A micromechanics and machine learning coupled approach for failure prediction of unidirectional CFRP composites under triaxial loading: A preliminary study

Chen, Jiayun; Wan, Lei; Ismail, Yaser; Ye, Jianqiao; Yang, Dongmin

Abstract

This study presents a hybrid method based on artificial neural network (ANN) and micro-mechanics for the failure prediction of IM7/8552 unidirectional (UD) composite lamina under triaxial loading. The ANN is trained offline by numerical data from a high-fidelity micromechanics-based representative volume element (RVE) model using the finite element method (FEM). The RVE adopts identified constituent parameters from inverse analysis and calibrated interface strengths form uniaxial and biaxial tests. A hybrid loading strategy is proposed for the RVE under triaxial loading to obtain the failure points on sliced surfaces whilst maintaining the constant stress at different surfaces. It has been found that the ANN algorithm is robust in the failure prediction of the UD lamina when subjected to different triaxial loading conditions, with over 97.5% accuracy being achieved by the shallow ANN model, where only two hidden layers and 560 samples are used. The predicted 3D failure surface based on trained ANN model has an elliptical paraboloid shape and shows an extremely high strength in biaxial compression. The approach could be used to inform the modification of existing failure criteria and to propose ANN-based failure criteria.

Citation

Chen, J., Wan, L., Ismail, Y., Ye, J., & Yang, D. (2021). A micromechanics and machine learning coupled approach for failure prediction of unidirectional CFRP composites under triaxial loading: A preliminary study. Composite Structures, 267, Article 113876. https://doi.org/10.1016/j.compstruct.2021.113876

• Journal Article Type: Article
• Acceptance Date: Mar 14, 2021
• Online Publication Date: Mar 19, 2021
• Publication Date: Jul 1, 2021
• Deposit Date: Apr 15, 2024
• Publicly Available Date: Apr 24, 2024
• Journal: Composite Structures
• Print ISSN: 0263-8223
• Electronic ISSN: 1879-1085
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 267
• Article Number: 113876
• DOI: https://doi.org/10.1016/j.compstruct.2021.113876
• Keywords: Machine learning; UD lamina; Failure prediction; Finite element modelling; Representative volume element; Triaxial loading
• Public URL: https://hull-repository.worktribe.com/output/4625431
• Related Public URLs: https://www.research.ed.ac.uk/en/publications/a-micromechanics-and-machine-learning-coupled-approach-for-failur