Journal of Nondestructive Evaluation最新文献_第7页

Immersive Inspection: Intuitive Material Analysis using X-Ray Computed Tomography Data in AR 沉浸式检测：使用AR中的x射线计算机断层扫描数据进行直观的材料分析

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-06-22 DOI: 10.1007/s10921-025-01220-x

Alexander Gall, Anja Heim, Patrick Weinberger, Bernhard Fröhler, Johann Kastner, Christoph Heinzl

{"title":"Immersive Inspection: Intuitive Material Analysis using X-Ray Computed Tomography Data in AR","authors":"Alexander Gall, Anja Heim, Patrick Weinberger, Bernhard Fröhler, Johann Kastner, Christoph Heinzl","doi":"10.1007/s10921-025-01220-x","DOIUrl":"10.1007/s10921-025-01220-x","url":null,"abstract":"<div><p>Material analyses based on X-ray computed tomography (XCT) imaging are typically conducted away from scanning facilities, in separate office environments on 2D displays. This separation hinders on-site analysis, and due to the lack of spatial representation, limits the effective exploration of the material structure. We present a novel augmented reality (AR) framework enabling in-situ visualization of non-destructive testing (NDT) data spatially registered with real specimens. Our approach facilitates comprehensive exploration of primary and secondary XCT data, enabling researchers to inspect material properties onsite and in-place. Coupling immersive visualization techniques with real physical objects allows for highly intuitive workflows in material analysis and inspection, which enables the identification of anomalies and accelerates informed decision making. The AR framework offers automatic material recognition, hands-free workflows and embodied interaction with physical samples, generating an engaging analytical experience. A case study on fiber-reinforced polymer datasets was used to validate the AR framework and its new workflow. Expert evaluations revealed significant improvements in spatial data comprehension and more natural interaction compared to conventional analysis systems. This study demonstrates the potential of immersive AR technologies to enhance industrial materials analysis, providing preliminary insights for integrating such immersive approaches.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-025-01220-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145168569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transformer Based on Multi-Scale Local Perception and Contrastive Learning for Train Axle Fatigue Crack Acoustic Emission Detection 基于多尺度局部感知和对比学习的变压器列车轴疲劳裂纹声发射检测

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-06-14 DOI: 10.1007/s10921-025-01199-5

Li Lin, Liwen Ding, Qingwei Peng

{"title":"Transformer Based on Multi-Scale Local Perception and Contrastive Learning for Train Axle Fatigue Crack Acoustic Emission Detection","authors":"Li Lin, Liwen Ding, Qingwei Peng","doi":"10.1007/s10921-025-01199-5","DOIUrl":"10.1007/s10921-025-01199-5","url":null,"abstract":"<div><p>Acoustic emission has become one of the most commonly used non-destructive testing techniques in the track crack detection industry due to its advantages in dynamic monitoring and real-time online detection. Transformer models construct global dependencies through self-attention layers, bringing more possibilities for feature extraction, but they are limited in the ability to extract local features. In order to further improve recognition accuracy and robustness, this paper designs a Transformer based on multi-scale local perception and contrastive learning for train axle fatigue crack acoustic emission detection. The core of this method is the collaborative design of its multi-scale local perception, local-global coupling architecture, and contrastive learning optimization, which breaks through the inherent limitations of traditional Transformer in acoustic emission signal processing and provides a highly robust solution for fatigue crack detection under complex working conditions. Specifically, after capturing global dependencies through the multi-head self-attention module, the convolutional module captures the local features of the sequence to provide more contextual information. By simultaneously incorporating multi-scale convolutional layers to enhance the generalization ability of the model. To eliminate the uncertainty of model predictions, this study also designed an optimization task that combines cross-entropy loss and supervised contrastive learning to enhance fine-grained feature representation capabilities. Finally, the proposed method was evaluated on the collected dataset. The experimental results show that the accuracy of the classification method reached 99.19%, achieving accurate identification and classification of fatigue crack signals and providing a novel and highly promising solution for the diagnosis of fatigue crack faults in train axles.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145165591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spectral Band Attention Networks for Efficient Multi-Feature Fusion in Hyperspectral and RGB Data with Ensemble Deep Learning Networks 基于集成深度学习网络的高光谱和RGB数据高效多特征融合的光谱带关注网络

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-06-14 DOI: 10.1007/s10921-025-01215-8

