Data-driven design of topologically optimized auxetic metamaterials for tailored stress–strain and Poisson’s ratio-strain behaviors
Conventional topology optimization methods often struggle to achieve simultaneous customization of stress–strain and Poisson’s ratio–strain curves in auxetic metamaterials, due to the complexity of nonlinear analysis, multi-objective coupling, and high computational costs. To overcome these limitati...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Materials & Design |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0264127525007105 |
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| _version_ | 1850120593762942976 |
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| author | Yueyou Tang Anfu Zhang Qi Zhou Mu He Liang Xia |
| author_facet | Yueyou Tang Anfu Zhang Qi Zhou Mu He Liang Xia |
| author_sort | Yueyou Tang |
| collection | DOAJ |
| description | Conventional topology optimization methods often struggle to achieve simultaneous customization of stress–strain and Poisson’s ratio–strain curves in auxetic metamaterials, due to the complexity of nonlinear analysis, multi-objective coupling, and high computational costs. To overcome these limitations, present work proposes a physics–data collaborative design framework that integrates nonlinear topology optimization with neural networks. This framework first generates baseline configuration approximating the target mechanical behavior via nonlinear topology optimization, thereby establishing a physically reliable initial design space. High-precision neural networks are then trained using geometric features extracted through PCA (principal component analysis), enabling real-time, simultaneous prediction of stress–strain and Poisson’s ratio–strain responses. By combining evolutionary strategies with an adaptive learning factor, we construct an efficient global optimization model that enables high-accuracy coupled customization of both curves under finite deformation. Compression experiments on polyurethane specimens fabricated via precision milling validate the feasibility of the proposed method, showing an average curve-matching accuracy of 98.2% between the experimental and target curves. Compared to traditional topology optimization, our approach improves customization accuracy by 71.51% to 93.24%, achieves simultaneous coupling of both mechanical responses, and delivers an overall design accuracy exceeding 99%. |
| format | Article |
| id | doaj-art-484cc0a57bfc46348b821bfb82503300 |
| institution | OA Journals |
| issn | 0264-1275 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials & Design |
| spelling | doaj-art-484cc0a57bfc46348b821bfb825033002025-08-20T02:35:19ZengElsevierMaterials & Design0264-12752025-08-0125611429010.1016/j.matdes.2025.114290Data-driven design of topologically optimized auxetic metamaterials for tailored stress–strain and Poisson’s ratio-strain behaviorsYueyou Tang0Anfu Zhang1Qi Zhou2Mu He3Liang Xia4State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaWuhan Second Ship Design and Research Institute, Wuhan 430062, ChinaSchool of Aerospace Engineering, Huazhong University of Science and Technology, 430074 Wuhan, ChinaState Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China; Corresponding authors.State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China; Corresponding authors.Conventional topology optimization methods often struggle to achieve simultaneous customization of stress–strain and Poisson’s ratio–strain curves in auxetic metamaterials, due to the complexity of nonlinear analysis, multi-objective coupling, and high computational costs. To overcome these limitations, present work proposes a physics–data collaborative design framework that integrates nonlinear topology optimization with neural networks. This framework first generates baseline configuration approximating the target mechanical behavior via nonlinear topology optimization, thereby establishing a physically reliable initial design space. High-precision neural networks are then trained using geometric features extracted through PCA (principal component analysis), enabling real-time, simultaneous prediction of stress–strain and Poisson’s ratio–strain responses. By combining evolutionary strategies with an adaptive learning factor, we construct an efficient global optimization model that enables high-accuracy coupled customization of both curves under finite deformation. Compression experiments on polyurethane specimens fabricated via precision milling validate the feasibility of the proposed method, showing an average curve-matching accuracy of 98.2% between the experimental and target curves. Compared to traditional topology optimization, our approach improves customization accuracy by 71.51% to 93.24%, achieves simultaneous coupling of both mechanical responses, and delivers an overall design accuracy exceeding 99%.http://www.sciencedirect.com/science/article/pii/S0264127525007105Auxetic metamaterialsCustomizationStress–strain curvesPoisson’s ratio–strain curvesNeural networksTopology optimization |
| spellingShingle | Yueyou Tang Anfu Zhang Qi Zhou Mu He Liang Xia Data-driven design of topologically optimized auxetic metamaterials for tailored stress–strain and Poisson’s ratio-strain behaviors Materials & Design Auxetic metamaterials Customization Stress–strain curves Poisson’s ratio–strain curves Neural networks Topology optimization |
| title | Data-driven design of topologically optimized auxetic metamaterials for tailored stress–strain and Poisson’s ratio-strain behaviors |
| title_full | Data-driven design of topologically optimized auxetic metamaterials for tailored stress–strain and Poisson’s ratio-strain behaviors |
| title_fullStr | Data-driven design of topologically optimized auxetic metamaterials for tailored stress–strain and Poisson’s ratio-strain behaviors |
| title_full_unstemmed | Data-driven design of topologically optimized auxetic metamaterials for tailored stress–strain and Poisson’s ratio-strain behaviors |
| title_short | Data-driven design of topologically optimized auxetic metamaterials for tailored stress–strain and Poisson’s ratio-strain behaviors |
| title_sort | data driven design of topologically optimized auxetic metamaterials for tailored stress strain and poisson s ratio strain behaviors |
| topic | Auxetic metamaterials Customization Stress–strain curves Poisson’s ratio–strain curves Neural networks Topology optimization |
| url | http://www.sciencedirect.com/science/article/pii/S0264127525007105 |
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