Abstract Additive manufacturing (AM) represents a category of manufacturing processes that fabricates parts in a layer-by-layer manner. As such, AM provides unique advantages over conventional manufacturing processes such as the ability to fabricate highly complex geometries, to minimize material waste, and to enable mass customization, while having some limitations, such as high costs and complexities. Advances in artificial intelligence (AI) and machine learning (ML) enable these limitations to be addressed due to the data-rich environment in modern commercial AM machines with multiple sensors. This paper surveys papers that apply AI/ML techniques to the topics of defect detection, AM process surrogate models and their application, generative design, and design for manufacturing in metal AM processes. The approach taken is to introduce these topics, provide a coarse survey, and then discuss specific applications in some depth, rather than to provide a fine-grained, comprehensive survey.
Rosen et al. (Sat,) studied this question.