Application of chord length distributions and principal component analysis for quantification and representation of diverse polycrystalline microstructures

Marat I. Latypov, Markus Kühbach, Irene J. Beyerlein, Jean Charles Stinville, Laszlo S. Toth, Tresa M. Pollock, Surya R. Kalidindi

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

Quantification of mesoscale microstructures of polycrystalline materials is important for a range of practical tasks of materials design and development. The current protocols of quantifying grain size and morphology often rely on microstructure metrics (e.g., mean grain diameter) that overlook important details of the mesostructure. In this work, we present a quantification framework based on directionally resolved chord length distribution and principal component analysis as a means of extracting additional information from 2-D microstructural maps. Towards this end, we first present in detail a method for calculating chord length distribution based on boundary segments available in modern digital datasets (e.g., from microscopy post-processing) and their low-rank representations by principal component analysis. The utility of the proposed framework for capturing grain size, morphology, and their anisotropy for efficient visualization, representation, and specification of polycrystalline microstructures is then demonstrated in case studies on datasets from synthetic generation, experiments (on Ni-base superalloys), and simulations (on steel during recrystallization).

Original languageEnglish (US)
Pages (from-to)671-685
Number of pages15
JournalMaterials Characterization
Volume145
DOIs
StatePublished - Nov 2018
Externally publishedYes

Keywords

  • Chord length distribution
  • EBSD
  • Grain size
  • Microstructure
  • Principal component analysis

ASJC Scopus subject areas

  • Materials Science(all)
  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering

Fingerprint

Dive into the research topics of 'Application of chord length distributions and principal component analysis for quantification and representation of diverse polycrystalline microstructures'. Together they form a unique fingerprint.

Cite this