Defeaturing of CAD Models Using Machine Learning

Abstract

Part features, such as dents, holes, and bumps, are integral to the thin-walled sheet metal components used in the automobile and aerospace industries. These features are often required to be suppressed for more agile and reasonably accurate results during the CAE analysis. However, identifying these features is a critical task usually performed by experts. It requires a detailed analysis of the CAD model, which largely depends on the importance of each feature, feature size, and domain boundary conditions. Thus, identifying such features is a challenging task. This work proposes a novel data-driven approach to create an intuitive model that identifies suppressible features on CAD models of sheet metal parts. The dataset to train the supervised learning model is generated by extensive finite element analysis of the part models. The case studies show that the trained Machine learning model gave good test accuracy.