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:
-
[1];
[2];
- Northwestern Univ., Evanston, IL (United States). Theoretical and Applied Mechanics
- Northwestern Univ., Evanston, IL (United States). Dept. of Mechanical Engineering
- Publication Date:
- Research Org.:
- Ford Motor Company, Dearborn, 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. doi: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. doi: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}
}
Free Publicly Available Full Text
Publisher's Version of Record
Other availability
Search WorldCat to find libraries that may hold this journal
Cited by: 68 works
Citation information provided by
Web of Science
Web of Science
Save to My Library
You must Sign In or Create an Account in order to save documents to your library.
Works referencing / citing this record:
A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites
journal, October 2018
- Lu, Xiaoxin; Giovanis, Dimitris G.; Yvonnet, Julien
- Computational Mechanics, Vol. 64, Issue 2
Transfer learning of deep material network for seamless structure–property predictions
journal, April 2019
- Liu, Zeliang; Wu, C. T.; Koishi, M.
- Computational Mechanics, Vol. 64, Issue 2
Principle of cluster minimum complementary energy of FEM-cluster-based reduced order method: fast updating the interaction matrix and predicting effective nonlinear properties of heterogeneous material
journal, May 2019
- Nie, Yinghao; Cheng, Gengdong; Li, Xikui
- Computational Mechanics, Vol. 64, Issue 2
Clustering discretization methods for generation of material performance databases in machine learning and design optimization
journal, May 2019
- Li, Hengyang; Kafka, Orion L.; Gao, Jiaying
- Computational Mechanics, Vol. 64, Issue 2
Fast calculation of interaction tensors in clustering-based homogenization
journal, May 2019
- Zhang, Lei; Tang, Shaoqiang; Yu, Cheng
- Computational Mechanics, Vol. 64, Issue 2
Derivation of heterogeneous material laws via data-driven principal component expansions
journal, May 2019
- Yang, Hang; Guo, Xu; Tang, Shan
- Computational Mechanics, Vol. 64, Issue 2
A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites
journal, October 2018
- Lu, Xiaoxin; Giovanis, Dimitris G.; Yvonnet, Julien
- Computational Mechanics, Vol. 64, Issue 2
Transfer learning of deep material network for seamless structure–property predictions
journal, April 2019
- Liu, Zeliang; Wu, C. T.; Koishi, M.
- Computational Mechanics, Vol. 64, Issue 2
Principle of cluster minimum complementary energy of FEM-cluster-based reduced order method: fast updating the interaction matrix and predicting effective nonlinear properties of heterogeneous material
journal, May 2019
- Nie, Yinghao; Cheng, Gengdong; Li, Xikui
- Computational Mechanics, Vol. 64, Issue 2
Clustering discretization methods for generation of material performance databases in machine learning and design optimization
journal, May 2019
- Li, Hengyang; Kafka, Orion L.; Gao, Jiaying
- Computational Mechanics, Vol. 64, Issue 2
Fast calculation of interaction tensors in clustering-based homogenization
journal, May 2019
- Zhang, Lei; Tang, Shaoqiang; Yu, Cheng
- Computational Mechanics, Vol. 64, Issue 2
Derivation of heterogeneous material laws via data-driven principal component expansions
journal, May 2019
- Yang, Hang; Guo, Xu; Tang, Shan
- Computational Mechanics, Vol. 64, Issue 2
A framework for self‐evolving computational material models inspired by deep learning
journal, August 2019
- Cho, In Ho
- International Journal for Numerical Methods in Engineering, Vol. 120, Issue 10
Accelerating crack growth simulations through adaptive model order reduction
journal, May 2020
- Rocha, Iuri. B. C. M.; Meer, Frans. P.; Mororó, Luiz. A. T.
- International Journal for Numerical Methods in Engineering, Vol. 121, Issue 10
Data science for finite strain mechanical science of ductile materials
journal, November 2018
- Shakoor, Modesar; Kafka, Orion L.; Yu, Cheng
- Computational Mechanics, Vol. 64, Issue 1
Virtual, Digital and Hybrid Twins: A New Paradigm in Data-Based Engineering and Engineered Data
journal, November 2018
- Chinesta, Francisco; Cueto, Elias; Abisset-Chavanne, Emmanuelle
- Archives of Computational Methods in Engineering, Vol. 27, Issue 1
Localization models for the plastic response of polycrystalline materials using the material knowledge systems framework
journal, August 2019
- Montes de Oca Zapiain, David; Kalidindi, Surya R.
- Modelling and Simulation in Materials Science and Engineering, Vol. 27, Issue 7
A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics
journal, May 2019
- Bock, Frederic E.; Aydin, Roland C.; Cyron, Christian J.
- Frontiers in Materials, Vol. 6
Hysteretic Behavior of Random Particulate Composites by the Stochastic Finite Element Method
journal, September 2019
- Sokołowski, Damian; Kamiński, Marcin
- Materials, Vol. 12, Issue 18