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Estimating microscale DE parameters of brittle adhesive joints using genetic expression programming

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Estimating microscale DE parameters of brittle adhesive joints using genetic expression programming. / Wang, X.-E.; Kanani, A.Y.; Gu, Z. et al.
In: International Journal of Adhesion and Adhesives, Vol. 118, 103230, 31.10.2022.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Wang X-E, Kanani AY, Gu Z, Yang J, Ye J, Hou X. Estimating microscale DE parameters of brittle adhesive joints using genetic expression programming. International Journal of Adhesion and Adhesives. 2022 Oct 31;118:103230. Epub 2022 Aug 4. doi: 10.1016/j.ijadhadh.2022.103230

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Bibtex

@article{c5982f39d2c54f4993c9bd63352797d5,
title = "Estimating microscale DE parameters of brittle adhesive joints using genetic expression programming",
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. ",
keywords = "Adhesive, Adhesive joint, Composite materials, Discrete element method, Genetic algorithm",
author = "X.-E. Wang and A.Y. Kanani and Z. Gu and J. Yang and J. Ye and X. Hou",
year = "2022",
month = oct,
day = "31",
doi = "10.1016/j.ijadhadh.2022.103230",
language = "English",
volume = "118",
journal = "International Journal of Adhesion and Adhesives",
issn = "0143-7496",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Estimating microscale DE parameters of brittle adhesive joints using genetic expression programming

AU - Wang, X.-E.

AU - Kanani, A.Y.

AU - Gu, Z.

AU - Yang, J.

AU - Ye, J.

AU - Hou, X.

PY - 2022/10/31

Y1 - 2022/10/31

N2 - 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.

AB - 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.

KW - Adhesive

KW - Adhesive joint

KW - Composite materials

KW - Discrete element method

KW - Genetic algorithm

U2 - 10.1016/j.ijadhadh.2022.103230

DO - 10.1016/j.ijadhadh.2022.103230

M3 - Journal article

VL - 118

JO - International Journal of Adhesion and Adhesives

JF - International Journal of Adhesion and Adhesives

SN - 0143-7496

M1 - 103230

ER -