Open Access

ARTICLE

Data-Driven Structural Topology Optimization Method Using Conditional Wasserstein Generative Adversarial Networks with Gradient Penalty

Qingrong Zeng, Xiaochen Liu, Xuefeng Zhu^*, Xiangkui Zhang, Ping Hu

School of Automotive Engineering, Dalian University of Technology, Dalian, 116024, China

* Corresponding Author: Xuefeng Zhu. Email:

Computer Modeling in Engineering & Sciences 2024, 141(3), 2065-2085. https://doi.org/10.32604/cmes.2024.052620

Received 09 April 2024; Accepted 19 August 2024; Issue published 31 October 2024

Abstract

Traditional topology optimization methods often suffer from the “dimension curse” problem, wherein the computation time increases exponentially with the degrees of freedom in the background grid. Overcoming this challenge, we introduce a real-time topology optimization approach leveraging Conditional Generative Adversarial Networks with Gradient Penalty (CGAN-GP). This innovative method allows for nearly instantaneous prediction of optimized structures. Given a specific boundary condition, the network can produce a unique optimized structure in a one-to-one manner. The process begins by establishing a dataset using simulation data generated through the Solid Isotropic Material with Penalization (SIMP) method. Subsequently, we design a conditional generative adversarial network and train it to generate optimized structures. To further enhance the quality of the optimized structures produced by CGAN-GP, we incorporate Pix2pixGAN. This augmentation results in sharper topologies, yielding structures with enhanced clarity, de-blurring, and edge smoothing. Our proposed method yields a significant reduction in computational time when compared to traditional topology optimization algorithms, all while maintaining an impressive accuracy rate of up to 85%, as demonstrated through numerical examples.

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Keywords

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