A novel genetic expression programming assisted calibration strategy for discrete element models of composite joints with ductile adhesives

Wang, X.-E. and Kanani, A.Y. and Pang, K. and Yang, J. and Ye, J. and Hou, X. (2022) A novel genetic expression programming assisted calibration strategy for discrete element models of composite joints with ductile adhesives. Thin-Walled Structures, 180: 109985. ISSN 0263-8231

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Abstract

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.

Item Type:
Journal Article
Journal or Publication Title:
Thin-Walled Structures
Additional Information:
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
Uncontrolled Keywords:
Data Sharing Template/yes
Subjects:
?? adhesive jointcomposite materialsdiscrete element methodgenetic algorithmmachine learningadhesive jointsadhesivescalibrationgenetic algorithmsregression analysiscalibration processcomposite jointcomposites materialdiscrete element modelsdiscrete elements ??
ID Code:
175687
Deposited By:
Deposited On:
08 Sep 2022 12:15
Refereed?:
Yes
Published?:
Published
Last Modified:
28 Mar 2024 01:02