Ndt & E International最新文献_第4页

Contour guided-deep radon prior: A robust unsupervised framework for limited-angle CT inspection of oil and gas pipelines 轮廓引导深氡先验：用于石油和天然气管道有限角度CT检查的鲁棒无监督框架

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.2026.103647

Jintao Fu, Tianchen Zeng, Jiahao Chang, Peng Cong, Ximing Liu, Yuewen Sun

Oil and gas pipelines serve as critical global energy infrastructure, where structural integrity is paramount for ensuring energy security and preventing catastrophic accidents. However, in-service pipelines operating in harsh environments present significant challenges for non-destructive testing due to severely constrained inspection spaces. Limited-angle computed tomography (CT) has emerged as a useful method for detecting pipeline defects, but incomplete projection data leads to severe reconstruction artifacts when using conventional algorithms, substantially compromising defect detection accuracy. While unsupervised deep learning methods show promise without requiring paired training data, existing approaches primarily rely on implicit network priors, making it difficult to guarantee geometric fidelity of reconstructed structures. To address this challenge, this study proposes a novel Contour Guided-Deep Radon Prior (CG-DRP) unsupervised reconstruction framework. The key innovation incorporates known geometric contours of pipeline structures as explicit physical constraints deeply integrated into the Deep Radon Prior (DRP) optimization process, achieving optimal fusion of physical prior accuracy and unsupervised learning flexibility. The framework additionally incorporates Convolutional Block Attention Module (CBAM) to enhance feature extraction capabilities. Experimental validation using simulated and real pipeline data under 90°and 120°limited-angle conditions demonstrates that CG-DRP comprehensively outperforms traditional algorithms (FBP, SART, ADMM-TV) and advanced unsupervised methods (DIP, RBP-DIP, DRP). Reconstructed images achieve optimal PSNR and SSIM performance, effectively suppressing artifacts while preserving structural details and minor defects. The research confirms CG-DRP’s robustness and superiority, providing an efficient solution for industrial CT applications in pipeline integrity assessment.

油气管道是全球重要的能源基础设施，其结构完整性对于确保能源安全和防止灾难性事故至关重要。然而，由于检测空间严重受限，在役管道在恶劣环境中运行，对无损检测提出了重大挑战。有限角度计算机断层扫描（CT）已成为检测管道缺陷的一种有用方法，但在使用传统算法时，不完整的投影数据会导致严重的重建伪影，从而大大降低缺陷检测的准确性。虽然无监督深度学习方法不需要配对训练数据，但现有方法主要依赖隐式网络先验，难以保证重建结构的几何保真度。为了解决这一挑战，本研究提出了一种新的轮廓引导-深度氡先验（CG-DRP）无监督重建框架。关键创新是将已知的管道结构几何轮廓作为明确的物理约束深度集成到Deep Radon Prior （DRP）优化过程中，实现物理先验精度和无监督学习灵活性的最佳融合。该框架还结合了卷积块注意模块（CBAM）来增强特征提取能力。利用90°和120°受限角度条件下的模拟和真实管道数据进行的实验验证表明，CG-DRP综合优于传统算法（FBP、SART、ADMM-TV）和先进的无监督方法（DIP、RBP-DIP、DRP）。重建后的图像具有最佳的PSNR和SSIM性能，在保留结构细节和微小缺陷的同时有效地抑制了伪影。研究证实了CG-DRP的鲁棒性和优越性，为工业CT在管道完整性评估中的应用提供了有效的解决方案。

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引用次数: 0

RF antenna array for contactless structural health monitoring: Ultrasonic benchmarking and application to airfoil structure 用于非接触式结构健康监测的射频天线阵列：超声基准测试及其在翼型结构中的应用

