Topologically optimal design and failure prediction using conditional generative adversarial networks

Among the various structural optimization tools, topology optimization is the widely used technique in obtaining the initial design of structural components. The resulting topologically optimal initial design will be the input for subsequent structural optimizations such as shape, size and layout optimizations. However, iterative solvers used in conventional topology optimization schemes are known to be computationally expensive, thus act as a bottleneck in the manufacturing process. In this paper, a novel deep learning-based accelerated topology optimization technique with the ability to predict ductile material failure is presented. A Conditional Generative Adversarial Network (cGAN) coupled with a Convolutional Neural Network (CNN) is used to predict the optimal topology of a given structure subject to a set of input variables. Subsequently, the same cGAN is trained to predict the Von-Mises stress contours on the optimal structure by means of color transformed image-to-image translations. The ductile failure criterion is evaluated by comparing the cGAN predicted maximum Von-Mises stress with the yield strength of the material. The proposed novel numerical method is proven to arrive at the topologically optimal design, accompanying the material failure decision within a negligible amount of time but also maintaining a higher prediction accuracy.

Automated summary

This paper introduces a novel accelerated topology optimization technique based on deep learning, which can predict material failure, thus improving the efficiency and accuracy of the design process.