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

Chen, J. and Wan, L. and Ismail, Y. and Ye, J. and 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: 113876. ISSN 0263-8223

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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.

Item Type:

Journal Article

Journal or Publication Title:

Composite Structures

Additional Information:

This is the author’s version of a work that was accepted for publication in Composite Structures. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Composite Structures, 267, 2021 DOI: 10.1016/j.compstruct.2021.113876

Uncontrolled Keywords:

/dk/atira/pure/subjectarea/asjc/2200/2205

Subjects:

?? failure predictionfinite element modellingmachine learningrepresentativevolume elementtriaxial loadingud laminafailure (mechanical)forecastingneural networksnumerical methodsstress analysisartificial neural network modelselement modelsfailure criteriafail ??

Departments:

Faculty of Science and Technology > Engineering

ID Code:

154792

Deposited By:

ep_importer_pure

Deposited On:

10 May 2021 12:45

Refereed?:

Yes

Published?:

Published