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A New Method for Rapid Detection of Surface Defects on Complex Textured Tiles 一种复杂纹理瓷砖表面缺陷快速检测新方法
IF 2.6 3区 材料科学 Q2 MATERIALS SCIENCE, CHARACTERIZATION & TESTING Pub Date : 2024-12-04 DOI: 10.1007/s10921-024-01145-x
Guanping Dong, Yuanzhi Wang, Sai Liu, Nanshou Wu, Xiangyu Kong, Xiangyang Chen, Zixi Wang

The surface of complex textured ceramic tiles contains numerous defects that exhibit low contrast with the background, making them easily confused with the textured background during detection. Traditional defect detection algorithms and convolutional neural networks are prone to texture interference in the defect detection of complex textured ceramic tiles, resulting in high false detection rates and missed detection rates. Inspired by the human eye’s ability to find surface defects on smooth objects against a high-light background, this paper proposes a new method for detecting surface defects of complex textured tiles. This method uses the high-light area generated by the reflection of the light source as the background for detecting textured tile defects, thereby increasing the threshold difference between the defect and the background and highlighting the defect. This method translates the position of the textured tiles horizontally and captures images while the reflection of the strip light source covering the surface of the tiles is in motion, thereby acquiring several tile images with light source reflections. Subsequently, after intercepting the images of the highlight areas covered by the light source reflection, the RANSAC algorithm is used to match the characteristic corners of these images, and after rigid splicing, a complete image of the textured tiles with the highlight area as the background is obtained. Finally, defects on textured tiles can be extracted through threshold segmentation and morphological filtering. Experimental results indicate that this method can ignore complex texture interference on ceramic tiles and achieve rapid detection of defects in textured ceramic tiles.

复杂纹理瓷砖表面含有大量与背景对比度较低的缺陷,在检测过程中容易与纹理背景混淆。传统的缺陷检测算法和卷积神经网络在复杂纹理瓷砖的缺陷检测中容易受到纹理干扰,导致高误检率和漏检率。受人眼在强光背景下发现光滑物体表面缺陷能力的启发,本文提出了一种检测复杂纹理瓷砖表面缺陷的新方法。该方法利用光源反射产生的高光区域作为检测纹理瓷砖缺陷的背景,从而增加缺陷与背景的阈值差,突出缺陷。该方法对纹理瓷砖的位置进行水平平移,并在覆盖瓷砖表面的条形光源的反射运动时捕获图像,从而获得多个具有光源反射的瓷砖图像。随后,截取光源反射覆盖的高光区域的图像,利用RANSAC算法对这些图像的特征角进行匹配,经过刚性拼接,得到以高光区域为背景的纹理瓷砖的完整图像。最后,通过阈值分割和形态滤波提取纹理瓦片上的缺陷。实验结果表明,该方法可以忽略瓷砖表面复杂的纹理干扰,实现纹理瓷砖缺陷的快速检测。
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引用次数: 0
Nondestructive Identification of Wheat Species using Deep Convolutional Networks with Oversampling Strategies on Near-Infrared Hyperspectral Imagery 基于过采样深度卷积网络的近红外高光谱图像小麦品种无损识别
IF 2.6 3区 材料科学 Q2 MATERIALS SCIENCE, CHARACTERIZATION & TESTING Pub Date : 2024-12-04 DOI: 10.1007/s10921-024-01143-z
Nitin Tyagi, Sarvagya Porwal, Pradeep Singh, Balasubramanian Raman, Neerja Garg

