Defect severity classification of complex composites using CWT and CNN

Abstract

Composite structures are prone to internal defects such as delamination. Due to this, it is vital to recognize internal flaws in composite materials accurately because there is possibility that these internal defects can severely degrade the composite structure’s strength. This work aims to develop an intelligent complex composite defect severity classification which will contribute to efficient monitoring of composite structures during their service life. Firstly, the behavior of guided ultrasonic waves is processed and transformed into image database using continuous wavelet transform method. Then, a defect classification framework is proposed by using convolutional neural network to classify six types of defect sizes. A total of 798, 342, and 90 images are used for training, validation, and testing, respectively. The results present that the proposed system achieved approximately above 86% of precision and recall for all six defects classes.