Fuzzy Logic Based Delamination Detection in CFRP Panels

This paper presents an intelligent interpretation of ultrasonic C-scan results for carbon-fiber-reinforced plastic (CFRP) panels by using fuzzy logic approach. Ultrasonic C-scan results have relatively low resolution and poor imaging quality in anisotropic composites due to the speckle noise produced by the interference of backscattered signals. In this study, fuzzy logic was implemented to accurately determine a defect’s shape and size and to avoid over-segmentation and under-segmentation. For this, first, a 3×3 mask was considered to define the central value and the mean value within the C-scan amplitude data. Then, five linguistic labels for the central value and mean value were defined as: very low, low, neutral, high, and very high so as to determine fuzzy sets for the fuzzy inference system (FIS). Combined with 25 fuzzy rules, the FIS was capable of making decisions based on fuzzy sets and fuzzy rules. Experimental results demonstrated this fuzzy logic method can detect the size and shape of sub-surface delamination correctly, and restrain the noises effectively. The authors believe this approach for automatic defect detection and classification can be an integral part of the development of an intelligent NDE expert system for composite structures in the future, thus making defect evaluation process much easier and more accurate.