Nondestructive Testing and Evaluation最新文献_第3页

An improved magnetic dipole model for MFL testing based on non-uniform magnetic charge distribution 基于非均匀磁荷分布的改进型磁偶极子模型用于 MFL 试验

IF 2.6 3区材料科学

Nondestructive Testing and Evaluation Pub Date : 2023-11-24 DOI: 10.1080/10589759.2023.2285336

Shengping Li, L. Bai, Chunrui Feng, Xu Zhang, Yiping Liang, Jiangshan Ai, Jie Zhang

引用次数: 0

Development of dual-main excitation mechanism modes electromagnet EMAT for testing ferromagnetic materials 开发用于铁磁材料测试的双主激励机制模式电磁铁 EMAT

IF 2.6 3区材料科学

Nondestructive Testing and Evaluation Pub Date : 2023-11-24 DOI: 10.1080/10589759.2023.2283712

Peiying Zhang, Zenghua Liu, Yanhong Guo, Xiaosai Wang, Yu Gong

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Using acoustic emission for condition monitoring of the main shaft bearings in 4-point suspension wind turbine drivetrains 利用声发射对 4 点悬挂式风力涡轮机传动系统中的主轴轴承进行状态监测

IF 2.6 3区材料科学

Nondestructive Testing and Evaluation Pub Date : 2023-11-23 DOI: 10.1080/10589759.2023.2283511

Housam Mohammad, Frantisek Vlasic, Jiří Žáček, Baraah Maya, P. Mazal

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Physics-based thermal noise effect reduction in sonic IR crack length estimation 在声波红外裂缝长度估算中降低基于物理的热噪声效应

IF 2.6 3区材料科学

Nondestructive Testing and Evaluation Pub Date : 2023-11-23 DOI: 10.1080/10589759.2023.2285340

S. Al-Said, B. Abu-Nabah

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Coupling pressure of an ultrasonic transducer excitation under the action of a loading device 加载装置作用下超声波传感器激励的耦合压力

IF 2.6 3区材料科学

Nondestructive Testing and Evaluation Pub Date : 2023-11-20 DOI: 10.1080/10589759.2023.2274010

Jiahui Liang, Yu Jia, Lei Tang, Jinyu Ge, Yulei Wang, Shenghang Zhang

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Low-velocity impact damage detection in CFRP composites by applying long pulsed thermography based on post-processing techniques 利用基于后处理技术的长脉冲热成像技术检测 CFRP 复合材料中的低速冲击损伤

IF 2.6 3区材料科学

Nondestructive Testing and Evaluation Pub Date : 2023-11-20 DOI: 10.1080/10589759.2023.2284248

Guozeng Liu, Weicheng Gao, Wei Liu, Yanze Wei, Xionghui Zou, Weiliang Bai, Peng Chen

引用次数: 0

Deformation failure and damage evolution law of weathered granite under triaxial compression 三轴压缩下风化花岗岩的变形破坏和损伤演化规律

IF 2.6 3区材料科学

Nondestructive Testing and Evaluation Pub Date : 2023-11-17 DOI: 10.1080/10589759.2023.2283710

Qizheng Huang, Kang Zhao, Weiling Xiao, Qiang Nie, Jiale Chen, Yang Liu, Juncheng Zhong

引用次数: 0

Influence of tensile stress on the magnetic flux leakage signal of the coated steel belt 拉伸应力对涂覆钢带漏磁信号的影响

3区材料科学

Nondestructive Testing and Evaluation Pub Date : 2023-11-14 DOI: 10.1080/10589759.2023.2274014

Zhaoting Liu, Yanlin Liu, Chuan Shen, Liming Wei, Jianbo Wu, Wenhui Yang, Kewen Huang, Piyu Miao

{"title":"Influence of tensile stress on the magnetic flux leakage signal of the coated steel belt","authors":"Zhaoting Liu, Yanlin Liu, Chuan Shen, Liming Wei, Jianbo Wu, Wenhui Yang, Kewen Huang, Piyu Miao","doi":"10.1080/10589759.2023.2274014","DOIUrl":"https://doi.org/10.1080/10589759.2023.2274014","url":null,"abstract":"ABSTRACTThe safety of coated steel belts (CSBs) is crucial for reliable elevator operation, as they are critical components in elevator systems. Magnetic flux leakage (MFL) testing is the preferred non-destructive method for evaluating CSBs due to its high sensitivity. Previous studies overlooked tensile stress's impact on in-service MFL testing of CSBs, potentially resulting in inaccurate defect assessments. This research aims to investigate the impact of tensile stress on the MFL signal of CSBs by developing a theoretical model, conducting finite element simulations, and performing experimental verification. In this paper, the rectangular edge defects are considered as the primary defect type in CSBs, and the applied stress on the CSBs varies from 30 MPa to 160 MPa. Under the above conditions, a linear relationship between MFL signal of CSBs and stress is established based on the simplified Jiles-Atherton model and magnetic dipole model suitable for CSBs. The finite element simulation and experiments further indicate that the MFL signal of CSBs increases linearly with the increasing tensile stress. The primary contributions of this study are establishing an MFL model suitable for CSBs and uncovering the linear relationship between the MFL signal of CSBs and tensile stress.KEYWORDS: Coated steel beltmagnetic field distribution mapmagnetic flux leakage testingtensile stress Disclosure statementThe authors report there are no competing interests to declare.Supplementary InformationSupplemental data for this article can be accessed online at https://doi.org/10.1080/10589759.2023.2274014.Additional informationFundingThis work was supported by the National Key Research and Development Program of China under Grant 2022YFF0605600; National Natural Science Foundation of China under Grant 92060114; Sichuan Science and Technology Program under Grant 2023YFQ0060, Grant 2023YFS0413, and Grant 2022YFG0044; Science and Technology Program of the State Administration for Market Regulation under Grant 2022MK153; and Science and Technology Program of the Administration for Market Regulation of Sichuan Province under Grant SCSJZ2023001.","PeriodicalId":49746,"journal":{"name":"Nondestructive Testing and Evaluation","volume":"50 16","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134902811","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

