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 Kam
N1 - Funding Information:
Modesar Shakoor and Wing Kam Liu warmly thank the support from National Institute of Standards and Technology and Center for Hierarchical Materials Design (CHiMaD) under grant No. 70NANB13Hl94 and 70NANB14H012. Jiaying Gao and Wing Kam Liu warmly thank the support from DOECF-ICME project under grant No. DE-EE0006867. Zeliang Liu would like to thank Dr. John O. Hallquist of LSTC for his support.
Funding Information:
Acknowledgements Modesar Shakoor and Wing Kam Liu warmly thank the support from National Institute of Standards and Technology and Center for Hierarchical Materials Design (CHiMaD) under grant No. 70NANB13Hl94 and 70NANB14H012. Jiaying Gao and Wing Kam Liu warmly thank the support from DOECF-ICME project under grant No. DE-EE0006867. Zeliang Liu would like to thank Dr. John O. Hallquist of LSTC for his support.
Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
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.
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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
T2 - 9th International Workshop on Meshfree Methods for Partial Differential Equations, IWMMPDE 2017
Y2 - 18 September 2017 through 20 September 2017
ER -