Qualitative and quantitative characterization of powder bed quality in laser powder-bed fusion additive manufacturing by multi-task learning
Author:
Funder
National Key R&D Program of China
Special support plan for high level talents in Shaanxi Province
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10845-024-02388-1.pdf
Reference47 articles.
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2. Amyar, A., Modzelewski, R., Li, H., & Ruan, S. (2020). Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation. Computers in Biology and Medicine, 126, 104037. https://doi.org/10.1016/j.compbiomed.2020.104037
3. Anwar, A. B., & Pham, Q.-C. (2018). Study of the spatter distribution on the powder bed during selective laser melting. Additive Manufacturing, 22, 86–97. https://doi.org/10.1016/j.addma.2018.04.036
4. Boschetto, A., Bottini, L., Vatanparast, S., & Veniali, F. (2022). Part defects identification in selective laser melting via digital image processing of powder bed anomalies. Production Engineering, 16(5), 691–704. https://doi.org/10.1007/s11740-022-01112-3
5. Buslaev, A., Iglovikov, V. I., Khvedchenya, E., Parinov, A., Druzhinin, M., & Kalinin, A. A. (2020). Albumentations: Fast and Flexible Image Augmentations. Information, 11(2), 125. https://doi.org/10.3390/info11020125
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