A deep learning–based method for the design of microstructural materials

• Published in: Structural and multidisciplinary optimization Vol. 61; no. 4; pp. 1417 - 1438
• Main Authors: Tan, Ren Kai, Zhang, Nevin L., Ye, Wenjing
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2020; Springer Nature B.V
• Subjects: Artificial neural networks; Computational Mathematics and Numerical Analysis; Computer simulation; Deep learning; Engineering; Engineering Design; Inverse design; Machine learning; Microstructure; Optimization; Optimization techniques; Research Paper; Tensors; Theoretical and Applied Mechanics; Deep learning; Microstructural materials; Material design; Convolutional neural network; Generative adversarial network
• DOI: 10.1007/s00158-019-02424-2
• ISSN: 1615-147X

Due to their designable properties, microstructural materials have emerged as an important class of materials that have the potential for used in a variety of applications. The design of such materials is challenged by the multifunctionality requirements and various constraints stemmed from manufacturing limitations and other practical considerations. Traditional design methods such as those based on topological optimization techniques rely heavily on high-dimensional physical simulations and can be inefficient. In addition, it is difficult to impose geometrical constraints in those methods. In this work, we propose a deep learning model based on deep convolutional generative adversarial network (DCGAN) and convolutional neural network (CNN) for the design of microstructural materials. The DCGAN is used to generate design candidates that satisfy geometrical constraints and the CNN is used as a surrogate model to link the microstructure to its properties. Once trained, the two networks are combined to form the design network which is utilized to for the inverse design. The advantages of the method include its high efficiency and the simplicity in handling geometrical constraints. In addition, no high-dimensional sensitivity simulations are required. The performance of the method is demonstrated on the design of microstructural materials with desired compliance tensor, subject to specified geometrical constraints.