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Initial state of charge estimation for prismatic LiFePO4 cells using ultrasonic velocity along internal layers 利用沿内层的超声速度估计棱柱形LiFePO4电池的初始电荷状态
IF 4.5 2区 材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING Pub Date : 2025-10-17 DOI: 10.1016/j.ndteint.2025.103580
Shengyuan Zhang , Peng Zuo , Zheng Fan
Prismatic Li-ion battery cells are increasingly employed in electric transportation, where accurate state of charge (SoC) estimation is critical for reliable and efficient operation. However, conventional SoC estimation methods, primarily based on voltage measurements, face challenges with LiFePO4 cells due to their characteristically flat voltage-SoC profile. To address this limitation, ultrasonic diagnosis offers a valuable alternative by leveraging the coupling between SoC and the mechanical properties of the cells. In this study, we propose a novel ultrasonic feature — the wave velocity along internal layers — as a robust indicator of SoC. Its sensitivity to SoC is validated experimentally, and the correlation is further confirmed through numerical simulations. A full-scale finite difference time domain (FDTD) model is developed to simulate ultrasonic wave propagation, capturing microscale fluid-solid interactions within the porous electrode and separator layers, as well as the coupling between them. Furthermore, multiscale modeling based on finite element model (FEM) and transfer matrix method (TMM) is introduced. Both simulation methods reveal that the observed correlation primarily arises from internal pressure variations, which are directly related to SoC and modulate the interfacial stiffness between the electrode and separator layers. As a result, ultrasonic wave velocity serves as a non-destructive proxy for internal pressure measurements and SoC estimation. With this theoretical insight, the repeatability and transferability of this ultrasonic velocity-based approach are demonstrated, positioning it as a promising complement to existing electrical and mechanical methods.
棱镜型锂离子电池越来越多地应用于电力运输,其中准确的充电状态(SoC)估算对于可靠和高效的运行至关重要。然而,传统的SoC估计方法主要基于电压测量,由于LiFePO4电池具有平坦的电压SoC分布特征,因此面临挑战。为了解决这一限制,超声诊断提供了一个有价值的替代方案,利用SoC和细胞的机械性能之间的耦合。在这项研究中,我们提出了一种新的超声特征-沿内层的波速-作为SoC的稳健指标。实验验证了其对SoC的敏感性,数值模拟进一步证实了其相关性。建立了一个全尺寸时域有限差分(FDTD)模型来模拟超声波的传播,捕捉了多孔电极和分离层内的微尺度流固相互作用以及它们之间的耦合。此外,还介绍了基于有限元模型和传递矩阵法的多尺度建模方法。两种模拟方法都表明,观察到的相关性主要来自内部压力变化,而内部压力变化与SoC直接相关,并调节电极和隔膜层之间的界面刚度。因此,超声波波速可以作为内部压力测量和SoC估算的非破坏性代理。有了这一理论见解,证明了这种基于超声速度的方法的可重复性和可转移性,将其定位为现有电气和机械方法的有前途的补充。
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引用次数: 0
THz super-transmission phenomenon in carbon fiber reinforced polymer for non-destructive testing 无损检测用碳纤维增强聚合物中太赫兹超透射现象
IF 4.5 2区 材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING Pub Date : 2025-10-15 DOI: 10.1016/j.ndteint.2025.103582
Xinke Zhao , Leijun Xu , Jianfeng Chen , Xue Bai , Xinyu Jin , Yiyang Kong , Hui Xiao
As an emerging class of materials, carbon fiber reinforced polymers (CFRPs) have found extensive applications in automotive, aviation, and other industries. However, the development of efficient and accurate non-destructive testing (NDT) methods for CFRPs remains a critical research area. Currently, THz-based detection devices are limited by low output power. Electromagnetic waves also experience significant losses in CFRPs. These factors make THz NDT extremely challenging. To address these issues, this article investigates the phenomenon of super-transmission in CFRPs, which mitigates the difficulty of THz electromagnetic waves penetrating this composite material. First, the electromagnetic properties of single-layer CFRP are analyzed by using the effective medium model and electrical concentric cylindrical model, resulting in the construction of the four-port scattering-matrices (S-matrices) for single-layer CFRP. To calculate the total transmission for double-layer CFRPs, the wave-matrices (W-matrices) are employed to construct a cascaded four-port transmission network, thereby providing a theoretical explanation for the super-transmission phenomenon. Furthermore, a THz transmission experiment is conducted by using a 350 GHz detection device and THz time-domain spectroscopy (THz-TDS). The experimental results align well with the theoretical predictions. The proposed super-transmission phenomenon presents a novel approach and methodology for NDT of CFRPs based on THz electromagnetic waves, thereby offering potential applications in the field of NDT.
