K. Mehrotra, Elements of artificial neural networks. Cambridge, Mass: MIT Press, 1997.
[9] K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Networks, vol. 2, no. 5, pp. 359–366, 1989, https://doi.org/10.1016/0893-6080(89)90020-8
[10] M. Knap, G. Kugler, H. Palkowski, and R. Turk, “Prediction of Material Spreading in Hot Open-Die Forging,” steel research international, vol. 75, no. 6, pp. 405–410, 2004, https://doi.org/10.1002/srin.200405787
[11] S. Lee, K. Kim, and N. Kim, “A Preform Design Approach for Uniform Strain Distribution in Forging Processes Based on Convolutional Neural Network,” J. Manuf. Sci. Eng, vol. 144, no. 12, 2022, https://doi.org/10.1115/1.4054904
[12] W. L. Chan, M. W. Fu, and J. Lu, “An integrated FEM and ANN methodology for metal-formed product design,” Engineering Applications of Artificial Intelligence, vol. 21, no. 8, pp. 1170–1181, 2008, https://doi.org/10.1016/j.engappai.2008.04.001
[13] M. Mozaffar, S. Liao, X. Xie, S. Saha, C. Park, J. Cao, W. K. Liu, and Z. Gan, “Mechanistic artificial intelligence (mechanistic-AI) for modeling, design, and control of advanced manufacturing processes: Current state and perspectives,” Journal of Materials Processing Technology, vol. 302, p. 117485, 2022, https://doi.org/10.1016/j.jmatprotec.2021.117485