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A review of artificial intelligence in composite finite element impact damage modelling: a five-role lifecycle framework
File(s)
Author(s)
Ding, Yuzhe
Dear, James
Xiong, Tianhao
Johnson, Michael
Blackman, Bamber
Type
Journal Article
Abstract
Finite Element (FE) simulation of impact damage in fibre-reinforced composite laminates is constrained by high computational cost, many difficult-to-calibrate material parameters and the absence of experimental evidence for different composite structures. Artificial Intelligence (AI) and Machine Learning (ML) methods are increasingly used to address these limitations. This review paper organises the field using a five-role lifecycle framework: Constitutive and Damage Modelling, Parameter Calibration, Computational Acceleration, Verification and Validation and Design Optimisation. The framework maps how each stage of the FE workflow can support the AI and ML approach and identifies the connections between the different roles. For each role, the current state of the art is assessed, gaps in knowledge are identified and where appropriate, example studies are highlighted.
Date Acceptance
2026-08-26
Citation
Composite Design and Manufacturing
URI
DOI
ISSN
2977-7305
Journal / Book Title
Composite Design and Manufacturing
Copyright Statement
Copyright This paper is embargoed until publication. Once published the Version of Record (VoR) will be available on immediate open access.
License URL
Publication Status
Accepted