Rights statement: This is the author’s version of a work that was accepted for publication in Thin-Walled 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 Thin-Walled Structures, 180, 2022 DOI: 10.1016/j.tws.2022.109985
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Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - A novel genetic expression programming assisted calibration strategy for discrete element models of composite joints with ductile adhesives
AU - Wang, X.-E.
AU - Kanani, A.Y.
AU - Pang, K.
AU - Yang, J.
AU - Ye, J.
AU - Hou, X.
N1 - This is the author’s version of a work that was accepted for publication in Thin-Walled 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 Thin-Walled Structures, 180, 2022 DOI: 10.1016/j.tws.2022.109985
PY - 2022/11/30
Y1 - 2022/11/30
N2 - Discrete element (DE) model has a great feasibility in modelling the microstructural behaviours of adhesive composite joints. However, it demands a sophisticated calibration process to determine microscale bond parameters, which involves massive efforts in both experimental and numerical investigations. This work developed a novel calibration strategy based on DE models and genetic expression programming (GEP) approach for predicting the behaviours of hybrid composite joints encompassing the material nonlinearity, large ductile deformation and multiple fracture modes. In the developed strategy, both the bulk and interlaminar-like properties of ductile adhesives were concerned to suit various joint configurations. GEP modelling was performed based on the datasets from virtual DE experiments. Symbolic regression models were subsequently developed to facilitate the parameters determination. A series lab tests were conducted to validate the numerical results. It shows that the calibrated DE model can adaptively simulate the featured behaviours of both the ductile adhesive and composite joints with different configurations well in most examined occasions. Therefore, it could be suggested to generalize the developed strategy in the development of other DE models for saving the massive efforts in the calibration process of microstructural parameters.
AB - Discrete element (DE) model has a great feasibility in modelling the microstructural behaviours of adhesive composite joints. However, it demands a sophisticated calibration process to determine microscale bond parameters, which involves massive efforts in both experimental and numerical investigations. This work developed a novel calibration strategy based on DE models and genetic expression programming (GEP) approach for predicting the behaviours of hybrid composite joints encompassing the material nonlinearity, large ductile deformation and multiple fracture modes. In the developed strategy, both the bulk and interlaminar-like properties of ductile adhesives were concerned to suit various joint configurations. GEP modelling was performed based on the datasets from virtual DE experiments. Symbolic regression models were subsequently developed to facilitate the parameters determination. A series lab tests were conducted to validate the numerical results. It shows that the calibrated DE model can adaptively simulate the featured behaviours of both the ductile adhesive and composite joints with different configurations well in most examined occasions. Therefore, it could be suggested to generalize the developed strategy in the development of other DE models for saving the massive efforts in the calibration process of microstructural parameters.
KW - Adhesive joint
KW - Composite materials
KW - Discrete element method
KW - Genetic algorithm
KW - Machine learning
KW - Adhesive joints
KW - Adhesives
KW - Calibration
KW - Genetic algorithms
KW - Regression analysis
KW - Calibration process
KW - Composite joint
KW - Composites material
KW - Discrete element models
KW - Discrete elements method
KW - Ductile adhesives
KW - Experimental investigations
KW - Genetic Expression Programming
KW - Machine-learning
KW - Micro-structural
U2 - 10.1016/j.tws.2022.109985
DO - 10.1016/j.tws.2022.109985
M3 - Journal article
VL - 180
JO - Thin-Walled Structures
JF - Thin-Walled Structures
SN - 0263-8231
M1 - 109985
ER -