Optical tomography for in-situ quality assurance in laser powder bed fusion: From defect detection to closed-loop control: A critical review

Abstract Metal additive manufacturing has become a disruptive manufacturing method for producing components in critical applications; therefore, quality assurance is increasingly important for ensuring part reliability and performance. Traditional methods for examining parts, such as destructive testing and X-ray computed tomography, can be costly, time-consuming, and impractical for inspecting every part in a production run. In-situ monitoring using optical tomography (OT) has gained increasing attention for quality control in processes such as laser powder bed fusion (LPBF) because it provides a relatively low-cost monitoring solution with relatively simple hardware integration into commercial systems for full-field, layer-wise monitoring of the build process. This review provides a comprehensive overview of OT for in situ monitoring in LPBF processes. It covers the fundamental principles, sensor configurations, research trends, and applications for quality assurance and defect detection. The discussion addresses the challenges and limitations these systems face, such as data management, signal interpretation, and calibration. The review highlights future opportunities to apply OT with machine learning to create digital twins or enable closed-loop control, thereby improving reliable build strategies in metal additive manufacturing.