Automated Recognition of Machining Features in Mechanical Components Utilizing 3D Convolutional Neural Networks

Machining feature recognition is crucial for achieving automation and intelligence in the realm of intelligent manufacturing. It serves as a fundamental technology for the integration of Computer-Aided Design (CAD), Computer-Aided Process Planning (CAPP), and Computer-Aided Manufacturing (CAM). Traditional methods of machining feature recognition largely depend on manual processes, resulting in low efficiency and limited reusability. In this paper, we propose a novel approach utilizing a 3D convolutional neural network (3D CNN) that is particularly well-suited for recognizing machining features of mechanical components. The network takes triangular mesh data as input and employs a voxelization algorithm to convert the 3D model into a 3D Boolean matrix. This matrix is subsequently processed by the 3D CNN for feature recognition. Experimental results demonstrate that the proposed machining feature recognition method achieves an accuracy exceeding 99% on the 3D models from an open machining dataset. This work holds significant implications for the automatic recognition of machining features in mechanical parts.