作为一种新兴的材料,碳纤维增强聚合物(CFRPs)在汽车、航空和其他工业中得到了广泛的应用。然而,开发高效、准确的碳纤维复合材料无损检测方法仍然是一个重要的研究领域。目前,基于太赫兹的探测设备受到低输出功率的限制。电磁波在cfrp中也会遭受重大损失。这些因素使得太赫兹无损检测极具挑战性。为了解决这些问题,本文研究了CFRPs中的超传输现象,从而减轻了太赫兹电磁波穿透CFRPs的困难。首先,采用有效介质模型和电同心圆柱模型对单层CFRP的电磁特性进行了分析,构建了单层CFRP的四端口散射矩阵(s矩阵)。为了计算双层cfrp的总传输,采用波矩阵(w矩阵)构建了一个级联的四端口传输网络,从而为超传输现象提供了理论解释。此外,利用350 GHz探测装置和太赫兹时域谱(THz- tds)进行了太赫兹传输实验。实验结果与理论预测相吻合。所提出的超传输现象为基于太赫兹电磁波的cfrp无损检测提供了一种新的途径和方法,从而在无损检测领域具有潜在的应用前景。
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引用次数: 0
An efficient nondestructive defect location method from X-ray images via an attention network 基于注意网络的x射线图像无损定位方法
IF 4.5 2区 材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING Pub Date : 2025-10-11 DOI: 10.1016/j.ndteint.2025.103566
Lei Yang , Shuai Xu , Junfeng Fan , En Li , Yanhong Liu
Accurate welding defect detection plays the key role to the welding quality control, which is also the basis of maintenance decision. However, the welding images from X-ray imaging system are always against poor texture and low contrast phenomenon, which causes weak feature representation ability. Meanwhile, the welding defects always occupy a small proportion of image pixels compared with backgrounds to bring the class imbalance issue. These complex factors bring a certain challenge to accurate welding defect detection. Recently, due to strong context feature representation ability, deep convolutional neural network (DCNN) has acquired a remarkable performance on defect segmentation. Nevertheless, high-precision defect segmentation based on DCNNs is still a challenging task due to insufficient processing of local contextual feature maps, limited receptive field, etc. To address these issues, considering the encoder–decoder framework, an effective welding defect segmentation network is proposed for end-to-end defect location from X-ray images. Specifically, an effective backbone with a bidirectional convolutional long short-term memory (BiConvLSTM) block is built to learn the global, long-range contexts and improve the network’s propagation ability of subtle context features. Meanwhile, to address the insufficient processing issue of local contextual feature maps, to imitate human visual attention, a global attention block is proposed for local feature enhancement to make the segmentation network emphasize the defective areas. In addition, aimed at the limited receptive field, a feature enhancement block is proposed for multi-scale feature representation and fusion. Experiments on public dataset of X-ray welding defects show that the proposed defect segmentation network could acquire a promising segmentation performance compared with other related segmentation models.
