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  • Data-driven failure predictions of composite failure_Chen_Wan_DY_1122

    Rights statement: 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

    Accepted author manuscript, 1.39 MB, PDF document

    Embargo ends: 19/03/22

    Available under license: CC BY-NC-ND

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

Research output: Contribution to journalJournal articlepeer-review

E-pub ahead of print
  • J. Chen
  • L. Wan
  • Y. Ismail
  • J. Ye
  • D. Yang
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Article number113876
<mark>Journal publication date</mark>1/07/2021
<mark>Journal</mark>Composite Structures
Volume267
Number of pages14
Publication StatusE-pub ahead of print
Early online date19/03/21
<mark>Original language</mark>English

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.

Bibliographic note

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