Nitin Tyagi, Sarvagya Porwal, Pradeep Singh, Balasubramanian Raman, Neerja Garg

{"title":"Spectral Band Attention Networks for Efficient Multi-Feature Fusion in Hyperspectral and RGB Data with Ensemble Deep Learning Networks","authors":"Nitin Tyagi, Sarvagya Porwal, Pradeep Singh, Balasubramanian Raman, Neerja Garg","doi":"10.1007/s10921-025-01215-8","DOIUrl":"10.1007/s10921-025-01215-8","url":null,"abstract":"<div><p>Rapid and nondestructive seed variety identification is crucial for improving agricultural efficiency. Hyperspectral imaging is a powerful tool for this task; however, its high dimensionality and redundant bands can lead to overfitting, while its lower spatial resolution makes it challenging to distinguish individual seeds. A spectral band attention network is proposed to overcome the issue of high dimensionality and redundant bands. Further, an ensemble model is developed that integrates the spectral and spatial features extracted from hyperspectral and RGB images, respectively. A large dataset comprising 96 Indian wheat varieties was prepared using RGB and hyperspectral imaging (900-1700 nm). The ensemble model comprises four deep convolutional neural networks, Customized DenseNet, GoogLeNet, ResNet34, and DenseNet121, with a Support Vector Machine classifier for the final prediction of the seed class. The model’s performance was evaluated using spectral band subsets selected through the band selection techniques, which included Spectral Band Attention Network, Sparse Band Attention Network, Principal Component Analysis-loading, Successive Projection Algorithm, and Triplet-attention. The proposed spectral band attention network outperformed other methods, identifying 25 optimal spectral bands, enabling the ensemble model to achieve a test accuracy of 95.75%. These findings highlight the potential of the proposed spectral band attention network and ensemble model for accurately identifying the wheat varieties. The source code is available at GitHub Repository: SBAN for Multi-Feature Fusion</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145165725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Physics-informed Wave Tomography Framework for Defect Reconstruction: A Collaborative Network Scheme 缺陷重建的物理信息波层析框架：协同网络方案

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-06-10 DOI: 10.1007/s10921-025-01210-z

Hairui Liu, Qi Li, Zhi Qian, Peng Li, Zhenghua Qian, Dianzi Liu

{"title":"A Physics-informed Wave Tomography Framework for Defect Reconstruction: A Collaborative Network Scheme","authors":"Hairui Liu, Qi Li, Zhi Qian, Peng Li, Zhenghua Qian, Dianzi Liu","doi":"10.1007/s10921-025-01210-z","DOIUrl":"10.1007/s10921-025-01210-z","url":null,"abstract":"<div>\u0000 \u0000 <p>It is challenging for guided wave tomography methods to intelligently solve problems in the area of structural defect detection, as this requires more data to achieve the high-accuracy reconstruction of defects. To meet this end, a physics-informed wave tomography framework (PIWT) with a collaborative network scheme is proposed in this paper to reconstruct defects in metal plates with high levels of accuracy and efficiency. First, taking the spatial coordinate information of the point source and sampling points as the inputs of the deep learning collaborative network, a physical principle-based prediction framework is established by minimizing the loss functions to realize the mapping of inputs to outputs, which are represented as the travel time and wave velocity in two collaborative networks for defect reconstruction. To effectively guide the convergence direction of the collaborative network for efficient computations, the Helmholtz equation and source condition are leveraged as the constraints on PIWT to realize the defect reconstruction. As the developed approach belongs to the class of mesh-free methods, its superiority over the conventional mesh-based ultrasonic Lamb wave tomography imaging (ULWTI) technique is demonstrated for defect reconstruction throughout the numerical and experimental examples in terms of accuracy. Moreover, the effects of pre-training on the accelerated convergence and accuracy of the PIWT framework are discussed to allow the training with few epochs and also help effectively achieve real-time high-precision defect reconstruction in the fields of non-destructive testing and structural health monitoring, thus offering a promising solution for broader engineering applications.</p>\u0000 </div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145164111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automated Robot-Based Computed Tomography Trajectory Optimization using Differential Evolution in 3D Radon Space 基于机器人的三维Radon空间差分演化计算机断层扫描轨迹优化

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-06-10 DOI: 10.1007/s10921-025-01204-x

Maximilian Linde, Wolfram Wiest, Anna Trauth, Markus G. R. Sause

{"title":"Automated Robot-Based Computed Tomography Trajectory Optimization using Differential Evolution in 3D Radon Space","authors":"Maximilian Linde, Wolfram Wiest, Anna Trauth, Markus G. R. Sause","doi":"10.1007/s10921-025-01204-x","DOIUrl":"10.1007/s10921-025-01204-x","url":null,"abstract":"<div><p>Limited accessibility of the X-ray hardware manipulating robots stemming from collision elements and the restricted workspace of the robots as well as areas of significant X-ray absorption are inherent characteristics of robot-based computed tomography scanning in subregions of large structures. The manual definition of trajectories is resource-intensive and results in substantial user influence on the resulting data quality. Therefore, this work proposes a method for the automated calculation of optimized (partial) circular scan trajectories for robot-based computed tomography. Specifically, a differential evolution algorithm is used to find global parametrization optima by estimating the reconstruction quality of trajectories. This estimation is based on a quantitative sampling quality metric in 3D Radon space, which is introduced in this work. The proposed method is evaluated on a test body from a region of limited accessibility within the strut mount of a car body. The reconstruction results are compared to those obtained from nearly 1000 reference trajectories. The results demonstrate that the proposed technique automatically generates trajectories that surpass the global optimum in data completeness of all reference trajectories. This methodology thus enables the elimination of user influence in trajectory parametrization.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-025-01204-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145164110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Thickness Measurement Method Immune to Lift-Off Fluctuation Using Sweep-Frequency Eddy Current Testing 一种不受升降波动影响的扫频涡流测厚方法

