| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Algorithm Development in Materials Science and Engineering
|
| Presentation Title |
Prediction of Cutting Surface Parameters in Punching Processes aided by Machine Learning |
| Author(s) |
Adrian Schenek, Marcel Görz, Mathias Liewald |
| On-Site Speaker (Planned) |
Adrian Schenek |
| Abstract Scope |
Punching represents one of the most frequently used manufacturing processes in the sheet metal processing industry. As an important quality criterion for shear cutting processes, the geometric shape of the cutting surface is considered. In this regard, the edge draw-in height, the clean cut proportion, the fracture surface height and the burr are relevant parameters for monitoring the production quality in punching processes. These parameters can easily be measured in shear cutting processes with an open cutting line (e.g. using laser triangulation). For processes with a closed cutting line, however, such a measurement is often not possible due to the limited accessibility. The present paper therefore proposes a machine learning approach, which enables a data-driven prediction of cutting surface parameters based on measurable process data. The new approach presented in this paper is to pre-train a neural network on numerically determined cutting force curves. As an output, the neural network predicts the mentioned quality parameters of punched sheet metal component edges. The output of the numerically pre-trained neural network is evaluated for numerically and experimentally determined process data and cutting surface parameters. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, Iron and Steel, |