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.

中文翻译：

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基于径向基函数神经网络的X射线荧光测铝厚度的无损研究

渗铝是航空发动机涡轮机叶片的常见保护涂层，但是没有方法可以精确测量渗铝厚度。X射线荧光无损检测技术是一种可以基本实现对金属基材上所有涂层的测​​量的方法。然而，渗铝涂层结构与常规涂层结构完全不同，这给通过常规计算模型测量渗铝厚度带来很大困难。因此，为实现镀铝厚度的测量，提出了一种基于径向基函数（RBF）神经网络的X射线荧光（XRF）建模方法。通过比较两个RBF计算模型和主成分分析（PCA）-RBF，结果表明这两个模型都可以实现铝化厚度的测量，PCA-RBF的精度优于RBF，预测结果的平均相对误差为3.99％。PCA-RBF模型的预测结果更适合训练值，可预测性更好。