Abstract
Abstract Variability in product quality continues to pose a major barrier to the widespread application of additive manufacturing (AM) processes in production environment. Towards addressing this barrier, monitoring AM processes and measuring AM materials and parts has become increasingly commonplace, and increasingly precise, making a new wave of AM-related data available. This newfound data provides a valuable resource for gaining new insight to AM processes and decision making. Machine Learning (ML) provides an avenue to gain this insight by 1) learning fundamental knowledge about AM processes and 2) identifying predictive and actionable recommendations to optimize part quality and process design. This report presents a literature review of ML applications in AM. The review identifies areas in the AM lifecycle, including design, process plan, build, post process, and test and validation, that have been researched using ML. Furthermore, this report discusses the benefits of ML for AM, as well as existing hurdles currently limiting applications.
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