To improve the accuracy of damage localization in parallel wire strands (PWS) used in cable-stayed bridges and optimize the arrangement of acoustic emission (AE) sensors, an analytical model describing the attenuation of AE signal amplitude across the PWS cross-section was developed. Attenuation tests were then conducted using pencil lead break (PLB) and center punch impacts as simulated damage sources, followed by a sensitivity analysis. The comparison between test results and analytical solutions shows that the analytical model is more suitable for low-frequency signal analysis, with deviations increasing as the signal frequency rises. The analytical model and test result both demonstrate that high-frequency components of AE signals attenuate more rapidly within the PWS cross-section, and sensors with lower resonant frequencies yield superior performance. As the AE signal frequency increases, so does the energy dissipation during propagation. When the frequency rises from 5 kHz to 100 kHz, the attenuation coefficient and acoustic impedance ratio increase by factors of 4.17 and 4.31, respectively. For damage monitoring of bridge PWS, both the resonant frequency of the sensor and the peak signal energy should be considered, with priority given to the resonant frequency.
{"title":"Propagation characteristics of acoustic emission signals across the cross-section of parallel wire strands","authors":"Zhitao Sun , Dongming Feng , Yixuan Zhao , Futang Wei","doi":"10.1016/j.ndteint.2026.103659","DOIUrl":"10.1016/j.ndteint.2026.103659","url":null,"abstract":"<div><div>To improve the accuracy of damage localization in parallel wire strands (PWS) used in cable-stayed bridges and optimize the arrangement of acoustic emission (AE) sensors, an analytical model describing the attenuation of AE signal amplitude across the PWS cross-section was developed. Attenuation tests were then conducted using pencil lead break (PLB) and center punch impacts as simulated damage sources, followed by a sensitivity analysis. The comparison between test results and analytical solutions shows that the analytical model is more suitable for low-frequency signal analysis, with deviations increasing as the signal frequency rises. The analytical model and test result both demonstrate that high-frequency components of AE signals attenuate more rapidly within the PWS cross-section, and sensors with lower resonant frequencies yield superior performance. As the AE signal frequency increases, so does the energy dissipation during propagation. When the frequency rises from 5 kHz to 100 kHz, the attenuation coefficient and acoustic impedance ratio increase by factors of 4.17 and 4.31, respectively. For damage monitoring of bridge PWS, both the resonant frequency of the sensor and the peak signal energy should be considered, with priority given to the resonant frequency.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"160 ","pages":"Article 103659"},"PeriodicalIF":4.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-05-01Epub Date: 2026-02-10DOI: 10.1016/j.ndteint.2026.103665
Vedran Tunukovic , Rylan Gomes , Shaun McKnight , Rastislav Zimermann , Euan Foster , Charalampos Loukas , Randika K.W. Vithanage , Ehsan Mohseni , S. Gareth Pierce , Charles N. MacLeod , Stewart Williams
Wire Arc Additive Manufacturing (WAAM) is a direct energy deposition method that enables the fabrication of large, complex metal components with minimal material waste, making it a key technology within Industry 4.0. However, WAAM is prone to weld-like defects, such as lack of fusion, keyholes, and porosities, which compromise structural integrity and require a reliable Non-Destructive Evaluation (NDE). Conventional post-process inspection methods, including Ultrasonic Testing (UT) and X-ray imaging, can detect such defects but often lead to costly rework once fabrication is complete. This work presents a dual-sensor robotic inspection system enabling simultaneous phased array UT and Eddy Current Testing (ECT) during WAAM deposition for early defect detection and efficient process monitoring. The system integrates an industrial manipulator with closed-loop force-torque control for repeatable layer-wise scanning without tool changes or process interruption. The system was evaluated using two Ti-6Al-4V reference blocks that replicated WAAM geometries and contained artificial defects. A depth-weighted C-scan data fusion approach, supported by targeted ECT denoising, improved contrast-to-noise ratio by 4.44 dB and 9.02 dB for the two samples, respectively. The approach was further validated on a titanium WAAM sample containing embedded tungsten inclusions, demonstrating the robustness of the methodology. A receiver operating characteristic analysis further confirmed the improved defect discrimination of the fused data, consistently resulting in higher area-under-curve values than either UT or ECT alone across all evaluated samples.
