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In metal additive manufacturing (AM), parts often exhibit quality variations, defects, intricate surface topography, and anisotropic properties influenced by factors such as process parameters, energy and fusion interactions, and material physics. These complexities make metal-AM processes challenging to manage consistently, leading to unacceptable levels of inconsistency. To address these issues and predict quality, in-situ process sensing and monitoring as well as post-process measurements are commonly employed, aiming to enhance process understanding, control, and reliability. This review paper surveys literature on deep learning (DL) methods used in AM processes, discussing current research challenges and future directions. The ultimate objective is to develop intelligent AM systems capable of using real-time process data for automated control decisions and interventions, advancing towards more reliable defect-free manufacturing outcomes.
Tuğrul Özel (Tue,) studied this question.