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    Rights statement: This is the author’s version of a work that was accepted for publication in Composite 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 Composite Structures, 373, 4-5, 2021 DOI: 10.1016/S0370-1573(02)00269-7

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Understanding mixed mode ratio of adhesively bonded joints using genetic programming (GP)

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Understanding mixed mode ratio of adhesively bonded joints using genetic programming (GP). / Liu, Yiding; Gu, Zewen; Hughes, Darren J. et al.

In: Composite Structures, Vol. 258, 113389, 15.02.2021.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Liu Y, Gu Z, Hughes DJ, Ye J, Hou X. Understanding mixed mode ratio of adhesively bonded joints using genetic programming (GP). Composite Structures. 2021 Feb 15;258:113389. Epub 2020 Dec 3. doi: 10.1016/j.compstruct.2020.113389

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Liu, Yiding ; Gu, Zewen ; Hughes, Darren J. et al. / Understanding mixed mode ratio of adhesively bonded joints using genetic programming (GP). In: Composite Structures. 2021 ; Vol. 258.

Bibtex

@article{7dae03c1759549adb452483e59a2a4b2,
title = "Understanding mixed mode ratio of adhesively bonded joints using genetic programming (GP)",
abstract = "Adhesively bonding has been increasingly used for numerous industrial applications to meet the high demand for lightweight and safer structures. Debonding of adhesively bonded joints is a typical mixed mode failure process. It is highly depended on the interactional effects of material properties and geometric definitions of the constituents, which is very complicated. The existing studies in identifying fracture modes of joints based on either experiments or finite element analysis are often prohibitively time and computational expensive. This paper proposed an innovate method by combining Finite Element Analysis (FEA), Latin Hypercube Sampling (LHS) and Genetic Programming (GP) to understand the effect of the physical attributes on the fracture modes of adhesively single lap joints. A dataset of 150 adhesive joint samples has been generated using LHS, including different combinations of adherend and adhesive{\textquoteright}s material properties and thicknesses. The mixed mode ratios of the 150 samples are calculated using Strain Energy Release Rate (SERR) outputs embedded in Linear Elastic Fracture Mechanics (LEFM), which has been validated by experimental tests. Finally, a GP model is developed and trained to provide an extracted explicit expression used for evaluating the early-state failure modes of the adhesively bonded joints against the design variables.",
keywords = "Adhesively bonded joints, Mixed mode ratio, Finite element analysis, Latin Hypercube Sampling, Genetic Programming, Strain Energy Release Rate",
author = "Yiding Liu and Zewen Gu and Hughes, {Darren J.} and Jianqiao Ye and Xiaonan Hou",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Composite 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 Composite Structures, 373, 4-5, 2021 DOI: 10.1016/S0370-1573(02)00269-7 ",
year = "2021",
month = feb,
day = "15",
doi = "10.1016/j.compstruct.2020.113389",
language = "English",
volume = "258",
journal = "Composite Structures",
issn = "0263-8223",
publisher = "Elsevier Ltd",

}

RIS

TY - JOUR

T1 - Understanding mixed mode ratio of adhesively bonded joints using genetic programming (GP)

AU - Liu, Yiding

AU - Gu, Zewen

AU - Hughes, Darren J.

AU - Ye, Jianqiao

AU - Hou, Xiaonan

N1 - This is the author’s version of a work that was accepted for publication in Composite 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 Composite Structures, 373, 4-5, 2021 DOI: 10.1016/S0370-1573(02)00269-7

PY - 2021/2/15

Y1 - 2021/2/15

N2 - Adhesively bonding has been increasingly used for numerous industrial applications to meet the high demand for lightweight and safer structures. Debonding of adhesively bonded joints is a typical mixed mode failure process. It is highly depended on the interactional effects of material properties and geometric definitions of the constituents, which is very complicated. The existing studies in identifying fracture modes of joints based on either experiments or finite element analysis are often prohibitively time and computational expensive. This paper proposed an innovate method by combining Finite Element Analysis (FEA), Latin Hypercube Sampling (LHS) and Genetic Programming (GP) to understand the effect of the physical attributes on the fracture modes of adhesively single lap joints. A dataset of 150 adhesive joint samples has been generated using LHS, including different combinations of adherend and adhesive’s material properties and thicknesses. The mixed mode ratios of the 150 samples are calculated using Strain Energy Release Rate (SERR) outputs embedded in Linear Elastic Fracture Mechanics (LEFM), which has been validated by experimental tests. Finally, a GP model is developed and trained to provide an extracted explicit expression used for evaluating the early-state failure modes of the adhesively bonded joints against the design variables.

AB - Adhesively bonding has been increasingly used for numerous industrial applications to meet the high demand for lightweight and safer structures. Debonding of adhesively bonded joints is a typical mixed mode failure process. It is highly depended on the interactional effects of material properties and geometric definitions of the constituents, which is very complicated. The existing studies in identifying fracture modes of joints based on either experiments or finite element analysis are often prohibitively time and computational expensive. This paper proposed an innovate method by combining Finite Element Analysis (FEA), Latin Hypercube Sampling (LHS) and Genetic Programming (GP) to understand the effect of the physical attributes on the fracture modes of adhesively single lap joints. A dataset of 150 adhesive joint samples has been generated using LHS, including different combinations of adherend and adhesive’s material properties and thicknesses. The mixed mode ratios of the 150 samples are calculated using Strain Energy Release Rate (SERR) outputs embedded in Linear Elastic Fracture Mechanics (LEFM), which has been validated by experimental tests. Finally, a GP model is developed and trained to provide an extracted explicit expression used for evaluating the early-state failure modes of the adhesively bonded joints against the design variables.

KW - Adhesively bonded joints

KW - Mixed mode ratio

KW - Finite element analysis

KW - Latin Hypercube Sampling

KW - Genetic Programming

KW - Strain Energy Release Rate

U2 - 10.1016/j.compstruct.2020.113389

DO - 10.1016/j.compstruct.2020.113389

M3 - Journal article

VL - 258

JO - Composite Structures

JF - Composite Structures

SN - 0263-8223

M1 - 113389

ER -