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Neural networks for ultrasonic grain size discrimination

In this study, we have developed the grain power spectrum neural network (GPSNN) to classify the ultrasonic backscattered grain signals for material characterization. The GPSNN has 32 input nodes, 13 hidden neurons determined adaptively, and one summing output node. A set of 4490 training sequences is utilized to train the neural network. A new set of 12572 testing sequences is used to test GPSNN performance. The samples tested for grain size discrimination are steel with grain sizes of 14 and 50 microns. GPSNN achieves an average recognition performance of over 98%. This high level of recognition suggests that the GPSNN is a promising method for ultrasonic nondestructive testing.