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research-article

Microstructural Materials Design via Deep Adversarial Learning Methodology

[+] Author and Article Information
Zijiang Yang

Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL, 60208 USA
zijiangyang2016@u.northwestern.edu

Xiaolin Li

Theoretical and Applied Mechanics, Northwestern University, Evanston, IL, 60208 USA
xiaolinli2018@u.northwestern.edu

L. Catherine Brinson

Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, 27708 USA
cate.brinson@duke.edu

Alok Choudhary

Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL, 60208 USA
a-choudhary@northwestern.edu

Wei Chen

Department of Mechanical Engineering, Northwestern University, Evanston, IL, 60208 USA
editor@asmejmd.org

Ankit Agrawal

Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL, 60208 USA
ankitag@eecs.northwestern.edu

1Corresponding author.

ASME doi:10.1115/1.4041371 History: Received March 24, 2018; Revised July 25, 2018

Abstract

Identifying the key microstructure representations is crucial for Computational Materials Design (CMD). However, existing microstructure characterization and reconstruction (MCR) techniques have limitations to be applied for materials design. Some MCR approaches are not applicable for material microstructural design because no parameters are available to serve as design variables, while others introduce significant information loss in either microstructure representation and/or dimensionality reduction. In this work, we present a deep adversarial learning methodology that overcomes the limitations of existing MCR techniques. In the proposed methodology, generative adversarial networks (GAN) are trained to learn the mapping between latent variables and microstructures. Thereafter, the low-dimensional latent variables serve as design variables, and a Bayesian optimization framework is applied to obtain microstructures with desired material property. Due to the special design of the network architecture, the proposed methodology is able to identify the latent (design) variables with desired dimensionality, as well as capturing complex material microstructural characteristics. The validity of the proposed methodology is tested numerically on a synthetic microstructure dataset and its effectiveness for materials design is evaluated through a case study of optimizing optical performance for energy absorption. Additional features, such as scalability and transferability, are also demonstrated in this work. In essence, the proposed methodology provides an end-to-end solution for microstructural design, in which GAN reduces information loss and preserves more microstructural characteristics, and the GP-Hedge optimization improves the efficiency of design exploration.

Copyright (c) 2018 by ASME
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