Development of Automatic Neural Network Classifier of Defects Detected by Ultrasonic Means Oleg KARPASH, Maksym KARPASH, Valentine MYNDJUK, National Technical University of Oil and Gas, Ivano-Frankivsk, Ukraine Abstract. The possibility of applying artificial neural network to the multistage processing of defect signals detected by ultrasonic means was analyzed in this paper. Two networks were used for that purpose: the first was the neural network for feature extraction and the second one was used for defect class estimation. Two major defect types were considered – plane and volumetric defects. Developed technique was used to process signals from artificial defects of specially made steel bars and real pipes as well. Classification results were fine in both cases. Introduction It is very important to know the type of defect (is it plane or volumetric) during ultrasonic testing of products and materials [1]. There is certain amount of main defect types for various products. But commonly there are only two main defect types that are characterized by their shape. The first type is presented by defects caused by improper production process. They have mostly volumetric shape and are not stress concentrators and therefore they can’t grow in metal. Defects caused by improper production process are not as dangerous as fatigue cracks are. Fatigue cracks are stress concentrators and they are the result of tested product exploitation. These defects can grow and cause equipment failure. 1 Theoretical Aspects Capabilities of acoustical method of non-destructive testing enable defects detection and estimation of their geometrical dimensions as well. In most cases it is very difficult to estimate defects’ type by analyzing measurements that were made during ultrasonic testing procedure. This is because of complex waveform of echo-signals reflected from defects. The amplitude of echo-signal depends on piezoelectric transducer frequency, wave incident angle, depth and orientation of defect etc. In other hand, defects of different size and shape can have the same equivalent area and reflect echo-signals with the same amplitude [2]. It is known that the shape form of defect is very determinative for evaluation of numerous structures, products etc. Nowadays to estimate the type of defect the analysis of single frequency echo-signals spatial distribution in selected directions within vertical and horizontal planes is mainly used [3]. Therefore it’s considered to be unreliable to estimate the defect type (plane or volumetric) by analyzing amplitudes of echo-signals from one transducer which emits acoustic waves in one direction (e.g. only in horizontal or vertical plane). ECNDT 2006 - Poster 142 1