Computational intelligence aspects for defect classification in aeronautic composites by using ultrasonic pulses

Production of carbon fiber reinforced polymers (i.e., one of the basic material of the modern airplanes) is an elaborate process unfree from faults and problems. Errors during the manufacturing or the plies' overlapping, in fact, can cause particular flaws in the resulting material, so compromising its same integrity. Within this framework, ultrasonic tests could be useful to characterize the presence of defect, depending on its dimensions. On the contrary, the requirement of a perfect state for used polymers is unavoidable in order to assure both transport reliability and passenger safety. Therefore, a real-time approach able to recognize and classify the defect starting from the measured ultrasonic echoes could be very useful in industrial applications. The ill-posedness of the so defined process induce a regularization method. In this paper, an heuristic approach is proposed for this aim. Particularly, the proposed method is based on the use of support vector machines. Obtained results assure good performances of the implemented classifier, with very interesting applications.