Computational Discovery of Microstructured Composites with Optimal Stiffness-Toughness Trade-Offs
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866909060500029440 |
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| author | Li, Beichen Deng, Bolei Shou, Wan Oh, Tae-Hyun Hu, Yuanming Luo, Yiyue Shi, Liang Matusik, Wojciech |
| author_facet | Li, Beichen Deng, Bolei Shou, Wan Oh, Tae-Hyun Hu, Yuanming Luo, Yiyue Shi, Liang Matusik, Wojciech |
| contents | The conflict between stiffness and toughness is a fundamental problem in engineering materials design. However, the systematic discovery of microstructured composites with optimal stiffness-toughness trade-offs has never been demonstrated, hindered by the discrepancies between simulation and reality and the lack of data-efficient exploration of the entire Pareto front. We introduce a generalizable pipeline that integrates physical experiments, numerical simulations, and artificial neural networks to address both challenges. Without any prescribed expert knowledge of material design, our approach implements a nested-loop proposal-validation workflow to bridge the simulation-to-reality gap and discover microstructured composites that are stiff and tough with high sample efficiency. Further analysis of Pareto-optimal designs allows us to automatically identify existing toughness enhancement mechanisms, which were previously discovered through trial-and-error or biomimicry. On a broader scale, our method provides a blueprint for computational design in various research areas beyond solid mechanics, such as polymer chemistry, fluid dynamics, meteorology, and robotics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_01078 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Computational Discovery of Microstructured Composites with Optimal Stiffness-Toughness Trade-Offs Li, Beichen Deng, Bolei Shou, Wan Oh, Tae-Hyun Hu, Yuanming Luo, Yiyue Shi, Liang Matusik, Wojciech Materials Science Machine Learning The conflict between stiffness and toughness is a fundamental problem in engineering materials design. However, the systematic discovery of microstructured composites with optimal stiffness-toughness trade-offs has never been demonstrated, hindered by the discrepancies between simulation and reality and the lack of data-efficient exploration of the entire Pareto front. We introduce a generalizable pipeline that integrates physical experiments, numerical simulations, and artificial neural networks to address both challenges. Without any prescribed expert knowledge of material design, our approach implements a nested-loop proposal-validation workflow to bridge the simulation-to-reality gap and discover microstructured composites that are stiff and tough with high sample efficiency. Further analysis of Pareto-optimal designs allows us to automatically identify existing toughness enhancement mechanisms, which were previously discovered through trial-and-error or biomimicry. On a broader scale, our method provides a blueprint for computational design in various research areas beyond solid mechanics, such as polymer chemistry, fluid dynamics, meteorology, and robotics. |
| title | Computational Discovery of Microstructured Composites with Optimal Stiffness-Toughness Trade-Offs |
| topic | Materials Science Machine Learning |
| url | https://arxiv.org/abs/2302.01078 |