Characterising delamination in composite materials: a combined genetic algorithm - finite element approach

Abstract

A novel delamination identification technique based on a low-population genetic algorithm for the quantitative characterisation of a single delamination in composite laminated panels is developed, and validated experimentally The damage identification method is formulated as an inverse problem through which system parameters are identified. The input of the inverse problem, the central geometric moments (CGM), is calculated from the surface out-of-plane displacements measurements of a delaminated panel obtained from Digital Speckle Pattern Interferometry (DSPI). The output parameters, the planar location, size and depth of the flaw, are the solution to the inverse problem to characterise an idealised elliptical flaw. The inverse problem is then reduced to an optimisation problem where the objective function is defined as the L2 norm of the difference between the CGM obtained from a finite element (FE) model with a trial delamination and the moments computed from the DSPI measurements. The optimum crack parameters are found by minimising the objective function through the use of a low-population real-coded genetic algorithm (LARGA). DSPI measurements of ten delaminated T700/LTM-45EL carbon/epoxy laminate panels with embedded delaminations are used to validate the methodology presented in this thesis