Differentiating between wheat species poses a significant challenge to the Indian grain industry. Visual inspection of wheat species has drawbacks, including inconsistency, low throughput, and labor intensiveness. In this study, near-infrared hyperspectral imaging (NIR-HSI) was utilized in conjunction with a deep learning approach to achieve precise predictions of wheat at the species level. A dataset comprising 40 different varieties from four Indian wheat species, namely Triticum aestivum (T. aestivum), Triticum durum (T. durum), Triticum dicocccum (T. dicoccum), and Triticale, was prepared using a NIR-HSI system that encompassed the wavelength ranging from 900–1700 nm. The imbalanced dataset is a common problem in the classification task, making it harder for the classifier to classify minority class data correctly. To address this issue, oversampling techniques such as Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic sampling (ADASYN) were employed. For the classification task, a 1D Convolutional Neural Network (1D-CNN), a 1D-ResNet, and four traditional machine learning models: Naive Bayes (NB), K-Nearest Neighbor (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) are utilized and compared. The performance of these models was assessed using both imbalanced and balanced datasets. The 1D-CNN model outperformed traditional machine learning models, achieving impressive test accuracies of 98.25% and 98.43% with the SMOTE and ADASYN approach, respectively. These findings underscore the efficacy of NIR-HSI in conjunction with an end-to-end 1D-CNN and oversampling techniques as a reliable and efficient method for the rapid, accurate, and nondestructive identification of various wheat species. The code is available at https://github.com/nitintyagi007-iitr/Wheat_species_classification

区分小麦品种对印度粮食工业构成了重大挑战。小麦品种目测检测存在不一致、产量低、劳动强度大等缺点。在这项研究中,近红外高光谱成像(NIR-HSI)与深度学习方法相结合,在品种水平上实现了小麦的精确预测。利用NIR-HSI系统制备了一个数据集,该数据集包括来自4个印度小麦品种的40个不同品种,即Triticum aestivum (T. aestivum)、Triticum durum (T. durum)、Triticum dicoccum (T. dicoccum)和Triticale,其波长范围为900-1700 nm。数据集不平衡是分类任务中常见的问题,使得分类器难以正确分类少数类数据。为了解决这个问题,采用了合成少数过采样技术(SMOTE)和自适应合成采样(ADASYN)等过采样技术。对于分类任务,使用1D卷积神经网络(1D- cnn), 1D- resnet和四种传统机器学习模型:朴素贝叶斯(NB), k -近邻(KNN),随机森林(RF)和极端梯度增强(XGBoost)进行比较。使用不平衡和平衡数据集评估这些模型的性能。1D-CNN模型优于传统的机器学习模型,使用SMOTE和ADASYN方法分别实现了令人印象深刻的98.25%和98.43%的测试准确率。这些发现强调了NIR-HSI结合端到端1D-CNN和过采样技术作为快速、准确和无损鉴定各种小麦品种的可靠有效方法的有效性。代码可在https://github.com/nitintyagi007-iitr/Wheat_species_classification上获得
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引用次数: 0
Statistical and Machine Learning-Based Imaging with Long Pulse Thermography for the Detection of Non-standardised Defects in CFRP Composites 基于统计和机器学习的长脉冲热成像CFRP复合材料非标准化缺陷检测
IF 2.6 3区 材料科学 Q2 MATERIALS SCIENCE, CHARACTERIZATION & TESTING Pub Date : 2024-12-04 DOI: 10.1007/s10921-024-01138-w
Afonso Espirito Santo, Weeliam Khor, Francesco Ciampa

In the last few years, infrared long pulse thermography (LPT) has attained high reliability and accuracy in the non-destructive inspection of low-thermally conductive materials such as carbon fibre reinforced polymer (CFRP) composites. However, to date, research investigations of LPT have been conducted on standardised and controlled material flaws such as flat bottom holes. Non-standardised defects in CFRPs are more common in real-life operations and, because of different nature, dimensions and complex shapes, their detection poses a significant challenge. This paper provides an in-depth analysis of LPT combined to advanced statistical and machine learning-based image processing tools for detection of non-standardised damage in CFRP composites. Statistical methods such as skewness and kurtosis, and machine learning algorithms such as principal component analysis and Fuzzy-c clustering were used to post-process thermal LPT signals. Damage scenarios that are likely to occur during manufacturing and in-service operations were analysed in terms of defect mapping characteristics using the signal-to-noise ratio and the Tanimoto criterion. Experimental results revealed that Fuzzy-c and LPT produced superior damage inspection performance.

