Abstract
This paper describes nondestructive inspection by means of the ultrasonic inspection of casting of cast steel using a neural network. Ultrasonic inspection is affected by surface roughness, and the surface roughness makes ultrasonic inspection of surface flaws difficult. Therefore, three kinds of reflection waveform data from surface based on roughness were examined as the NN training data. In addition, two kinds of probes were tested in order to investigate the proper one. The results were compared in order to discuss classification rate.
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