Ndt & E International最新文献_第3页

Fatigue damage detection and assessment of standard plate specimens via metal magnetic memory testing 基于金属磁记忆试验的标准板试件疲劳损伤检测与评定

IF 4.5 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International

Pub Date : 2026-01-23 DOI: 10.1016/j.ndteint.2026.103656

Jinbo Li , Nan Zhang , Yuling Zhang

Metal magnetic memory testing (MMMT) has demonstrated considerable potential for the identification and quantitative evaluation of hidden fatigue damage; however, the applicability of current damage evaluation indicators in actual structural inspections remains insufficiently explored. In this study, this issue was examined by designing two types of plate specimens to model the fatigue damage characteristics of orthotropic steel bridge decks. Prefabricated gaps were incorporated to simulate hidden fatigue damage in actual components, and the initial magnetic fields of the specimens were retained. The specimens were subjected to tensile‒tensile fatigue testing, and their surface magnetic fields were monitored online via a three-dimensional probe along predefined scanning paths. Digital image correlation was concurrently utilized on the opposite side of the specimens to verify the capability of the MMMT for fatigue damage detection and to evaluate the reliability of the fatigue life predictions. Analysis of the measured data revealed the limitations within the existing damage evaluation indicators, and new indicators of Mc, Div, and Curl were proposed. To minimize missed and false detections in the MMMT, a joint analysis of local contour maps for these indicators was conducted. By extracting the first-order longitudinal difference characteristic values of the proposed indicators and applying Bayes' theorem, a characteristic value database was established to assess the fatigue life of the specimens. The field detection from three fatigue designs in the orthotropic steel bridge deck of an in-service cable-stayed bridge indicated that the proposed MMMT-based scheme is highly efficacious for detecting the fatigue damage in the steel structures.

金属磁记忆检测（MMMT）在隐性疲劳损伤的识别和定量评价方面具有相当大的潜力；然而，目前的损伤评价指标在实际结构检测中的适用性还没有得到充分的探讨。在本研究中，通过设计两种类型的板试件来模拟正交各向异性钢桥面的疲劳损伤特征，对这一问题进行了研究。采用预制间隙模拟实际构件的隐性疲劳损伤，并保留试件的初始磁场。试样进行拉伸-拉伸疲劳试验，并通过三维探针沿预定扫描路径在线监测其表面磁场。同时利用数字图像相关技术在试件的反面验证了MMMT检测疲劳损伤的能力，并评估了疲劳寿命预测的可靠性。通过对实测数据的分析，发现了现有损伤评价指标的局限性，提出了Mc、Div、Curl等新的损伤评价指标。为了最大限度地减少MMMT中的漏检和误检，对这些指标的局部等高线图进行了联合分析。通过提取上述指标的一阶纵向差分特征值，应用贝叶斯定理，建立特征值数据库，对试件进行疲劳寿命评估。对某在役斜拉桥正交各向异性钢桥面三种疲劳设计的现场检测表明，基于mmmt的方案对钢结构的疲劳损伤检测是非常有效的。

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引用次数: 0

Monitoring Tensile-Induced Subsurface Damages of Woven Glass Fiber Reinforced Polymer Using Terahertz Time-of-Flight Tomography 利用太赫兹飞行时间层析成像技术监测编织玻璃纤维增强聚合物拉伸引起的亚表面损伤

IF 4.5 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International

Pub Date : 2026-01-22 DOI: 10.1016/j.ndteint.2026.103645

Min Zhai , Haoyue Pan , Bin Xiao , Haolian Shi , Zhang Qu , Wenlong He , Cong Zhai , Yi Tang

Woven Glass Fiber Reinforced Polymer (GFRP) composites were studied using terahertz time-of-flight tomography to characterize failure modes in GFRP composite in a nondestructive and contactless fashion during in-situ tensile testing. The fracture morphologies of GFRP composite under different applied stresses were discussed by comparing terahertz C-and B-scan images to evaluate the dynamic evolution of tensile-induced microstructure. Our results show that significant THz-detectable damage initiation was observed at stress levels exceeding 60 MPa. In addition, tensile-induced damage can be observed not only on the surface, but also within the inner piles of GFRP composites. Finally, our work verifies the effectiveness of THz-based approach on three-dimensional dynamic monitoring the quality of GFRP composite in service and evaluating the influence of different loading conditions on structural properties and failure pattern of composite materials.

