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Title: Self-consistent clustering analysis: An efficient multi-scale scheme for inelastic heterogeneous materials

Abstract

The discovery of efficient and accurate descriptions for the macroscopic behavior of materials with complex microstructure is an outstanding challenge in mechanics of materials. A mechanistic, data-driven, two-scale approach is developed for predicting the behavior of general heterogeneous materials under irreversible processes such as inelastic deformation. The proposed approach includes two major innovations: 1) the use of a data compression algorithm, k-means clustering, during the offline stage of the method to homogenize the local features of the material microstructure into a group of clusters; and 2) a new method called self-consistent clustering analysis used in the online stage that is valid for any local plasticity laws of each material phase without the need for additional calibration. A particularly important feature of the proposed approach is that the offline stage only uses the linear elastic properties of each material phase, making it efficient. This work is believed to open new avenues in parameter-free multi-scale modeling of complex materials, and perhaps in other fields that require homogenization of irreversible processes.

Authors:
ORCiD logo [1]; ORCiD logo [2];  [2]
  1. Northwestern Univ., Evanston, IL (United States). Theoretical and Applied Mechanics
  2. Northwestern Univ., Evanston, IL (United States). Dept. of Mechanical Engineering
Publication Date:
Research Org.:
Ford Motor Company, Detroit, MI (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
OSTI Identifier:
1503879
Grant/Contract Number:  
EE0006867
Resource Type:
Accepted Manuscript
Journal Name:
Computer Methods in Applied Mechanics and Engineering
Additional Journal Information:
Journal Volume: 306; Journal Issue: C; Journal ID: ISSN 0045-7825
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; 36 MATERIALS SCIENCE

Citation Formats

Liu, Zeliang, Bessa, M. A., and Liu, Wing Kam. Self-consistent clustering analysis: An efficient multi-scale scheme for inelastic heterogeneous materials. United States: N. p., 2016. Web. doi:10.1016/j.cma.2016.04.004.
Liu, Zeliang, Bessa, M. A., & Liu, Wing Kam. Self-consistent clustering analysis: An efficient multi-scale scheme for inelastic heterogeneous materials. United States. https://doi.org/10.1016/j.cma.2016.04.004
Liu, Zeliang, Bessa, M. A., and Liu, Wing Kam. Wed . "Self-consistent clustering analysis: An efficient multi-scale scheme for inelastic heterogeneous materials". United States. https://doi.org/10.1016/j.cma.2016.04.004. https://www.osti.gov/servlets/purl/1503879.
@article{osti_1503879,
title = {Self-consistent clustering analysis: An efficient multi-scale scheme for inelastic heterogeneous materials},
author = {Liu, Zeliang and Bessa, M. A. and Liu, Wing Kam},
abstractNote = {The discovery of efficient and accurate descriptions for the macroscopic behavior of materials with complex microstructure is an outstanding challenge in mechanics of materials. A mechanistic, data-driven, two-scale approach is developed for predicting the behavior of general heterogeneous materials under irreversible processes such as inelastic deformation. The proposed approach includes two major innovations: 1) the use of a data compression algorithm, k-means clustering, during the offline stage of the method to homogenize the local features of the material microstructure into a group of clusters; and 2) a new method called self-consistent clustering analysis used in the online stage that is valid for any local plasticity laws of each material phase without the need for additional calibration. A particularly important feature of the proposed approach is that the offline stage only uses the linear elastic properties of each material phase, making it efficient. This work is believed to open new avenues in parameter-free multi-scale modeling of complex materials, and perhaps in other fields that require homogenization of irreversible processes.},
doi = {10.1016/j.cma.2016.04.004},
journal = {Computer Methods in Applied Mechanics and Engineering},
number = C,
volume = 306,
place = {United States},
year = {2016},
month = {4}
}

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