Acoustic Analysis of Adhesive Surface in Granular Inhomogeneous Structural Material using Neural Network

Abstract

Difficulty in quality assessment of adhesive interface between granular inhomogeneous materials and a disk plate has been pointed out in nondestructive ultrasonic inspection because of the effect of multiple-reflection at the contact point of each granular particle. A simulation technique is presented in this paper for estimating the adhesion quality of a CBN grinding wheel at the interface between the granular material and the disk plate. The grinding wheel was transposed to one dimensional serial model consisted of each segment of particle with random acoustic impedances at the interface. The propagation of incident ultrasonic waves in the grinding wheel was calculated and echoes from adhesive interface were analyzed. The domestic echo waveform from the adhesive interface was specified and learned by a neural network corresponding adhesion quality and the characteristic of the echo waveform. The effect of the grinding wheel structure to the accuracy of presumption was investigated by using the network. It was revealed that the neural network is effective to assess the adhesive quality of such inhomogeneous structural materials.