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Title: Localized keyhole pore prediction during laser powder bed fusion via multimodal process monitoring and X-ray radiography

Journal Article · · Additive Manufacturing
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  1. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
  2. SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States). Stanford Synchrotron Radiation Lightsource (SSRL)
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Systematic fault detection and control during laser powder bed fusion (L-PBF) has been a long-standing objective for system manufacturers and researchers in the additive manufacturing (AM) industry. This manuscript investigates a data fusion approach for detection of keyhole porosity formation during laser irradiation of Ti-6Al-4V substrates by concurrent recording of thermally induced optical emission measured using both off-axis and coaxial photodiode sensors, and acoustic emission. Subsurface defect formation was monitored via high-speed synchrotron X-ray imaging at 20,000 frames per second, enabling temporal registration of keyhole pore formation events to the monitoring signals at a resolution of 50 µs. We developed data fusion machine learning (ML) models for localized prediction of keyhole pore formation at various time scales ranging from 0.5 ms to 2 ms. The signal segments were featurized using two independent approaches: (1) power spectral density (PSD) and (2) highly comparative time series analysis (HCTSA) framework. The extracted features from different sensor modalities were fused together to construct a multimodal feature space and sequential feature selection was used to determine the most informative features for training the ML models. The predictive performance was evaluated for three classifying algorithms: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Gaussian Naive Bayes (GNB). As a result, pore formation events were predicted with up to 0.95 F1-score, 1.0 recall and 0.94 accuracy. The most heavily weighted features indicate that model performance is chiefly governed by the acoustic monitoring signal, with a secondary contribution from the optical emission sensors.

Research Organization:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Laboratory Directed Research and Development (LDRD) Program
Grant/Contract Number:
AC52-07NA27344; AC02-76SF00515
OSTI ID:
2280478
Report Number(s):
LLNL-JRNL-847264; 1070781
Journal Information:
Additive Manufacturing, Vol. 78, Issue N/A; ISSN 2214-8604
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

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36 MATERIALS SCIENCE
laser powder bed fusion
in situ monitoring
x-ray radiography
keyhole pore identification
sensor fusion