A DNN-based Image Retrieval Approach for Detection of Defective Area in Carbon Fiber Reinforced Polymers through LDV Data

Carbon fiber reinforced polymer (CFRP) materials, due to their specific strength and high consistency against erosion and corrosion, are widely used in industrial applications and high-tech engineering structures. However, there are also disadvantages: e.g. they are prone to different kinds of internal defects which could jeopardize the structural integrity of the CFRP material and therefore early detection of such defects can be an important task. Recently, local defect resonance (LDR), which is a subcategory of ultrasonic nondestructive testing, has been successfully used to solve this issue. However, the drawback of utilizing this technique is that the frequency at which the LDR occurs must be known. Further, the LDR-based technique has difficulty in assessing deep defects. In this paper, deep neural network (DNN) methodology is employed to remove this limitation and to acquire a better defect image retrieval process and also to achieve a model for the approximate depth estimation of such defects. In these regards, two types of defects called flat bottom holes (FBH) and barely visible impact damage (BVID) which are made in two CFRP coupons are used to evaluate the ability of the proposed method. Then, these two CFRPs are excited with a piezoelectric patch, and their corresponding laser Doppler vibrometry (LDV) response is collected through a scanning laser Doppler vibrometer (SLDV). Eventually, the superiority of our DNN-based approach is evaluated in comparison with other well-known classification methodologies.