Stochastic microstructure characterization and reconstruction via supervised learning

Ramin Bostanabad, Anh Tuan Bui, Wei Xie, Daniel W. Apley*, Wei Chen

*Corresponding author for this work

Research output: Contribution to journalArticle

77 Scopus citations

Abstract

Microstructure characterization and reconstruction have become indispensable parts of computational materials science. The main contribution of this paper is to introduce a general methodology for practical and efficient characterization and reconstruction of stochastic microstructures based on supervised learning. The methodology is general in that it can be applied to a broad range of microstructures (clustered, porous, and anisotropic). By treating the digitized microstructure image as a set of training data, we generically learn the stochastic nature of the microstructure via fitting a supervised learning model to it (we focus on classification trees). The fitted supervised learning model provides an implicit characterization of the joint distribution of the collection of pixel phases in the image. Based on this characterization, we propose two different approaches to efficiently reconstruct any number of statistically equivalent microstructure samples. We test the approach on five examples and show that the spatial dependencies within the microstructures are well preserved, as evaluated via correlation and lineal-path functions. The main advantages of our approach stem from having a compact empirically-learned model that characterizes the stochastic nature of the microstructure, which not only makes reconstruction more computationally efficient than existing methods, but also provides insight into morphological complexity.

Original languageEnglish (US)
Pages (from-to)89-102
Number of pages14
JournalActa Materialia
Volume103
DOIs
StatePublished - Jan 15 2016

Keywords

  • Characterization and reconstruction
  • Statistical equivalency
  • Stochastic microstructure
  • Supervised learning

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Ceramics and Composites
  • Polymers and Plastics
  • Metals and Alloys

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