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

doi:10.1126/science.add4667

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

Journal Article · Fri Jan 06 00:00:00 EST 2023 · Science

DOI:https://doi.org/10.1126/science.add4667· OSTI ID:1963710

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Univ. of Virginia, Charlottesville, VA (United States)
Argonne National Laboratory (ANL), Argonne, IL (United States)
Kansas City National Security Campus (KCNSC), Kansas City, MO (United States); Carnegie Mellon Univ., Pittsburgh, PA (United States)
Kansas City National Security Campus (KCNSC), Kansas City, MO (United States)
Carnegie Mellon Univ., Pittsburgh, PA (United States)
Univ. of Wisconsin, Madison, WI (United States)

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. Finally, 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.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States); Kansas City Nuclear Security Campus (KCNSC), Kansas City, MO (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)

Grant/Contract Number:: AC02-06CH11357; NA0002839

OSTI ID:: 1963710

Alternate ID(s):: OSTI ID: 2404323

Report Number(s):: NSC--614-4577

Journal Information:: Science, Journal Name: Science Journal Issue: 6627 Vol. 379; ISSN 0036-8075

Publisher:: AAASCopyright Statement

Country of Publication:: United States

Language:: English

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