Research on the efficiency of combined convolutional neural network and traditional optimization methods

Yanyan Zhao; Hao Chen; Yue Cao; Wenjun Tan

doi:10.1117/12.3006587

10 October 2023 Research on the efficiency of combined convolutional neural network and traditional optimization methods

Yanyan Zhao, Hao Chen, Yue Cao, Wenjun Tan

Author Affiliations +

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127992A (2023) https://doi.org/10.1117/12.3006587
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

The iterative algorithm used in traditional topology optimization methods takes a long time. With the increase of the degree of freedom of the finite element mesh, the calculation time will also exhibit exponential growth, resulting in the problem of dimension explosion. To solve this problem, this paper adopts a fast topology optimization method based on Convolutional Neural Network (CNN). By comparing the optimized results and the computational costs between the traditional SIMP method and the Level Set method, this paper proposes to use the fuzzy images obtained from the initial iteration of the SIMP method as training dataset and uses CNN to train them. Based on the fast prediction of the training model, reasonable structural topology can be obtained with a small number of iterations. Based on the classical 88 lines of MATLAB code and the discrete level set MATLAB code, the differences between the traditional iterative optimization algorithm and the CNN-based optimization algorithm are compared by classical cantilever beam examples used as benchmarks. In terms of computational efficiency, these examples are iterated up to 247 times by using the SIMP method and 74 times by using the Level Set method. These two methods took a maximum of 20.72 seconds and 29.36 seconds, respectively. As an improvement, the CNN prediction mode only iterated 10 times with a calculation time of 1.02 seconds. Meanwhile, the model prediction time was negligible. Compared with the traditional iterative modes, the optimization method coupling with SIMP and CNN can significantly improve the computational efficiency.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yanyan Zhao, Hao Chen, Yue Cao, and Wenjun Tan "Research on the efficiency of combined convolutional neural network and traditional optimization methods", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127992A (10 October 2023); https://doi.org/10.1117/12.3006587

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