自适应焊接中电阻点焊过程监测超声数据的实时人工智能驱动解释

IF 0.5 4区 材料科学 Q4 MATERIALS SCIENCE, CHARACTERIZATION & TESTING
R. Scott, D. Stocco, A. Chertov, Roman Gr. Maev
{"title":"自适应焊接中电阻点焊过程监测超声数据的实时人工智能驱动解释","authors":"R. Scott, D. Stocco, A. Chertov, Roman Gr. Maev","doi":"10.32548/2023.me-04344","DOIUrl":null,"url":null,"abstract":"Adaptive resistance spot welding systems typically rely on real-time analysis of dynamic resistance curves and other indirect measurements to estimate weld progress and guide adaptive weld control algorithms. Though efficient, these approaches are not always reliable, and consequently there is a need for improved feedback systems to drive adaptive welding algorithms. As an alternative, an advanced in-line integrated ultrasonic monitoring system is proposed, with real-time weld process characterization driven by artificial intelligence (AI) to create actionable feedback for the weld controller. Such a system would require real-time ultrasonic data interpretation, and for this a solution using deep learning was investigated. The proposed solution monitors the ultrasonic data for key process events and estimates the vertical size of the weld nugget proportional to the stack size throughout the welding process. This study shows that adaptive welding using ultrasonic process monitoring backed by AI-based data interpretation has immense potential. This research highlights the importance of nondestructive evaluation (NDE) in the zero-defect manufacturing paradigm.","PeriodicalId":49876,"journal":{"name":"Materials Evaluation","volume":" ","pages":""},"PeriodicalIF":0.5000,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Real-Time AI driven Interpretation of Ultrasonic Data from Resistance Spot Weld Process Monitoring For Adaptive Welding\",\"authors\":\"R. Scott, D. Stocco, A. Chertov, Roman Gr. Maev\",\"doi\":\"10.32548/2023.me-04344\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Adaptive resistance spot welding systems typically rely on real-time analysis of dynamic resistance curves and other indirect measurements to estimate weld progress and guide adaptive weld control algorithms. Though efficient, these approaches are not always reliable, and consequently there is a need for improved feedback systems to drive adaptive welding algorithms. As an alternative, an advanced in-line integrated ultrasonic monitoring system is proposed, with real-time weld process characterization driven by artificial intelligence (AI) to create actionable feedback for the weld controller. Such a system would require real-time ultrasonic data interpretation, and for this a solution using deep learning was investigated. The proposed solution monitors the ultrasonic data for key process events and estimates the vertical size of the weld nugget proportional to the stack size throughout the welding process. This study shows that adaptive welding using ultrasonic process monitoring backed by AI-based data interpretation has immense potential. This research highlights the importance of nondestructive evaluation (NDE) in the zero-defect manufacturing paradigm.\",\"PeriodicalId\":49876,\"journal\":{\"name\":\"Materials Evaluation\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.5000,\"publicationDate\":\"2023-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Materials Evaluation\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.32548/2023.me-04344\",\"RegionNum\":4,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MATERIALS SCIENCE, CHARACTERIZATION & TESTING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Materials Evaluation","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.32548/2023.me-04344","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATERIALS SCIENCE, CHARACTERIZATION & TESTING","Score":null,"Total":0}
引用次数: 0

摘要

自适应电阻点焊系统通常依赖于动态电阻曲线的实时分析和其他间接测量来估计焊接进度并指导自适应焊接控制算法。尽管这些方法是有效的,但并不总是可靠的,因此需要改进的反馈系统来驱动自适应焊接算法。作为一种替代方案,提出了一种先进的在线集成超声波监测系统,该系统由人工智能(AI)驱动实时焊接过程表征,为焊接控制器创建可操作的反馈。这样的系统需要实时超声数据解释,为此研究了一种使用深度学习的解决方案。所提出的解决方案监测关键工艺事件的超声波数据,并估计整个焊接过程中熔核的垂直尺寸与堆叠尺寸成比例。这项研究表明,使用基于人工智能的数据解释支持的超声波过程监测的自适应焊接具有巨大的潜力。这项研究强调了无损评估(NDE)在零缺陷制造模式中的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-Time AI driven Interpretation of Ultrasonic Data from Resistance Spot Weld Process Monitoring For Adaptive Welding
Adaptive resistance spot welding systems typically rely on real-time analysis of dynamic resistance curves and other indirect measurements to estimate weld progress and guide adaptive weld control algorithms. Though efficient, these approaches are not always reliable, and consequently there is a need for improved feedback systems to drive adaptive welding algorithms. As an alternative, an advanced in-line integrated ultrasonic monitoring system is proposed, with real-time weld process characterization driven by artificial intelligence (AI) to create actionable feedback for the weld controller. Such a system would require real-time ultrasonic data interpretation, and for this a solution using deep learning was investigated. The proposed solution monitors the ultrasonic data for key process events and estimates the vertical size of the weld nugget proportional to the stack size throughout the welding process. This study shows that adaptive welding using ultrasonic process monitoring backed by AI-based data interpretation has immense potential. This research highlights the importance of nondestructive evaluation (NDE) in the zero-defect manufacturing paradigm.
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
Materials Evaluation
Materials Evaluation 工程技术-材料科学:表征与测试
CiteScore
0.90
自引率
16.70%
发文量
35
审稿时长
6-12 weeks
期刊介绍: Materials Evaluation publishes articles, news and features intended to increase the NDT practitioner’s knowledge of the science and technology involved in the field, bringing informative articles to the NDT public while highlighting the ongoing efforts of ASNT to fulfill its mission. M.E. is a peer-reviewed journal, relying on technicians and researchers to help grow and educate its members by providing relevant, cutting-edge and exclusive content containing technical details and discussions. The only periodical of its kind, M.E. is circulated to members and nonmember paid subscribers. The magazine is truly international in scope, with readers in over 90 nations. The journal’s history and archive reaches back to the earliest formative days of the Society.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信