利用太赫兹飞行时间层析成像技术，研究了编织玻璃纤维增强聚合物（GFRP）复合材料在现场拉伸测试中的无损和无接触方式的失效模式。通过比较太赫兹c扫描和b扫描图像，探讨了GFRP复合材料在不同外加应力下的断裂形貌，以评估拉伸诱导微观结构的动态演变。我们的研究结果表明，在超过60 MPa的应力水平下，观察到明显的太赫兹可探测的损伤起裂。此外，GFRP复合材料不仅在表面存在拉伸损伤，而且在桩内也存在拉伸损伤。最后，验证了基于太赫兹的GFRP复合材料在役质量三维动态监测方法的有效性，并评估了不同载荷条件对复合材料结构性能和破坏模式的影响。

引用次数: 0

A micromagnetic feature–excitation mapping framework for separate non-destructive characterization of lamellar spacing and cluster size in pearlitic steel 用于单独无损表征珠光体钢片层间距和团簇尺寸的微磁特征激发映射框架

IF 4.5 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International

Pub Date : 2026-01-21 DOI: 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.

对珠光体钢的关键组织特征进行有效的无损评价是质量控制和性能预测的基础。本研究开发了一种微磁特征激发映射方法，利用单个多功能传感器来表征片层间距和簇大小。制备了具有可控微观结构（片层间距和簇大小）的样品，并在不同的激发频率和振幅下进行了测试。采集了4类磁信号，提取了41个磁特征。通过线性和灵敏度分析，确定了独立评价片层间距和簇大小的最佳特征激励组合。论证了两种实用的策略：在固定励磁条件下选择不同的磁特征参数或在单一参数下调整励磁条件。所提出的方法能够在一个集成检测系统中实现灵活的多参数表征，并为工业无损检测提供实用指导。虽然该方法仅适用于珠光体钢，但也适用于其他微观结构或力学参数，在结构健康监测和过程控制方面具有更广泛的应用潜力。

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引用次数: 0

A novel non-destructive testing method for the uniaxial compressive strength of cemented paste backfill based on hyperspectral imaging and artificial intelligence 基于高光谱成像和人工智能的胶结膏体充填体单轴抗压强度无损检测新方法

IF 4.5 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International

Pub Date : 2026-01-19 DOI: 10.1016/j.ndteint.2026.103654

Qing Na , Qiusong Chen , Daolin Wang , Jilong Pan , Yunbo Tao , Aixiang Wu

Accurate and rapid evaluation of the uniaxial compressive strength (UCS) of cemented paste backfill (CPB) is crucial for ensuring safe and efficient underground mining. Traditional UCS tests, however, are contact-based, destructive and time-consuming, which severely limits real-time UCS monitoring and rapid feedback for in-situ backfill quality control. To overcome these limitations, this study innovatively introduces hyperspectral imaging (HSI) technology into the real-time, in-situ UCS monitoring, establishing a non-destructive testing (NDT) method by integrating HSI with artificial intelligence. A total of 120 CPB groups with five mass concentrations (61–73 %) and eight curing ages (3–28 d) were tested to obtain both UCS and corresponding hyperspectral data. The spectral response characteristics of the CPB under varying concentrations were analyzed, revealing that the reflectance gradually increased with concentration, and two distinct absorption peaks were observed near 1400 nm and 1950 nm. Two-dimensional correlation spectroscopy indicated that, under concentration interference, the spectral sensitivity was highest at 1900 nm and lowest at 1600 nm. Subsequently, the effect of various spectral preprocessing techniques and feature extraction algorithms on UCS prediction accuracy was investigated using the PSO-SVM-Bagging algorithm. The results demonstrated that the 2nd D-SG-Nor + UVE model exhibited the best performance, with R_p² = 0.9487 and RPD = 4.4132. The three most important bands for UCS prediciton were identified as 1855.24 nm, 1240.29 nm, and 1589.91 nm respectively. Finally, comparison between the PSO-SVM-Bagging and CNN-LSTM algorithms revealed that the PSO-SVM-Bagging approach presented superior accuracy and generalization ability. This study validates the feasibility and scientific merit of applying HSI-based intelligent modeling as a NDT method for the UCS of CPB, providing a practical pathway for real-time monitoring, on-site feedback, and intelligent regulation of backfill performance in underground mining.

