用加权全聚焦法对粗晶钢缺陷进行高分辨率超声成像

Jiawei Zhang, J. Jiao, Xiang Gao, Bin Wu, C. He, Changhua Chen
{"title":"用加权全聚焦法对粗晶钢缺陷进行高分辨率超声成像","authors":"Jiawei Zhang, J. Jiao, Xiang Gao, Bin Wu, C. He, Changhua Chen","doi":"10.1784/insi.2023.65.1.19","DOIUrl":null,"url":null,"abstract":"Ultrasonic testing of coarse-grained materials is strongly influenced by high-level scattering noise. In addition, the signalto-noise ratio (SNR) and spatial resolution of imaging by the traditional total focusing method (TFM) are relatively low. In this study, we focused on the reconstruction\n of high-resolution ultrasonic images from full matrix capture datasets. A weighted TFM image by combining the inverse problem-based method and traditional TFM is proposed to detect defects in coarse-grained steel. The proposed method was used to image defects with the full matrix data obtained\n through simulations and experiments. The simulation and experimental results show that the weighted total focusing method can significantly improve the SNR of ultrasonic imaging in coarse-grained steel and, moreover, it can improve the resolution of imaging and distinguish adjacent defects\n with a centre distance less than the Rayleigh criteria.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"81 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"High-resolution ultrasonic imaging of the defects in coarse-grained steel by a weighted total focusing method\",\"authors\":\"Jiawei Zhang, J. Jiao, Xiang Gao, Bin Wu, C. He, Changhua Chen\",\"doi\":\"10.1784/insi.2023.65.1.19\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Ultrasonic testing of coarse-grained materials is strongly influenced by high-level scattering noise. In addition, the signalto-noise ratio (SNR) and spatial resolution of imaging by the traditional total focusing method (TFM) are relatively low. In this study, we focused on the reconstruction\\n of high-resolution ultrasonic images from full matrix capture datasets. A weighted TFM image by combining the inverse problem-based method and traditional TFM is proposed to detect defects in coarse-grained steel. The proposed method was used to image defects with the full matrix data obtained\\n through simulations and experiments. The simulation and experimental results show that the weighted total focusing method can significantly improve the SNR of ultrasonic imaging in coarse-grained steel and, moreover, it can improve the resolution of imaging and distinguish adjacent defects\\n with a centre distance less than the Rayleigh criteria.\",\"PeriodicalId\":344397,\"journal\":{\"name\":\"Insight - Non-Destructive Testing and Condition Monitoring\",\"volume\":\"81 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Insight - Non-Destructive Testing and Condition Monitoring\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1784/insi.2023.65.1.19\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Insight - Non-Destructive Testing and Condition Monitoring","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1784/insi.2023.65.1.19","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

粗粒材料的超声检测受到高强度散射噪声的强烈影响。此外,传统全聚焦法成像的信噪比(SNR)和空间分辨率较低。在这项研究中,我们专注于从全矩阵捕获数据集重建高分辨率超声图像。将基于逆问题的方法与传统的TFM相结合,提出了一种加权TFM图像来检测粗粒钢的缺陷。利用仿真和实验得到的全矩阵数据对缺陷进行了图像处理。仿真和实验结果表明,加权全聚焦方法能显著提高粗晶钢超声成像的信噪比,提高成像分辨率,并能以小于瑞利准则的中心距离分辨出相邻缺陷。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
High-resolution ultrasonic imaging of the defects in coarse-grained steel by a weighted total focusing method
Ultrasonic testing of coarse-grained materials is strongly influenced by high-level scattering noise. In addition, the signalto-noise ratio (SNR) and spatial resolution of imaging by the traditional total focusing method (TFM) are relatively low. In this study, we focused on the reconstruction of high-resolution ultrasonic images from full matrix capture datasets. A weighted TFM image by combining the inverse problem-based method and traditional TFM is proposed to detect defects in coarse-grained steel. The proposed method was used to image defects with the full matrix data obtained through simulations and experiments. The simulation and experimental results show that the weighted total focusing method can significantly improve the SNR of ultrasonic imaging in coarse-grained steel and, moreover, it can improve the resolution of imaging and distinguish adjacent defects with a centre distance less than the Rayleigh criteria.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Multi-criterion analysis-based artificial intelligence system for condition monitoring of electrical transformers MFL detection of adjacent pipeline defects: a finite element simulation of signal characteristics A multi-frequency balanced electromagnetic field measurement for arbitrary angles of pipeline cracks with high sensitivity Ultrasonic total focusing method for internal defects in composite insulators Developments in ultrasonic and eddy current testing in the 1970s and 1980s with emphasis on the requirements of the UK nuclear power industry
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1