{"title":"Distortion Mechanisms and Physics-Informed Intelligent Compensation of Eddy Current Testing Signals for Curved Surface Components","authors":"Tuan Zhu, Chao Lu, Wenze Shi, Jianshen Liu, Gongpeng Yang, Yiping Liang, 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.
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.