准确、快速地评价胶结膏体充填体的单轴抗压强度是保证地下矿山安全高效开采的关键。然而，传统的UCS测试是基于接触的、破坏性的和耗时的，这严重限制了对UCS的实时监测和对原位充填体质量控制的快速反馈。为了克服这些局限性，本研究创新性地将高光谱成像（HSI）技术引入到实时、原位UCS监测中，将高光谱成像与人工智能相结合，建立了一种无损检测（NDT）方法。共测试了120个CPB组，5种质量浓度（61 - 73%）和8种固化年龄（3-28 d），以获得UCS和相应的高光谱数据。对CPB在不同浓度下的光谱响应特性进行了分析，发现随着浓度的增加，反射率逐渐增大，在1400 nm和1950 nm附近有两个明显的吸收峰。二维相关光谱分析表明，在浓度干扰下，光谱灵敏度在1900 nm处最高，在1600 nm处最低。随后，利用PSO-SVM-Bagging算法研究了各种光谱预处理技术和特征提取算法对UCS预测精度的影响。结果表明，第2代D-SG-Nor + UVE模型表现最佳，Rp2 = 0.9487, RPD = 4.4132。对UCS预测最重要的三个波段分别为1855.24 nm、1240.29 nm和1589.91 nm。最后，将PSO-SVM-Bagging方法与CNN-LSTM算法进行比较，发现PSO-SVM-Bagging方法具有更好的准确率和泛化能力。本研究验证了将基于hsi的智能建模作为CPB单轴充填体无损检测方法的可行性和科学价值，为地下采矿充填体性能的实时监测、现场反馈和智能调控提供了一条实用途径。

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引用次数: 0

An improved time integrated energy method for imaging extended defect in multilayer composites 多层复合材料扩展缺陷成像的改进时间积分能量法

IF 4.5 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International

Pub Date : 2026-01-17 DOI: 10.1016/j.ndteint.2026.103644

Kang An, Changyou Li

Microwave time reversal based on time integrated energy method (TIEM) was used for the detection of extended defects through combining time-resolved information from multiple sources. However, the wrong localization problem caused by the strong reflection from metal still appears when it is applied for non-destructive testing of multilayer composites backed by metal. In this paper, an improved TIEM (ITIEM) is proposed by properly combining TIEM and the time constraint information obtained from target initial reflection method to overcome the wrong localization problem and ensure the correct localization. Then, microwave time reversal with multiple sources based on ITIEM (ITIEM-MS-MTR) is proposed for the detection of extended cracks with different shapes, such as “V”-shaped crack and “W”-shaped crack. Its effectiveness and noise tolerance is proved through multiple investigations in two-dimensional cases. Furthermore, the proposed ITIEM-MS-MTR is investigated in the detection of extended defect in the multilayer composite skin of an aircraft wing model, and its effectiveness and noise tolerance is finally validated in 2D and 3D cases.

基于时间积分能量法（TIEM）的微波时间反演，结合多源时间分辨信息，对扩展缺陷进行检测。然而，在应用于金属背衬多层复合材料的无损检测时，由于金属的强反射，仍然存在定位错误的问题。本文将TIEM与目标初始反射法获得的时间约束信息合理结合，提出了一种改进的TIEM (ITIEM)，克服了错误定位问题，保证了正确定位。然后，提出了基于ITIEM的多源微波时间反演方法（ITIEM- ms - mtr），用于“V”型裂纹和“W”型裂纹等不同形状扩展裂纹的检测。通过对二维情况的多次研究，证明了该方法的有效性和耐噪性。最后，将所提出的ITIEM-MS-MTR方法应用于某飞机机翼模型多层复合材料蒙皮扩展缺陷的检测，并在二维和三维实例中验证了其有效性和噪声容限。

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引用次数: 0

Modulus back-calculation method for asphalt pavements with limited surface layer thickness based on interlayer stiffness coordination factors 基于层间刚度协调因子的有限面层厚度沥青路面模量反计算方法

IF 4.5 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International

Pub Date : 2026-01-17 DOI: 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 K_v 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, K_v 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 K_v reaches 10⁶ MPa/cm, the interface behaves as fully coordinated. Furthermore, K_v 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.