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

Ndt & E International

Pub Date : 2026-01-09 DOI: 10.1016/j.ndteint.2026.103643

Deepak Kumar , Yogesh Kumar Yadav , Sahil Kalra , Prabhat Munshi

Sensors are the primary component in the study of health monitoring of various structures. Numerous cutting-edge smart sensors have been utilized to improve monitoring technologies, however, the necessity to patch them to the structure in close contact still creates major complications in their actual deployment. Moreover, the contact sensors add a mass penalty to the structural element, causing a challenge for thin and flexible structures. In this paper, we introduce a contactless approach for damage localization in metallic plates using ultra-wide-band (UWB) antennas to overcome the limitations of contact-based approaches. The UWB antenna array is placed at a distance from the structure and is used to transmit and receive electromagnetic (EM) waves in the radio frequency (RF) range. Additionally, an imaging algorithm is developed to locate the damage in the structure. The simulation and experimental results demonstrate that the algorithm accurately estimates the damage locations. Furthermore, the estimated results of the proposed RF-based approach are comparatively validated with the existing ultrasonic sensor-based contact approach. Our simulation and experimental results show that both techniques (ultrasonic and RF) have a par accuracy of 99.93% for damage localization with respect to actual damage locations. The comparative study confirms that the UWB antennas are equally efficient in multi-damage localization in metallic plates, with the additional advantage of eliminating sensor patching onto the structure. This leads to the conviction that the UWB antennas are a novel addition to contactless SHM for various metallic structures. The technique is further extended to non-metallic airfoil structures for damage localization, and the computed accuracy of located damage is 95.1% with respect to actual damage location.

传感器是各种结构健康监测研究的主要组成部分。许多尖端智能传感器已被用于改进监测技术，然而，需要将它们贴合到密切接触的结构上，这在实际部署中仍然造成了很大的复杂性。此外，接触式传感器增加了结构元件的质量损失，对薄而灵活的结构造成了挑战。本文介绍了一种利用超宽带天线进行金属板损伤定位的非接触式方法，克服了基于接触式方法的局限性。超宽带天线阵列放置在离建筑物一定距离的位置，用于发射和接收射频（RF）范围内的电磁波。此外，还开发了一种成像算法来定位结构中的损伤。仿真和实验结果表明，该算法能够准确地估计出损伤位置。此外，将基于射频的估计结果与现有的基于超声传感器的接触方法进行了比较验证。我们的仿真和实验结果表明，两种技术（超声波和射频）相对于实际损伤位置的损伤定位精度均达到99.93%。对比研究证实，超宽带天线在金属板的多损伤定位中同样有效，并且具有消除传感器贴片到结构上的额外优势。这使我们确信，超宽带天线是用于各种金属结构的非接触式SHM的新补充。将该技术进一步推广到非金属翼型结构的损伤定位中，损伤定位计算精度为实际损伤定位的95.1%。

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引用次数: 0

Nonlinear ultrasound to detect hydrogen embrittlement in Al2024 非线性超声检测Al2024中氢脆

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

Ndt & E International

Pub Date : 2026-01-09 DOI: 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.

氢脆包括许多材料退化机制，导致延性损失和脆性断裂。超声检测，作为一种结构完整性评估方法，将显示检测扩散氢。从冷拔Al2024棒材中提取立方样品并充氢。在充氢前和充氢后，分别对立方体样品进行了三个主要方向的超声检测：L（纵向，平行于拉长晶粒的拉伸方向）、T（长横向）和S（短横向）。线性超声测试-特别是使用脉冲回波模式进行波速和衰减测量-显示出对氢气充注的中等灵敏度。非线性超声检测-特别是二次谐波产生(SHG) -对氢在L方向上传播的充氢具有高灵敏度，在T方向上具有中等灵敏度，在S方向上具有低灵敏度。我们根据最近对晶界附近溶质H原子对声学非线性参数的影响的预测来解释这些SHG结果。模型结果表明，平行于晶界的声波非线性参数显著增大。此外，预测声非线性参数在垂直于晶界的超声波中略有减小。冷拔棒材平行于l方向的晶界较多，平行于s方向的晶界较少。因此，L方向和s方向的SHG结果大致对应于模型中晶界平行波和法向波。这项研究提高了我们对非线性超声检测如何有效地应用于检测氢脆的诊断工具的理解。

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引用次数: 0

A fast numerical method for low-power vibrothermography nondestructive testing of fatigue cracks 疲劳裂纹小功率振动热成像无损检测的快速数值方法

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

Ndt & E International

Pub Date : 2026-01-08 DOI: 10.1016/j.ndteint.2026.103642

Yang Yang , Bofan Liu , Zongfei Tong , Hong-En Chen , Jianguo Zhu , Cuixiang Pei , Shejuan Xie , Hao Su , Zhenmao Chen