{"title":"Automated robotic system for dual ultrasonic and eddy current array integration and data fusion in wire arc additive manufacturing material inspection","authors":"Vedran Tunukovic , Rylan Gomes , Shaun McKnight , Rastislav Zimermann , Euan Foster , Charalampos Loukas , Randika K.W. Vithanage , Ehsan Mohseni , S. Gareth Pierce , Charles N. MacLeod , Stewart Williams","doi":"10.1016/j.ndteint.2026.103665","DOIUrl":"10.1016/j.ndteint.2026.103665","url":null,"abstract":"<div><div>Wire Arc Additive Manufacturing (WAAM) is a direct energy deposition method that enables the fabrication of large, complex metal components with minimal material waste, making it a key technology within Industry 4.0. However, WAAM is prone to weld-like defects, such as lack of fusion, keyholes, and porosities, which compromise structural integrity and require a reliable Non-Destructive Evaluation (NDE). Conventional post-process inspection methods, including Ultrasonic Testing (UT) and X-ray imaging, can detect such defects but often lead to costly rework once fabrication is complete. This work presents a dual-sensor robotic inspection system enabling simultaneous phased array UT and Eddy Current Testing (ECT) during WAAM deposition for early defect detection and efficient process monitoring. The system integrates an industrial manipulator with closed-loop force-torque control for repeatable layer-wise scanning without tool changes or process interruption. The system was evaluated using two Ti-6Al-4V reference blocks that replicated WAAM geometries and contained artificial defects. A depth-weighted C-scan data fusion approach, supported by targeted ECT denoising, improved contrast-to-noise ratio by 4.44 dB and 9.02 dB for the two samples, respectively. The approach was further validated on a titanium WAAM sample containing embedded tungsten inclusions, demonstrating the robustness of the methodology. A receiver operating characteristic analysis further confirmed the improved defect discrimination of the fused data, consistently resulting in higher area-under-curve values than either UT or ECT alone across all evaluated samples.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"160 ","pages":"Article 103665"},"PeriodicalIF":4.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-05-01Epub Date: 2026-02-06DOI: 10.1016/j.ndteint.2026.103663
Xinyun Xie , Qinghua Wang , M.J. Mohammad Fikry , Shinji Ogihara , Xiaojun Yan
Accurate deformation field quantification in high-noise environments persists as a critical limitation for grid-based optical metrology. In this study, we develop a grid enhanced sampling moiré (GE-SM) method that enables robust microscale deformation mapping under complex background noise. This method employs Fourier-domain global periodic frequency extraction to isolate deformation-carrying grid signals from contaminating noise sources, achieving superior noise immunity compared to the traditional sampling moiré (SM) method. Detailed theoretical principles are presented, and numerical simulations verify that the GE-SM method can reduce the local errors from over 100% to within ±5% under simulated noise. Furthermore, carbon fiber reinforced plastic (CFRP) specimens in-situ heating experiments were performed, and the micro-scale thermal expansion strain field evolutions of this material at room temperature up to 130 °C were quantitatively characterized by the GE-SM method. The results confirmed that the GE-SM method can significantly reduce moiré phase disturbances and measurement errors induced by the complex fiber background, elucidating the distinct microscale thermal deformation behaviors of the resin and fiber in CFRP materials. The proposed method provides a promising solution for precise deformation retrieval in extreme noise scenarios, advancing capabilities in grid-based deformation measurement techniques.
