Development of a melt pool characteristics detection platform based on multi-information fusion of temperature fields and photodiode signals in plasma arc welding

IF 5.9 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Intelligent Manufacturing Pub Date : 2024-03-24 DOI:10.1007/s10845-024-02342-1
{"title":"Development of a melt pool characteristics detection platform based on multi-information fusion of temperature fields and photodiode signals in plasma arc welding","authors":"","doi":"10.1007/s10845-024-02342-1","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>Melt pool characteristics reflect the formation mechanisms and potential issues of flaws. Long-term, high-precision, and real-time detection of melt pool characteristics is one of the major challenges in the industrial application of additive manufacturing technology. This work proposes, for the first time, the melt pool characteristics detection platform based on multi-information fusion in the plasma arc welding (PAW) process, which fully utilizes real-time photodiode signals and high-precision, information-rich melt pool temperature fields. By optimizing the detection area and wavelength selection of the platform, particularly through the unique photodiode signal acquisition system capable of detecting the high-sensitivity area of the melt pool, we effectively mitigate the influences of intense arc light and welding wire obstruction on the temperature signals and photodiode signals. Through applying machine learning, the trained model integrates photodiode signals with temperature signals from the high-sensitivity area, thereby achieving real-time acquisition of high-precision average temperature. By combining the fused signals collected from the platform and the scanning results from micro-computed tomography (CT), we evaluate and verify the influence of flaws and droplets on the melt pool characteristics, realizing the determination of flaw occurrence based on the abnormal variations of average temperature. The experimental results demonstrated that the platform fully utilized the advantages of long-term and real-time acquisition of the photodiode signal and the high-precision and information-rich of the melt pool temperature field, achieving long-term, high-precision, and real-time detection of melt pool characteristics.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"62 1","pages":""},"PeriodicalIF":5.9000,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Intelligent Manufacturing","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s10845-024-02342-1","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Melt pool characteristics reflect the formation mechanisms and potential issues of flaws. Long-term, high-precision, and real-time detection of melt pool characteristics is one of the major challenges in the industrial application of additive manufacturing technology. This work proposes, for the first time, the melt pool characteristics detection platform based on multi-information fusion in the plasma arc welding (PAW) process, which fully utilizes real-time photodiode signals and high-precision, information-rich melt pool temperature fields. By optimizing the detection area and wavelength selection of the platform, particularly through the unique photodiode signal acquisition system capable of detecting the high-sensitivity area of the melt pool, we effectively mitigate the influences of intense arc light and welding wire obstruction on the temperature signals and photodiode signals. Through applying machine learning, the trained model integrates photodiode signals with temperature signals from the high-sensitivity area, thereby achieving real-time acquisition of high-precision average temperature. By combining the fused signals collected from the platform and the scanning results from micro-computed tomography (CT), we evaluate and verify the influence of flaws and droplets on the melt pool characteristics, realizing the determination of flaw occurrence based on the abnormal variations of average temperature. The experimental results demonstrated that the platform fully utilized the advantages of long-term and real-time acquisition of the photodiode signal and the high-precision and information-rich of the melt pool temperature field, achieving long-term, high-precision, and real-time detection of melt pool characteristics.

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
开发基于等离子弧焊中温度场和光电二极管信号多信息融合的熔池特征检测平台
摘要 熔池特征反映了缺陷的形成机制和潜在问题。熔池特性的长期、高精度和实时检测是增材制造技术工业化应用的主要挑战之一。本研究首次提出了基于等离子弧焊(PAW)过程中多信息融合的熔池特征检测平台,充分利用了实时光电二极管信号和高精度、信息丰富的熔池温度场。通过优化平台的检测区域和波长选择,特别是通过能够检测熔池高灵敏度区域的独特光电二极管信号采集系统,我们有效地减轻了强烈弧光和焊丝阻挡对温度信号和光电二极管信号的影响。通过应用机器学习,训练有素的模型将光电二极管信号与来自高灵敏度区域的温度信号进行整合,从而实现高精度平均温度的实时采集。结合平台采集的融合信号和微计算机断层扫描(CT)的扫描结果,我们评估并验证了缺陷和液滴对熔池特性的影响,实现了根据平均温度的异常变化判断缺陷的发生。实验结果表明,该平台充分发挥了光电二极管信号长期、实时采集和熔池温度场高精度、信息丰富的优势,实现了熔池特性的长期、高精度和实时检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Journal of Intelligent Manufacturing
Journal of Intelligent Manufacturing 工程技术-工程:制造
CiteScore
19.30
自引率
9.60%
发文量
171
审稿时长
5.2 months
期刊介绍: The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.
期刊最新文献
Industrial vision inspection using digital twins: bridging CAD models and realistic scenarios Reliability-improved machine learning model using knowledge-embedded learning approach for smart manufacturing Smart scheduling for next generation manufacturing systems: a systematic literature review An overview of traditional and advanced methods to detect part defects in additive manufacturing processes A systematic multi-layer cognitive model for intelligent machine tool
×
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