Artificial intelligence and ultrasonic tests in detection of defects

Abstract

One of the most serious problems in the quality control of welded unions is the location, identification and classification of defects. As a solution to this problem, a technique for classification, applicable to welded unions done by electric arc welding as well as by friction, is proposed; it is based on ultrasonic signals. The neuronal networks proposed are Kohonen and Multilayer Perceptron, all in a virtual instrument environment. Currently the techniques most used in this field are: radiological analysis (X-rays) and ultrasonic analysis (ultrasonic waves). The X-ray technique in addition to being dangerous requires highly specialized personnel and equipment, therefore its use is restricted. The ultrasonic technique, in spite of being one of the most used for detection of discontinuities, requires personnel with wide experience in the interpretation of ultrasonic signals; this is a timeconsuming process which necessarily increases its operation cost. The classification techniques that we propose turn out to be safe, reliable, inexpensive and easy to implement for the solution of this important problem.