Machining feature recognition using BRepNet

Numerous attempts have been made to recognize the machining features in three-dimensional (3D) computer-aided design (CAD) models using various methods since the 1980s. Recently, deep learning approaches have been explored for machining feature recognition. However, the boundary representation (BRep) model, the most common representation of 3D CAD models, is difficult to use directly in deep learning because of its complex structure. To solve this problem, BRepNet was recently proposed. This study proposes a method for recognizing machining features in 3D CAD models represented by BRep using BRepNet. In the proposed method, BRepNet is used to classify each face of a 3D CAD model based on the machining features. Next, the classified faces are combined into machining features using connected-component analysis. In addition, a dataset is generated to train the BRepNet model. Subsequently, the proposed method is implemented and tested for verification. The proposed method exhibits an accuracy of 96.03 % and part intersection over union (pIoU) of 90.57 %.