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-06-09 DOI: 10.1007/s10921-025-01201-0

Pu Huang, Zhenyu Bao, Jinqin Guo, Yuedong Xie

{"title":"A Thickness Measurement Method Immune to Lift-Off Fluctuation Using Sweep-Frequency Eddy Current Testing","authors":"Pu Huang, Zhenyu Bao, Jinqin Guo, Yuedong Xie","doi":"10.1007/s10921-025-01201-0","DOIUrl":"10.1007/s10921-025-01201-0","url":null,"abstract":"<div><p>Eddy current testing (ECT) is a highly effective technique for measuring the thickness of metal samples. However, the fluctuation of lift-off distance easily affects the accuracy of measurement. In this paper, a thickness measurement immune to lift-off strategy based on the sweep frequency eddy current testing is investigated. First of all, we conducted an analysis on the relationship between the peak frequency of mutual inductance variation and the thickness of metal plates in line with Dodd-Deeds analytical solution. Moreover, we have demonstrated that the real part of mutual inductance variation at high frequencies (~ MHz) can be directly employed to invert and estimate lift-off, which is immune to the thickness and electromagnetic properties of metal samples. According to the estimated lift-off, the instrument factor for thickness measurement can be compensated to improve accuracy of thickness measurement. Both experiment and numerical solution have been applied to verify the proposed method, and the results indicate the relative error is only within 2.4%, which provides an approach to actual online measurement in the future.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145163524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Eddy Current Method in Non-Magnetic Aluminide Coating Thickness Assessment 涡流法在非磁性铝化物涂层厚度评估中的应用

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-06-09 DOI: 10.1007/s10921-025-01211-y

Grzegorz Tytko, Małgorzata Adamczyk-Habrajska, Yong Li, Shi Pengpeng, Mateusz Kopec

{"title":"Eddy Current Method in Non-Magnetic Aluminide Coating Thickness Assessment","authors":"Grzegorz Tytko, Małgorzata Adamczyk-Habrajska, Yong Li, Shi Pengpeng, Mateusz Kopec","doi":"10.1007/s10921-025-01211-y","DOIUrl":"10.1007/s10921-025-01211-y","url":null,"abstract":"<div><p>This study investigates the use of eddy current testing (ECT) as a non-destructive technique to evaluate the thickness and structural variations of non-magnetic aluminide coatings on MAR-M247 nickel-based superalloy. Coatings with thicknesses of 20 μm and 40 μm were applied to substrates exhibiting fine, coarse, and columnar grain structures. Using sensors of different geometries, impedance measurements were performed within a frequency range of 11.5 MHz to 12.5 MHz. Results demonstrated the designed sensor’s superior sensitivity, with the highest values of absolute resistance difference significantly exceeding the threshold for reliable distinction due to coating thicknesses or grain structures. The study highlights the impact of eddy current penetration depth and edge effects on the measurement accuracy, emphasizing the need for optimized sensor design and frequency selection. Findings confirm the efficacy of ECT in differentiating coatings of varying thicknesses and substrate structures, offering a reliable tool for quality control in high-temperature applications.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-025-01211-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145163526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Review of Ex-situ, In situ and Artificial Intelligence-based Thermographic Measurements in Additively Manufactured Parts 增材制造零件非原位、原位和人工智能热像测量综述

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-06-09 DOI: 10.1007/s10921-025-01195-9

Manuela Galati, Simone De Giorgi, Giovanni Rizza, Emanuele Tognoli, Giulia Colombini, Lucia Denti, Elena Bassoli, Luca Iuliano

