Formability Assessment of Variable Geometries Using Machine Learning-Analysis of the Influence of the Database

Abstract

Surrogate modelling has proven to be an effective strategy for time-efficient analysis and optimisation of expensive functions such as manufacturing process simulations. However, most surrogate approaches generate problem-specific “one-off” models, which cannot be reused in other, even similar scenarios. Hence, variations of the problem, e.g. minor geometry changes, instantly invalidate the surrogate. Image-based machine learning (ML) techniques have been proposed as an option to train a surrogate for variable geometries. However, it is currently unclear how to construct a sufficiently diverse set of generic training geometries and what effect different databases have. This work investigates the effect of different databases on the prediction accuracy of an ML-assessment of component manufacturability. The considered use-case is textile forming (draping) of a woven fabric. Sampling plans generate different numbers of training geometries, which are in turn evaluated in draping simulations. An image-based ML-algorithm is trained on these process samples and evaluated on a set of validation geometries. Results show that the diversity of the training geometries has a greater impact on the prediction accuracy than the number of samples. The results also hint that a comparably low number of geometry samples suffices to give meaningful results. With these findings, ML-techniques are considered a promising and time-efficient tool for manufacturability assessment at early stages of part and process design.