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Research Papers: Design Automation

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
e-mail: zijiangyang2016@u.northwestern.edu

Xiaolin Li

Theoretical and Applied Mechanics,
Northwestern University,
Evanston, IL 60208
e-mail: xiaolinli2018@u.northwestern.edu

L. Catherine Brinson

Department of Mechanical Engineering
and Materials Science,
Duke University,
Durham, NC 27708
e-mail: cate.brinson@duke.edu

Alok N. Choudhary

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

Wei Chen

Department of Mechanical Engineering,
Northwestern University,
Evanston, IL 60208
e-mail: weichen@northwestern.edu

Ankit Agrawal

Department of Electrical Engineering and
Computer Science,
Northwestern University,
Evanston, IL 60208
e-mail: ankitag@eecs.northwestern.edu

1Z. Yang and X. Li contributed equally to this work.

2Corresponding authors.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received March 24, 2018; final manuscript received July 25, 2018; published online October 1, 2018. Special Editor: Carolyn Seepersad.

J. Mech. Des 140(11), 111416 (Oct 01, 2018) (10 pages) Paper No: MD-18-1252; doi: 10.1115/1.4041371 History: Received March 24, 2018; Revised July 25, 2018

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 microstructural materials design. Some MCR approaches are not applicable for microstructural materials 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 microstructural 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 materials design, in which GAN reduces information loss and preserves more microstructural characteristics, and the GP-Hedge optimization improves the efficiency of design exploration.

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Figures

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Fig. 3

The composition of loss function and information flow in the proposed architecture

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Fig. 2

The architecture of the proposed GAN

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Fig. 1

The flowchart of the proposed design methodology

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Fig. 4

Examples of original (training) microstructures and microstructures produced by the generator

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Fig. 5

Comparison of correlation functions of original microstructures and microstructures generated by the proposed generator: (a) two-point correlation function and (b) lineal-path correlation function

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Fig. 6

The integration of Gaussian process metamodeling and GP-Hedge Bayesian optimization

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Fig. 8

The comparison of the optical absorption property between (1) 30 randomly generated microstructures, (2) 30 microstructures generated by the trained generator, and (3) optimal design

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Fig. 9

An illustration of microstructures of different sizes generated by the scalable generator: (a) 64 × 64, (b) 96 × 96, (c) 128 × 128, (d) 192 × 192, (e) 256 × 356, and (f) 512 × 512

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Fig. 10

The comparison of the MAE for training from scratch and transfer learning. Outliers, higher error and variance are observed from the results of “training from scratch” control group.

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Fig. 7

The microstructure optimization history and microstructure designs indicated at selected iterations

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