M3AM: Materials, Monitoring, and Machine Learning for Flaw-Free Additive Manufacturing

The long-term goal of this research is to achieve flaw-free, industrial-scale production of metal parts using additive manufacturing processes (metal AM/ 3D printing). This dissertation focuses on two key metal AM processes: directed energy deposition, and laser powder bed fusion. Despite demonstrated design and processing advantages over conventional subtractive and formative manufacturing techniques, metal AM remains to be widely adopted in safety-critical industries, such as aerospace and biomedical, owing to its tendency to create flaws. The prevalence of flaws in turn results in a large variation in functional properties. As a step towards the long-term goal of flaw-free, production-level scaling of metal AM, the goal of this dissertation is to understand, monitor, control, and predict the causal effect of thermal phenomena on flaw formation. The flaws studied in this work transcend across scales from the microstructure-level to bulk part-level, e.g., microstructure heterogeneity, porosity, inter-granular cracking (microcracking), and thermal-induced part distortion (warping). Accordingly, the objective of this research is three-fold: (1) understand and explain the fundamental process phenomena and metallurgical interactions that lead to flaw formation, and subsequently use this insight to mitigate flaws; (2) mitigate flaw formation via in-situ closed-loop process control; and (3) in-situ monitoring and prediction of flaw formation using real-time sensor data and machine learning