Deep Learning Approach for Feature Recognition and Extraction in Computer-Aided Process Planning and Manufacturing: Current State and Future Directions

Abstract

With the global rise in demand for various parts products, manufacturing industries must adopt modern manufacturing approaches to achieve customer satisfaction, timely production, reliability and cost-effectiveness. Features have been the distinguishing attributes of components; these attributes require adequate recognition and careful planning to ease production lag times. Computer-Aided Process Planning transforms these features from design specification through Computer-Aided Design model into manufacturing sequences for a computer-aided manufacturing system. This review paper highlights the deep learning approach adopted to aid computer-aided process planning methodologies in feature extraction and recognition, and proposes the potential of using deep learning methodologies in feature recognition, extraction, and specification using convolutional neural networks, it also presents future directions for further study.