Machine-Learning-Based Monitoring of Laser Powder Bed Fusion

Yuan, Bodi; Guss, Gabriel M.; Wilson, Aaron C.; Hau-Riege, Stefan P.; DePond, Phillip J.; McMains, Sara; Matthews, Manyalibo J.; Giera, Brian

doi:10.1002/admt.201800136

Title: Machine-Learning-Based Monitoring of Laser Powder Bed Fusion

Journal Article · Wed Sep 05 00:00:00 EDT 2018 · Advanced Materials Technologies

DOI:https://doi.org/10.1002/admt.201800136· OSTI ID:1481059

Yuan, Bodi ^[1]; Guss, Gabriel M. ^[1]; Wilson, Aaron C. ^[1]; Hau-Riege, Stefan P. ^[1]; DePond, Phillip J. ^[1]; McMains, Sara ^[1]; Matthews, Manyalibo J. ^[1];

^[1]

Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

A two-step machine learning approach to monitoring Laser Powder Bed Fusion (LPBF) additive manufacturing is demonstrated that enables on-the-fly assessments of laser track welds. First, in situ video melt pool data acquired during LPBF is labeled according to the (1) average and (2) standard deviation of individual track width and also (3) whether or not the track is continuous, measured post-build through an ex situ height map analysis algorithm. This procedure generates three ground truth labeled datasets for supervised machine learning. Using a portion of the labeled 10-millisecond video clips, a single Convolutional Neural Network architecture is trained to generate three distinct networks. With the remaining in situ LPBF data, the trained neural networks are tested and evaluated and found to predict track width, standard deviation, and continuity without the need for ex situ measurements. This two-step approach should benefit any LPBF system – or any additive manufacturing technology – where height-map-derived properties can serve as useful labels for in situ sensor data.

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Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 1481059

Alternate ID(s):: OSTI ID: 1468826

Report Number(s):: LLNL-JRNL-748383; 933429

Journal Information:: Advanced Materials Technologies, Vol. 3, Issue 12; ISSN 2365-709X

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 90 works

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