Rapid measurement of the fourth-order texture coefficient by laser ultrasonic surface acoustic waves based on a neural network expert system.

In anisotropic materials, texture components and orientation density directly affect the surface acoustic waves' (SAW) velocity dispersion and SAW velocity variation. In this paper, a texture feature recognition and analysis system for a neural network is constructed based on the corresponding characteristics of texture components and orientation density and SAW velocity dispersion and variation by combining laser ultrasound technology with the partial texture analysis method, which is used for the identification and analysis of texture type and feature. At the same time, based on the relationship between surface wave velocity and the fourth-order texture coefficient, an expert system for accurate prediction of the fourth-order texture coefficient is constructed. Then, the fourth-order texture coefficients predicted by the neural network expert system (NNES) are compared with the texture coefficients measured by electron backscattered diffraction. The results show that the NNES can not only quickly identify and analyze texture features, but also accurately predict the fourth-order texture coefficients.