{"title":"Grid-enhanced sampling moiré method for robust micro-deformation mapping under complex background noise","authors":"Xinyun Xie , Qinghua Wang , M.J. Mohammad Fikry , Shinji Ogihara , Xiaojun Yan","doi":"10.1016/j.ndteint.2026.103663","DOIUrl":"10.1016/j.ndteint.2026.103663","url":null,"abstract":"<div><div>Accurate deformation field quantification in high-noise environments persists as a critical limitation for grid-based optical metrology. In this study, we develop a grid enhanced sampling moiré (GE-SM) method that enables robust microscale deformation mapping under complex background noise. This method employs Fourier-domain global periodic frequency extraction to isolate deformation-carrying grid signals from contaminating noise sources, achieving superior noise immunity compared to the traditional sampling moiré (SM) method. Detailed theoretical principles are presented, and numerical simulations verify that the GE-SM method can reduce the local errors from over 100% to within ±5% under simulated noise. Furthermore, carbon fiber reinforced plastic (CFRP) specimens in-situ heating experiments were performed, and the micro-scale thermal expansion strain field evolutions of this material at room temperature up to 130 °C were quantitatively characterized by the GE-SM method. The results confirmed that the GE-SM method can significantly reduce moiré phase disturbances and measurement errors induced by the complex fiber background, elucidating the distinct microscale thermal deformation behaviors of the resin and fiber in CFRP materials. The proposed method provides a promising solution for precise deformation retrieval in extreme noise scenarios, advancing capabilities in grid-based deformation measurement techniques.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"160 ","pages":"Article 103663"},"PeriodicalIF":4.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-05-01Epub Date: 2026-01-12DOI: 10.1016/j.ndteint.2026.103646
Mingtao Liu , Xue Bai , Fei Shao , Jian Ma
This paper addresses the challenge of accurately detecting surface and subsurface defects in laser additively manufactured components characterized by high surface roughness. A novel laser ultrasonic imaging method is proposed based on regularized Expectation-Maximization (EM) clustering. The theoretical foundation exploits the observation that ultrasonic feature signal intensities, derived from both transmitted Rayleigh waves (for surface defects) and time-delayed superposed scattered echo signals (for subsurface defects), conform to a Gaussian Mixture Model (GMM). By constructing a GMM and implementing the EM algorithm, the proposed method enables the adaptive separation of defect signals from background noise arising from surface roughness. To improve algorithmic stability and robustness, an adaptive regularization technique based on differential evolution was incorporated, addressing covariance singularity and accelerating convergence. The performance of the proposed method was validated on AlSi10Mg and Ti6Al4V samples. Even under challenging conditions of high surface roughness (Ra = 37.5 μm), the method successfully detects submillimeter surface defects with diameters as small as 0.4 mm. Additionally, the regularized EM clustering approach demonstrates excellent resolution for subsurface defects from 0.5 mm down to sub-wavelength depths (1.1 mm, ∼0.9λ) with a diameter of 0.5 mm. The method also shows strong adaptability in limited sample and high-noise scenarios, outperforming a convolutional neural network-based benchmark in detection accuracy and false detection rate. The core innovation of this approach lies in clustering feature signal data to distinguish defect-related signals from noise, enabling adaptive noise reduction on rough surfaces and minimizing the false detection rate. The proposed method offers a promising application pathway for both online defect detection during the laser additive manufacturing process and comprehensive defect evaluation in components with high surface roughness.
针对激光增材制造零件表面粗糙度高的特点，提出了精确检测表面和亚表面缺陷的难题。提出了一种基于正则化期望最大化聚类的激光超声成像方法。理论基础是基于对透射瑞利波（用于表面缺陷）和延时叠加散射回波信号（用于亚表面缺陷）的超声特征信号强度符合高斯混合模型（GMM）的观察。该方法通过构造GMM和实现EM算法，实现了缺陷信号与表面粗糙度引起的背景噪声的自适应分离。为了提高算法的稳定性和鲁棒性，引入了基于差分进化的自适应正则化技术，解决了协方差奇异性，加快了收敛速度。在AlSi10Mg和Ti6Al4V样品上验证了该方法的性能。即使在具有挑战性的高表面粗糙度条件下（Ra = 37.5 μm），该方法也能成功检测到直径小至0.4 mm的亚毫米表面缺陷。此外，正则化EM聚类方法对直径为0.5 mm的从0.5 mm到亚波长深度（1.1 mm, ~ 0.9λ）的亚表面缺陷具有出色的分辨率。该方法在有限样本和高噪声场景下也表现出较强的适应性，在检测精度和误检率方面优于基于卷积神经网络的基准。该方法的核心创新点在于对特征信号数据进行聚类，将缺陷相关信号与噪声区分开来，实现粗糙表面的自适应降噪，最大限度地降低误检率。该方法为激光增材制造过程中的在线缺陷检测和高表面粗糙度部件的综合缺陷评估提供了一条有前景的应用途径。
{"title":"Regularized expectation-maximization clustering enhanced laser ultrasonic imaging for defects in laser additively manufactured components with high surface roughness","authors":"Mingtao Liu , Xue Bai , Fei Shao , Jian Ma","doi":"10.1016/j.ndteint.2026.103646","DOIUrl":"10.1016/j.ndteint.2026.103646","url":null,"abstract":"<div><div>This paper addresses the challenge of accurately detecting surface and subsurface defects in laser additively manufactured components characterized by high surface roughness. A novel laser ultrasonic imaging method is proposed based on regularized Expectation-Maximization (EM) clustering. The theoretical foundation exploits the observation that ultrasonic feature signal intensities, derived from both transmitted Rayleigh waves (for surface defects) and time-delayed superposed scattered echo signals (for subsurface defects), conform to a Gaussian Mixture Model (GMM). By constructing a GMM and implementing the EM algorithm, the proposed method enables the adaptive separation of defect signals from background noise arising from surface roughness. To improve algorithmic stability and robustness, an adaptive regularization technique based on differential evolution was incorporated, addressing covariance singularity and accelerating convergence. The performance of the proposed method was validated on AlSi10Mg and Ti6Al4V samples. Even under challenging conditions of high surface roughness (Ra = 37.5 μm), the method successfully detects submillimeter surface defects with diameters as small as 0.4 mm. Additionally, the regularized EM clustering approach demonstrates excellent resolution for subsurface defects from 0.5 mm down to sub-wavelength depths (1.1 mm, ∼0.9λ) with a diameter of 0.5 mm. The method also shows strong adaptability in limited sample and high-noise scenarios, outperforming a convolutional neural network-based benchmark in detection accuracy and false detection rate. The core innovation of this approach lies in clustering feature signal data to distinguish defect-related signals from noise, enabling adaptive noise reduction on rough surfaces and minimizing the false detection rate. The proposed method offers a promising application pathway for both online defect detection during the laser additive manufacturing process and comprehensive defect evaluation in components with high surface roughness.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"160 ","pages":"Article 103646"},"PeriodicalIF":4.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-05-01Epub Date: 2026-01-21DOI: 10.1016/j.ndteint.2026.103655
Lin Wang , Xiucheng Liu , Shurui Zhang , Yangyang Zhang , Zhongqi Xu , Yang Yu
Effective non-destructive evaluation of key microstructural features is essential for quality control and performance prediction of pearlitic steels. This study develops a micromagnetic feature-excitation mapping method to characterize lamellar spacing and cluster size using a single multifunctional sensor. Specimens with controlled microstructures-lamellar spacing and cluster size-were prepared and tested under varied excitation frequencies and amplitudes. Four types of magnetic signals were acquired, and 41 magnetic features were extracted. Analysis of linearity and sensitivity identified optimal feature–excitation combinations for independently evaluating lamellar spacing and cluster size. Two practical strategies are demonstrated: selecting different magnetic feature parameters under fixed excitation or adjusting excitation conditions for a single parameter. The proposed approach enables flexible multi-parameter characterization within one integrated detection system and offers practical guidance for industrial non-destructive testing. Although demonstrated for pearlitic steels, the method can be adapted to other microstructural or mechanical parameters, showing strong potential for broader applications in structural health monitoring and process control.
{"title":"A micromagnetic feature–excitation mapping framework for separate non-destructive characterization of lamellar spacing and cluster size in pearlitic steel","authors":"Lin Wang , Xiucheng Liu , Shurui Zhang , Yangyang Zhang , Zhongqi Xu , Yang Yu","doi":"10.1016/j.ndteint.2026.103655","DOIUrl":"10.1016/j.ndteint.2026.103655","url":null,"abstract":"<div><div>Effective non-destructive evaluation of key microstructural features is essential for quality control and performance prediction of pearlitic steels. This study develops a micromagnetic feature-excitation mapping method to characterize lamellar spacing and cluster size using a single multifunctional sensor. Specimens with controlled microstructures-lamellar spacing and cluster size-were prepared and tested under varied excitation frequencies and amplitudes. Four types of magnetic signals were acquired, and 41 magnetic features were extracted. Analysis of linearity and sensitivity identified optimal feature–excitation combinations for independently evaluating lamellar spacing and cluster size. Two practical strategies are demonstrated: selecting different magnetic feature parameters under fixed excitation or adjusting excitation conditions for a single parameter. The proposed approach enables flexible multi-parameter characterization within one integrated detection system and offers practical guidance for industrial non-destructive testing. Although demonstrated for pearlitic steels, the method can be adapted to other microstructural or mechanical parameters, showing strong potential for broader applications in structural health monitoring and process control.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"160 ","pages":"Article 103655"},"PeriodicalIF":4.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-05-01Epub Date: 2026-01-17DOI: 10.1016/j.ndteint.2026.103652
Yue Hu, Lijun Sun, Huailei Cheng, Ruikang Yang
