Study on the Nondestructive Measurement of Aluminized Thickness Based on Radial Basis Function Neural Network by X-ray Fluorescence

Abstract

Aluminizing is a common protective coating for aeroengine turbine blades, but there is no method to accurately measure the aluminized thickness. X-ray fluorescence nondestructive testing technology is a method which can basically realize the measurement of all coatings on the metal substrate. However, the aluminized coating structure is completely different from the conventional coating structure, which causes great difficulties in measuring the aluminized thickness by conventional calculation models. Therefore, to realize the measurement of aluminized thickness, a new modeling method based on radial basis function (RBF) neural network by X-ray fluorescence (XRF) is proposed. By comparing two calculation models of RBF and principal component analysis (PCA)-RBF, the results show that both models can realize the measurement of aluminized thickness, but the accuracy of PCA-RBF is better than that of RBF, and the average relative error of the predicted results is 3.99%; the predicted results of the PCA-RBF model fit the training values better, and its predictability is better.