Shear cutting: Model-based prediction of material parameters based on synthetic process force signals

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Shear cutting: Model-based prediction of material parameters based on synthetic process force signals

RIEMER Matthias, SILBERMANN Katja, KRÄUSEL Verena, LANGHAMMER Dominic, KOSCHMIDER Agnes

Abstract. Data-driven process monitoring is an approach in the field of forming technology for increasing process efficiency. In shear cutting processes surrogate models based on process force signals can be used for process monitoring. Currently, the data basis for developing such models has to be generated within experiments. The generation of synthetic training data using numerical methods seems to be a more efficient alternative approach. In this work, it is investigated whether virtual training data for the prediction of material properties can be generated by numerical methods. An FE model of the investigated shear cutting process has been designed and validated based on experiments. It is shown that especially the consideration of the tool stiffness has a significant influence on the simulated process force signal. The validated FE model is used to generate synthetic training data. Based on this data, different prediction models are trained to predict the material model parameters based on the force signals. Different model types are compared and the hyperparameters are optimized for the preferred model.

Keywords
Shear Cutting, Punching Force, Synthetic Data, Model-Based Process Monitoring, Machine-Learning

Published online 4/24/2024, 9 pages
Copyright © 2024 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: RIEMER Matthias, SILBERMANN Katja, KRÄUSEL Verena, LANGHAMMER Dominic, KOSCHMIDER Agnes, Shear cutting: Model-based prediction of material parameters based on synthetic process force signals, Materials Research Proceedings, Vol. 41, pp 1334-1342, 2024

DOI: https://doi.org/10.21741/9781644903131-148

The article was published as article 148 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.

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