Abstract
This paper is dedicated to out-of-plane waviness defect detection within composite materials by ultrasonic testing. We present here an in-house experimental database of ultrasonic data built on composite pieces with/without elaborated defects. Using this dataset, we have developed several defect detection methods using the C-scan representation, where the defect is clearly observable. We compare here the defect detection performance of unsupervised, classical machine learning methods and deep learning approaches. In particular, we have investigated the use of semantic segmentation networks that provides a classification of the data at the “pixel level”, hence at each C-scan measure. This technique is used to classify if a defect is detected, and to produce a precise localization of the defect within the material. The results we obtained with the various detection methods are compared, and we discuss the drawbacks and advantages of each method.
Probability of defect detection in pulse compression favourable thermal excitation schemes for infra-red non-destructive testing