Predicting self-healing strength recovery using a multi-objective genetic algorithm

C Knipprath; GP McCombe; RS Trask; IP Bond

doi:10.1016/j.compscitech.2012.02.002

Predicting self-healing strength recovery using a multi-objective genetic algorithm

C Knipprath, GP McCombe, RS Trask, IP Bond

Research output: Contribution to journal › Article (Academic Journal) › peer-review

17 Citations (Scopus)

Abstract

This study aims to identify the optimum location and distribution of a healing agent within the delamination of a fibre reinforced plastic to ensure effective self-healing by utilising a multi-objective Genetic Algorithm (GA). Two optimisation problems were formulated and addressed with a different set of objectives. A simple finite element (FE) model is used to evaluate the mechanical performance of the healing component. The FE model consists of an idealised delamination region, which allows the direct discretisation of the problem used for the optimisation algorithm. Effective healing locations are found for a specific load case with a healing efficiency of up to 95% for the best performing solution.

Translated title of the contribution	Predicting self-healing strength recovery using a multi-objective genetic algorithm
Original language	English
Pages (from-to)	752 - 759
Number of pages	8
Journal	Composites Science and Technology
Volume	72
DOIs	https://doi.org/10.1016/j.compscitech.2012.02.002
Publication status	Published - Mar 2012

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Publisher: Elsevier

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10.1016/j.compscitech.2012.02.002

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CRACK ARREST AND SELF-HEALING IN COMPOSITE STRUCTURES (CRASHCOMPS)
Bond, I. P. (Principal Investigator)
1/01/09 → 1/01/13
Project: Research

Cite this

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title = "Predicting self-healing strength recovery using a multi-objective genetic algorithm",

abstract = "This study aims to identify the optimum location and distribution of a healing agent within the delamination of a fibre reinforced plastic to ensure effective self-healing by utilising a multi-objective Genetic Algorithm (GA). Two optimisation problems were formulated and addressed with a different set of objectives. A simple finite element (FE) model is used to evaluate the mechanical performance of the healing component. The FE model consists of an idealised delamination region, which allows the direct discretisation of the problem used for the optimisation algorithm. Effective healing locations are found for a specific load case with a healing efficiency of up to 95% for the best performing solution.",

author = "C Knipprath and GP McCombe and RS Trask and IP Bond",

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doi = "10.1016/j.compscitech.2012.02.002",

language = "English",

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T1 - Predicting self-healing strength recovery using a multi-objective genetic algorithm

AU - Knipprath, C

AU - McCombe, GP

AU - Trask, RS

AU - Bond, IP

N1 - Publisher: Elsevier

PY - 2012/3

Y1 - 2012/3

N2 - This study aims to identify the optimum location and distribution of a healing agent within the delamination of a fibre reinforced plastic to ensure effective self-healing by utilising a multi-objective Genetic Algorithm (GA). Two optimisation problems were formulated and addressed with a different set of objectives. A simple finite element (FE) model is used to evaluate the mechanical performance of the healing component. The FE model consists of an idealised delamination region, which allows the direct discretisation of the problem used for the optimisation algorithm. Effective healing locations are found for a specific load case with a healing efficiency of up to 95% for the best performing solution.

AB - This study aims to identify the optimum location and distribution of a healing agent within the delamination of a fibre reinforced plastic to ensure effective self-healing by utilising a multi-objective Genetic Algorithm (GA). Two optimisation problems were formulated and addressed with a different set of objectives. A simple finite element (FE) model is used to evaluate the mechanical performance of the healing component. The FE model consists of an idealised delamination region, which allows the direct discretisation of the problem used for the optimisation algorithm. Effective healing locations are found for a specific load case with a healing efficiency of up to 95% for the best performing solution.

U2 - 10.1016/j.compscitech.2012.02.002

DO - 10.1016/j.compscitech.2012.02.002

M3 - Article (Academic Journal)

VL - 72

SP - 752

EP - 759

JO - Composites Science and Technology

JF - Composites Science and Technology

ER -

Predicting self-healing strength recovery using a multi-objective genetic algorithm

Abstract

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CRACK ARREST AND SELF-HEALING IN COMPOSITE STRUCTURES (CRASHCOMPS)

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