3D convolutional neural network for machining feature recognition with gradient-based visual explanations from 3D CAD models

Abstract

In the manufacturing industry, all things related to a product manufactured are generated and managed with a three-dimensional (3D) computer-aided design (CAD) system. CAD models created in a 3D CAD system are represented as geometric and topological information for exchange between different CAD systems. Although 3D CAD models are easy to use for product design, it is not suitable for direct use in manufacturing since information on machining features is absent. This study proposes a novel deep learning model to recognize machining features from a 3D CAD model and detect feature areas using gradient-weighted class activation mapping (Grad-CAM). To train the deep learning networks, we construct a dataset consisting of single and multi-feature. Our networks comprised of 12 layers classified the machining features with high accuracy of 98.81% on generated datasets. In addition, we estimated the area of the machining feature by applying Grad-CAM to the trained model. The deep learning model for machining feature recognition can be utilized in various fields such as 3D model simplification, computer-aided engineering, mechanical part retrieval, and assembly component identification.