Design of auxetic metamaterial for enhanced low cycle fatigue life and negative Poisson’s ratio through multi-objective Bayesian optimization
Auxetic metamaterials (AM) with negative Poisson’s ratio (NPR) offer promising mechanical properties but often suffer from significant stress concentrations, compromising durability and fatigue life. Conventional design approaches, including topology optimization and empirical geometry-based methods...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Materials & Design |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0264127525002187 |
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| author | Sukheon Kang Hyeonbin Moon Seonho Shin Mahmoud Mousavi Hyokyung Sung Seunghwa Ryu |
| author_facet | Sukheon Kang Hyeonbin Moon Seonho Shin Mahmoud Mousavi Hyokyung Sung Seunghwa Ryu |
| author_sort | Sukheon Kang |
| collection | DOAJ |
| description | Auxetic metamaterials (AM) with negative Poisson’s ratio (NPR) offer promising mechanical properties but often suffer from significant stress concentrations, compromising durability and fatigue life. Conventional design approaches, including topology optimization and empirical geometry-based methods, struggle with exploring complex design spaces, while data-driven techniques demand extensive datasets, making fatigue life prediction computationally expensive. To address these challenges, we propose a novel framework that integrates Bézier curve-based geometric parameterization, multi-objective Bayesian optimization (MBO), and fatigue life prediction via elastoplastic homogenization and critical distance theory. This approach systematically explores the design space, simultaneously enhancing NPR and optimizing fatigue resistance while alleviating localized stress concentrations. MBO efficiently balances exploration and exploitation with limited data, making it particularly suitable for computationally intensive fatigue analysis. Optimized AM structures exhibited an 85.11% increase in NPR and a 12.07% improvement in low-cycle fatigue (LCF) life compared to initial designs. Experimental validation confirmed up to 30 times the LCF life and a 2.5-fold NPR increase over conventional AM structures. These findings establish a scalable methodology for AM design, advancing the development of durable, high-performance metamaterials for biomedical, aerospace, and energy-harvesting applications. |
| format | Article |
| id | doaj-art-2e00f76ba5ef4a8ab48d9ed84d68842d |
| institution | DOAJ |
| issn | 0264-1275 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials & Design |
| spelling | doaj-art-2e00f76ba5ef4a8ab48d9ed84d68842d2025-08-20T02:50:56ZengElsevierMaterials & Design0264-12752025-04-0125211379810.1016/j.matdes.2025.113798Design of auxetic metamaterial for enhanced low cycle fatigue life and negative Poisson’s ratio through multi-objective Bayesian optimizationSukheon Kang0Hyeonbin Moon1Seonho Shin2Mahmoud Mousavi3Hyokyung Sung4Seunghwa Ryu5Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of KoreaDepartment of Mechanical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of KoreaDepartment of Materials Science and Engineering, Kookmin University, 02707 Seoul, Republic of KoreaDivision of Applied Mechanics, Department of Materials Science and Engineering, Uppsala University, 751 03 Uppsala, SwedenDepartment of Materials Science and Engineering, Kookmin University, 02707 Seoul, Republic of Korea; Corresponding authors.Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea; Corresponding authors.Auxetic metamaterials (AM) with negative Poisson’s ratio (NPR) offer promising mechanical properties but often suffer from significant stress concentrations, compromising durability and fatigue life. Conventional design approaches, including topology optimization and empirical geometry-based methods, struggle with exploring complex design spaces, while data-driven techniques demand extensive datasets, making fatigue life prediction computationally expensive. To address these challenges, we propose a novel framework that integrates Bézier curve-based geometric parameterization, multi-objective Bayesian optimization (MBO), and fatigue life prediction via elastoplastic homogenization and critical distance theory. This approach systematically explores the design space, simultaneously enhancing NPR and optimizing fatigue resistance while alleviating localized stress concentrations. MBO efficiently balances exploration and exploitation with limited data, making it particularly suitable for computationally intensive fatigue analysis. Optimized AM structures exhibited an 85.11% increase in NPR and a 12.07% improvement in low-cycle fatigue (LCF) life compared to initial designs. Experimental validation confirmed up to 30 times the LCF life and a 2.5-fold NPR increase over conventional AM structures. These findings establish a scalable methodology for AM design, advancing the development of durable, high-performance metamaterials for biomedical, aerospace, and energy-harvesting applications.http://www.sciencedirect.com/science/article/pii/S0264127525002187Inverse designFatigueTheory of critical distanceElastoplastic homogenizationMachine learningBayesian optimization |
| spellingShingle | Sukheon Kang Hyeonbin Moon Seonho Shin Mahmoud Mousavi Hyokyung Sung Seunghwa Ryu Design of auxetic metamaterial for enhanced low cycle fatigue life and negative Poisson’s ratio through multi-objective Bayesian optimization Materials & Design Inverse design Fatigue Theory of critical distance Elastoplastic homogenization Machine learning Bayesian optimization |
| title | Design of auxetic metamaterial for enhanced low cycle fatigue life and negative Poisson’s ratio through multi-objective Bayesian optimization |
| title_full | Design of auxetic metamaterial for enhanced low cycle fatigue life and negative Poisson’s ratio through multi-objective Bayesian optimization |
| title_fullStr | Design of auxetic metamaterial for enhanced low cycle fatigue life and negative Poisson’s ratio through multi-objective Bayesian optimization |
| title_full_unstemmed | Design of auxetic metamaterial for enhanced low cycle fatigue life and negative Poisson’s ratio through multi-objective Bayesian optimization |
| title_short | Design of auxetic metamaterial for enhanced low cycle fatigue life and negative Poisson’s ratio through multi-objective Bayesian optimization |
| title_sort | design of auxetic metamaterial for enhanced low cycle fatigue life and negative poisson s ratio through multi objective bayesian optimization |
| topic | Inverse design Fatigue Theory of critical distance Elastoplastic homogenization Machine learning Bayesian optimization |
| url | http://www.sciencedirect.com/science/article/pii/S0264127525002187 |
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