Adjustable mechanical properties design of microstructure by using generative and adversarial network with gradient penalty

Abstract

An intelligent microstructural design method based on deep learning is proposed considering performance indicators that contains boundary information and homogenized elastic modules. Microstructure dataset is established by random boundary method and homogenization method. Random boundary method is proposed to design microstructures under given boundary information, and homogenization method is utilized to acquire homogenized elastic modules. A generative and adversarial network with gradient penalty is developed to establish the high-dimensional mapping between performance indicators and microstructure. The Wasserstein distance is imported to overcome mode collapse. Numerical simulation shows that the pre-trained network successfully achieved corresponding microstructure design by given performance indicators.

Keywords:

• Microstructure
• deep learning
• random boundary method
• homogenization
• Wasserstein distance

Disclosure statement

The authors declare that they have no conflict of interest.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (11872080) and Natural Science Foundation of Beijing, China (3192005).