| Abstract Scope |
Transitioning laser powder bed fusion (PBF-LB) towards reliable serial production requires moving beyond traditional qualifications toward robust model-based strategies. This presentation details EOS's advancements in multi-modal in-situ process monitoring (ISPM) for enhanced PBF-LB qualification. We investigate the use of Optical Tomography (OT), Melt Pool Monitoring (MPM), and Powder Bed (PB) imaging data acquired from various EOS platforms, analyzing process variations across machines and builds. Our research demonstrates the potential of statistical regression models to correlate ISPM data, specifically OT, with mechanical properties. Building on these correlations, we present a data-driven framework to improve mechanical property prediction, potentially reducing destructive testing. We also demonstrate the pivotal role of Closed Loop Control (i.e., Smart Fusion), which allows for greater thermal management and better control of anomalies. Overall, this research focuses on the fundamental understanding of ISPM signals and their relationship to material properties, contributing to developing robust, model-based qualification methodologies for PBF-LB. |