| About this Abstract |
| Meeting |
13th International Conference on the Technology of Plasticity (ICTP 2021)
|
| Symposium
|
13th International Conference on the Technology of Plasticity (ICTP 2021)
|
| Presentation Title |
Geometry Representation Techniques for Machine Learning Application in Incremental Sheet Forming |
| Author(s) |
Dennis Möllensiep, Philipp Kulessa, Bernd Kuhlenkötter |
| On-Site Speaker (Planned) |
Dennis Möllensiep |
| Abstract Scope |
Incremental sheet forming is a promising process for manufacturing sheet metal parts small batch sizes. However, the industrial application is prevented by the low geometric accuracy resulting of the lack of precise simulations. Many researches have shown the potential of artificial neural networks for predicting the geometric accuracy. Although, existing approaches are imprecise as they only consider general process parameters, neglecting the influence of the part's geometry. To improve the prediciton quality, the submitted publication will present geometry representation techniques for incremental sheet forming and demonstrate their applicability for machine learning. |
| Proceedings Inclusion? |
Definite: At-meeting proceedings |