Research Team Detects Additive Manufacturing Defects in Real Time

 Another interesting area for machine learning had not considered .... 

Research Team Detects Additive Manufacturing Defects in Real Time

University of Virginia Engineering, January 6, 2023

A research team led by the University of Virginia's Tao Sun employed machine learning to detect defects in additive manufacturing (also known as three-dimensional printing) in real time. The research focused on the formation of keyhole pores, one of the major defects in laser powder bed fusion, which uses metal powder and lasers to three-dimensionally print metal parts. Said Sun, "By integrating operando synchrotron x-ray imaging, near-infrared imaging, and machine learning, our approach can capture the unique thermal signature associated with keyhole pore generation with sub-millisecond temporal resolution and 100% prediction rate.” Sun said the approach “provides a viable solution for high-fidelity, high-resolution detection of keyhole pore generation that can be readily applied in many additive manufacturing scenarios." .... '