Development of CAE Method Using Physical Simulation by Artificial Intelligence
We aim to develop a high-speed CAE system of microstructure of materials by employing artificial intelligence (AI) to investigate the suitable microstructures for specific macroscopic properties, and establish reinforcement guidelines for various devices.
We are developing a framework that deals with forward analysis to predict a property from a polymer alloy’s phase separation structure and inverse design to generate the structure from the property. We only consider Young’s modulus as the property in this study. The forward analysis is performed using a convolutional neural network (CNN) and the inverse design is realized by a random search toward a model combining a generative adversarial network (GAN) and a CNN. This framework is applicable to other properties at a low computational cost, and latent variables belonging to the GAN are useful for feature extraction.
Machine Learning, Microstructure
Kojima, Y., Hirayama, K., Endo, K., Hiraide, K., Muramatsu, M., “Inverse Estimation Method for Internal Defects Based on Surface Stress of Carbon-Fiber-Reinforced Plastics Using Machine Learning”, Advanced Composite Materials, Vol. 31, pp. 617-629, (2022).
Hiraide, K., Hirayama, K., Endo, K., Muramatsu, M., “Application of deep learning to inverse design of phase separation structure in polymer alloy”, Computational Materials Science, Vol. 190, pp. 110278, 1-9, (2021).