根据挠度反向计算路面层模量是评估在用路面性能的关键技术，但对于沥青面层厚度有限（通常小于18厘米）的路面，其可靠性往往会下降。通过机理分析，本研究确定地表与下伏基层层间协调不足是主要原因。显著的模量差异导致层间变形不连续，偏离了传统模型的完全连续性假设。为了解决这一问题，引入了一种受多层弹性理论中部分连续层间建模方法启发的方法。定义层间刚度协调系数Kv来量化层间协同程度，并将该参数纳入SimuAPSO反算软件。利用实测挠度数据和室内动模量试验，确定了不同路面结构的Kv值。回归分析表明，沥青层厚度和地表温度是主要影响变量，所建立的预测模型具有较强的稳健性和统计稳定性。结果表明：当Kv达到106 MPa/cm时，界面表现为完全协调；Kv随沥青层厚度和表面温度的增加而增大，揭示了结构和环境因素对层间力学行为的综合影响。最后，利用长期路面性能（LTPP）数据库和中国某路段实测数据进行验证，结果表明，引入层间刚度协调机制显著提高了路面反算模量的准确性和稳定性，为改进路面评价提供了实用框架。

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引用次数: 0

Delamination detection in composite laminates using Lamb wave tomographic method based on sparse and probabilistic reconstruction 基于稀疏重建和概率重建的Lamb波层析检测复合材料层合板的分层检测

IF 4.5 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International

Pub Date : 2026-01-16 DOI: 10.1016/j.ndteint.2026.103650

Tong Tong , Wan Qu , Jiadong Hua , Daogui Chen , Jinghan Tan , Jing Lin

Composite materials are widely employed in many industrial fields, and transmitted Lamb wave-based methods, represented by tomography, have been widely utilized for delamination detection in composite laminates. Nevertheless, conventional Lamb wave tomography may suffer from large artifacts and other problems. To break these limitations, a Lamb wave tomographic method based on sparse and probabilistic reconstruction for delamination detection in composite laminates is proposed in this study. Firstly, Lamb wave propagation in delaminated laminates is analyzed, from which it can be derived that delamination can cause the time-of-flight (ToF) delay of A0 mode. Then, differences in ToF between intact and delaminated laminates are calculated and constitute the time difference vector, which can be represented by the product of the length matrix and the slowness difference vector. Since the delamination distribution is sparse, the slowness difference vector satisfies the sparse assumption, which indicates that it can be solved with sparse reconstruction techniques. Furthermore, to improve the quality of sparse reconstruction, the probability distribution is introduced as a prior weight during the solving procedure. Finally, numerical and experimental investigations are implemented. The imaging results can provide a more precise estimation of delamination size and location, which demonstrates the performance improvement of the presented approach.

复合材料广泛应用于许多工业领域，以层析成像为代表的基于透射兰姆波的方法已广泛用于复合材料层合板的分层检测。然而，传统的兰姆波断层扫描可能存在较大的伪影和其他问题。为了突破这些局限性，本文提出了一种基于稀疏重建和概率重建的Lamb波层析检测方法。首先，分析了Lamb波在分层层压板中的传播，推导出分层会导致A0模式的飞行时间延迟。然后，计算完整层合板和分层层合板之间的ToF差，并构成时间差向量，该时间差向量可以用长度矩阵和慢度差向量的乘积表示。由于分层分布是稀疏的，因此慢度差向量满足稀疏假设，可以用稀疏重建技术求解。此外，为了提高稀疏重建的质量，在求解过程中引入了概率分布作为先验权值。最后，进行了数值和实验研究。成像结果可以更精确地估计分层的大小和位置，这表明了该方法的性能改进。

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引用次数: 0

The methodology of defect thermal characterization in pulsed thermal NDT based on 3D numerical solutions and polynomial approximation 基于三维数值解和多项式近似的脉冲热无损检测缺陷热表征方法

IF 4.5 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International

Pub Date : 2026-01-14 DOI: 10.1016/j.ndteint.2026.103639

Vladimir Vavilov, Arsenii Chulkov, Olesia Ganina, Marina Kuimova, Oleg Makushev

This study presents a comprehensive methodology for characterizing air-filled finite-size defects in materials with varying thermal properties using pulsed thermal nondestructive testing (TNDT). We numerically solve the three-dimensional heat transfer problem for 729 test cases encompassing defects with different lateral dimensions, depths, and thicknesses in both metallic and non-metallic materials. The analysis yields maximum temperature contrasts and their corresponding observation times, while investigating the influence of defect geometry on thermal signatures. An analytical expression for predicting observation times is derived to complement the numerical results.