Back-calculating pavement layer moduli from deflections is a key technique for evaluating in-service pavement performance, yet its reliability often declines for pavements with limited asphalt surface layer thickness (typically less than 18 cm). Through mechanistic analysis, this study identifies insufficient interlayer coordination between the surface and underlying base layer as the primary cause. The significant modulus differences lead to discontinuous interlayer deformation, deviating from the full continuity assumption of conventional models. To resolve this, a method inspired by the partial-continuous interlayer modeling approach in multi-layer elastic theory was introduced. An interlayer stiffness coordination factor Kv was defined to quantify the degree of interlayer synergy, and this parameter was incorporated into the SimuAPSO back-calculation software. Using measured deflection data and laboratory dynamic modulus tests, Kv values were determined across various pavement structures. Regression analysis revealed asphalt layer thickness and surface temperature as the dominant influencing variables, and the developed predictive model demonstrated strong robustness and statistical stability. Results indicate that when Kv reaches 106 MPa/cm, the interface behaves as fully coordinated. Furthermore, Kv increases with both asphalt layer thickness and surface temperature, revealing the combined influence of structural and environmental factors on interlayer mechanical behavior. Finally, validation using Long-Term Pavement Performance (LTPP) database and measured data from a Chinese highway section shows that incorporating the interlayer stiffness coordination mechanism markedly enhances the accuracy and stability of back-calculated moduli for the pavements, providing a practical framework for improved pavement evaluation.
{"title":"Modulus back-calculation method for asphalt pavements with limited surface layer thickness based on interlayer stiffness coordination factors","authors":"Yue Hu, Lijun Sun, Huailei Cheng, Ruikang Yang","doi":"10.1016/j.ndteint.2026.103652","DOIUrl":"10.1016/j.ndteint.2026.103652","url":null,"abstract":"<div><div>Back-calculating pavement layer moduli from deflections is a key technique for evaluating in-service pavement performance, yet its reliability often declines for pavements with limited asphalt surface layer thickness (typically less than 18 cm). Through mechanistic analysis, this study identifies insufficient interlayer coordination between the surface and underlying base layer as the primary cause. The significant modulus differences lead to discontinuous interlayer deformation, deviating from the full continuity assumption of conventional models. To resolve this, a method inspired by the partial-continuous interlayer modeling approach in multi-layer elastic theory was introduced. An interlayer stiffness coordination factor <em>K</em><sub><em>v</em></sub> was defined to quantify the degree of interlayer synergy, and this parameter was incorporated into the SimuAPSO back-calculation software. Using measured deflection data and laboratory dynamic modulus tests, <em>K</em><sub><em>v</em></sub> values were determined across various pavement structures. Regression analysis revealed asphalt layer thickness and surface temperature as the dominant influencing variables, and the developed predictive model demonstrated strong robustness and statistical stability. Results indicate that when <em>K</em><sub><em>v</em></sub> reaches 10<sup>6</sup> MPa/cm, the interface behaves as fully coordinated. Furthermore, <em>K</em><sub><em>v</em></sub> increases with both asphalt layer thickness and surface temperature, revealing the combined influence of structural and environmental factors on interlayer mechanical behavior. Finally, validation using Long-Term Pavement Performance (LTPP) database and measured data from a Chinese highway section shows that incorporating the interlayer stiffness coordination mechanism markedly enhances the accuracy and stability of back-calculated moduli for the pavements, providing a practical framework for improved pavement evaluation.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"160 ","pages":"Article 103652"},"PeriodicalIF":4.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the laser ultrasonic system, the phase velocity of the guided wave propagating in composite structures can be conveniently extracted, exhibiting the potential for evaluating and predicting the fatigue state of composites. In this work, a condition-based fatigue prediction framework for composites is proposed based on the measurement of guided wave phase velocity. The framework incorporates the consideration of uncertainty factors and establishes a prediction and update method for fatigue evolution using the particle filter (PF) algorithm. Firstly, the state transition equation and the measurement equation are introduced to describe the process of fatigue evolution and observation. The state equation utilizes an empirical stiffness degradation model, while the measurement equation employs a Gaussian process regression (GPR) model to estimate the structural stiffness with the input of guided wave phase velocity. Subsequently, the PF algorithm is employed to integrate the measurement error and the inherent uncertainty of the stiffness degradation model. This enables the tracking of stiffness evolution and facilitates the update of stiffness degradation prediction based on the guided wave measurement. Finally, controlled fatigue tests are conducted in conjunction with in-situ guided wave measurements to validate the proposed condition-based prediction framework. The results demonstrate the effectiveness of utilizing guided wave phase velocity for fatigue characterization and validate the ability of the proposed framework to predict fatigue evolution.
