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Title: Heterogeneous Sensing and Scientific Machine Learning for Quality Assurance in Laser Powder Bed Fusion – A Single-track Study

Journal Article · · Additive Manufacturing
 [1];  [2];  [2];  [2];  [2];  [3]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Univ. of Nebraska, Lincoln, NE (United States)
  2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  3. Univ. of Nebraska, Lincoln, NE (United States)
+ Show Author Affiliations

Laser Powder Bed Fusion (LPBF) is the predominant metal additive manufacturing technique that benefits from a significant body of academic study and industrial investment given its ability to create complex geometry parts. Despite LPBF’s widespread use, there still exists a need for process monitoring to ensure reliable part production and reduce post-build quality assessments. Towards this end, we develop and evaluate machine learning-based predictive models using height map-derived quality metrics for single tracks and the accompanying pyrometer and high-speed video camera data collected under a wide range of laser power and laser velocity settings. We extract physically intuitive low-level features representative of the meltpool dynamics from these sensing modalities and explore how these vary with the linear energy density. We find our Sequential Decision Analysis Neural Network (SeDANN) model – a scientific machine learning model that incorporates physical process insights – outperforms other purely data-driven black box models in both accuracy and speed. The general approach to data curation and adaptable nature of SeDANN’s scientifically informed architecture should benefit LPBF systems with an evolving suite of sensing modalities and post-build quality measurements.

Research Organization:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC52-07NA27344; AC52-07-NA27344; LLNL-JRNL-808643
OSTI ID:
1702002
Alternate ID(s):
OSTI ID: 1678853
Report Number(s):
LLNL-JRNL-808643; 1014653
Journal Information:
Additive Manufacturing, Vol. na, Issue na; ISSN 2214-8604
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

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Figures / Tables (24)

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36 MATERIALS SCIENCE
Laser powder bed fusion
in-situ quality monitoring
process-mapping
machine learning