The computational results are fitted with polynomial functions to enable rapid estimation of optimal TNDT parameters. This approach provides a practical framework for evaluating detection limits across a wide range of material properties and defect geometries. System-wide analysis reveals mean errors of 60 % for temperature contrast evaluation and 36 % for determination of observation times. Experimental validation using reference samples demonstrates measurement accuracies of 14–35 % for temperature contrasts and 2–8 % for observation times. The proposed inverse solution achieves particularly accurate depth characterization (<14 % error), though thickness estimation shows greater variability (up to 61 % error).

本研究提出了一种综合的方法，用于表征具有不同热性能的材料中的充气有限尺寸缺陷，使用脉冲热无损检测（TNDT）。我们对729个测试用例的三维传热问题进行了数值求解，这些测试用例包括金属和非金属材料中具有不同横向尺寸、深度和厚度的缺陷。分析得出了最大温度对比和相应的观察时间，同时研究了缺陷几何形状对热特征的影响。推导了预测观测次数的解析表达式，以补充数值结果。计算结果用多项式函数拟合，以便快速估计最优TNDT参数。这种方法为评估各种材料特性和缺陷几何形状的检测极限提供了一个实用的框架。全系统分析显示，温度对比评估的平均误差为60%，确定观测时间的平均误差为36%。使用参考样品的实验验证表明，温度对比的测量精度为14 - 35%，观察时间的测量精度为2 - 8%。所提出的反解实现了特别精确的深度表征（误差为14%），尽管厚度估计显示出更大的可变性（误差高达61%）。

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引用次数: 0

Method and application of data conversion between modulated and flash thermal imaging 调制热成像与闪光热成像数据转换方法及应用

IF 4.5 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International

Pub Date : 2026-01-12 DOI: 10.1016/j.ndteint.2026.103648

Wenyi Xu, Jing Yu, Yuxin Chang, Ruiyuan Niu, Guanglei Zhu, Ning Tao, Jiangang Sun

In this study, a data conversion method between modulated thermal imaging and flash thermal imaging is derived theoretically and demonstrated experimentally. The method allows for modulated data acquired at one frequency to be forwardly converted to a full flash data which can then be backwardly converted to a modulated data at a different frequency. The experimental demonstrations were carried out using a glass fiber reinforced plastic (GFRP) plate sample that contains flat bottom holes located at various depths. From a forward conversion of measured modulated data, the converted flash data was processed for defect detection by using the thermal effusivity tomography method and the results were compared with the corresponding ones obtained from a flash experiment on the same sample. In addition, backward conversions from the converted flash data to new sets of modulated data at various other frequencies were demonstrated and verified. The results show that this data-conversion method can address the detection of subsurface defects within different depths, which will eradicate the blind-frequency problem and eliminate the need for performing multiple tests with different modulation frequencies.

本文从理论上推导了一种调制热成像与闪烁热成像之间的数据转换方法，并进行了实验验证。所述方法允许在一个频率上获取的调制数据向前转换为完整的闪存数据，该闪存数据随后可向后转换为不同频率上的调制数据。实验演示使用玻璃纤维增强塑料（GFRP）板样品，其中包含位于不同深度的平底孔。对测量的调制数据进行正演转换，利用热溢率层析成像方法对转换后的闪光数据进行缺陷检测，并与同一样品的闪光实验结果进行比较。此外，还演示并验证了从转换后的闪存数据到各种其他频率的新调制数据集的反向转换。结果表明，该数据转换方法可以解决不同深度的地下缺陷检测问题，消除了频率盲问题，避免了使用不同调制频率进行多次测试的需要。

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引用次数: 0

Regularized expectation-maximization clustering enhanced laser ultrasonic imaging for defects in laser additively manufactured components with high surface roughness 正则化期望最大化聚类增强激光超声成像对高表面粗糙度激光增材制造部件缺陷的影响

IF 4.5 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International

Pub Date : 2026-01-12 DOI: 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。

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引用次数: 0