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 synchrotro...

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Bibliographic Details
Published in:	Science (American Association for the Advancement of Science) Vol. 379; no. 6627; p. 89
Main Authors:	Ren, Zhongshu, Gao, Lin, Clark, Samuel J, Fezzaa, Kamel, Shevchenko, Pavel, Choi, Ann, Everhart, Wes, Rollett, Anthony D, Chen, Lianyi, Sun, Tao
Format:	Journal Article
Language:	English
Published:	United States 06.01.2023
ISSN:	1095-9203, 1095-9203
Online Access:	Get more information
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Summary:	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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1095-9203 1095-9203
DOI:	10.1126/science.add4667