Author ORCID Identifier
Document Type
Article
Publication Date
7-19-2024
Abstract
Metal-based additive manufacturing requires active monitoring solutions for assessing part quality. Multiple sensors and data streams, however, generate large heterogeneous data sets that are impractical for manual assessment and characterization. In this work, an automated pipeline is developed that enables feature extraction from high-speed camera video and multi-modal data analysis. The framework removes the need for manual assessment through the utilization of deep learning techniques and training models in a weakly supervised paradigm. We demonstrate this pipeline’s capability over 700,000 high-speed camera frames. The pipeline successfully extracts melt pool and spatter geometries and links them to corresponding pyrometry, radiography, and processparameter information. 715 individual prints are examined to reveal melt pool areas that exceeds 0.07 mm2 and pyrometry signal over a threshold (375 pyrometry units) were more likely to have defects. These automated processes enable massive throughput of characterization techniques.
Keywords
data integration, deep learning, image segmentation, laser powder bed fusion, melt pool morphology
Language
English
Publication Title
Integrating Materials and Manufacturing Innovation
Grant
DE-NA0004104
Rights
© The Author(s). This is an Open Access work distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Hernandez, K.J., Ciardi, T.G., Yamamoto, R. et al. L-PBF High-Throughput Data Pipeline Approach for Multi-modal Integration. Integr Mater Manuf Innov 13, 758–772 (2024). https://doi.org/10.1007/s40192-024-00368-0
Manuscript Version
Final Publisher Version