Predicting fine blanking process signals from sheet metal thickness

Predicting fine blanking process signals from sheet metal thickness

MOON Jiyoung, GELBICH Daria, BECKER Marco, NIEMIETZ Philipp, BERGS Thomas

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Abstract. In sheet metal forming and blanking processes, the direct assessment of process conditions and product quality poses a challenge due to high production rates and the inaccessibility of the tool. In this context, the process signals generated by the manufacturing process, such as force and acoustic emissions, have the potential to serve as a valuable source of information, containing important insights into the quality of the final product as well as the complexity of the process itself. To date, it is not yet fully understood how these process signals depend on different influencing factors, such as process parameters. However, knowing how process signals, which reflect the process state, change with influencing factors is relevant to put observed signals into context and make informed decisions with respect to parameter adjustments. Conditional generative AI models, such as conditional generative adversarial networks (CGANs) offer a promising approach to the aforementioned issue by generating probable process signals based on specified conditions. In this study, thin metal sheets with three different thicknesses were provided into a fine blanking process, and corresponding punching force signals were measured. With these signals, a conditional-deep convolutional GAN (C-DCGAN), a model that combines the principles of both CGAN and deep convolutional GAN (DCGAN), is trained with sheet metal thickness specified as a condition. The trained generator is employed to predict process signals for different sheet thickness values. The presented model is evaluated with respect to thickness values that were known during training time as well as with thickness values that were not presented to the model during training.

Sheet Metal Forming, Process Monitoring, Process Signal Modeling, CGAN

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: MOON Jiyoung, GELBICH Daria, BECKER Marco, NIEMIETZ Philipp, BERGS Thomas, Predicting fine blanking process signals from sheet metal thickness, Materials Research Proceedings, Vol. 41, pp 1436-1445, 2024


The article was published as article 159 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] F. Klocke, Fertigungsverfahren, Springer, Berlin, 2017.
[2] C. Kubik, D.A. Molitor, M. Rojahn, P. Groche, Towards a real-time tool state detection in sheet metal forming processes validated by wear classification during blanking, IOP Conf. Ser.: Mater. Sci. Eng. 1238 (2022) 12067.
[3] M. Unterberg, M. Becker, P. Niemietz, T. Bergs, Data-driven indirect punch wear monitoring in sheet-metal stamping processes, Journal of Intelligent Manufacturing (2023). Https://
[4] C. Kubik, M. Becker, D.-A. Molitor, P. Groche, Towards a systematical approach for wear detection in sheet metal forming using machine learning, Prod. Eng. Res. Devel. 17 (2023) 21–36.
[5] R. Alizadeh, J.K. Allen, F. Mistree, Managing computational complexity using surrogate models: a critical review, Res Eng Design 31 (2020) 275–298.
[6] T. Wuest, C. Irgens, K.-D. Thoben, An approach to monitoring quality in manufacturing using supervised machine learning on product state data, Journal of Intelligent Manufacturing 25 (2014) 1167–1180.
[7] J. Tang, X. Geng, D. Li, Y. Shi, J. Tong, H. Xiao, F. Peng, Machine learning-based microstructure prediction during laser sintering of alumina, Scientific Reports 11 (2021) 10724.
[8] P. Link, J. Bodenstab, L. Penter, S. Ihlenfeldt, Metamodeling of a deep drawing process using conditional Generative Adversarial Networks, IOP Conf. Ser.: Mater. Sci. Eng. 1238 (2022) 12064.
[9] D.A. Molitor, C. Kubik, M. Becker, R.H. Hetfleisch, F. Lyu, P. Groche, Towards high-performance deep learning models in tool wear classification with generative adversarial networks, Journal of Materials Processing Technology 302 (2022) 117484.
[10] J. Luo, J. Huang, H. Li, A case study of conditional deep convolutional generative adversarial networks in machine fault diagnosis, Journal of Intelligent Manufacturing 32 (2021) 407–425.
[11] M. Li, Z. Siqin, Research on Manufacturing Matrix Data Complementation Method Based on Generative Adversarial Network, in: 2023 4th International Symposium on Computer Engineering and Intelligent Communications (ISCEIC), Nanjing, China, pp. 134–138.
[12] M. Mirza, S. Osindero, Conditional Generative Adversarial Nets, 2014.
[13] A. Radford, L. Metz, S. Chintala, Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, 2015.
[14] Sahil Sidheekh, Aroof Aimen, Narayanan C Krishnan, On Characterizing GAN Convergence Through Proximal Duality Gap, International Conference on Machine Learning (2021) 9660–9670.
[15] A. Ramdas, N. Trillos, M. Cuturi, On Wasserstein Two-Sample Testing and Related Families of Nonparametric Tests, Entropy 19 (2017) 47.
[16] R. Fu, J. Chen, S. Zeng, Y. Zhuang, A. Sudjianto, Time Series Simulation by Conditional Generative Adversarial Net, 2019.
[17] J.L. Hodges, The significance probability of the smirnov two-sample test, Ark. Mat. 3 (1958) 469–486.