| 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 |
Comparison of Linear Regression and Neural Networks as Surrogates for Sensor Modeling on a Deep Drawn Part |
| Author(s) |
Matthias Ryser, Markus Bambach |
| On-Site Speaker (Planned) |
Matthias Ryser |
| Abstract Scope |
Several developments in deep drawing aim at systematically determining modifications during tool tryout. Recent work deals with a simulation based method to discover the current state parameters based on characteristic measurement quantities and infer a tryout proposal by comparison with the simulated robust optimum. Whereas the simulation provides an accurate model of the drawing process, a low-fidelity surrogate model is required to predict the influence of process parameters on the targets in a computationally efficient manner. In this work, training data is generated by a stochastic finite element simulation in AutoForm. The datapoints are used to fit and evaluate linear models as well as neural networks for regression. These models use process parameters as predictors to estimate the target parameters draw-in and local blank holder forces. Results show that simple models outperform complex models. No evidence was found that the model accuracy increases by using neural networks. |
| Proceedings Inclusion? |
Definite: At-meeting proceedings |