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 this 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.
Owing to the high sensitivity of carbon fiber reinforced polymer (CFRP) to internal damages, defect detection through Non-destructive testing (NDT) is deemed an essential task. One of the common methods in NDT to achieve this aim is measuring and analyzing the full-field guided waves propagation in CFRP plates. Scattered waves corresponding to deep defects are usually obscured by other waves due to their weak amplitude. A successful method to highlight these waves is to use wavenumber filtering (WF). However, WF suffers from the assumption that the optimal frequency range of excitation signal is known beforehand, which is not always available. Another drawback is that when more than one type of guided waves mode exist, this method is not capable of highlighting desirable waves or vibrations sufficiently. In this paper, full wavefield images are first constituted by exciting the guided waves via broadband chirp signal and registering them with scanning laser Doppler vibrometery (SLDV). Then, a successive wavenumber filtering (SWF) approach is introduced, which efficiently removes undesirable higher order guided wave modes, and removes the need to know a priori the optimal excitation frequency. Moreover, it is quantitatively and qualitatively shown that the proposed approach could lead to better discrimination between damaged and healthy area than conventional WF.
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