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

C. Knipprath; G. P. McCombe; R. S. Trask; I. P. Bond

doi:10.1016/j.compscitech.2012.02.002

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

C. Knipprath, G. P. McCombe, R. S. Trask, I. P. Bond

Department of Mechanical Engineering

University of Bristol

Research output: Contribution to journal › Article › peer-review

17 Citations (SciVal)

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.

Original language	English
Pages (from-to)	752-759
Number of pages	8
Journal	Composites Science and Technology
Volume	72
Issue number	6
DOIs	https://doi.org/10.1016/j.compscitech.2012.02.002
Publication status	Published - 27 Mar 2012

Keywords

A. Smart materials
C. Damage tolerance
C. Finite element analysis (FEA)
C. Probabilistic methods
Self-healing

ASJC Scopus subject areas

General Engineering
Ceramics and Composites

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10.1016/j.compscitech.2012.02.002

Cite this

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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.",

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AU - McCombe, G. P.

AU - Trask, R. S.

AU - Bond, I. P.

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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.

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KW - C. Damage tolerance

KW - C. Finite element analysis (FEA)

KW - C. Probabilistic methods

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

Abstract

Keywords

ASJC Scopus subject areas

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