近年来,红外长脉冲热成像技术(LPT)在低导热材料(如碳纤维增强聚合物(CFRP)复合材料)的无损检测中取得了较高的可靠性和准确性。然而,迄今为止,LPT的研究都是在标准化和可控的材料缺陷上进行的,如平底孔。cfrp的非标准化缺陷在实际操作中更为常见,由于其不同的性质、尺寸和复杂的形状,其检测提出了重大挑战。本文结合先进的统计和基于机器学习的图像处理工具,对LPT进行了深入分析,用于检测CFRP复合材料的非标准化损伤。利用偏度和峰度等统计方法,以及主成分分析和Fuzzy-c聚类等机器学习算法对热LPT信号进行后处理。使用信噪比和谷本准则,根据缺陷映射特征,分析了在制造和服役操作过程中可能发生的损坏情况。实验结果表明,Fuzzy-c和LPT具有较好的损伤检测性能。
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引用次数: 0
Nondestructive Evaluation of Adhesive Joints Using Nonlinear Non-collinear Wave Mixing Technique 基于非线性非共线波混频技术的粘接接头无损评价
IF 2.6 3区 材料科学 Q2 MATERIALS SCIENCE, CHARACTERIZATION & TESTING Pub Date : 2024-12-04 DOI: 10.1007/s10921-024-01142-0
Jingpin Jiao, Zhiqiang Li, Li Li, Guanghai Li, Xinyuan Lu

Adhesive joints are extensive used in various industrial applications. Bonded quality is crucial for ensuring structural integrity and safety. In this study, the nonlinear non-collinear wave mixing techniques were developed to nondestructive evaluate micro imperfections in adhesive joints, including adhesive degradation and bond weakening. Two testing schemes were proposed via the resonance conditions of the adhesive and substrate respectively, following the classical nonlinearity theories. Numerical simulations and experiments of non-collinear wave mixing were conducted to explore the feasibility of the proposed testing schemes for accessing two typical micro imperfections in adhesive joints. Both the simulation and experimental results demonstrate that the proposed nonlinear non-collinear wave mixing method is effective for nondestructive evaluation of the micro imperfections in adhesive joints. Moreover, the scheme via resonance conditions of adhesive exhibits a higher sensitive to the adhesive degradation, whereas the one relying on the resonance conditions of substrate exhibits a higher sensitive to bond weakening.

粘接接头广泛应用于各种工业应用中。粘结质量是保证结构完整性和安全性的关键。本研究采用非线性非共线波混合技术对粘接接头的微缺陷进行无损评价,包括粘接降解和粘接弱化。根据经典非线性理论,分别在胶粘剂和基材的共振条件下提出了两种测试方案。通过数值模拟和非共线波混合实验,探讨了所提出的检测方案对胶合接头中两种典型微缺陷检测的可行性。仿真和实验结果均表明,所提出的非线性非共线波混合方法对于粘接接头微缺陷的无损检测是有效的。此外,基于粘合剂共振条件的方案对粘合剂降解表现出更高的敏感性,而依赖基材共振条件的方案对粘结弱化表现出更高的敏感性。
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引用次数: 0
Electromagnetic Radiation Characteristics and Mechanical Properties of Cement-Mortar Under Impact Load 冲击载荷下水泥砂浆的电磁辐射特性和力学性能
IF 2.6 3区 材料科学 Q2 MATERIALS SCIENCE, CHARACTERIZATION & TESTING Pub Date : 2024-11-11 DOI: 10.1007/s10921-024-01140-2
Amit Kumar, Vishal S. Chauhan, Rajeev Kumar, Kamal Prasad

