A Data-Driven Multiscale Theory for Modeling Damage and Fracture of Composite Materials
- 459 Downloads
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.
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.
- 5.O. Goury, D. Amsallem, S.P.A. Bordas, W.K. Liu, P. Kerfriden, Automatised selection of load paths to construct reduced-order models in computational damage micromechanics: from dissipation-driven random selection to Bayesian optimization. Comput. Mech. 58(2), 213–234 (2016)MathSciNetCrossRefGoogle Scholar
- 12.J. Macqueen, Some methods for classification and analysis of multivariate observations, in Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1 (University of California Press, Berkeley, 1967), pp. 281–297Google Scholar