Vibration-based delamination detection in curved composite plates

Delamination is one of common damages in fibre-reinforced composite laminates. It is known to cause changes in the vibration characteristics of the laminates, which allows for the use of vibration-based indicators for assessing structural health conditions and identifying potential risk of catastrophic failures. This paper presents a vibration-based approach to assess delamination in fibre reinforced composite curved plates through frequency shifts as indicative parameters. Two algorithms based on computational intelligence, namely artificial neural network (ANN) and surrogate assisted genetic algorithm (SAGA), were employed as inverse algorithms for predicting the location, size and interface of the delamination. The validation of the two algorithms is realized numerically through finite element (FE) studies conducted using a structured selection of parameters of genetic algorithms, number of frequency shifts and database size. Experimental modal testing was conducted using scanning laser vibrometer on five carbon fibre reinforced polymer (CFRP) curved plates. Among these, one was intact and the other four were manufactured with artificially induced laminations. It was confirmed that the two inverse algorithms were able to reasonably predict delamination parameters in curved composite plates. Among the two, SAGA's performance was observed to be better. A sensitivity analysis was further conducted by adding artificial noise to the numerical data to simulate measurement errors for investigating the influence of noise on the accuracy of the two algorithms.