A novel vibration based non-destructive testing for predicting glass fibre/matrix volume fraction in composites using a neural network model.

This study proposes a novel approach to determine the fibre volume fraction in composites using vibration based non-destructive technique with a neural network. Currently, the volume fraction of a glass fibre/matrix based composite material is assessed using destructive techniques. Instead of changing or destroying the structure, a new non-destructive approach based on vibration analysis is proposed. Complete experimental protocols were developed to capture the vibration pattern. An auto-regressive model was developed as a feature extraction tool to classify the fibre volume fractions and as a pole tracking algorithm. The classification performances were within the range of 90–98%. For NDT method to be efficient, the classification results were then compared with destructive burn-out technique. The results of non-destructive test showed good agreement with those obtained through destructive test suggesting that the proposed method is an alternative to ASTM D2584-11 for determining the volume fraction of a glass fibre/matrix composite. [ABSTRACT FROM AUTHOR]