A Deep 3D Convolutional Neural Network Based Design for Manufacturability Framework
Deep 3D Convolutional Neural Networks (3D CNN) are traditionally used for object recognition, video data analytics and human gesture recognition. In this paper, we present a novel application of 3D CNNs in understanding difficultto manufacture features from computer aided design (CAD) models to develop a decision support tool for cyber enabled manufacturing. Traditionally, design for manufacturability (DFM) rules are hand crafted and used to accelerate the engineering product design cycle by integrating manufacturability analysis during the design stage. Such a practice relies on the experience and training of the designer to create a complex component that is manufacturable. However, even after careful design, the inclusion of certain features might cause the part to be non manufacturable. In this paper, we develop a framework using Deep 3D CNNs to learn salient features from a CAD model of a mechanical part and determine if the part can be manufactured or not. CAD models of different manufacturable and nonmanufacturable parts are generated using a solid modeling kernel and then converted into 3D voxel data using a fast GPU accelerated voxelization algorithm. The voxel data is used to train a 3D CNN model for manufacturability classification. Feature space and filter visualization is also performed to understand the learning capability in the context of manufacturability features. We demonstrate that the proposed 3D CNN based DFM framework is able to learn the DFM rules for non manufacturable features without a human prior. The framework can be extended to identify a large variety of difficult to manufacture features at multiple spatial scales leading to a real time decision support system for DFM.
ArXiv