Fatigue crack is a typical defect initiated in key engineering structures under dynamic loads. The propagation of fatigue cracks would significantly shorten the structural service life and even cause serious accidents. The vibrothermography (VT), as a promising non-destructive testing (NDT) technique, presents great potential for fatigue crack inspection due to its internal heating mode and applicable for both metallic and nonmetallic materials. However, the multi-parameters optimization and agent model building of VT system put forward higher requirements of an efficient numerical simulation technique for VT signals. In this paper, a fast numerical method for low-power VT under high frequency excitation is proposed and validated. For efficient simulation of dynamic displacement, the element birth and death method is utilized to adjust the coefficient matrix of finite element based on the contact or separation state of crack surface. This method can cope with the complex nonlinear phenomenon of crack closing properly while maintaining computational feasibility during vibration analysis. For the simulation of temperature field of VT, the energy equivalent method proposed by authors is employed to address the efficiency problem of the direct time domain integration for the high-frequency excitation. By linearizing the heat source, the present method can reduce computational burden while preserving numerical accuracy, enabling efficient simulation of the thermal field during VT process. Finally, the proposed method is validated via numerical simulations and experiments which show that the method is over six times faster than the commercial software but with a comparablenumerical precision.

疲劳裂纹是关键工程结构在动荷载作用下产生的一种典型缺陷。疲劳裂纹的扩展会大大缩短结构的使用寿命，甚至造成严重的事故。振动热像仪（VT）作为一种很有前途的无损检测技术，由于其内部加热的方式，在检测金属和非金属材料的疲劳裂纹方面具有很大的潜力。然而，VT系统的多参数优化和智能体模型的建立对VT信号的高效数值仿真技术提出了更高的要求。本文提出并验证了高频激励下小功率VT的快速数值计算方法。为了有效地模拟动态位移，采用单元生死法根据裂纹表面的接触或分离状态调整有限元系数矩阵。该方法能较好地处理复杂的非线性裂纹闭合现象，同时保持振动分析的计算可行性。对于VT温度场的仿真，采用了作者提出的能量等效法解决了高频激励直接时域积分的效率问题。该方法通过对热源进行线性化处理，在保证数值精度的同时减少了计算量，实现了VT过程热场的高效模拟。最后，通过数值模拟和实验验证了该方法的有效性，结果表明，该方法的计算速度比商业软件快6倍以上，且具有相当的数值精度。

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引用次数: 0

Infrared thermography coupled with deep learning for fast and reliable predictive monitoring of lubricating oils in dual-use heavy-duty vehicles 红外热成像与深度学习相结合，用于两用重型车辆润滑油的快速可靠预测监测

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

Ndt & E International

Pub Date : 2026-01-07 DOI: 10.1016/j.ndteint.2026.103638

Alicia Ortiz-Chiliquinga , Fernando Moreno-Haya , Carlos López-Pingarrón , Carlos Jesús Vega-Vera , José S. Torrecilla

The reliable evaluation of lubricating oil condition is critical for ensuring the safety and operational efficiency of heavy-duty equipment in both civilian and defense sectors. Conventional laboratory-based physicochemical analyses, although effective, are inherently time-consuming and do not enable real-time diagnostics or on-site decision-making. In this work, we introduce an innovative approach that leverages infrared thermography coupled with deep learning to achieve rapid, non-destructive, and fully automated classification of lubricating oil samples as either “compliant” (fit for use) or “non-compliant” (unfit for use). The study focuses on two reference lubricants (O-1178 (5W30), gearbox oil and O-1236 (15W40), engine oil) widely deployed in military vehicles, with ground-truth class labels established via standardized laboratory protocols. A comprehensive dataset of over 10,000 thermographic images was generated through controlled cooling cycles, providing the foundation for model development. After comparative analysis of several state-of-the-art convolutional neural network architectures, ResNet-34 and ResNet-50 were selected for their superior performance. The models, trained and validated on stratified and balanced datasets, consistently achieved classification accuracies above 99 %, with the ResNet-34 model delivering 100 % sensitivity and specificity for the detection of non-compliant samples in both oil types. Complementary metrics, including ROC/AUC (≈1.0) and F1-scores near unity, together with stable training–validation loss convergence, confirmed that the classifiers operated in a saturated performance regime with robust generalization. Interpretation with Grad-CAM heatmaps revealed that the model's decisions are grounded in physically meaningful thermal micropatterns directly linked to lubricant degradation. This strategy not only minimizes unnecessary oil changes and associated environmental impact, but also elevates predictive maintenance capabilities by enabling immediate, reliable diagnostics in dual-use (civil and military) settings. The proposed methodology establishes a robust and versatile framework for advanced lubricant condition monitoring, readily adaptable to other industrial fluids and diverse operational scenarios requiring rapid, on-site assessment. Future work will extend this framework to additional lubricant types and broader real-world conditions to further consolidate these findings.