This study investigates the changes in electromagnetic radiation (EMR) emissions from cement-mortar subjected to impact throughout its curing process. The generation of EMR signals in hydrated samples is primarily driven by the accelerated motion of charged particles through the pore spaces and the time-dependent variation in dipole moments formed at the electrical double layer. As the hydration (curing) progresses, there is a noticeable decrease in EMR voltage, average EMR energy release rate, and dominant frequency. However, these EMR parameters exhibit an increasing trend with the application of higher mechanical impact energy. It was further observed that as hydration advances, the non-evaporable water content and degree of hydration increase, whereas the evaporable water content decreases. Additionally, EMR voltage recorded after fracture was consistently lower than that measured before fracture across all curing days, indicating that crack formation during repetitive loading suppresses EMR emissions. This suggests that cracks formed in the cement-mortar do not facilitate EMR generation. Moreover, the study found an inverse relationship between impact-dependent mechanical parameters and EMR voltage, highlighting that as mechanical resistance to impact increases, EMR voltage decreases. These findings suggest that the EMR technique has significant potential for non-contact, early-age monitoring of civil structures, providing critical insights into their mechanical integrity and performance under load.

本研究调查了水泥砂浆在整个固化过程中受到冲击时发出的电磁辐射(EMR)的变化情况。水化样品中电磁辐射信号的产生主要是由带电粒子在孔隙中的加速运动和电双层形成的偶极矩随时间变化所驱动的。随着水合(固化)的进行,电磁辐射电压、平均电磁辐射能量释放率和主频会明显下降。不过,随着机械冲击能量的增加,这些电磁辐射参数呈现出增加的趋势。进一步观察还发现,随着水化的推进,非蒸发水含量和水化程度增加,而蒸发水含量减少。此外,在所有固化天数中,断裂后记录到的电磁辐射电压始终低于断裂前测量到的电压,这表明在重复加载过程中形成的裂缝抑制了电磁辐射。这表明,水泥砂浆中形成的裂缝不会促进电磁辐射的产生。此外,研究还发现与冲击有关的机械参数与电磁辐射电压之间存在反比关系,这表明随着机械抗冲击性的增加,电磁辐射电压也会降低。这些研究结果表明,EMR 技术在非接触、早期监测土木工程结构方面具有巨大潜力,可为了解其负载下的机械完整性和性能提供重要依据。
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引用次数: 0
Instance Segmentation XXL-CT Challenge of a Historic Airplane 历史飞机的实例分割 XXL-CT 挑战赛
IF 2.6 3区 材料科学 Q2 MATERIALS SCIENCE, CHARACTERIZATION & TESTING Pub Date : 2024-11-05 DOI: 10.1007/s10921-024-01136-y
Roland Gruber, Johann Christopher Engster, Markus Michen, Nele Blum, Maik Stille, Stefan Gerth, Thomas Wittenberg

Instance segmentation of compound objects in XXL-CT imagery poses a unique challenge in non-destructive testing. This complexity arises from the lack of known reference segmentation labels, limited applicable segmentation tools, as well as partially degraded image quality. To asses recent advancements in the field of machine learning-based image segmentation, the ‘Instance Segmentation XXL-CT Challenge of a Historic Airplane’ was conducted. The challenge aimed to explore automatic or interactive instance segmentation methods for an efficient delineation of the different aircraft components, such as screws, rivets, metal sheets or pressure tubes. We report the organization and outcome of this challenge and describe the capabilities and limitations of the submitted segmentation methods.

