AI-based real-time prediction of cross-sectional phase distribution during laser heat treatment via sequential thermal imaging and image-to-image synthesis
Real-time monitoring of laser heat treatment is essential for ensuring microstructural consistency and process stability, but remains challenging due to complex thermal behavior and phase transformations. While deep learning has been applied to various laser-based processes, no prior study has addre...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-10-01
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| Series: | Materials & Design |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0264127525009669 |
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| author | Myeonggyun Son Hyungson Ki |
| author_facet | Myeonggyun Son Hyungson Ki |
| author_sort | Myeonggyun Son |
| collection | DOAJ |
| description | Real-time monitoring of laser heat treatment is essential for ensuring microstructural consistency and process stability, but remains challenging due to complex thermal behavior and phase transformations. While deep learning has been applied to various laser-based processes, no prior study has addressed the real-time prediction of cross-sectional phase distributions in laser heat treatment. This study proposes the first AI-driven framework for real-time phase mapping during laser heat treatment of S45C steel, using sequential infrared surface temperature images. A convolutional gated recurrent unit architecture was developed to extract both spatial and temporal features, enabling pixel-wise image-to-image translation from thermal images to phase maps. Experimental data were collected from 16 experimental conditions by varying laser power and scanning speed. The model was trained using cross-sectional phase maps obtained from metallographic analysis, achieving high prediction accuracy across heat-affected, heat-treated, and melted regions. The model successfully captured key phenomena such as heat accumulation, geometric growth, and asymmetric phase evolution due to directional thermal gradients. This approach demonstrates a non-invasive, data-driven method for high-resolution monitoring of phase evolution in laser heat treatment. It offers new insights into thermally driven phase evolution and holds promise for integration into intelligent thermal processing and materials design strategies. |
| format | Article |
| id | doaj-art-a659619c5bdb4ef9bd4af56c6d7f21cb |
| institution | DOAJ |
| issn | 0264-1275 |
| language | English |
| publishDate | 2025-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials & Design |
| spelling | doaj-art-a659619c5bdb4ef9bd4af56c6d7f21cb2025-08-20T03:03:01ZengElsevierMaterials & Design0264-12752025-10-0125811454610.1016/j.matdes.2025.114546AI-based real-time prediction of cross-sectional phase distribution during laser heat treatment via sequential thermal imaging and image-to-image synthesisMyeonggyun Son0Hyungson Ki1Department of Mechanical Engineering, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Ulsan 44919, South KoreaCorresponding author.; Department of Mechanical Engineering, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Ulsan 44919, South KoreaReal-time monitoring of laser heat treatment is essential for ensuring microstructural consistency and process stability, but remains challenging due to complex thermal behavior and phase transformations. While deep learning has been applied to various laser-based processes, no prior study has addressed the real-time prediction of cross-sectional phase distributions in laser heat treatment. This study proposes the first AI-driven framework for real-time phase mapping during laser heat treatment of S45C steel, using sequential infrared surface temperature images. A convolutional gated recurrent unit architecture was developed to extract both spatial and temporal features, enabling pixel-wise image-to-image translation from thermal images to phase maps. Experimental data were collected from 16 experimental conditions by varying laser power and scanning speed. The model was trained using cross-sectional phase maps obtained from metallographic analysis, achieving high prediction accuracy across heat-affected, heat-treated, and melted regions. The model successfully captured key phenomena such as heat accumulation, geometric growth, and asymmetric phase evolution due to directional thermal gradients. This approach demonstrates a non-invasive, data-driven method for high-resolution monitoring of phase evolution in laser heat treatment. It offers new insights into thermally driven phase evolution and holds promise for integration into intelligent thermal processing and materials design strategies.http://www.sciencedirect.com/science/article/pii/S0264127525009669Deep learningLaser heat treatmentInfrared thermal imagingPhase distributionProcess monitoring |
| spellingShingle | Myeonggyun Son Hyungson Ki AI-based real-time prediction of cross-sectional phase distribution during laser heat treatment via sequential thermal imaging and image-to-image synthesis Materials & Design Deep learning Laser heat treatment Infrared thermal imaging Phase distribution Process monitoring |
| title | AI-based real-time prediction of cross-sectional phase distribution during laser heat treatment via sequential thermal imaging and image-to-image synthesis |
| title_full | AI-based real-time prediction of cross-sectional phase distribution during laser heat treatment via sequential thermal imaging and image-to-image synthesis |
| title_fullStr | AI-based real-time prediction of cross-sectional phase distribution during laser heat treatment via sequential thermal imaging and image-to-image synthesis |
| title_full_unstemmed | AI-based real-time prediction of cross-sectional phase distribution during laser heat treatment via sequential thermal imaging and image-to-image synthesis |
| title_short | AI-based real-time prediction of cross-sectional phase distribution during laser heat treatment via sequential thermal imaging and image-to-image synthesis |
| title_sort | ai based real time prediction of cross sectional phase distribution during laser heat treatment via sequential thermal imaging and image to image synthesis |
| topic | Deep learning Laser heat treatment Infrared thermal imaging Phase distribution Process monitoring |
| url | http://www.sciencedirect.com/science/article/pii/S0264127525009669 |
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