A review of artificial intelligence in composite finite element impact damage modelling: a five-role lifecycle framework
File(s)
Published version
Author(s)
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 Issued
2026-09-01
Date Acceptance
2026-08-26
Citation
Composite Design and Manufacturing, 2026, 2 (3), pp.35-55
ISSN
2977-7305
Publisher
Oxford University Press
Start Page
35
End Page
55
Journal / Book Title
Composite Design and Manufacturing
Volume
2
Issue
3
Copyright Statement
© The Author(s) 2026. Published by Oxford University Press on behalf of Taiyuan University of Technology. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
License URL
Identifier
10.1093/cdm/wqag019
Subjects
composite impact
finite element modelling
artificial intelligence
machine learning
damage modelling
Publication Status
Published
Date Publish Online
2026-09-22