In silico optimization of actuation performance in dielectric elastomer composites via integrated finite element modeling and deep learning
Dielectric elastomers (DEs) require balanced electric actuation performance and mechanical integrity under applied voltages. Incorporating high dielectric particles as fillers provides extensive design space to optimize concentration, morphology, and distribution for improved actuation performance a...
| Những tác giả chính: | , |
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| Định dạng: | Bài viết |
| Ngôn ngữ: | English |
| Được phát hành: |
Elsevier
2024-01-01
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| Loạt: | Theoretical and Applied Mechanics Letters |
| Những chủ đề: | |
| Truy cập trực tuyến: | http://www.sciencedirect.com/science/article/pii/S2095034924000011 |
| _version_ | 1827250370047377408 |
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| author | Jiaxuan Ma Sheng Sun |
| author_facet | Jiaxuan Ma Sheng Sun |
| author_sort | Jiaxuan Ma |
| collection | DOAJ |
| description | Dielectric elastomers (DEs) require balanced electric actuation performance and mechanical integrity under applied voltages. Incorporating high dielectric particles as fillers provides extensive design space to optimize concentration, morphology, and distribution for improved actuation performance and material modulus. This study presents an integrated framework combining finite element modeling (FEM) and deep learning to optimize the microstructure of DE composites. FEM first calculates actuation performance and the effective modulus across varied filler combinations, with these data used to train a convolutional neural network (CNN). Integrating the CNN into a multi-objective genetic algorithm generates designs with enhanced actuation performance and material modulus compared to the conventional optimization approach based on FEM approach within the same time. This framework harnesses artificial intelligence to navigate vast design possibilities, enabling optimized microstructures for high-performance DE composites. |
| first_indexed | 2024-03-08T12:52:46Z |
| format | Article |
| id | doaj.art-cc67b56f00004769a6fd62085fd6f61d |
| institution | Directory Open Access Journal |
| issn | 2095-0349 |
| language | English |
| last_indexed | 2025-03-22T00:02:27Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Theoretical and Applied Mechanics Letters |
| spelling | doaj.art-cc67b56f00004769a6fd62085fd6f61d2024-05-17T04:18:28ZengElsevierTheoretical and Applied Mechanics Letters2095-03492024-01-01141100490In silico optimization of actuation performance in dielectric elastomer composites via integrated finite element modeling and deep learningJiaxuan Ma0Sheng Sun1Materials Genome Institute, Shanghai University, Shanghai 200444, ChinaCorresponding author at: Materials Genome Institute, Shanghai University, Shanghai 200444, China.; Materials Genome Institute, Shanghai University, Shanghai 200444, China; Zhejiang Laboratory, Hangzhou 311100, China; Shanghai Frontier Science Center of Mechanoinformatics, Shanghai University, Shanghai 200444, ChinaDielectric elastomers (DEs) require balanced electric actuation performance and mechanical integrity under applied voltages. Incorporating high dielectric particles as fillers provides extensive design space to optimize concentration, morphology, and distribution for improved actuation performance and material modulus. This study presents an integrated framework combining finite element modeling (FEM) and deep learning to optimize the microstructure of DE composites. FEM first calculates actuation performance and the effective modulus across varied filler combinations, with these data used to train a convolutional neural network (CNN). Integrating the CNN into a multi-objective genetic algorithm generates designs with enhanced actuation performance and material modulus compared to the conventional optimization approach based on FEM approach within the same time. This framework harnesses artificial intelligence to navigate vast design possibilities, enabling optimized microstructures for high-performance DE composites.http://www.sciencedirect.com/science/article/pii/S2095034924000011Dielectric elastomer compositesMulti-objective optimizationFinite element modelingConvolutional neural network |
| spellingShingle | Jiaxuan Ma Sheng Sun In silico optimization of actuation performance in dielectric elastomer composites via integrated finite element modeling and deep learning Theoretical and Applied Mechanics Letters Dielectric elastomer composites Multi-objective optimization Finite element modeling Convolutional neural network |
| title | In silico optimization of actuation performance in dielectric elastomer composites via integrated finite element modeling and deep learning |
| title_full | In silico optimization of actuation performance in dielectric elastomer composites via integrated finite element modeling and deep learning |
| title_fullStr | In silico optimization of actuation performance in dielectric elastomer composites via integrated finite element modeling and deep learning |
| title_full_unstemmed | In silico optimization of actuation performance in dielectric elastomer composites via integrated finite element modeling and deep learning |
| title_short | In silico optimization of actuation performance in dielectric elastomer composites via integrated finite element modeling and deep learning |
| title_sort | in silico optimization of actuation performance in dielectric elastomer composites via integrated finite element modeling and deep learning |
| topic | Dielectric elastomer composites Multi-objective optimization Finite element modeling Convolutional neural network |
| url | http://www.sciencedirect.com/science/article/pii/S2095034924000011 |
| work_keys_str_mv | AT jiaxuanma insilicooptimizationofactuationperformanceindielectricelastomercompositesviaintegratedfiniteelementmodelinganddeeplearning AT shengsun insilicooptimizationofactuationperformanceindielectricelastomercompositesviaintegratedfiniteelementmodelinganddeeplearning |