Machine learning in design for additive manufacturing: A state-of-the-art discussion for a support tool in product design lifecycle

Abstract Additive manufacturing represents one of the most significant improvements in Industry 4.0. Design for additive manufacturing is the discipline that studies integrated CAD/CAE tools with guidelines for optimizing 3D printing in terms of cost, process time, quality, and precision. In this context, machine learning is used to support control and decision-making activities in additive manufacturing. However, the use of machine learning methods is generally limited to one single process phase. No studies are proposing a machine learning approach focused on different phases of the product lifecycle, from the early design phase to manufactured parts. In the literature, machine learning applications for additive manufacturing regard only one specific phase of the production process. This paper describes current improvements in the integration of additive manufacturing and machine learning, highlighting limitations, and proposes to include different phases of the product lifecycle while designing with machine learning tools. The research provides a guide to develop a new design platform where machine learning supports the engineers in the definition of the product design and process parameters. Finally, the paper also introduces the informatics infrastructure and necessary capabilities to implement the proposed model.