Multi-objective optimization of corrosion resistance, strength, ductility properties of weathering steel utilizing interpretable attention-based deep learning model
Abstract A high-performance, low-cost weathering steel was developed using a deep learning-based interpretable Attention mechanism, which identifies key compositional factors and captures complex feature interactions. By revealing the intrinsic drivers of strength, ductility, and corrosion resistanc...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
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| Series: | npj Materials Degradation |
| Online Access: | https://doi.org/10.1038/s41529-025-00654-y |
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| Summary: | Abstract A high-performance, low-cost weathering steel was developed using a deep learning-based interpretable Attention mechanism, which identifies key compositional factors and captures complex feature interactions. By revealing the intrinsic drivers of strength, ductility, and corrosion resistance, and introducing a utility function to balance performance and cost, the newly designed steel achieved a UTS of 837 MPa, 20% elongation, and a corrosion rate of 0.54 g/(m2·h) after 576 hours in a simulated marine environment, demonstrating excellent overall properties. |
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| ISSN: | 2397-2106 |