XXL-CT 图像中复合物体的实例分割给无损检测带来了独特的挑战。这种复杂性源于缺乏已知的参考分割标签、适用的分割工具有限以及部分图像质量下降。为了评估基于机器学习的图像分割领域的最新进展,举办了 "历史飞机实例分割 XXL-CT 挑战赛"。挑战赛旨在探索自动或交互式实例分割方法,以有效划分不同的飞机部件,如螺丝、铆钉、金属片或压力管。我们报告了这次挑战赛的组织情况和结果,并介绍了提交的分割方法的能力和局限性。
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引用次数: 0
Publisher Correction: Intelligent Extraction of Surface Cracks on LNG Outer Tanks Based on Close-Range Image Point Clouds and Infrared Imagery 出版商更正:基于近距离图像点云和红外图像的液化天然气外罐表面裂缝智能提取技术
IF 2.6 3区 材料科学 Q2 MATERIALS SCIENCE, CHARACTERIZATION & TESTING Pub Date : 2024-11-03 DOI: 10.1007/s10921-024-01128-y
Ming Guo, Li Zhu, Youshan Zhao, Xingyu Tang, Kecai Guo, Yanru Shi, Liping Han
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引用次数: 0
Acoustic Emission Signal Feature Extraction for Bearing Faults Using ACF and GMOMEDA 利用 ACF 和 GMOMEDA 提取轴承故障的声发射信号特征
IF 2.6 3区 材料科学 Q2 MATERIALS SCIENCE, CHARACTERIZATION & TESTING Pub Date : 2024-11-02 DOI: 10.1007/s10921-024-01134-0
Yun Li, Yang Yu, Ping Yang, Fanzi Pu, Yunpeng Ben

In industry, rolling bearing damage acoustic emission (AE) signals are interfered with by complex transmission paths and strong noise. The signal-to-noise ratio of the AE signal is low. The bearing periodic fault pulse is weak, and fault feature extraction is challenging. To address these issues, combined with the characteristics of impulsiveness and rapid attention of the AE signal, an enhancement of the bearing weak fault signal based on the autocorrelation function (ACF) and improved multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) method is proposed in this contribution. Firstly, in low signal-to-noise ratio, the target vector of the MOMEDA method is not optimal, and the diagnostic accuracy is low. To address this problem, this paper improves MOMEDA by using the gradient descent method, called GMOMEDA. Rolling bearing fault AE pulse signals are enhanced. Then, a method combination of ACF and GMOMEDA highlights the periodic elastic wave in the signal. Finally, the enhanced AE signal is processed by envelope demodulation to extract the frequency of the bearing fault signal. The experimental results show that the performance of the ACF-GMOMEDA method is better than the other five methods. The frequency features of bearing fault AE signal can be accurately extracted.

在工业中,滚动轴承损坏声发射(AE)信号会受到复杂传输路径和强噪声的干扰。AE 信号的信噪比很低。轴承周期性故障脉冲微弱,故障特征提取具有挑战性。针对这些问题,结合 AE 信号的脉冲性和快速关注的特点,本文提出了一种基于自相关函数(ACF)和改进的多点最优最小熵解卷积调整(MOMEDA)方法的轴承微弱故障信号增强方法。首先,在低信噪比情况下,MOMEDA 方法的目标向量不是最优的,诊断精度较低。针对这一问题,本文采用梯度下降法对 MOMEDA 进行了改进,称为 GMOMEDA。对滚动轴承故障 AE 脉冲信号进行了增强。然后,结合 ACF 和 GMOMEDA 方法,突出信号中的周期性弹性波。最后,对增强的 AE 信号进行包络解调处理,以提取轴承故障信号的频率。实验结果表明,ACF-GMOMEDA 方法的性能优于其他五种方法。轴承故障 AE 信号的频率特性可以被准确提取出来。
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引用次数: 0
Modeling and Analysis of Ellipticity Dispersion Characteristics of Lamb Waves in Pre-stressed Plates 预应力板中λ波的椭圆度频散特性建模与分析
IF 2.6 3区 材料科学 Q2 MATERIALS SCIENCE, CHARACTERIZATION & TESTING Pub Date : 2024-10-25 DOI: 10.1007/s10921-024-01133-1
Yizheng Zhang, Yan Lyu, Jie Gao, Yang Zheng, Yongkang Wang, Bin Wu, Cunfu He