润滑油状态的可靠评估对于确保民用和国防重型设备的安全和运行效率至关重要。传统的基于实验室的物理化学分析虽然有效，但本质上是耗时的，不能实现实时诊断或现场决策。在这项工作中，我们引入了一种创新的方法，利用红外热成像和深度学习来实现对润滑油样品的快速、无损和全自动分类，将其分为“符合”（适合使用）或“不符合”（不适合使用）。该研究的重点是两种参考润滑油(O-1178 （5W30)，变速箱油和O-1236 (15W40)，发动机油）广泛应用于军用车辆，并通过标准化实验室协议建立了地面真实等级标签。通过控制冷却循环生成了超过10,000张热成像图像的综合数据集，为模型开发提供了基础。经过对几种最先进的卷积神经网络架构的比较分析，我们选择了ResNet-34和ResNet-50，因为它们的性能更优越。经过分层和平衡数据集的训练和验证，该模型的分类准确率始终高于99%，其中ResNet-34模型在检测两种油类型的不合规样品时具有100%的灵敏度和特异性。互补指标，包括ROC/AUC（≈1.0）和f1分数接近统一，以及稳定的训练-验证损失收敛，证实了分类器在具有鲁棒泛化的饱和性能状态下运行。对Grad-CAM热图的解释表明，该模型的决策是基于与润滑剂降解直接相关的有物理意义的热微模式。该策略不仅最大限度地减少了不必要的换油和相关的环境影响，而且通过在军民两用环境中实现即时、可靠的诊断，提高了预测性维护能力。所提出的方法为先进的润滑油状态监测建立了一个强大而通用的框架，易于适应其他工业流体和需要快速现场评估的各种操作场景。未来的工作将把这个框架扩展到更多的润滑剂类型和更广泛的现实条件，以进一步巩固这些发现。

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引用次数: 0

Features extraction for characterizing partial-circumferential pipe wall thinning using TM01 mode microwaves 基于TM01模式微波的部分周向管壁减薄特征提取

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

Ndt & E International

Pub Date : 2026-01-07 DOI: 10.1016/j.ndteint.2026.103641

Weiying Cheng

In the inspection of partial-circumferential pipe wall thinning (PCPWT) using

{T M}_{01}

mode microwaves, higher-order mode microwaves are excited at various frequencies when the primary

{T M}_{01}

mode interacts with the thinning region. This alters the reflection of

{T M}_{01}

mode waves, and consequently affects the

S_{11}

signals. The behavior of

S_{11}

signals varies across different frequency ranges. Therefore, in this study, we analyzed the signals within specific corresponding frequency bands: (1) low frequencies, where higher-order modes have not yet been generated; (2) intermediate frequencies, where the

{T E}_{21}

mode is excited but the

{T M}_{11}

mode is not yet; and (3) higher frequencies, where both the

{T E}_{21}

and

{T M}_{11}

mode are excited. In the signal analysis, Singular Spectral Analysis (SSA) was employed to decompose the simulated

S_{11}

signal into two components: a slowly oscillating component - exhibiting beating patterns particularly in the low-frequency range - and a residual component, characterized by irregular oscillation attributed to higher-order modes, especially at intermediate and higher frequencies. The results showed that both the thinning thickness and circumferential extent can be characterized using features derived from the two components. In the experimental study, a variety of signal processing techniques have been applied to measurement signals, which include reflections other than from the PCPWT. By using SSA and transforming the measurement signals across various domains – namely, frequency, spatial, and

Ω

- domains – signals most strongly associated with PWT were successfully extracted. These signals exhibited features consistent with simulation results, validating their potential for characterizing higher order modes and, consequently, PCPWT.