{"title":"A Review of Ex-situ, In situ and Artificial Intelligence-based Thermographic Measurements in Additively Manufactured Parts","authors":"Manuela Galati, Simone De Giorgi, Giovanni Rizza, Emanuele Tognoli, Giulia Colombini, Lucia Denti, Elena Bassoli, Luca Iuliano","doi":"10.1007/s10921-025-01195-9","DOIUrl":"10.1007/s10921-025-01195-9","url":null,"abstract":"<div><p>Additive manufacturing (AM) encompasses a range of advanced production methods that are increasingly applied across various sectors, particularly where customisation, high-strength materials, or complex parts are required. However, a key challenge remains the need for rapid methods and non-destructive testing (NDT) technologies to ensure part quality, particularly for detecting internal defects. Among these methods, infrared thermography (IRT) is gaining popularity due to its ease of use and low overall system cost (hardware, data acquisition, and processing) when compared to more complex techniques like tomography. AM can greatly benefit from IRT, both ex-situ for quality control and in-situ for process monitoring. This paper reviews the current literature on the application of IRT in the AM field. It examines IRT as a standard method for detecting typical defects in AM parts ex-situ, after the manufacturing process. The effectiveness of IRT techniques is evaluated in terms of their ability to detect defects based on size and depth. The paper also explores the use of IRT for in-situ process monitoring, where thermograms are captured during production and analysed to identify defects early. The advantages and limitations of IRT are discussed and compared with other NDT techniques. Additionally, the use of numerical simulation and artificial intelligence (AI) in enhancing IRT applications is reviewed. The findings highlight that while IRT is a valuable tool for defect characterisation in AM, significant potential remains for developing more advanced and efficient approaches that integrate data from multiple sources.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-025-01195-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145163982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-response Optimization of Ultrasonic Case Depth Measurement 超声壳体深度测量的多响应优化

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-06-09 DOI: 10.1007/s10921-025-01207-8

Mahdi Bayat-Kazazi, Farhang Honarvar, Alireza Bahadory

{"title":"Multi-response Optimization of Ultrasonic Case Depth Measurement","authors":"Mahdi Bayat-Kazazi, Farhang Honarvar, Alireza Bahadory","doi":"10.1007/s10921-025-01207-8","DOIUrl":"10.1007/s10921-025-01207-8","url":null,"abstract":"<div><p>This paper presents a methodology for optimizing the parameters of the immersion ultrasonic backscattering technique used in measuring the case depth of induction-hardened parts. The accuracy of an immersion ultrasonic backscattering technique is governed by two major parameters: the probe angle and probe distance from the part surface. The required equations are derived to calculate the probe distance based on the focal length of the probe. A design of experiments (DOE) process is also performed by conducting 81 tests on three surface-hardened steel shafts, to identify the significant factors affecting the measurement results. The findings demonstrate the effectiveness of the probe angle, probe distance and their interaction in the backscattering technique. The desirability function approach is then used to optimize the multi-response results of case depth measurements. The optimal settings for this specific case are determined as a probe angle of 16° and a probe distance of 10 mm, resulting in a desirability function value of 0.90. The methodology introduced in this paper can be applied to other backscattering applications, providing optimal configurations for ultrasonic testing, and improving measurement accuracy.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145163525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detection of Anode Coating Defects in Batteries Electrode Production and their Effect on Cell Performance 电池电极生产中阳极涂层缺陷的检测及其对电池性能的影响

IF 2.4 3区材料科学

Journal of Nondestructive Evaluation Pub Date : 2025-06-09 DOI: 10.1007/s10921-025-01208-7

Stefan W. Zangerle, Lea Weinzierl, Armin Summer, Simon Schmid, Peter Jahnke, Christian U. Grosse

{"title":"Detection of Anode Coating Defects in Batteries Electrode Production and their Effect on Cell Performance","authors":"Stefan W. Zangerle, Lea Weinzierl, Armin Summer, Simon Schmid, Peter Jahnke, Christian U. Grosse","doi":"10.1007/s10921-025-01208-7","DOIUrl":"10.1007/s10921-025-01208-7","url":null,"abstract":"<div><p>The transition from combustion engines to electric vehicles drives the growing demand for high-quality lithium-ion batteries. Currently, defective cells are identified at the end of the battery production process during the aging step. Recycling these defective cells is costly and resource-intensive. Therefore, detecting defects earlier in the production process leads to significant cost savings. For that purpose, Non-Destructive Testing (NDT) methods can be applied in-line during battery production. This study examines two NDT methods for detecting coating defects on the anode: laser thermography and optical cameras. Both methods are suitable for in-line inspection. Artificial line and pinhole defects, as well as particle contamination, are introduced for testing the performance of the method using probability of detection curves. The results demonstrate that the optical camera system is superior at detecting particle contamination and point defects, while laser thermography is more effective for identifying line defects. Besides the detectability, the effect of these particles on the cell performance is investigated. The assembled cylindrical cells underwent life cycle testing and were benchmarked against defect-free reference cells. The findings indicate that point defects do not significantly affect cell behaviour. However, line defects and particle contamination on the anodes were observed to impact both the specific capacity over cycles and it’s thermal behaviour.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"44 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10921-025-01208-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145163523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0