准确的焊接缺陷检测对焊接质量控制起着关键作用,也是维修决策的依据。然而,x射线成像系统的焊接图像往往存在纹理差、对比度低的现象,导致特征表示能力较弱。同时,与背景相比,焊接缺陷在图像像素中所占的比例总是很小,从而带来了类不平衡问题。这些复杂的因素给焊接缺陷的准确检测带来了一定的挑战。近年来,深度卷积神经网络(deep convolutional neural network, DCNN)由于其强大的上下文特征表征能力,在缺陷分割方面取得了令人瞩目的成绩。然而,由于局部上下文特征图处理不足、接受域有限等原因,基于DCNNs的高精度缺陷分割仍然是一个具有挑战性的任务。为了解决这些问题,考虑到编码器-解码器框架,提出了一种有效的焊接缺陷分割网络,用于x射线图像的端到端缺陷定位。具体而言,构建了一个双向卷积长短期记忆(BiConvLSTM)块的有效主干,用于学习全局、远程上下文,提高网络对细微上下文特征的传播能力。同时,针对局部上下文特征图处理不足的问题,为了模仿人类的视觉注意,提出了一种全局注意块对局部特征进行增强,使分割网络突出缺陷区域。此外,针对有限的接收野,提出了一种多尺度特征表示和融合的特征增强块。在公开的x射线焊接缺陷数据集上的实验表明,与其他相关的缺陷分割模型相比,所提出的缺陷分割网络可以获得良好的分割性能。
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引用次数: 0
Gold purity detection via pulsed eddy current testing with adaptive compensation residual shrinkage network 基于自适应补偿残余收缩网络的脉冲涡流检测金纯度
IF 4.5 2区 材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING Pub Date : 2025-10-11 DOI: 10.1016/j.ndteint.2025.103563
Wei Shen , Jian Li , Zhongsheng Zhai , Xuanze Wang , Wei Feng , Zili Lei
Accurate and non-invasive assessment of gold purity remains a critical challenge in nondestructive testing (NDT), particularly in scenarios involving interlayer adulteration. Pulsed eddy current testing (PECT), due to its strong penetration capability and sensitivity to electrical conductivity, shows promising potential for this application. In this study, a portable and highly integrated PECT system is developed for practical gold purity inspection. A series of multilayer gold samples with varying purities and thicknesses were prepared, yielding 14,400 signal acquisitions. To address signal distortions caused by noise and probe temperature fluctuations, an Adaptive Compensation Residual Shrinkage Network (AC-RSN) is proposed. The method integrates a learnable compensation mechanism and a two-stage residual shrinkage architecture to suppress signal deviations and enhance feature representation. Experimental results demonstrate that the AC-RSN achieves a classification accuracy of 99.27%, outperforming conventional approaches in robustness and reliability. This work highlights the feasibility of combining advanced deep learning techniques with PECT for intelligent and reliable nondestructive evaluation of gold purity.
准确和无创的金纯度评估仍然是无损检测(NDT)的一个关键挑战,特别是在涉及层间掺假的情况下。脉冲涡流测试(PECT)由于其强大的穿透能力和对电导率的敏感性,在这一应用中显示出很大的潜力。在本研究中,开发了一种便携式,高度集成的PECT系统,用于实际的金纯度检测。制备了一系列不同纯度和厚度的多层金样品,产生了14,400个信号采集。针对噪声和探头温度波动引起的信号失真,提出了一种自适应补偿残余收缩网络(AC-RSN)。该方法集成了可学习补偿机制和两阶段剩余收缩结构,以抑制信号偏差并增强特征表示。实验结果表明,AC-RSN的分类准确率达到99.27%,鲁棒性和可靠性均优于传统方法。这项工作强调了将先进的深度学习技术与PECT相结合,对黄金纯度进行智能、可靠的无损评估的可行性。
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引用次数: 0
Tilt effects analysis and evaluation in Pulsed Eddy Current measurements 脉冲涡流测量中的倾斜效应分析与评价
IF 4.5 2区 材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING Pub Date : 2025-10-10 DOI: 10.1016/j.ndteint.2025.103570
Kuohai Yu , Rui Guo , Saibo She , Lei Xiong , Xinnan Zheng , Xun Zou , Jialong Shen , Wuliang Yin
Sensor tilt is regarded as one of the major causes of noise in eddy current testing. A tilted Pulsed Eddy Current (PEC) probe can lead to signal distortion, resulting in errors in measurements and evaluations. For the first time, this paper investigates the tilt effect on PEC signals by developing an analytical solution for tilted PEC sensors. The analytical solution combines the theory of mutual impedance variation of tilted coils with PEC testing. It is found that the impact of tilt angles on PEC signals follows a double-exponential function in terms of both amplitude and the decreasing rate of transient voltage change. Corresponding experiments have been conducted, which agree well with the numerical results and validate the analytical solutions. Additionally, a sensor tilt angle estimation method based on the double-exponential relationship curve is developed and an average absolute error of 0.2829° has been achieved.
