Fuzzy Similarity Measures for Detection and Classification of Defects in CFRP

The systematic use of non-destructive testing assumes a remarkable importance where the on-line manufacturing quality control is associated to the maintenance of complex equipments. For this reason, Non-Destructive Testing and Evaluation (NDT/NDE), together with accuracy and precision of measurements of the specimen, results as a strategic activity in many fields of industrial and civil interest. It is well known that non-destructive research methodologies are able to provide information on the state of a manufacture without compromising its integrity and functionality. Moreover, exploitation of algorithms with a low computational complexity for detecting the integrity of a specimen plays a crucial role in real-time working. In such context, the production of Carbon Fiber Resin Epoxy (CFRP) is a complex process not free from defects and faults above all during the process of manufacturing, because it could be kept compromised that could compromise the integrity of the manufactured specimen. Following the said principle, Ultrasonic Tests (UTs) give an effective contribution in identifying the presence of a defect. In this work a, Fuzzy Similarity approach is proposed being used with the aim of localizing and classifying defects in CFRP in terms of a sort of distance among signals (measure of ultrasonic echoes). A FPGA based board will be also presented, which implements the described algorithms on a hardware device. The good performance of the detection and classification achieved assures the comparability of the results with the ones obtained using the heuristic