TY - GEN
T1 - Deep network-based feature extraction and reconstruction of complex material microstructures
AU - Cang, Ruijin
AU - Ren, Max Yi
N1 - Funding Information:
This work has been financially supported by the startup funding from the Arizona State University. This support is gratefully acknowledged. We would also like to thank Dr. Honglak Lee, Dr. Kihyuk Sohn and Ye Liu for their support on the CDBN implementation, and Dr. Wei Chen, Dr. Hongyi Xu, Ramin Bostanabad, Dr. Yang Jiao and Dr. Yongming Liu for valuable discussion. All source code and datasets are available at https://github.com/DesignInformaticsLab/Material-Design.
Publisher Copyright:
© Copyright 2016 by ASME.
PY - 2016
Y1 - 2016
N2 - Computational material design (CMD) aims to accelerate optimal design of complex material systems by integrating material science and design automation. For tractable CMD, it is required that (1) a feature space be identified to allow reconstruction of new designs, and (2) the reconstruction process be property-preserving. Existing solutions rely on the designer's understanding of specific material systems to identify geometric and statistical features, which could be insufficient for reconstructing physically meaningful microstructures of complex material systems. This paper develops a feature learning mechanism that automates a two-way conversion between microstructures and their lower-dimensional feature representations. The proposed model is applied to four material systems: Ti-6Al-4V alloy, Pb-Sn alloy, Fontainebleau sandstone, and spherical colloids, to produce random reconstructions that are visually similar to the samples. This capability is not achieved by existing synthesis methods relying on the Markovian assumption of material systems. For Ti-6Al-4V alloy, we also show that the reconstructions preserve the mean critical fracture force of the system for a fixed processing setting. Source code and datasets are available.
AB - Computational material design (CMD) aims to accelerate optimal design of complex material systems by integrating material science and design automation. For tractable CMD, it is required that (1) a feature space be identified to allow reconstruction of new designs, and (2) the reconstruction process be property-preserving. Existing solutions rely on the designer's understanding of specific material systems to identify geometric and statistical features, which could be insufficient for reconstructing physically meaningful microstructures of complex material systems. This paper develops a feature learning mechanism that automates a two-way conversion between microstructures and their lower-dimensional feature representations. The proposed model is applied to four material systems: Ti-6Al-4V alloy, Pb-Sn alloy, Fontainebleau sandstone, and spherical colloids, to produce random reconstructions that are visually similar to the samples. This capability is not achieved by existing synthesis methods relying on the Markovian assumption of material systems. For Ti-6Al-4V alloy, we also show that the reconstructions preserve the mean critical fracture force of the system for a fixed processing setting. Source code and datasets are available.
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U2 - 10.1115/DETC2016-59404
DO - 10.1115/DETC2016-59404
M3 - Conference contribution
AN - SCOPUS:85007600018
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 42nd Design Automation Conference
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2016
Y2 - 21 August 2016 through 24 August 2016
ER -