传感器倾斜被认为是涡流检测中产生噪声的主要原因之一。倾斜的脉冲涡流(PEC)探头会导致信号失真,从而导致测量和评估误差。本文首次通过建立倾斜式脉冲电位传感器的解析解,研究了倾斜对脉冲电位信号的影响。解析解将倾斜线圈的互阻抗变化理论与PEC测试相结合。结果表明,倾斜角度对瞬态电压变化幅度和衰减速率的影响均呈双指数函数关系。进行了相应的实验,与数值结果吻合较好,验证了解析解的正确性。此外,提出了一种基于双指数关系曲线的传感器倾角估计方法,平均绝对误差为0.2829°。
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引用次数: 0
EM sensor array for non-destructive evaluation of spatially varying steel phase transformation 空间变化钢相变无损评价的电磁传感器阵列
IF 4.5 2区 材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING Pub Date : 2025-10-10 DOI: 10.1016/j.ndteint.2025.103578
Fanfu Wu, Lei Zhou, Claire Davis
An electromagnetic (EM) sensor array system, consisting of four sensor heads, has been used to non-destructively characterise spatial variations in steel strips, with differences in phase transformation rates due to differences in local cooling rates being reported in this work. An S355-grade steel strip was subjected to different local cooling conditions (air cooling and water cooling, with half the strip being insulated) on a lab-based run-out table (ROT). The sensor array was able to monitor the different phase transformation behaviour across the width of the steel strip due to the non-uniform cooling. Thermocouples were used to determine the local cooling rates, and these were used with continuous cooling transformation (CCT) diagrams to predict the local phase transformation behaviour. It is known that the zero-crossing frequency (ZCF) from EM sensors can be related to the phase transformation; therefore, the ZCF values from the separate EM sensor heads have been compared to the predicted phase transformation behaviour. Microstructural validation for the predicted phase transformation products (fractions of ferrite, pearlite, bainite and/or martensite) was performed using optical microscopy. The spatial resolution performance of the EM sensor array has been compared to that of the commercial EMspec™ system for the case of varying phase transformation across a strip sample. This work demonstrates the potential for EM sensors to be used in arrays without interference between signals, allowing the characterisation of spatially varying behaviour in steel during cooling.
电磁(EM)传感器阵列系统,由四个传感器头组成,已被用于非破坏性地表征钢带的空间变化,由于局部冷却速率的差异,在这项工作中报告了相变速率的差异。一个s355级钢带在实验室运行台上经受了不同的局部冷却条件(风冷和水冷,其中一半钢带是绝缘的)。由于不均匀冷却,传感器阵列能够监测钢带宽度上不同的相变行为。热电偶用于确定局部冷却速率,并将其与连续冷却转变(CCT)图一起用于预测局部相变行为。已知电磁传感器的过零频率(ZCF)与相变有关;因此,将来自单独的EM传感器头的ZCF值与预测的相变行为进行了比较。使用光学显微镜对预测的相变产物(铁素体、珠光体、贝氏体和/或马氏体的部分)进行了显微组织验证。在条带样品的相变变化情况下,EM传感器阵列的空间分辨率性能与商用EMspec™系统进行了比较。这项工作证明了电磁传感器在没有信号之间干扰的情况下用于阵列的潜力,允许表征钢在冷却过程中的空间变化行为。
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引用次数: 0
3D reconstruction of subsurface pipes and cavities using ground penetrating radar based on deep learning 基于深度学习的探地雷达地下管道和空腔三维重建
IF 4.5 2区 材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING Pub Date : 2025-10-10 DOI: 10.1016/j.ndteint.2025.103579
Zhigang Cheng , Zhizhou He , Peng Pan
Detecting subsurface pipes and cavities is important in urban infrastructure management, but existing methods struggle to accurately reconstruct the 3D shapes of deep subsurface objects. This study pioneers a new paradigm for this task by reformulating the ill-posed permittivity regression problem as a 3D semantic segmentation problem. A novel neural network, 3DReconNet, to predict the material type of each subsurface voxel from ground penetrating radar (GPR) data was proposed. This approach leverages the intrinsic relationship between material composition and reflected signal intensity to simultaneously recover both geometry and material properties. A dataset of 3150 synthetic cases was generated using full-scale simulation models and a Markov model-based algorithm to simulate irregular cavities. The 3DReconNet adopts a U-shaped architecture and incorporates residual connections to reduce information loss. The network is trained using the Dice Loss function regularized with total variation (TV) constraints, which enhances geometric consistency and reconstruction accuracy. The proposed method was validated using both simulated and experimental data, and the qualitative as well as quantitative results confirmed its effectiveness, robustness, and generalizability.