{"title":"Condition-based prediction of fatigue evolution in composites utilizing the particle filter algorithm and laser ultrasonic technology","authors":"Yuxiang Huang, Chao Zhang, Boda Wang, Chongcong Tao, Hongli Ji, Jinhao Qiu","doi":"10.1016/j.ndteint.2026.103666","DOIUrl":"10.1016/j.ndteint.2026.103666","url":null,"abstract":"<div><div>With the laser ultrasonic system, the phase velocity of the guided wave propagating in composite structures can be conveniently extracted, exhibiting the potential for evaluating and predicting the fatigue state of composites. In this work, a condition-based fatigue prediction framework for composites is proposed based on the measurement of guided wave phase velocity. The framework incorporates the consideration of uncertainty factors and establishes a prediction and update method for fatigue evolution using the particle filter (PF) algorithm. Firstly, the state transition equation and the measurement equation are introduced to describe the process of fatigue evolution and observation. The state equation utilizes an empirical stiffness degradation model, while the measurement equation employs a Gaussian process regression (GPR) model to estimate the structural stiffness with the input of guided wave phase velocity. Subsequently, the PF algorithm is employed to integrate the measurement error and the inherent uncertainty of the stiffness degradation model. This enables the tracking of stiffness evolution and facilitates the update of stiffness degradation prediction based on the guided wave measurement. Finally, controlled fatigue tests are conducted in conjunction with in-situ guided wave measurements to validate the proposed condition-based prediction framework. The results demonstrate the effectiveness of utilizing guided wave phase velocity for fatigue characterization and validate the ability of the proposed framework to predict fatigue evolution.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"160 ","pages":"Article 103666"},"PeriodicalIF":4.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-05-01Epub Date: 2026-02-12DOI: 10.1016/j.ndteint.2026.103674
Hongseok Kim , Dooyoul Lee
Reliable inspection is essential for ensuring the safety of critical infrastructure, such as nuclear power plants. Field inspectors must be able to detect defects with a high probability of detection (POD) while minimizing the probability of false alarms. Therefore, establishing an appropriate detection threshold is crucial. Inspectors cannot make any decisions without a clearly defined threshold level. Traditionally, noise analysis has been used to determine this threshold. However, with the introduction of risk-based maintenance, considering overall operation costs has become necessary. Hence, we utilized Bayesian decision analysis to determine the optimal inspection threshold. We developed a straightforward yet effective method for representing POD curves. Additionally, we explored repeated inspection, a common but sometimes controversial technique aimed at improving inspection reliability. The proposed framework elucidates how thresholds should be learned, calibrated, and applied. We investigated how the choice of threshold can influence maintenance decisions based on nondestructive inspection results. By determining inspection thresholds through Bayesian decision analysis, our framework enables the optimization of inspection and maintenance planning.
