基于X射线荧光和极端梯度提升的渗铝层厚度预测模型

Prediction Model of Aluminized Layer Thickness Based on X-Ray Fluorescence and Extreme Gradient Boosting

渗铝作为航空发动机涡轮叶片高温防护的重要手段,其质量与飞行安全密切相关。渗铝层厚度是评估渗层性能的重要因素,但目前的无损检测方法难以对其进行准确测量。针对该问题,将X射线荧光技术与极端梯度提升(XGBoost)算法相结合,通过Pearson相关系数筛选(PCCS)提取特征元素,构建渗铝层厚度预测模型。将该模型与K近邻回归、线性回归、支持向量机、随机森林模型预测结果的平均相对误差进行对比。结果表明,相比其他模型,PCCSXGBoost模型预测渗层厚度的平均相对误差最小,为1.60%。该研究为渗铝层厚度的无损检测提供了一种新的预测方法。

As an important means of hightemperature protection for aeroengine turbine blades,the quality of aluminized coatings is closely related to flight safety.The thickness of the aluminized layer is an essential factor in evaluating its performance.However,it is not easy to measure it accurately by current nondestructive testing methods.For this problem,the Xray fluorescence technology is combined with the extreme gradient boosting(XGBoost)algorithm,and the feature element extraction by Pearson correlation coefficient screening(PCCS)is used to build a prediction model for the thickness of the aluminized layer.The average relative error of the prediction results is compared with K nearest neighbor regression,linear regression,support vector machine,and random forest models.The results show that the PCCSXGBoost model had the smallest average error of 1.60%in predicting thickness compared with other models.The study provides a new prediction method for nondestructive testing of the thickness of the aluminized layer.