Fuzzy similarity measures for detection and classification of defects in CFRP.

The systematic use of nondestructive testing assumes a remarkable importance where on-line manufacturing quality control is associated with the maintenance of complex equipment. For this reason, nondestructive 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 nondestructive research methodologies are able to provide information on the state of a manufacturing process 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 work. In such a context, the production of carbon fiber resin epoxy (CFRP) is a complex process that is not free from defects and faults that could compromise the integrity of the manufactured specimen. Ultrasonic tests provide an effective contribution in identifying the presence of a defect. In this work, a fuzzy similarity approach is proposed with the goal of localizing and classifying defects in CFRP in terms of a sort of distance among signals (measure of ultrasonic echoes). A field-programmable gate array (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 results obtained using heuristic techniques with a higher computational load.