| Autor: |
Runbo Jiang, John Smith, Yu-Tsen Yi, Tao Sun, Brian J. Simonds, Anthony D. Rollett |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
npj Computational Materials, Vol 10, Iss 1, Pp 1-13 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2057-3960 |
| DOI: |
10.1038/s41524-023-01172-8 |
| Popis: |
Abstract The quantification of absorbed light is essential for understanding laser-material interactions and melt pool dynamics in order to minimize defects in additively manufactured metal components. The geometry of a vapor depression formed during laser melting is closely related to laser energy absorption. This relationship has been observed by the state-of-the-art in situ high-speed synchrotron X-ray visualization and integrating sphere radiometry. These two techniques create a temporally resolved dataset consisting of vapor depression images and corresponding laser absorptance. In this work, we propose two different approaches to predict instantaneous laser absorptance. The end-to-end approach uses deep convolutional neural networks to learn implicit features of X-ray images automatically and predict the laser energy absorptance. The two-stage approach uses a semantic segmentation model to engineer geometric features and predict absorptance using classical regression models. While having distinct advantages, both approaches achieved a consistently low mean absolute error of less than 3.3%. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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