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

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

Research output: Contribution to journalArticle

13 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.

Original languageEnglish
Pages (from-to)752-759
Number of pages8
JournalComposites Science and Technology
Volume72
Issue number6
DOIs
Publication statusPublished - 27 Mar 2012

Fingerprint

Delamination
Genetic algorithms
Recovery
Fiber reinforced plastics

Keywords

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

ASJC Scopus subject areas

  • Engineering(all)
  • Ceramics and Composites

Cite this

Predicting self-healing strength recovery using a multi-objective genetic algorithm. / Knipprath, C.; McCombe, G. P.; Trask, R. S.; Bond, I. P.

In: Composites Science and Technology, Vol. 72, No. 6, 27.03.2012, p. 752-759.

Research output: Contribution to journalArticle

Knipprath, C. ; McCombe, G. P. ; Trask, R. S. ; Bond, I. P. / Predicting self-healing strength recovery using a multi-objective genetic algorithm. In: Composites Science and Technology. 2012 ; Vol. 72, No. 6. pp. 752-759.
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