The use of the neural networks in the recognition of the austenitic steel types

The work shows the results obtained in recognition of different types of au stenitic steels with an ultrasonic system that provides the necessary data towards two different neural networks. One of the neural networks (RNAU) us ed as input a vector containing processed data (propagation velocity and ul trasonic attenuation). The second neural network (AUFRAN) used the amplitud e of digitized radio-frequency signal and its numerical Fourier transform a s input vector, Two thirds of data obtained from three kinds of steels (W.1.4541, W.1.6903 and HP50) were used in the learning process. The last third of acquired dat a on these samples were used in the testing process, The obtained classific ation probabilities were above 98.3%. As a supplement, the testing process was extended to three other types of austenitic steels having different che mical compositions than those used in the learning process, (C) 2000 Elsevi er Science Ltd, All rights reserved.