Semantic segmentation of corrosive critical designs in body‐in‐white structures for corrosion simulation

Scaling finite element method (FEM) based corrosion simulations to whole body-in-white structures lead to extremely high computational costs. As corrosion only appears in corrosive critical areas, the FEM is restricted to these. The objective is a semantic segmentation of corrosive critical designs, which are part edges and flanges in body-in-white structures. Different deterministic as well as Machine Learning and Deep Learning approaches are proposed and compared with respect to their ability to segment critical designs based on the geometry and the spatial relations of the parts only. The deterministic edge detection provides a fast and highly accurate way to segment part edges, whereas the described deterministic flange detection is not suitable for capturing the full diversity of flange structures. As the feature-based Machine Learning approach evaluates more properties in a more flexible way, the performance of the flange detection is significantly increased and even the edge segmentation obtains slightly better results. The evaluation of graph structures with a Geometric Deep Learning method fails as the train set is too small to sufficiently represent the complex and various structures in the test set.