探测地下管道和空腔在城市基础设施管理中很重要,但现有方法难以准确地重建深层地下物体的三维形状。本研究通过将不适定介电常数回归问题重新表述为三维语义分割问题,为该任务开辟了新的范例。提出了一种新的神经网络3DReconNet,用于从探地雷达(GPR)数据中预测每个地下体素的材料类型。这种方法利用材料成分和反射信号强度之间的内在关系,同时恢复几何形状和材料特性。利用全尺寸模拟模型和基于马尔可夫模型的算法模拟不规则空腔,生成了3150个合成病例的数据集。3DReconNet采用u型结构,并结合剩余连接,减少信息丢失。该网络采用全变分(TV)约束正则化的Dice Loss函数进行训练,增强了网络的几何一致性和重构精度。仿真和实验数据验证了该方法的有效性、稳健性和泛化性。
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引用次数: 0
Material-resolving computed tomography of lithium-ion batteries using deep learning 使用深度学习的锂离子电池材料分辨计算机断层扫描
IF 4.5 2区 材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING Pub Date : 2025-10-09 DOI: 10.1016/j.ndteint.2025.103565
M. Weiss , K. Mrzljak , M. von Schmid , G. Erbach , N. Brierley , T. Meisen
The demand for batteries as portable energy storages increases drastically. Especially for electric mobility, battery safety is crucial which begins at seamless quality control during and after manufacturing. Recent developments in high-speed computed tomography (high-speed CT) enable scan times around 10 s, roughly matching the speed of a typical battery production line. While the majority of defects in batteries can be detected using the CT scan data directly, data post-processing such as material identification can reveal further insights. As the complexity of modern battery production grows, traditional material-resolving CT methods face challenges in delivering the precision and efficiency required. To meet these demands, more advanced, data-driven approaches are becoming essential. This has led to an ongoing paradigm shift in material-resolving CT, introducing deep learning techniques that promise enhanced accuracy and processing speed. In the scope of this paper, we propose an end-to-end deep learning approach, which is designed to resolve materials in CT scans in presence of heavy CT artifacts by exploiting context knowledge with a convolution-based neural network. The model computes atomic numbers and densities directly from the dual-energy CT volume slices for each pixel. Our approach uses simulation-generated training data, thereby avoiding the need for manual annotation. CT scans from two fundamentally different systems – one providing slow, high-quality scans and the other fast, medium-quality scans – are compared in terms of material identification performance. Especially for high-speed CT, increasing the scanning time can influence the data quality drastically. We believe, that the combination of a high-speed scanner for pre-screening together with a slower high-quality scanner provides comprehensive in-line inspection, where only critical candidates, revealing anomalies in the high-speed scan, will be send to the high-quality scanner.
