Scalable microstructure reconstruction with multi-scale pattern preservation

Ruijin Cang, Aditya Vipradas, Yi Ren

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

A key challenge in computational material design is to optimize for particular material properties by searching in an often high-dimensional design space of microstructures. A tractable approach to this optimization task is to identify an encoder that maps from microstructures, which are 2D or 3D images, to a lower-dimensional feature space, and a decoder that generates new microstructures based on samples from the feature space. This two-way mapping has been achieved through feature learning, as common features often exist in microstructures from the same material system. Yet existing approaches limit the size of the generated images to that of the training samples, making it less applicable to designing microstructures at an arbitrary scale. This paper proposes a hybrid model that learns both common features and the spatial distributions of them. We show through various material systems that unlike existing reconstruction methods, our method can generate new microstructure samples of arbitrary sizes that are both visually and statistically close to the training samples while preserving local microstructure patterns.

Original languageEnglish (US)
Title of host publication43rd Design Automation Conference
PublisherAmerican Society of Mechanical Engineers (ASME)
Volume2B-2017
ISBN (Electronic)9780791858134
DOIs
StatePublished - Jan 1 2017
EventASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2017 - Cleveland, United States
Duration: Aug 6 2017Aug 9 2017

Other

OtherASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2017
CountryUnited States
CityCleveland
Period8/6/178/9/17

ASJC Scopus subject areas

  • Mechanical Engineering
  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Modeling and Simulation

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  • Cite this

    Cang, R., Vipradas, A., & Ren, Y. (2017). Scalable microstructure reconstruction with multi-scale pattern preservation. In 43rd Design Automation Conference (Vol. 2B-2017). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/DETC201768286