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| Cross-resolution acceleration design for structural topology optimization based on deep learning |
| Received:May 09, 2021 Revised:May 26, 2021 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7511/jslx20210509403 |
| KeyWord:topology optimization ICM method deep learning cross-resolution acceleration design |
| Author | Institution |
| 叶红玲 |
北京工业大学 材料与制造学部, 北京 |
| 李继承 |
北京工业大学 材料与制造学部, 北京 |
| 魏南 |
北京工业大学 材料与制造学部, 北京 |
| 隋允康 |
北京工业大学 材料与制造学部, 北京 |
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| Abstract: |
| In the traditional topology optimization design, iterative calculations cost too much time with the increase of structural elements. In this paper, a cross-resolution acceleration method based on deep learning is proposed to shorten the iterative process of topology optimization design, and to generate a high-resolution topological configuration. A deep learning model is introduced to create a high-dimensional mapping relationship between the low-resolution intermediate configuration and the high-resolution topological configuration, and the dataset is established by Independent Continuous Mapping (ICM) method to train the deep learning model. The topology optimization design problem is transformed into the problem of style transfer in image processing when the pre-trained deep learning model is acquired. Conditional generative and adversarial neural network (CGAN) is used to solve the problem of cross-resolution topology optimization Numerical experiment verifies the feasibility of the cross-resolution acceleration method for topology optimization. The method has good generalization performance, and the deep learning model is generalizable to other optimization design problems. |