Coupling effect between highly nonlinear solitary waves and functionally graded porous plates reinforced with graphene platelets 高度非线性孤立波与石墨烯增强功能梯度多孔板的耦合效应

3区材料科学

Nondestructive Testing and Evaluation Pub Date : 2023-11-09 DOI: 10.1080/10589759.2023.2274009

Xingyu Xiao, Yan Wang

{"title":"Coupling effect between highly nonlinear solitary waves and functionally graded porous plates reinforced with graphene platelets","authors":"Xingyu Xiao, Yan Wang","doi":"10.1080/10589759.2023.2274009","DOIUrl":"https://doi.org/10.1080/10589759.2023.2274009","url":null,"abstract":"ABSTRACTSolitary waves possess extensive potential for application in non-destructive testing due to their role as efficient information carriers. This study investigates the coupling effect between highly nonlinear solitary waves and functionally graded porous plates reinforced with graphene platelets (FGP-GPLs). An improved Halpin–Tsai micromechanics model and an improved two-variable precision plate theory are employed to derive a differential equation system for the coupling of particle chains and FGP-GPLs. The system is solved using the fourth-order Runge–Kutta method to obtain velocity and displacement solutions of the particles. The time and amplitude of the rebound waves are analysed, and it is found that the pore distribution, graphene distribution, porosity coefficient, thickness ratio, and graphene weight fraction impact the solitary wave. The results of this study provide a theoretical basis for the non-destructive detection of FGP-GPLs by solitary waves, which enables rapid inspection and controllability studies of structures. Moreover, this technology expands the application fields of nonlinear solitary waves based on one-dimensional spherical particle chains.KEYWORDS: Highly nonlinear solitary wavefunctionally graded porous plates reinforced with graphene plateletsHertz’s lawnon-destructive testingone-dimensional spherical particle chains Disclosure statementThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","PeriodicalId":49746,"journal":{"name":"Nondestructive Testing and Evaluation","volume":" 21","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135242906","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

Semi-supervised instance segmentation algorithm based on transfer learning 基于迁移学习的半监督实例分割算法

3区材料科学

Nondestructive Testing and Evaluation Pub Date : 2023-11-09 DOI: 10.1080/10589759.2023.2274013

Bing Liu, Ren Yi, Zhongquan Yu, Shiyu Wang, Xuewen Yang, Fuwen Wang

{"title":"Semi-supervised instance segmentation algorithm based on transfer learning","authors":"Bing Liu, Ren Yi, Zhongquan Yu, Shiyu Wang, Xuewen Yang, Fuwen Wang","doi":"10.1080/10589759.2023.2274013","DOIUrl":"https://doi.org/10.1080/10589759.2023.2274013","url":null,"abstract":"ABSTRACTSemi-supervised instance segmentation algorithms are mainly divided into algorithms based on pseudo-label generation and algorithms based on transfer learning. The algorithms based on pseudo-label generation need to design a specific pseudo-label generation process, but the process is not scalable for different types of source tasks. The algorithms based on transfer learning that started late have relatively high scalability, but the algorithm research ideas are relatively simple. To expand the research on semi-supervised instance segmentation based on transfer learning, this paper proposes a feature transfer-based semi-supervised instance segmentation algorithm Feature Transfer Mask R-CNN (FT-Mask). The FT-Mask algorithm is more scalable than algorithms based on pseudo-label generation and can be used to transfer knowledge from different types of source tasks. Compared with other semi-supervised instance segmentation algorithms based on transfer learning, FT-Mask uses the feature transfer method to achieve semi-supervised instance segmentation for the first time. The experimental results show that the FT-Mask model improves the semi-supervised instance segmentation accuracy of the Mask R-CNN benchmark model through the semi-supervised learning process, and can achieve effective transfer learning.KEYWORDS: Semi-supervised learninginstance segmentationtransfer learning Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThis work was supported in part by the National Natural Science Foundation of China under Grant 62072319; the Sichuan Science and Technology Program under Grant 2023YFQ0022 and 2022YFG0041; the Luzhou Science and Technology Innovation R&D Program (No. 2022CDLZ-6).","PeriodicalId":49746,"journal":{"name":"Nondestructive Testing and Evaluation","volume":" 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135242074","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