在利用TM01模式微波检测部分周向管壁减薄（PCPWT）时，当主TM01模式与减薄区域相互作用时，激发不同频率的高阶模式微波。这改变了TM01模式波的反射，从而影响了S11信号。S11信号的行为在不同的频率范围内是不同的。因此，在本研究中，我们对特定对应频段内的信号进行了分析：(1)低频，其中尚未产生高阶模态；(2)中频，TE21模式被激发，但TM11模式尚未被激发；(3)更高频率，其中TE21和TM11模式都被激发。在信号分析中，使用奇异谱分析（SSA）将模拟的S11信号分解为两个分量：缓慢振荡分量（特别是在低频范围内表现出跳动模式）和残差分量（特别是在中频和高频范围内表现出高阶模态的不规则振荡）。结果表明，利用这两个分量的特征可以表征薄化厚度和周向程度。在实验研究中，多种信号处理技术已被应用于测量信号，其中包括来自PCPWT以外的反射。通过对测量信号在不同域（即频率域、空间域和Ω域）的变换，成功地提取了与PWT最密切相关的信号。这些信号显示出与仿真结果一致的特征，验证了它们表征高阶模式的潜力，从而验证了PCPWT。

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引用次数: 0

Assessment of out-of-plane ply waviness in carbon-fibre reinforced plastics: Comparing different non-destructive evaluation modalities 碳纤维增强塑料的面外铺层波纹度评价：不同无损评价方法的比较

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

Ndt & E International

Pub Date : 2026-01-02 DOI: 10.1016/j.ndteint.2026.103630

Rylan C.V.V. Gomes , Ehsan Mohseni , Vedran Tunukovic , Vincent Maes , Matteo Contino , S. Gareth Pierce , Kenneth Burnham , Randika K.W. Vithanage , Charles N. Macleod , Gavin Munro , Tom O'Hare

Out-of-plane waviness (ply wrinkles) reduces tensile and compressive strength in Carbon Fibre Reinforced Polymers (CFRPs), with maximum out-of-plane ply angle governing failure mechanisms. This study comparatively evaluates three Non-Destructive Evaluation (NDE) techniques: Eddy Current Array Testing (ECAT), Phased Array Ultrasonic Testing (PAUT) and X-ray Digital Tomosynthesis (DT) for detecting and characterising ply wrinkles across three parameters: amplitude, wavelength, and maximum out-of-plane ply angle. Nine unidirectional CFRP coupons containing induced ply wrinkles of controlled amplitudes (0.13–1.31 mm) were inspected, addressing a critical gap in comparative NDE performance for sub-2 mm amplitude defects in thin laminates.

PAUT achieved the highest overall characterisation success rate of 96.3 % (26/27 measurements) and a detection success rate of 88.9 % (8/9 samples). Critically, PAUT achieved 100 % success in characterising maximum out-of-plane ply angle - the parameter governing compressive/tensile failure across all samples, including the lowest amplitude wrinkle (0.13 mm). However, systematic overestimation in wrinkle amplitude characterisation occurred (+55.3 % mean percentage error). ECAT achieved an equivalent 88.9 % detection success and 33.3 % characterisation success, successfully measuring wrinkle wavelength (100 %) but unable to quantify wrinkle amplitude or out-of-plane ply angle from complex impedance data alone, positioning it as a rapid automated screening tool. X-ray DT achieved 88.9 % detection and characterisation success, with moderate overestimation in wrinkle amplitude characterisation (+24.8 %). However, complete detection and characterisation failure occurred on the lowest amplitude ply wrinkle.

A critical finding establishes that reliable characterisation requires ply wrinkle amplitudes ≥0.32 mm across all techniques, with implications for the wrinkle parameter hierarchy in manufacturing quality control.

碳纤维增强聚合物（CFRPs）的面外波浪形（铺层皱褶）会降低其抗拉和抗压强度，最大的面外铺层角控制其失效机制。本研究比较评估了三种无损检测（NDE）技术：涡流阵列检测（ECAT）、相控阵超声检测（PAUT）和x射线数字断层合成（DT），用于检测和表征三个参数：振幅、波长和最大面外铺层角的铺层褶皱。测试了9种单向CFRP片材，其中包含可控振幅（0.13-1.31 mm）的诱导层皱，解决了薄层压板中低于2 mm振幅缺陷的比较NDE性能的关键差距。PAUT达到了96.3%（26/27次测量）的最高总体表征成功率和88.9%（8/9个样本）的检测成功率。关键的是，pat在表征最大面外铺层角方面取得了100%的成功，这是控制所有样品的压缩/拉伸破坏的参数，包括最低幅度褶皱（0.13 mm）。然而，系统高估皱纹振幅特征发生（+ 55.3%平均百分比误差）。ECAT的检测成功率为88.9%，表征成功率为33.3%，成功测量了皱纹波长（100%），但无法仅从复杂阻抗数据量化皱纹幅度或面外厚度角，将其定位为快速自动化筛选工具。x射线DT检测和表征成功率为88.9%，皱纹振幅表征有中度高估（+ 24.8%）。然而，完全检测和表征失败发生在最低振幅皱褶。一项重要的发现表明，在所有技术中，可靠的表征要求皱褶幅度≥0.32 mm，这对制造质量控制中的皱褶参数层次具有重要意义。

