Junkai Tong , Jian Li , Min Lin , Shili Chen , Guoan Chu , Lingling Lv , Pengfei Zhang , Zhifeng Tang , Yang Liu
{"title":"利用剪切水平波和深度卷积下降全波形反演进行定量导波成像","authors":"Junkai Tong , Jian Li , Min Lin , Shili Chen , Guoan Chu , Lingling Lv , Pengfei Zhang , Zhifeng Tang , Yang Liu","doi":"10.1016/j.ndteint.2024.103141","DOIUrl":null,"url":null,"abstract":"<div><p>Effectively determining the size and thickness distributions of corrosion damages is a vital problem in nondestructive testing (NDT). In this article, a new approach is introduced that employs magnetostrictive transducers (MST) to excite the first dispersive shear horizontal mode (SH1), and utilizes the DCD-FWI (deep convolutional descent full waveform inversion) to provide comprehensive thickness mapping in just 1 s. Compared with traditional imaging techniques, DCD-FWI network is an intrinsically full waveform inversion (FWI) based method, which yields reliable imaging results in large scale finite element simulations. Moreover, the shorter wavelength and resistance to water loading of SH guided waves bring about reliable imaging results. Combined with these advantages, the high energy transfer efficiency of the MST enhances the proposed method's robustness, as demonstrated by guided wave tomography experiments on aluminum plates.</p></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"145 ","pages":"Article 103141"},"PeriodicalIF":4.1000,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Quantitative guided wave imaging with shear horizontal waves and deep convolutional descent full waveform inversion\",\"authors\":\"Junkai Tong , Jian Li , Min Lin , Shili Chen , Guoan Chu , Lingling Lv , Pengfei Zhang , Zhifeng Tang , Yang Liu\",\"doi\":\"10.1016/j.ndteint.2024.103141\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Effectively determining the size and thickness distributions of corrosion damages is a vital problem in nondestructive testing (NDT). In this article, a new approach is introduced that employs magnetostrictive transducers (MST) to excite the first dispersive shear horizontal mode (SH1), and utilizes the DCD-FWI (deep convolutional descent full waveform inversion) to provide comprehensive thickness mapping in just 1 s. Compared with traditional imaging techniques, DCD-FWI network is an intrinsically full waveform inversion (FWI) based method, which yields reliable imaging results in large scale finite element simulations. Moreover, the shorter wavelength and resistance to water loading of SH guided waves bring about reliable imaging results. Combined with these advantages, the high energy transfer efficiency of the MST enhances the proposed method's robustness, as demonstrated by guided wave tomography experiments on aluminum plates.</p></div>\",\"PeriodicalId\":18868,\"journal\":{\"name\":\"Ndt & E International\",\"volume\":\"145 \",\"pages\":\"Article 103141\"},\"PeriodicalIF\":4.1000,\"publicationDate\":\"2024-05-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ndt & E International\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0963869524001063\",\"RegionNum\":2,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATERIALS SCIENCE, CHARACTERIZATION & TESTING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ndt & E International","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0963869524001063","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, CHARACTERIZATION & TESTING","Score":null,"Total":0}
Quantitative guided wave imaging with shear horizontal waves and deep convolutional descent full waveform inversion
Effectively determining the size and thickness distributions of corrosion damages is a vital problem in nondestructive testing (NDT). In this article, a new approach is introduced that employs magnetostrictive transducers (MST) to excite the first dispersive shear horizontal mode (SH1), and utilizes the DCD-FWI (deep convolutional descent full waveform inversion) to provide comprehensive thickness mapping in just 1 s. Compared with traditional imaging techniques, DCD-FWI network is an intrinsically full waveform inversion (FWI) based method, which yields reliable imaging results in large scale finite element simulations. Moreover, the shorter wavelength and resistance to water loading of SH guided waves bring about reliable imaging results. Combined with these advantages, the high energy transfer efficiency of the MST enhances the proposed method's robustness, as demonstrated by guided wave tomography experiments on aluminum plates.
期刊介绍:
NDT&E international publishes peer-reviewed results of original research and development in all categories of the fields of nondestructive testing and evaluation including ultrasonics, electromagnetics, radiography, optical and thermal methods. In addition to traditional NDE topics, the emerging technology area of inspection of civil structures and materials is also emphasized. The journal publishes original papers on research and development of new inspection techniques and methods, as well as on novel and innovative applications of established methods. Papers on NDE sensors and their applications both for inspection and process control, as well as papers describing novel NDE systems for structural health monitoring and their performance in industrial settings are also considered. Other regular features include international news, new equipment and a calendar of forthcoming worldwide meetings. This journal is listed in Current Contents.