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- W4313555724 abstract "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." @default.
- W4313555724 created "2023-01-06" @default.
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- W4313555724 date "2023-01-06" @default.
- W4313555724 modified "2023-10-14" @default.
- W4313555724 title "Machine learning–aided real-time detection of keyhole pore generation in laser powder bed fusion" @default.
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- W4313555724 doi "https://doi.org/10.1126/science.add4667" @default.
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