Abstract
The advent of advanced processing and manufacturing techniques has led to new material classes with complex microstructures across scales from nanometers to meters. In this paper, a data-driven computational framework for the analysis of these complex material systems is presented. A mechanistic concurrent multiscale method called Self-consistent Clustering Analysis (SCA) is developed for general inelastic heterogeneous material systems. The efficiency of SCA is achieved via data compression algorithms which group local microstructures into clusters during the training stage, thereby reducing required computational expense. Its accuracy is guaranteed by introducing a self-consistent method for solving the Lippmann–Schwinger integral equation in the prediction stage. The proposed framework is illustrated for a composite cutting process where fracture can be analyzed simultaneously at the microstructure and part scales.
| Original language | English (US) |
|---|---|
| Title of host publication | Meshfree Methods for Partial Differential Equations IX, 2017 |
| Editors | Michael Griebel, Marc Alexander Schweitzer, Michael Griebel, Marc Alexander Schweitzer |
| Publisher | Springer Verlag |
| Pages | 135-148 |
| Number of pages | 14 |
| ISBN (Print) | 9783030151188 |
| DOIs | |
| State | Published - Jan 1 2019 |
| Event | 9th International Workshop on Meshfree Methods for Partial Differential Equations, IWMMPDE 2017 - Bonn, Germany Duration: Sep 18 2017 → Sep 20 2017 |
Publication series
| Name | Lecture Notes in Computational Science and Engineering |
|---|---|
| Volume | 129 |
| ISSN (Print) | 1439-7358 |
Conference
| Conference | 9th International Workshop on Meshfree Methods for Partial Differential Equations, IWMMPDE 2017 |
|---|---|
| Country | Germany |
| City | Bonn |
| Period | 9/18/17 → 9/20/17 |
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ASJC Scopus subject areas
- Modeling and Simulation
- Engineering(all)
- Discrete Mathematics and Combinatorics
- Control and Optimization
- Computational Mathematics
Cite this
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A data-driven multiscale theory for modeling damage and fracture of composite materials. / Shakoor, Modesar; Gao, Jiaying; Liu, Zeliang; Liu, Wing K.
Meshfree Methods for Partial Differential Equations IX, 2017. ed. / Michael Griebel; Marc Alexander Schweitzer; Michael Griebel; Marc Alexander Schweitzer. Springer Verlag, 2019. p. 135-148 (Lecture Notes in Computational Science and Engineering; Vol. 129).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - A data-driven multiscale theory for modeling damage and fracture of composite materials
AU - Shakoor, Modesar
AU - Gao, Jiaying
AU - Liu, Zeliang
AU - Liu, Wing K
PY - 2019/1/1
Y1 - 2019/1/1
N2 - The advent of advanced processing and manufacturing techniques has led to new material classes with complex microstructures across scales from nanometers to meters. In this paper, a data-driven computational framework for the analysis of these complex material systems is presented. A mechanistic concurrent multiscale method called Self-consistent Clustering Analysis (SCA) is developed for general inelastic heterogeneous material systems. The efficiency of SCA is achieved via data compression algorithms which group local microstructures into clusters during the training stage, thereby reducing required computational expense. Its accuracy is guaranteed by introducing a self-consistent method for solving the Lippmann–Schwinger integral equation in the prediction stage. The proposed framework is illustrated for a composite cutting process where fracture can be analyzed simultaneously at the microstructure and part scales.
AB - The advent of advanced processing and manufacturing techniques has led to new material classes with complex microstructures across scales from nanometers to meters. In this paper, a data-driven computational framework for the analysis of these complex material systems is presented. A mechanistic concurrent multiscale method called Self-consistent Clustering Analysis (SCA) is developed for general inelastic heterogeneous material systems. The efficiency of SCA is achieved via data compression algorithms which group local microstructures into clusters during the training stage, thereby reducing required computational expense. Its accuracy is guaranteed by introducing a self-consistent method for solving the Lippmann–Schwinger integral equation in the prediction stage. The proposed framework is illustrated for a composite cutting process where fracture can be analyzed simultaneously at the microstructure and part scales.
UR - http://www.scopus.com/inward/record.url?scp=85068309885&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068309885&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-15119-5_8
DO - 10.1007/978-3-030-15119-5_8
M3 - Conference contribution
AN - SCOPUS:85068309885
SN - 9783030151188
T3 - Lecture Notes in Computational Science and Engineering
SP - 135
EP - 148
BT - Meshfree Methods for Partial Differential Equations IX, 2017
A2 - Griebel, Michael
A2 - Schweitzer, Marc Alexander
A2 - Griebel, Michael
A2 - Schweitzer, Marc Alexander
PB - Springer Verlag
ER -