| Abstract Scope |
Laser powder bed fusion (LPBF) adoption faces hurdles from repeatability issues and slow, costly traditional qualification methods. In-situ process monitoring (ISPM) using multiple high-resolution sensors—like Optical Tomography (OT), Melt Pool Monitoring (MPM), photodiodes, and IR cameras—offers a path to faster, model-based qualification. This research utilizes multi-modal ISPM data to detect process anomalies and variations and predict final part quality. Strong correlations were found between OT signals and mechanical properties, predicting Yield Strength (R²=83%) and Tensile Strength (R²=74%). This enables a data-driven framework that has been proven to reduce destructive mechanical testing by 50%. Furthermore, these real-time signals facilitate active process correction during the build, such as adjusting laser power. This integrated monitoring, prediction, and control approach significantly accelerates qualification, reduces testing burdens, and enhances LPBF reliability for industrial applications. |