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引用次数: 0

Ultrasonic characterisation of process-induced pores in selective laser melted TiB2/Al composites 选择性激光熔化TiB2/Al复合材料中工艺诱导气孔的超声表征

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

Ndt & E International

Pub Date : 2025-12-31 DOI: 10.1016/j.ndteint.2025.103629

Kaiwen Ni , Qiuyu Li , Ming Huang , Yuan Liu

Process-induced pores in metal additive manufacturing (AM) components critically compromise mechanical performance, necessitating reliable characterisation methods for quality assurance. While ultrasound offers promising advantages for rapid, non-destructive evaluation with deep penetration, existing studies struggle to isolate pore effects from confounding grain scattering. This work overcomes these limitations by investigating selective laser melted TiB

_{2}

/Al composites, whose ceramic-reinforced microstructure exhibits refined equiaxed grains and minimal texture, effectively suppressing grain scattering to reveal fundamental pore-ultrasound interactions. We systematically examined how porosity (0.22%–2.21%), morphology, and size distribution influence ultrasonic attenuation and velocity. This was achieved through integrated experimental measurements and three-dimensional pore-scale finite element simulations incorporating realistic pores derived from stereological transformation of microscopy data. Our findings reveal hierarchical pore effects: porosity exhibits strong linear correlations with both attenuation coefficient and phase velocity under the same pore morphology conditions; irregular morphologies amplify these effects, generating fivefold higher attenuation sensitivity and twofold higher velocity sensitivity compared to spherical pores; size variations primarily affect attenuation with minimal velocity impact. Additional, we demonstrated ultrasound’s spatial mapping capability for detecting subtle microstructural heterogeneities, with attenuation exhibiting superior porosity sensitivity. These quantitative pore-ultrasound relationships establish a robust framework for non-destructive evaluation in metal AM, enabling morphology-sensitive quality control and process optimisation for safety-critical applications.

金属增材制造（AM）部件中的工艺诱发孔隙严重影响机械性能，需要可靠的表征方法来保证质量。虽然超声在快速、无损的深穿透评估方面具有很好的优势，但现有的研究很难从混杂的颗粒散射中分离出孔隙效应。这项工作通过研究选择性激光熔化TiB2/Al复合材料克服了这些限制，其陶瓷增强微观结构表现出精致的等轴晶粒和最小的纹理，有效地抑制了晶粒散射，揭示了基本的孔-超声相互作用。我们系统地研究了孔隙度（0.22%-2.21%）、形貌和尺寸分布对超声衰减和速度的影响。这是通过综合实验测量和三维孔隙尺度的有限元模拟来实现的，其中包括来自显微镜数据的立体变换的真实孔隙。我们的研究结果揭示了分层孔隙效应：在相同孔隙形态条件下，孔隙度与衰减系数和相速度均表现出很强的线性相关性；不规则的孔隙形态放大了这些影响，与球形孔隙相比，衰减灵敏度提高了5倍，速度灵敏度提高了2倍；尺寸变化主要影响衰减，速度影响最小。此外，我们还证明了超声波的空间测绘能力，可以检测细微的微观结构非均质性，衰减显示出优越的孔隙度敏感性。这些定量的孔隙-超声关系为金属增材制造的无损评估建立了一个强大的框架，为安全关键应用实现了形态敏感的质量控制和工艺优化。

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引用次数: 0

On acoustic fields of Lamb wave scattering in plates based on Convolutional Neural Network-Transformer 基于卷积神经网络-变压器的板材Lamb波散射声场研究

