Wang, Xing-er, Yousefi Kanani, Armin, Gu, Zewen, Yang, Jian, Ye, Jianqiao, Hou, Xiaonan (2022) Estimating microscale DE parameters of brittle adhesive joints using genetic expression programming. International Journal of Adhesion and Adhesives, 118 . Article Number 103230. ISSN 0143-7496. (doi:10.1016/j.ijadhadh.2022.103230) (KAR id:96748)
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Language: English
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Official URL: https://doi.org/10.1016/j.ijadhadh.2022.103230 |
Abstract
Particle-based model has strength and flexibility in modelling the microstructures of adhesives and interface in adhesive joints. In this work, a procedure with genetic expression programming (GEP) technique to calibrate the microscale parameters of discrete element (DE) model was proposed for brittle adhesives. Two categories of adhesive properties, the bulk property of thick adhesive and interlaminar-like property of thin adhesive, were discussed. For the bulk property, three target properties of adhesives, i.e. tensile strength, peak strain, secant modulus, were set as the reproduced features. 300 sets of adjustable microscale parameters were produced to run the numerical tests and generate datasets. GEP was then employed to find regression formulas for predicting the target properties as a function of the microscale parameters. For the interlaminar-like property, fracture energies of the cohesive failure of thin adhesives were approximated. A similar procedure of combined DE modelling and GEP was performed to find the regression models to estimate the fracture energy. The developed regression formulas can cover a general range of brittle adhesives. Loctite EA 9497 adhesive was selected to perform a series of lab tests, of which the results were subsequently used to examine the applicability of the DE model with calibrated parameters. The numerical results exhibit good agreements with testing data and observation.
Item Type: | Article |
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DOI/Identification number: | 10.1016/j.ijadhadh.2022.103230 |
Additional information: | For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. |
Uncontrolled keywords: | Adhesive joint, Discrete element method, Genetic algorithm, Composite materials, Adhesive |
Subjects: |
Q Science T Technology |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts |
Funders: | Engineering and Physical Sciences Research Council (https://ror.org/0439y7842) |
Depositing User: | Armin Yousefi Kanani |
Date Deposited: | 16 Sep 2022 12:35 UTC |
Last Modified: | 27 Feb 2024 11:17 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/96748 (The current URI for this page, for reference purposes) |
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