Multimodal deep learning for enhanced temperature prediction with uncertainty quantification in directed energy deposition (DED) process
The accurate and reliable prediction of temperature history is crucial in meeting the ever-increasing demands for part quality and process reliability in metal additive manufacturing (AM). While many recent studies based on deep learning approaches have shown promise, they are subject to major limit...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Virtual and Physical Prototyping |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17452759.2025.2474532 |
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