基于多模式 TFM 检测的不规则焊缝缺陷可视化

Shaofeng Wang, Yaowen Zhang, Shenrong Zhou, Wenjing Liu, Fei Du, Jian Wang, Fei Hui, Mingyuan Yang
{"title":"基于多模式 TFM 检测的不规则焊缝缺陷可视化","authors":"Shaofeng Wang, Yaowen Zhang, Shenrong Zhou, Wenjing Liu, Fei Du, Jian Wang, Fei Hui, Mingyuan Yang","doi":"10.32548/2024.me-04391","DOIUrl":null,"url":null,"abstract":"An image reconstruction method based on the multimode total focusing method (MTFM) is proposed to overcome the limitations of traditional total focusing method (TFM) imaging in detecting tiny discontinuities at complex locations. We conducted MTFM detection and TFM image reconstruction experiments for irregular welds containing multiple discontinuities. In an experiment using four 1 mm diameter manufactured defects fabricated on two aluminum alloy welded test blocks, we achieved two significant contributions. First, we accurately detected small discontinuities by combining CIVA simulation with robotic arm assistance. Second, we proposed fusion factor coefficients for TFM image processing, which considered different modal weights for image fusion and de-noising, thereby preserving the integrity of the fused images. Our experimental results demonstrate that the reconstructed TFM images effectively represented all defect information. Compared with other modal TFM images with the highest signal-to-noise ratio (SNR), the amplitude-corrected optimized TFM image exhibits an improved SNR of 51.95% without losing defect information.","PeriodicalId":505083,"journal":{"name":"Materials Evaluation","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Visualizing Defects of Irregular Weld Seams Based on MultiMode TFM Detection\",\"authors\":\"Shaofeng Wang, Yaowen Zhang, Shenrong Zhou, Wenjing Liu, Fei Du, Jian Wang, Fei Hui, Mingyuan Yang\",\"doi\":\"10.32548/2024.me-04391\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An image reconstruction method based on the multimode total focusing method (MTFM) is proposed to overcome the limitations of traditional total focusing method (TFM) imaging in detecting tiny discontinuities at complex locations. We conducted MTFM detection and TFM image reconstruction experiments for irregular welds containing multiple discontinuities. In an experiment using four 1 mm diameter manufactured defects fabricated on two aluminum alloy welded test blocks, we achieved two significant contributions. First, we accurately detected small discontinuities by combining CIVA simulation with robotic arm assistance. Second, we proposed fusion factor coefficients for TFM image processing, which considered different modal weights for image fusion and de-noising, thereby preserving the integrity of the fused images. Our experimental results demonstrate that the reconstructed TFM images effectively represented all defect information. Compared with other modal TFM images with the highest signal-to-noise ratio (SNR), the amplitude-corrected optimized TFM image exhibits an improved SNR of 51.95% without losing defect information.\",\"PeriodicalId\":505083,\"journal\":{\"name\":\"Materials Evaluation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Materials Evaluation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.32548/2024.me-04391\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Materials Evaluation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32548/2024.me-04391","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

我们提出了一种基于多模全聚焦法(MTFM)的图像重建方法,以克服传统全聚焦法(TFM)成像在检测复杂位置的微小不连续性方面的局限性。我们对包含多个不连续面的不规则焊缝进行了 MTFM 检测和 TFM 图像重建实验。在使用两个铝合金焊接试块上制造的四个直径为 1 毫米的人造缺陷进行的实验中,我们做出了两项重大贡献。首先,我们将 CIVA 仿真与机械臂辅助相结合,准确检测出了小的不连续性。其次,我们提出了用于 TFM 图像处理的融合因子系数,该系数考虑了图像融合和去噪的不同模态权重,从而保持了融合图像的完整性。实验结果表明,重建的 TFM 图像有效地代表了所有缺陷信息。与信噪比(SNR)最高的其他模态 TFM 图像相比,经过振幅校正的优化 TFM 图像在不丢失缺陷信息的情况下,信噪比提高了 51.95%。
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
Visualizing Defects of Irregular Weld Seams Based on MultiMode TFM Detection
An image reconstruction method based on the multimode total focusing method (MTFM) is proposed to overcome the limitations of traditional total focusing method (TFM) imaging in detecting tiny discontinuities at complex locations. We conducted MTFM detection and TFM image reconstruction experiments for irregular welds containing multiple discontinuities. In an experiment using four 1 mm diameter manufactured defects fabricated on two aluminum alloy welded test blocks, we achieved two significant contributions. First, we accurately detected small discontinuities by combining CIVA simulation with robotic arm assistance. Second, we proposed fusion factor coefficients for TFM image processing, which considered different modal weights for image fusion and de-noising, thereby preserving the integrity of the fused images. Our experimental results demonstrate that the reconstructed TFM images effectively represented all defect information. Compared with other modal TFM images with the highest signal-to-noise ratio (SNR), the amplitude-corrected optimized TFM image exhibits an improved SNR of 51.95% without losing defect information.
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
Machine Vision–Based Tools for Automotive Service and Repair Visual Testing Method Personnel Qualification and Certification: An Overview Robotic Crawlers For Visual Testing RVI For Internal Health Monitoring Of Industrial Gas Turbines Robotic Visual Inspection in Confined Spaces
×
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