Distortion Mechanisms and Physics-Informed Intelligent Compensation of Eddy Current Testing Signals for Curved Surface Components

IF 3 3区 材料科学 Q2 MATERIALS SCIENCE, CHARACTERIZATION & TESTING
Tuan Zhu, Chao Lu, Wenze Shi, Jianshen Liu, Gongpeng Yang, Yiping Liang, Bingwen Wang
{"title":"Distortion Mechanisms and Physics-Informed Intelligent Compensation of Eddy Current Testing Signals for Curved Surface Components","authors":"Tuan Zhu,&nbsp;Chao Lu,&nbsp;Wenze Shi,&nbsp;Jianshen Liu,&nbsp;Gongpeng Yang,&nbsp;Yiping Liang,&nbsp;Bingwen Wang","doi":"10.1007/s10921-026-01441-8","DOIUrl":null,"url":null,"abstract":"<div><p>The curvature characteristics of aero-engine turbine blades introduce significant interference in eddy current testing signals, readily causing waveform distortion and thereby hindering quantitative defect assessment. To address this issue, a physics-informed random forest compensation method is proposed, and the signal distortion mechanism induced by curvature variation is systematically investigated. Considering that non-differentiable models such as random forests cannot directly incorporate physical priors, a physics information embedding mechanism based on iterative target-value correction is developed. A curvature-domain Laplace operator is employed to construct the physical residual, which is subsequently fed back to the training labels to dynamically refine the prediction targets. To validate the effectiveness of the proposed approach, comparative scanning simulations on curved and flat surfaces as well as experimental verification using Inconel 718 specimens with artificial defects were conducted. Experimental results demonstrate that, compared with the standard random forest model, the improved method reduces the root-mean-square error (RMSE) for flat reference signal compensation under varying curvatures by an average of 72.4% and decreases the standard deviation (STD) by 18.9%. Under the extreme curvature condition of 5 mm, the RMSE improvement reaches 75.5%. In experimental validation, the RMSE values of the compensated signals for different curvatures are reduced by 18% and 96.7%, respectively, while the STD values are reduced by 14% and 18.9%. The compensated signals obtained by the proposed algorithm exhibit substantially higher fidelity to the flat reference signal, effectively mitigating distortion phenomena and facilitating quantitative defect signal analysis. The algorithm demonstrates pronounced physical robustness in curved-surface eddy current signal compensation, providing an effective technical pathway for the correction of eddy current signals acquired on curved geometries.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"45 4","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Nondestructive Evaluation","FirstCategoryId":"88","ListUrlMain":"https://link.springer.com/article/10.1007/s10921-026-01441-8","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, CHARACTERIZATION & TESTING","Score":null,"Total":0}
引用次数: 0

Abstract

The curvature characteristics of aero-engine turbine blades introduce significant interference in eddy current testing signals, readily causing waveform distortion and thereby hindering quantitative defect assessment. To address this issue, a physics-informed random forest compensation method is proposed, and the signal distortion mechanism induced by curvature variation is systematically investigated. Considering that non-differentiable models such as random forests cannot directly incorporate physical priors, a physics information embedding mechanism based on iterative target-value correction is developed. A curvature-domain Laplace operator is employed to construct the physical residual, which is subsequently fed back to the training labels to dynamically refine the prediction targets. To validate the effectiveness of the proposed approach, comparative scanning simulations on curved and flat surfaces as well as experimental verification using Inconel 718 specimens with artificial defects were conducted. Experimental results demonstrate that, compared with the standard random forest model, the improved method reduces the root-mean-square error (RMSE) for flat reference signal compensation under varying curvatures by an average of 72.4% and decreases the standard deviation (STD) by 18.9%. Under the extreme curvature condition of 5 mm, the RMSE improvement reaches 75.5%. In experimental validation, the RMSE values of the compensated signals for different curvatures are reduced by 18% and 96.7%, respectively, while the STD values are reduced by 14% and 18.9%. The compensated signals obtained by the proposed algorithm exhibit substantially higher fidelity to the flat reference signal, effectively mitigating distortion phenomena and facilitating quantitative defect signal analysis. The algorithm demonstrates pronounced physical robustness in curved-surface eddy current signal compensation, providing an effective technical pathway for the correction of eddy current signals acquired on curved geometries.

曲面元件涡流检测信号畸变机理及基于物理的智能补偿
航空发动机涡轮叶片的曲率特性对涡流检测信号产生了明显的干扰,容易造成波形畸变,从而阻碍了缺陷的定量评估。针对这一问题,提出了一种基于物理信息的随机森林补偿方法,并对曲率变化引起的信号畸变机制进行了系统研究。针对随机森林等不可微模型不能直接包含物理先验的问题,提出了一种基于迭代目标值校正的物理信息嵌入机制。利用曲率域拉普拉斯算子构造物理残差,并将残差反馈给训练标签,动态细化预测目标。为了验证该方法的有效性,对曲面和平面进行了对比扫描模拟,并对含人工缺陷的Inconel 718试样进行了实验验证。实验结果表明,与标准随机森林模型相比,改进后的方法可将变曲率下平坦参考信号补偿的均方根误差(RMSE)平均降低72.4%,标准差(STD)平均降低18.9%。在曲率极值为5 mm的条件下,RMSE的改善达到75.5%。在实验验证中,不同曲率下补偿信号的RMSE值分别降低了18%和96.7%,STD值分别降低了14%和18.9%。通过该算法获得的补偿信号对平坦参考信号具有更高的保真度,有效地减轻了失真现象,便于对缺陷信号进行定量分析。该算法在曲面涡流信号补偿中具有明显的物理鲁棒性,为曲面涡流信号的校正提供了有效的技术途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Journal of Nondestructive Evaluation
Journal of Nondestructive Evaluation 工程技术-材料科学:表征与测试
CiteScore
4.90
自引率
7.10%
发文量
67
审稿时长
9 months
期刊介绍: Journal of Nondestructive Evaluation provides a forum for the broad range of scientific and engineering activities involved in developing a quantitative nondestructive evaluation (NDE) capability. This interdisciplinary journal publishes papers on the development of new equipment, analyses, and approaches to nondestructive measurements.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信
小红书