Metal additive manufacturing (AM) facilitates the production of geometrically complex components, yet its broader industrial use remains limited by the risk of defect formation and uncertainties in their detection, originating from the highly dynamic and high-temperature process environment. To make additive manufacturing more reliable and establish high-quality parts, it is important to understand how these defects form and how their characteristics appear during the process. This review explains the main causes of common defects, such as cracking, porosity, lack of fusion, and inclusions in metal AM processes, including Powder Bed Fusion and Directed Energy Deposition. It also connects main defect formation mechanisms to the optical, thermal, acoustic, and spectroscopic signals that can be measured during the process. Moreover, it is described how commonly used in situ monitoring systems work and how their signals correspond to melt pool dynamics, vapor plume, particle movement, and the solidification process for each kind of defect. An overview is provided of how data from these systems are analyzed, including the extraction of features from images, the evaluation of temperature fields, and the use of time and frequency domain techniques for various signals. By linking the physics of defect formation to measurable process signals, the interpretation of sensor data is enabled, and potential strategies for monitoring specific problems are outlined. Finally, recent developments are examined, including the integration of multiple sensors, advanced feature-representation approaches, and real-time data interpretation coupled with adaptive control. Together, these directions represent promising advances towards more intelligent and reliable monitoring systems for the future of metal AM.
Nobari et al. (Wed,) studied this question.