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

Ndt & E International

Pub Date : 2025-12-23 DOI: 10.1016/j.ndteint.2025.103628

Zhen Zhang , Linfeng Wang , Jian Li , Zhoumo Zeng , Yang Liu

This paper presents a hybrid deep learning model that combines Convolutional Neural Network (CNN) and Transformer to enable efficient prediction of far-field scattered signals of S0 mode Lamb waves from defects of thin plates. The proposed model combines CNN for local spatial feature extraction with Transformer to model global temporal dependencies, enhancing the ability to predict scattering from irregularly-shaped defects beyond the limitations of traditional methods. A three-dimensional (3D) finite element model of an aluminum plate with irregularly-shaped defects was developed to generate scattering fields with diverse morphologies and parameters for model training and testing. CNN-Transformer model successfully predicted the scattering behavior of S0 mode Lamb wave, demonstrating high accuracy in scenarios with irregularly-shaped defects. The model's performance was further validated through laser Doppler experiments, demonstrating strong consistency with the predicted scattering characteristics. Furthermore, the model was extended to solve the scattering matrix, enabling accurate prediction of scattered signals across multiple incident angles. This study introduces a new approach to defect scattering in ultrasonic guided wave detection. It provides both theoretical insights and practical support for engineering applications.

本文提出了一种结合卷积神经网络（CNN）和变压器（Transformer）的混合深度学习模型，能够有效预测薄板缺陷的50模兰姆波远场散射信号。该模型结合CNN局部空间特征提取和Transformer建模全局时间依赖性，提高了不规则形状缺陷散射预测能力，超越了传统方法的局限性。建立了不规则缺陷铝板的三维有限元模型，生成了具有多种形态和参数的散射场，用于模型训练和测试。CNN-Transformer模型成功地预测了S0模式Lamb波的散射行为，在不规则形状缺陷情况下显示出较高的精度。通过激光多普勒实验进一步验证了模型的性能，与预测的散射特性有较强的一致性。此外，将模型扩展到求解散射矩阵，实现了对多个入射角散射信号的准确预测。介绍了超声导波检测中缺陷散射的一种新方法。它为工程应用提供了理论见解和实践支持。

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引用次数: 0

Exploiting critical interference in bounded ultrasonic beam scattering for near-surface damage detection in curved structures 利用有界超声波束散射的临界干扰进行曲面结构近表面损伤检测

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

Ndt & E International

Pub Date : 2025-12-19 DOI: 10.1016/j.ndteint.2025.103627

Jiangcheng Cai, Mingxi Deng

Advances in modern manufacturing have expanded the application of components with curvature surfaces in engineering, thereby increasing the demand for ultrasonic techniques capable of detecting near-surface damage in curved structures. This paper proposes a damage detection method based on the critical scattering fields generated by the interaction between ultrasonic waves and curvature surfaces. In this paper, we analyzed stainless steel structure comprised by two quarter-cylindrical segments and joined adhesively. Under fluid–solid coupling conditions, an obliquely incident acoustic wave interacts with a curved structure, splitting into a directly reflected wave and leaky Rayleigh waves. Our analysis shows that under specific conditions, re-radiated waves induced by leaky Rayleigh waves interfere with the direct reflected wave, forming a critical constructive interference field. We specifically investigate the origins of constructive interference angles in the studied structure and demonstrate that early-stage surface damage can be effectively identified using precisely configured transducer pairs under these constructive interference angle conditions. Conducted at three inspection points on specimen, Finite element simulations confirm that the proposed method can detect damage within a near-surface region extending up to one wavelength of the circumferential Rayleigh waves. Experimental results further validate the method's ability to reliably identify corrosion damage.

现代制造业的发展扩大了曲率表面构件在工程中的应用，从而增加了对能够检测弯曲结构近表面损伤的超声技术的需求。提出了一种基于超声与曲率面相互作用产生的临界散射场的损伤检测方法。在本文中，我们分析了由两个四分之一圆柱段组成并粘合连接的不锈钢结构。在流固耦合条件下，斜入射声波与弯曲结构相互作用，分裂成直接反射波和泄漏瑞利波。我们的分析表明，在特定条件下，由泄漏瑞利波诱导的再辐射波与直接反射波发生干涉，形成临界干涉场。我们特别研究了所研究结构中构造干涉角的来源，并证明在这些构造干涉角条件下，使用精确配置的传感器对可以有效地识别早期表面损伤。在试件的三个检查点上进行有限元模拟，证实了所提出的方法可以检测到圆周瑞利波一个波长的近表面区域内的损伤。实验结果进一步验证了该方法可靠识别腐蚀损伤的能力。

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引用次数: 0