A deep learning empowered smart representative volume element method for long fiber woven composites
In response to the global trend of carbon reduction over the last few years, various industries, including the aviation and automobile industries, have gradually begun research, design, and production of carbon fiber composite materials. These have excellent mechanical properties, such as being ligh...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Materials |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmats.2023.1179710/full |
| _version_ | 1797833032027078656 |
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| author | Mao-Ken Hsu Wei Chen Bo-Yu Huang Li-Hsuan Shen Chia-Hsiang Hsu Rong-Yeu Chang Chi-Hua Yu Chi-Hua Yu |
| author_facet | Mao-Ken Hsu Wei Chen Bo-Yu Huang Li-Hsuan Shen Chia-Hsiang Hsu Rong-Yeu Chang Chi-Hua Yu Chi-Hua Yu |
| author_sort | Mao-Ken Hsu |
| collection | DOAJ |
| description | In response to the global trend of carbon reduction over the last few years, various industries, including the aviation and automobile industries, have gradually begun research, design, and production of carbon fiber composite materials. These have excellent mechanical properties, such as being lightweight, high strength, and of high rigidity, which provide weight reduction and energy savings in applications across many fields. When used as a load-beam structure, the weave pattern determines the primary mechanical properties of the composite material. Therefore, the production of diverse products and components can be carried out using different patterns of weaving and manufacturing according to an application’s requirements. The mechanical properties of woven fiber composites can be obtained by using simulation analysis software, which can reduce unnecessary waste during design and manufacturing. However, difficulties arise in the simulation analysis due to the complexity of the weaving method. With the continuous improvement of computer technology in recent years and the enormous amount of training data available, many research teams have begun to implement artificial intelligence (AI) technology, which has been widely used to overcome long-standing obstacles in many different fields. For example, the problems involved in the prediction of protein folding sequences and the prediction of the physics of structural materials have all been resolved by AI. We implement a convolutional neural network (CNN), a deep learning method, to establish a model that utilizes a representative volume element for the prediction of the mechanical properties of a woven fiber composite material. The predictive model significantly streamlines the computational complexity involved in analyzing woven composite materials, resulting in a substantial reduction in processing time compared to conventional methods. Unlike traditional finite element simulations, which necessitate intricate boundary conditions and interactions on a case-by-case basis, our research simplifies these complex procedures and accommodates a wide range of scenarios. This research offers substantial advantages for industrial manufacturing, particularly in the design and mass production of woven fiber composite materials. |
| first_indexed | 2024-04-09T14:18:19Z |
| format | Article |
| id | doaj.art-3f65987ae21843668faa42d0e4c0dd8b |
| institution | Directory Open Access Journal |
| issn | 2296-8016 |
| language | English |
| last_indexed | 2024-04-09T14:18:19Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Materials |
| spelling | doaj.art-3f65987ae21843668faa42d0e4c0dd8b2023-05-05T05:50:43ZengFrontiers Media S.A.Frontiers in Materials2296-80162023-05-011010.3389/fmats.2023.11797101179710A deep learning empowered smart representative volume element method for long fiber woven compositesMao-Ken Hsu0Wei Chen1Bo-Yu Huang2Li-Hsuan Shen3Chia-Hsiang Hsu4Rong-Yeu Chang5Chi-Hua Yu6Chi-Hua Yu7Department of Engineering Science, National Cheng Kung University, Tainan, TaiwanProgram on Smart and Sustainable Manufacturing, Academy of Innovative Semiconductor and Sustainable Manufacturing, National Cheng Kung University, Tainan, TaiwanDepartment of Engineering Science, National Cheng Kung University, Tainan, TaiwanCoreTech System (Moldex3D), Hsinchu, TaiwanCoreTech System (Moldex3D), Hsinchu, TaiwanCoreTech System (Moldex3D), Hsinchu, TaiwanDepartment of Engineering Science, National Cheng Kung University, Tainan, TaiwanProgram on Smart and Sustainable Manufacturing, Academy of Innovative Semiconductor and Sustainable Manufacturing, National Cheng Kung University, Tainan, TaiwanIn response to the global trend of carbon reduction over the last few years, various industries, including the aviation and automobile industries, have gradually begun research, design, and production of carbon fiber composite materials. These have excellent mechanical properties, such as being lightweight, high strength, and of high rigidity, which provide weight reduction and energy savings in applications across many fields. When used as a load-beam structure, the weave pattern determines the primary mechanical properties of the composite material. Therefore, the production of diverse products and components can be carried out using different patterns of weaving and manufacturing according to an application’s requirements. The mechanical properties of woven fiber composites can be obtained by using simulation analysis software, which can reduce unnecessary waste during design and manufacturing. However, difficulties arise in the simulation analysis due to the complexity of the weaving method. With the continuous improvement of computer technology in recent years and the enormous amount of training data available, many research teams have begun to implement artificial intelligence (AI) technology, which has been widely used to overcome long-standing obstacles in many different fields. For example, the problems involved in the prediction of protein folding sequences and the prediction of the physics of structural materials have all been resolved by AI. We implement a convolutional neural network (CNN), a deep learning method, to establish a model that utilizes a representative volume element for the prediction of the mechanical properties of a woven fiber composite material. The predictive model significantly streamlines the computational complexity involved in analyzing woven composite materials, resulting in a substantial reduction in processing time compared to conventional methods. Unlike traditional finite element simulations, which necessitate intricate boundary conditions and interactions on a case-by-case basis, our research simplifies these complex procedures and accommodates a wide range of scenarios. This research offers substantial advantages for industrial manufacturing, particularly in the design and mass production of woven fiber composite materials.https://www.frontiersin.org/articles/10.3389/fmats.2023.1179710/fullfinite element methoddeep learningrepresentative volume element (RVE)carbon fiber woven composite materialmechanical properties |
| spellingShingle | Mao-Ken Hsu Wei Chen Bo-Yu Huang Li-Hsuan Shen Chia-Hsiang Hsu Rong-Yeu Chang Chi-Hua Yu Chi-Hua Yu A deep learning empowered smart representative volume element method for long fiber woven composites Frontiers in Materials finite element method deep learning representative volume element (RVE) carbon fiber woven composite material mechanical properties |
| title | A deep learning empowered smart representative volume element method for long fiber woven composites |
| title_full | A deep learning empowered smart representative volume element method for long fiber woven composites |
| title_fullStr | A deep learning empowered smart representative volume element method for long fiber woven composites |
| title_full_unstemmed | A deep learning empowered smart representative volume element method for long fiber woven composites |
| title_short | A deep learning empowered smart representative volume element method for long fiber woven composites |
| title_sort | deep learning empowered smart representative volume element method for long fiber woven composites |
| topic | finite element method deep learning representative volume element (RVE) carbon fiber woven composite material mechanical properties |
| url | https://www.frontiersin.org/articles/10.3389/fmats.2023.1179710/full |
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