Machine learning for additive manufacturing of implants: a review of metal and ceramic applications

Additive manufacturing is a promising technology with impact in several industries, and it is being increasingly applied to the healthcare sector. Despite the high potential, its application in the manufacturing of implants is limited by the variability of the process and challenges related to the biological integration. In this context, machine learning has been postulated as a tool that may overcome these problems by giving support in process monitoring, optimization, and control of the implant additive manufacturing process itself. This review systematically analyses the current state of the art regarding machine learning applications in the additive manufacturing of metallic and ceramic implants. We evaluate the integration of different machine learning algorithms and additive manufacturing technologies providing study cases where machine learning has been applied to additive manufactured bone implants. The discussion highlights the differences between the application of machine learning for additive manufacturing of metals and ceramics and how the strategies used for metal can be employed in ceramic additive manufacturing.