Physics-enhanced AI/ML tool for additive manufacturing defect detection and prediction

Through this proposed SBIR effort, QuesTek Innovations LLC will develop a physics-enhanced machine learning (ML) software to reduce exhaustive typical on-destructive testing or enable feed forward control methodologies for fabrication of high-quality additive manufacturing (AM) component. Process induced defects like keyholing and lack of fusion porosity are shown to be strongly correlated with AM process parameters and powder characteristics. On the other hand, stochastic flaws can often form even with optimized process conditions. The inability to fabricate defect-free, fully functional AM component has resulted in a requirement for tedious inspection and certification steps before final application. To address this problem, QuesTek will develop deep learning (DL) based models to process real-time multi-source sensor data for stochastic defect detection, classification, and localization. In addition, QuesTek proposes to fuse physics-based modeling (i.e., processibility map modeling) for predicting non-stochastic defects into the ML models for a comprehensive defect detection framework. In the Phase I program, proof-of-concept will be demonstrated by developing model framework for defect detection and model framework validation via powder bed fusion (PBF) AM fabrication and post build defect characterization