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

Abstract The work shows the results obtained in recognition of different types of austenitic steels with an ultrasonic system that provides the necessary data towards two different neural networks. One of the neural networks (RNAU) used as input a vector containing processed data (propagation velocity and ultrasonic attenuation). The second neural network (AUFRAN) used the amplitude of digitized radio-frequency signal and its numerical Fourier transform as 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 data on these samples were used in the testing process. The obtained classification probabilities were above 98.3%. As a supplement, the testing process was extended to three other types of austenitic steels having different chemical compositions than those used in the learning process.