English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

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

Latypov, M. I., Kühbach, M., Beyerlein, I. J., Stinville, J. C., Tóth, L. S., Pollock, T. M., et al. (2018). Application of chord length distributions and principal component analysis for quantification and representation of diverse polycrystalline microstructures. Materials Characterization, 145, 671-685. doi:10.1016/j.matchar.2018.09.020.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/21.11116/0000-0003-A363-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-A364-4
Genre: Journal Article

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Latypov, Marat I.1, 2, Author              
Kühbach, Markus3, Author              
Beyerlein, Irene J.4, Author              
Stinville, Jean Charles1, Author              
Tóth, L. S.5, Author              
Pollock, Tresa M.1, Author              
Kalidindi, Surya R.6, 7, Author              
Affiliations:
1Materials Department, University of California, Santa Barbara, CA 93106, USA, ou_persistent22              
2Mechanical Engineering Department, University of California, Santa Barbara, CA 93106, USA, ou_persistent22              
3Theory and Simulation, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863392              
4Department of Mechanical Engineering, MaterialsDepartment, University of California, Santa Barbara, CA, USA, ou_persistent22              
5University of Lorraine, Laboratory of Excellence on Design of Alloy Metals for Low-mAss Structures (DAMAS), Metz, 57073, France, ou_persistent22              
6Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA, ou_persistent22              
7School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA, ou_persistent22              

Content

show
hide
Free keywords: Crystal microstructure; Grain size and shape; Microstructure; Nickel alloys; Polycrystalline materials, Chord length distribution; EBSD; Grain size; Low-rank representations; Mean grain diameter; Ni-base superalloys; Polycrystalline microstructure; Synthetic generation, Principal component analysis
 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). © 2018 Elsevier Inc.

Details

show
hide
Language(s): eng - English
 Dates: 2018-11
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.matchar.2018.09.020
BibTex Citekey: Latypov2018671
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Materials Characterization
  Abbreviation : Mater. Charact.
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: New York, NY : Elsevier
Pages: - Volume / Issue: 145 Sequence Number: - Start / End Page: 671 - 685 Identifier: ISSN: 1044-5803
CoNE: https://pure.mpg.de/cone/journals/resource/954928499483