AI/ML for In-Situ Additive Manufacturing Defect Detection

Additive manufacturing (AM) increases the speed and flexibility of producing complex parts at scale. The ability for U.S. manufacturers to 3D-print advanced components in-house reduces reliance on traditional supply chains and bolsters national security readiness. But, AM-produced parts can be subject to various defects, such as lack of fusion, gas entrapment, powder agglomeration, internal cracks, and thermal stress, which can alter the mechanical properties. Post-build nondestructive inspection methodologies can be used, but they greatly increase cost and production time. The ideal solution is monitoring part quality in real-time as the part is being built using in situ sensors. Kitware, along with Princeton University, proposes to bring the latest advances in deep neural network artificial intelligence and signal fusion to optimize and extend 3D metal additive manufacturing systems for the NavyÆs unique needs. Our system is a platform-independent, interactive, in-process quality assurance system that combines data collection, inspection, feedback, and critical analysis. Optimizing defect detection accuracy will improve confidence in part performance and reduce part-rejection false-alarm rates. Our proposed method builds on an existing proof of concept for in-situ defect detection and extends our capabilities to cover a wider range of builds, printers, locations, and sensors.