Abstract & Overview
Additive manufacturing (AM) is a promising digital manufacturing approach that has seen recent rapid growth. Despite the fast-growing nature of the technology, AM has been slowed by the qualification and certification due to various defects observed in printed parts. On the other hand, Convolutional Neural Networks (CNN), as a deep learning method, have received a great deal of attention over the last decade and demonstrated excellent performance in dealing with image data. Deep learning is a subset of machine learning and refers to any Artificial Neural Network with more than two hidden layers. This article provides a comprehensive overview of CNN’s application to several aspects of the AM process since the emergence of this field. This review also highlights current challenges and possible solutions to provide a horizon for future studies.