Machine learning-aided real-time detection of keyhole pore generation in laser powder bed fusion - Ren, Zhongshu; Gao, Lin; Clark, Samuel J; Fezzaa, Kamel; Shevchenko, Pavel; Choi, Ann; Everhart, Wes; Rollett, Anthony D; Chen, Lianyi; Sun, Tao (Science (American Association for the Advancement of Science), 2023)
Porosity defects are currently a major factor that hinders the widespread adoption of laser-based metal additive manufacturing technologies. One common porosity occurs when an unstable vapor depression zone (keyhole) forms because of excess laser energy input. With simultaneous high-speed synchrotron x-ray imaging and thermal imaging, coupled with multiphysics simulations, we discovered two types of keyhole oscillation in laser powder bed fusion of Ti-6Al-4V. Amplifying this understanding with machine learning, we developed an approach for detecting the stochastic keyhole porosity generation events with submillisecond temporal resolution and near-perfect prediction rate. The highly accurate data labeling enabled by operando x-ray imaging allowed us to demonstrate a facile and practical way to adopt our approach in commercial systems.