A Machine Learning-Based Design Representation Method for Designing Heterogeneous Microstructures

Hongyi Xu, Ruoqian Liu, Alok Nidhi Choudhary, Wei Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

72 Scopus citations

Abstract

In designing microstructural materials systems, one of the key research questions is how to represent the microstructural design space quantitatively using a descriptor set that is sufficient yet small enough to be tractable. Existing approaches describe complex microstructures either using a small set of descriptors that lack sufficient level of details, or using generic high order microstructure functions of infinite dimensionality without explicit physical meanings. We propose a new machine learning-based method for identifying the key microstructure descriptors from vast candidates as potential microstructural design variables. With a large number of candidate microstructure descriptors collected from literature covering a wide range of microstructural material systems, a four-step machine learning-based method is developed to eliminate redundant microstructure descriptors via image analyses, to identify key microstructure descriptors based on structure-property data, and to determine the microstructure design variables. The training criteria of the supervised learning process include both microstructure correlation functions and material properties. The proposed methodology effectively reduces the infinite dimension of the microstructure design space to a small set of descriptors without a significant information loss. The benefits are demonstrated by an example of polymer nanocomposites optimization. We compare designs using key microstructure descriptors versus using empirically chosen microstructure descriptors as a demonstration of the proposed method.

Original languageEnglish (US)
Article number051403
JournalJournal of Mechanical Design, Transactions Of the ASME
Volume137
Issue number5
DOIs
StatePublished - May 1 2015

Keywords

  • informatics
  • machine learning
  • material design
  • microstructure descriptors

ASJC Scopus subject areas

  • Mechanics of Materials
  • Mechanical Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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