{"title":"Bayesian decision analysis of nondestructive inspection threshold for structural reliability analysis","authors":"Hongseok Kim , Dooyoul Lee","doi":"10.1016/j.ndteint.2026.103674","DOIUrl":"10.1016/j.ndteint.2026.103674","url":null,"abstract":"<div><div>Reliable inspection is essential for ensuring the safety of critical infrastructure, such as nuclear power plants. Field inspectors must be able to detect defects with a high probability of detection (POD) while minimizing the probability of false alarms. Therefore, establishing an appropriate detection threshold is crucial. Inspectors cannot make any decisions without a clearly defined threshold level. Traditionally, noise analysis has been used to determine this threshold. However, with the introduction of risk-based maintenance, considering overall operation costs has become necessary. Hence, we utilized Bayesian decision analysis to determine the optimal inspection threshold. We developed a straightforward yet effective method for representing POD curves. Additionally, we explored repeated inspection, a common but sometimes controversial technique aimed at improving inspection reliability. The proposed framework elucidates how thresholds should be learned, calibrated, and applied. We investigated how the choice of threshold can influence maintenance decisions based on nondestructive inspection results. By determining inspection thresholds through Bayesian decision analysis, our framework enables the optimization of inspection and maintenance planning.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"160 ","pages":"Article 103674"},"PeriodicalIF":4.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-05-01Epub Date: 2026-01-09DOI: 10.1016/j.ndteint.2026.103640
Seyed Hamidreza Afzalimir, Parisa Shokouhi, Cliff J. Lissenden
Hydrogen embrittlement encompasses many material degradation mechanisms that lead to loss of ductility and brittle fracture. Ultrasound testing, as a structural integrity evaluation method, will be shown to detect diffused hydrogen. Cubic samples were extracted from a cold-drawn Al2024 bar and charged with hydrogen. Ultrasound testing was performed in the three principal directions of the cubic samples: L (longitudinal, parallel to the drawing direction that elongated the grains), T (long transverse), and S (short transverse), both before and after hydrogen charging. Linear ultrasound testing – specifically using the pulse-echo mode for wave speed and attenuation measurements – shows moderate sensitivity to hydrogen charging. Nonlinear ultrasound testing – specifically for second-harmonic generation (SHG) – exhibits high sensitivity to hydrogen charging with wave propagation in the L direction, moderate sensitivity in the T direction, and low sensitivity in the S direction. We interpret these SHG results with respect to recent predictions of the effect that solute H atoms near a grain boundary have on the acoustic nonlinearity parameter. Model results show that the acoustic nonlinearity parameter increases dramatically for waves parallel to the grain boundary. Moreover, the acoustic nonlinearity parameter is predicted to decrease modestly for ultrasonic waves normal to the grain boundary. The cold-drawn bar has many grain boundaries parallel to the L-direction, but relatively few parallel to the S-direction. Thus, the SHG results in the L- and S-directions correspond roughly to the waves parallel and normal, respectively, to the grain boundary in the model. This study improves our understanding of how nonlinear ultrasound testing can be applied effectively as a diagnostic tool to detect hydrogen embrittlement.
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Intelligent weld defect assessment is a growing research focus. However, existing methods overlook non-destructive testing (NDT) radiographic interpretation standards and defect formation mechanisms, leading to missed or false detections in low-contrast or blurred-boundary regions, and misclassification of defect types. This study proposes an artificial intelligence (AI)-based method for detecting pipeline girth weld defects, integrating NDT domain knowledge with data and learning algorithms. First, inspired by how human inspectors visually scan long-scale images locally and sequentially, a semi-overlapping sliding window strategy is designed to preprocess full-length images while preserving original information. Second, inspired by the dynamic film evaluation process, a defect detection model based on the You Only Look Once (YOLO)v8 architecture is proposed, incorporating multi-image decomposition, keyframe selection, and multi-image feature fusion strategies. Finally, by analyzing the formation mechanisms of weld defects, a classification rule set covering eight typical defect types is established to support final defect-type determination. Experimental results demonstrate that the proposed “NDT domain knowledge + data + AI” paradigm outperforms state-of-the-art approaches, particularly in detecting concave, porosity, and slag defects. In addition, it achieves 100 % recall in burn-through and crack detection. This study provides new insights and technical support for the future development of intelligent weld defect recognition systems.
焊缝缺陷智能评估是一个日益发展的研究热点。然而，现有的方法忽略了无损检测（NDT）射线成像解释标准和缺陷形成机制，导致在低对比度或模糊边界区域漏检或误检，以及缺陷类型的错误分类。本研究提出了一种基于人工智能（AI）的管道环焊缝缺陷检测方法，将无损检测领域知识与数据和学习算法相结合。首先，受人类检查员在局部和顺序上视觉扫描长尺度图像的启发，设计了半重叠滑动窗口策略，在保留原始信息的情况下对全长图像进行预处理。其次，受动态胶片评价过程的启发，提出了一种基于You Only Look Once (YOLO)v8架构的缺陷检测模型，该模型融合了多图像分解、关键帧选择和多图像特征融合策略。最后，通过分析焊接缺陷的形成机理，建立了涵盖八种典型缺陷类型的分类规则集，以支持最终缺陷类型的确定。实验结果表明，提出的“无损检测领域知识+数据+人工智能”模式优于当前的方法，特别是在检测凹、孔隙和渣缺陷方面。此外，它在烧透和裂纹检测中实现100%召回。该研究为未来智能焊缝缺陷识别系统的发展提供了新的见解和技术支持。
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