Machine learning–aided real-time detection of keyhole pore generation in laser powder bed fusion

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…

• UD
  U.S. Department of Energy

  Awards: AC02-06CH11357, DE-NA0002839, DE-AC02, 06CH11357, DE-AC02-06CH11357, DE-AC02-
• DT
  Danmarks Tekniske Universitet
• NI
  National Institute of Standards and Technology

  Award: DE-AC02-06CH11357
• OO
  Office of Science

  Awards: DE-AC02-06CH11357, DE-AC02, 06CH11357, AC02-06CH11357
• AN
  Argonne National Laboratory

  Awards: DE-AC02, 06CH11357, AC02-06CH11357