Comparison of Linear Regression and Neural Networks as Surrogates for Sensor Modeling on a Deep Drawn Part

Abstract

AbstractSeveral 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 comparing with the simulated robust optimum. While 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 data points 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.KeywordsDeep drawingSurrogate modelingFE simulationRegressionBias-variance-tradeoff