基于PSO-SVR的巴克豪森效应特征信号预测环轧件残余应力  

Prediction of Residual Stress of Ring-rolled Parts Based on PSO-SVR Characteristic Signal of Barkhausen Effect

作　　者：穆晨光 陈曦[1] 邬冠华[1] 邢金华 MU Chen-guang;CHEN Xi;WU Guan-hua;XING Jin-hua(Key laboratory of Nondestructive Testing(Ministry of Education),Nanchang Hangkong University,Nanchang 330063,China)

机构地区：[1]南昌航空大学无损检测技术教育部重点实验室,南昌330063

出　　处：《南昌航空大学学报（自然科学版）》2024年第2期1-9,共9页Journal of Nanchang Hangkong University(Natural Sciences)

基　　金：国家自然科学基金(62061033)。

摘　　要：高温合金环轧件中的残余应力会影响构件的后续加工性能和使用寿命,其定量检测是无损检测应用中的难点。为了提高残余应力巴克豪森噪声定量检测的准确性,以高温合金GH2907环轧件为母材制作拉伸试样,在加载状态下原位采集巴克豪森噪声信号,提取均方根、脉冲因子等15种巴克豪森信号特征值;利用核主成分分析法进行检测信号特征数据降维,利用降维结果与对应的试样平均应力构建样本集,将粒子群优化(PSO)与支持向量回归(SVR)结合进行参数寻优,建立巴克豪森信号特征与残余应力间的连续变量回归模型;以实际工件为检测对象,以盲孔法测得残余应力结果为对比,验证预测模型的有效性和准确性。结果表明,所建立的应力预测模型拟合优度为0.9318,测试样本残余应力评价的平均相对误差为7.1%。与单一参数的巴克豪森残余应力线性评价模型相比,PSO-SVR应力预测模型拟合优度高,误差小,泛化性能好,可实现残余应力的检测与高精度评价。The residual stress in high-temperature alloy ring rolled parts can affect the subsequent processing performance and service life of the components,quantitative detection of which in high-temperature alloy ring-rolled components is a challenging task in non-destructive testing applications.To improve the accuracy of Barkhausen noise quantitative detection of residual stress,tensile specimens were fabricated using high-temperature alloy GH2907 as the parent material.Barkhausen noise signals were collected in situ under loading conditions,and 15 Barkhausen signal characteristics such as root mean square and pulse factor were extracted.Kernel principal component analysis was employed to reduce the dimensionality of the detection signal feature data.The result of dimension-decrease and the corresponding average stress of the specimens were used to construct a sample set.A continuous variable regression model between Barkhausen signal features and residual stresses was established by combining particle swarm optimization(PSO)with support vector regression(SVR)to optimize parameters.With actual workpieces as detection objects,and the residual stress results measured by blind hole method as comparison,the effectiveness and accuracy of the predictive model were validated.The results showed that the goodness of fit of the established stress prediction model is 0.9318,and the average relative error of residual stress evaluation for the test samples was 7.1%.Compared with the linear evaluation model of Barkhausen residual stress with a single parameter,the PSO-SVR stress prediction model had higher goodness of fit,smaller error,and better generalization performance,enabling the detection and high-precision evaluation of residual stresses.

关 键 词：巴克豪森噪声 残余应力预测 粒子群算法 支持向量机回归 高温合金环轧件 

分 类 号：V214.9[航空宇航科学与技术—航空宇航推进理论与工程]