mcGANs: Topology Optimization Using Multi-Stage CGAN
The traditional methods of solving topology optimization depends on finite element method (FEM). However, the iterative calculation of FEM increases the time cost of topology optimization significantly. In this paper, a novel strategy based on deep learning is proposed to speed up the topology optim...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11002494/ |
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| author | Jiandong Zhu Ting Ding Fuhao Cui Zhi Wang |
| author_facet | Jiandong Zhu Ting Ding Fuhao Cui Zhi Wang |
| author_sort | Jiandong Zhu |
| collection | DOAJ |
| description | The traditional methods of solving topology optimization depends on finite element method (FEM). However, the iterative calculation of FEM increases the time cost of topology optimization significantly. In this paper, a novel strategy based on deep learning is proposed to speed up the topology optimization process. Conditional Generative Adversarial Network (CGAN) is used as the basic network in which the constraints of optimization are taken into consideration as the conditions of generation. Additionally, the stress and strain are generated from initial physical information by networks, and used as the inputs of optimization network. The cascaded network can realize structural topology optimization without iterative calculations in the whole process. In the experiments, the traditional Solid Isotropic Material with Penalization (SIMP) method was replicated and implemented multiple times. It was found that the SIMP method takes 30 - 40 seconds to conduct topology optimization, while the proposed mcGANs only need 1.7682 seconds. Moreover, performance indicators such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Peak Signal-to-Noise Ratio (PSNR) are used to evaluate the performance of mcGANs, which demonstrate the effectiveness and superiority of this strategy. |
| format | Article |
| id | doaj-art-e9b6e2a3202d4af9a764d383222963d3 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e9b6e2a3202d4af9a764d383222963d32025-08-20T03:07:06ZengIEEEIEEE Access2169-35362025-01-0113839058391510.1109/ACCESS.2025.356940411002494mcGANs: Topology Optimization Using Multi-Stage CGANJiandong Zhu0https://orcid.org/0009-0009-1722-0695Ting Ding1Fuhao Cui2https://orcid.org/0009-0002-4891-965XZhi Wang3https://orcid.org/0000-0002-9652-7274Henan High-Speed Railway Operation and Maintenance Engineering Research Center, Zhengzhou, ChinaHenan High-Speed Railway Operation and Maintenance Engineering Research Center, Zhengzhou, ChinaHenan High-Speed Railway Operation and Maintenance Engineering Research Center, Zhengzhou, ChinaYantai City College of Science and Technology, Yantai, ChinaThe traditional methods of solving topology optimization depends on finite element method (FEM). However, the iterative calculation of FEM increases the time cost of topology optimization significantly. In this paper, a novel strategy based on deep learning is proposed to speed up the topology optimization process. Conditional Generative Adversarial Network (CGAN) is used as the basic network in which the constraints of optimization are taken into consideration as the conditions of generation. Additionally, the stress and strain are generated from initial physical information by networks, and used as the inputs of optimization network. The cascaded network can realize structural topology optimization without iterative calculations in the whole process. In the experiments, the traditional Solid Isotropic Material with Penalization (SIMP) method was replicated and implemented multiple times. It was found that the SIMP method takes 30 - 40 seconds to conduct topology optimization, while the proposed mcGANs only need 1.7682 seconds. Moreover, performance indicators such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Peak Signal-to-Noise Ratio (PSNR) are used to evaluate the performance of mcGANs, which demonstrate the effectiveness and superiority of this strategy.https://ieeexplore.ieee.org/document/11002494/Deep learningtopology optimizationcGANU-NetSE-ResNet |
| spellingShingle | Jiandong Zhu Ting Ding Fuhao Cui Zhi Wang mcGANs: Topology Optimization Using Multi-Stage CGAN IEEE Access Deep learning topology optimization cGAN U-Net SE-ResNet |
| title | mcGANs: Topology Optimization Using Multi-Stage CGAN |
| title_full | mcGANs: Topology Optimization Using Multi-Stage CGAN |
| title_fullStr | mcGANs: Topology Optimization Using Multi-Stage CGAN |
| title_full_unstemmed | mcGANs: Topology Optimization Using Multi-Stage CGAN |
| title_short | mcGANs: Topology Optimization Using Multi-Stage CGAN |
| title_sort | mcgans topology optimization using multi stage cgan |
| topic | Deep learning topology optimization cGAN U-Net SE-ResNet |
| url | https://ieeexplore.ieee.org/document/11002494/ |
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