In this research, based on the weak nonlinear elasticity theory and finite deformation theory, a dynamic equation of guided waves in pre-stressed plates is established. Legendre orthogonal polynomials expansion method is employed to analytically solve wave equations. The dispersion curves and particle trajectory are obtained without root-finding algorithm. The solution is validated by comparing with the superposition of partial bulk wave method, and its convergence properties are analyzed. The effects of the pre-stress states on particle trajectory and ellipticity dispersion curves are investigated. Results confirm that the behavior of particle trajectory and ellipticity dispersion depends not only on the pre-stress states but also on frequency and mode. Next, ellipticity dispersion curve of fundamental Lamb modes with various stress state and propagation directions are calculated. Finally, the sensitivity of ellipticity as an indicator of stress is also analyzed. These results provide useful reference for the development of innovative nondestructive testing method for pre-stress states based on Lamb waves.

本研究基于弱非线性弹性理论和有限变形理论,建立了预应力板中的导波动态方程。采用 Legendre 正交多项式展开法对波方程进行解析求解。在不使用寻根算法的情况下,得到了频散曲线和粒子轨迹。通过与部分体波叠加法进行比较,验证了求解结果,并分析了其收敛特性。研究了预应力状态对粒子轨迹和椭圆度频散曲线的影响。结果证实,粒子轨迹和椭圆度频散的行为不仅取决于预应力状态,还取决于频率和模式。接着,计算了不同应力状态和传播方向的基本 Lamb 模式的椭圆度频散曲线。最后,还分析了椭圆度作为应力指标的敏感性。这些结果为开发基于 Lamb 波的创新型预应力状态无损检测方法提供了有益的参考。
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引用次数: 0
Self-Calibrating Stress Measurement System Based on Multidirectional Barkhausen Noise Measurements 基于多向巴尔豪森噪声测量的自校准应力测量系统
IF 2.6 3区 材料科学 Q2 MATERIALS SCIENCE, CHARACTERIZATION & TESTING Pub Date : 2024-10-22 DOI: 10.1007/s10921-024-01137-x
Leszek Piotrowski, Marek Chmielewski

The system presented in this paper enables automatization of the two-dimensional calibration process (determination of Barkhausen noise (BN) intensity dependence on in-plane components of strain). Then, using dedicated software created by the authors in LabVIEW environment, and with the help of two dimensional calibration data one can effectively determine strain and stress distribution i.e. magnitude and orientation of main strain/stress components relative to measurement direction. BN signal measurements are performed using an advanced, multidirectional Barkhausen noise (BN) measuring sensor and a measurement system dedicated for cooperation with it. The system uses a robust algorithm for the strain components determination based on calibration surfaces, instead of usually applied curves, thus taking the influence of normal strain component directly into account instead of treating it as a correction factor (if not completely neglecting). The originality of the system arises also from the fact that this is the first BN measurement system that is self-calibrating (i.e. automatically loads the calibration sample in a pre-programmed way, performs BN signal measurements and calculates calibration planes), provided that the user possesses enough of the investigated material for calibration sample preparation.

本文介绍的系统可实现二维校准过程的自动化(确定巴克豪森噪声 (BN) 强度与应变面内分量的关系)。然后,使用作者在 LabVIEW 环境中创建的专用软件,并在二维校准数据的帮助下,可以有效地确定应变和应力分布,即相对于测量方向的主要应变/应力分量的大小和方向。BN 信号测量使用先进的多方向巴尔豪森噪声(BN)测量传感器和专用测量系统进行。该系统使用一种基于校准面而不是通常应用的曲线的稳健算法来确定应变分量,从而直接考虑到法向应变分量的影响,而不是将其作为校正因子(即使不是完全忽略)。该系统的独创性还在于,它是首个可进行自我校准的 BN 测量系统(即以预先编程的方式自动加载校准样品,执行 BN 信号测量并计算校准平面),前提是用户拥有足够的被测材料用于校准样品制备。
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引用次数: 0
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Journal of Nondestructive Evaluation
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