Title: Qualification of Metal Laser Powder Bed Fusion via In-Situ Melt Pool Monitoring When: Friday, August 21, 2026 at 11:00 AM Where: GTMI Building, Room 114

Abstract:
In metal additive manufacturing, qualifying a machine to manufacture components is a critical process to ensure parts will meet high complexity, dimensional accuracy, and performance requirements. The machine qualification and process development can be costly in terms of both time and resources, often requiring repeated test build evaluations that involve parameter space exploration, test builds and quality evaluations by destructive and/or non-destructive evaluation methods. Of substantial interest to accelerating machine qualification and process development are rapid techniques for supporting machine setup qualification and in situ process monitoring. While melt pool monitoring approaches have been heavily investigated to identify build process defects and support in situ part qualification, its use for supporting machine qualification and component geometry quality assessment has not been as strongly investigated and is not well understood. This dissertation addressed these shortcomings through a dedicated experimental and modeling study that did the following: (1) develop and validate model frameworks for mapping laser power, scan speed, and laser defocus to respective melting conditions using melt pool monitoring, (2) investigate capability of melt pool monitoring sensors to assess laser focusing variabilities through the build volume, and (3) understand capability of melt pool monitoring to measure the impact of laser defocus on surface roughness. Photodiodes were shown to provide sufficient data for models to be at least 85\% effective in predicting melting conditions with variable parameter combinations, while melt pool intensity measurements were able to predict laser focal error metrics within 0.3mm of the actual values. The results of these investigations further establish the utility of melt pool monitoring approaches to inform machine and process qualification and will provide industry practitioners new capabilities for machine health assessment.