作为便携式能源储存装置,对电池的需求急剧增加。特别是对于电动汽车来说,电池安全至关重要,从制造过程中和生产后的无缝质量控制开始。高速计算机断层扫描(高速CT)的最新发展使扫描时间约为10秒,大致与典型电池生产线的速度相匹配。虽然电池中的大多数缺陷可以直接使用CT扫描数据检测到,但数据后处理(如材料识别)可以揭示进一步的见解。随着现代电池生产的复杂性不断增加,传统的材料分辨CT方法在提供所需的精度和效率方面面临挑战。为了满足这些需求,更先进的、数据驱动的方法变得至关重要。这导致了材料分辨CT的持续范式转变,引入了有望提高准确性和处理速度的深度学习技术。在本文的范围内,我们提出了一种端到端深度学习方法,该方法旨在通过使用基于卷积的神经网络利用上下文知识来解决存在大量CT伪影的CT扫描中的材料。该模型直接从每个像素的双能CT体切片计算原子序数和密度。我们的方法使用模拟生成的训练数据,从而避免了手动注释的需要。CT扫描来自两个完全不同的系统——一个提供慢速、高质量的扫描,另一个提供快速、中等质量的扫描——在材料识别性能方面进行比较。特别是对于高速CT,增加扫描时间会极大地影响数据质量。我们相信,将用于预筛选的高速扫描仪与较慢的高质量扫描仪相结合,可以提供全面的在线检查,只有在高速扫描中发现异常的关键候选者才会被发送到高质量的扫描仪。
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引用次数: 0
Ultrasonic imaging using a phased array probe with a buffer consisting of a bundle of circular cylinders 超声成像使用相控阵探头与一个由一束圆柱组成的缓冲器
IF 4.5 2区 材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING Pub Date : 2025-10-08 DOI: 10.1016/j.ndteint.2025.103576
Mingqian Xia, Kohei Nishiuchi, Takahiro Hayashi, Naoki Mori
The authors have previously investigated defect imaging using a phased array probe with a buffer consisting of thin plates. Although the phased array probe with a stacked plate buffer works well in defect imaging, there remains the issue of spurious images due to trailing waves generating at the side walls of a plate, and large stacked plate buffers are required to avoid the trailing waves. To solve these issues, a buffer consisting of circular cylinders is introduced. Considering dispersion characteristics of longitudinal vibration mode of guided waves in a circular cylinder and dimensions of phased array probe, cylinder buffers were designed and fabricated. Using the buffer consisting of circular cylinders, defects were well visualized with two imaging algorithms, plane wave imaging and total focusing method.
作者先前已经研究了使用相控阵探针与由薄板组成的缓冲的缺陷成像。虽然带叠层板缓冲的相控阵探头在缺陷成像中表现良好,但由于在板侧壁处产生尾波,存在像伪的问题,需要较大的叠层板缓冲来避免尾波。为了解决这些问题,介绍了一种由圆柱组成的缓冲器。考虑导波在圆柱内纵向振动模态的色散特性和相控阵探头的尺寸,设计并制作了圆柱缓冲器。利用圆柱体构成的缓冲层,采用平面波成像和全聚焦成像两种成像算法对缺陷进行了较好的可视化处理。
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引用次数: 0
Nonlinear ultrasonic C-scan imaging based on sideband peak intensity for fatigue damage evaluation 基于边带峰值强度的非线性超声c扫描疲劳损伤评价
IF 4.5 2区 材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING Pub Date : 2025-10-08 DOI: 10.1016/j.ndteint.2025.103577
Fengling Wang , Shuzeng Zhang , Mingzhu Sun , Tribikram Kundu
This study proposes a nonlinear ultrasonic imaging method for the detection of material or structural damage. The contact-based frequency-mismatched pulse-echo sideband peak intensity (PE-SPI) technique is extended and implemented on an ultrasonic immersion C-scan platform, enabling non-contact scanning and imaging based on nonlinear parameters. Fatigue test specimens were examined using both the proposed method and conventional linear scanning approaches. The results indicate that, when linear parameters such as signal amplitude are used, the outcomes from both methods are consistent. However, the proposed method enables the extraction of nonlinear features by measuring the amplitudes of harmonic peaks in the frequency spectrum, thereby realizing an imaging approach fundamentally different from traditional linear ultrasonics. Experimental results demonstrate that the proposed technique more effectively identifies the locations of fatigue cracks, showing particularly enhanced sensitivity in detecting early-stage cracks and assessing crack extension.
本研究提出一种用于材料或结构损伤检测的非线性超声成像方法。将基于接触式频率不匹配脉冲回波边带峰值强度(PE-SPI)技术扩展并实现在超声浸入式c扫描平台上,实现基于非线性参数的非接触式扫描和成像。采用本文提出的方法和传统的线性扫描方法对疲劳试样进行了检测。结果表明,当使用信号幅度等线性参数时,两种方法的结果是一致的。然而,该方法通过测量频谱中谐波峰的幅值来提取非线性特征,从而实现了一种与传统线性超声有本质区别的成像方法。实验结果表明,该方法能更有效地识别疲劳裂纹的位置,尤其在早期裂纹检测和裂纹扩展评估方面具有更高的灵敏度。
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引用次数: 0
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