Nozzle-to-Work Distance Measurement and Control in Wire Arc Additive Manufacturing

Raven T. Reisch, T. Hauser, Jürgen Franke, F. Heinrich, Konstantinos Theodorou, T. Kamps, Alois Knoll
{"title":"Nozzle-to-Work Distance Measurement and Control in Wire Arc Additive Manufacturing","authors":"Raven T. Reisch, T. Hauser, Jürgen Franke, F. Heinrich, Konstantinos Theodorou, T. Kamps, Alois Knoll","doi":"10.1145/3501774.3501798","DOIUrl":null,"url":null,"abstract":"In multi-axes Wire Arc Additive Manufacturing, keeping the correct nozzle-to-work distance is crucial to avoid collisions and process defects. Measuring this distance is challenging as the welding arc complicates the usage of conventional distance measurements without positional offset in-process. For that reason, this study investigated and evaluated the usage of several sensors (wire feed sensor, current and voltage sensor, microphone, welding camera, spectrometer, structural acoustic sensor) for a direction independent in-process measurement. Features were extracted based on domain knowledge and selected by means of a correlation analysis. The spectrometer (Pearson’s r = −0.90) showed the most robust measurements for stable process parameters when computing the relative intensity at a wavelength of 960 nm, followed by the welding camera (Pearson’s r = 0.84) when analyzing the images with a convolutional neural network. Based on the findings, a closed-loop-control was created. As a system identification revealed a high impact of the welding speed on the track height in comparison to the wire feed rate (Pearson’s r − 0.90 < > − 0.16), the closed-loop-control was realized by means of a simple P-control for the welding speed. The proposed approach enabled the manufacturing of multi-layer multi-bead parts with multi-axes deposition paths.","PeriodicalId":255059,"journal":{"name":"Proceedings of the 2021 European Symposium on Software Engineering","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2021 European Symposium on Software Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3501774.3501798","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

In multi-axes Wire Arc Additive Manufacturing, keeping the correct nozzle-to-work distance is crucial to avoid collisions and process defects. Measuring this distance is challenging as the welding arc complicates the usage of conventional distance measurements without positional offset in-process. For that reason, this study investigated and evaluated the usage of several sensors (wire feed sensor, current and voltage sensor, microphone, welding camera, spectrometer, structural acoustic sensor) for a direction independent in-process measurement. Features were extracted based on domain knowledge and selected by means of a correlation analysis. The spectrometer (Pearson’s r = −0.90) showed the most robust measurements for stable process parameters when computing the relative intensity at a wavelength of 960 nm, followed by the welding camera (Pearson’s r = 0.84) when analyzing the images with a convolutional neural network. Based on the findings, a closed-loop-control was created. As a system identification revealed a high impact of the welding speed on the track height in comparison to the wire feed rate (Pearson’s r − 0.90 < > − 0.16), the closed-loop-control was realized by means of a simple P-control for the welding speed. The proposed approach enabled the manufacturing of multi-layer multi-bead parts with multi-axes deposition paths.
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
焊丝电弧增材制造中喷嘴到工件距离的测量与控制
在多轴电弧增材制造中,保持正确的喷嘴到工件的距离对于避免碰撞和工艺缺陷至关重要。测量这一距离是具有挑战性的,因为焊接电弧使传统距离测量的使用复杂化,而不需要在过程中进行位置偏移。因此,本研究调查并评估了几种传感器(导线馈送传感器、电流和电压传感器、麦克风、焊接相机、光谱仪、结构声学传感器)用于方向独立的过程测量的使用情况。基于领域知识提取特征,并通过相关性分析选择特征。在计算波长为960 nm的相对强度时,光谱仪(Pearson’s r = - 0.90)对稳定工艺参数的测量结果最稳健,其次是焊接相机(Pearson’s r = 0.84),使用卷积神经网络对图像进行分析。在此基础上,建立了闭环控制系统。由于系统辨识表明,与送丝速度相比,焊接速度对轨道高度的影响较大(Pearson’s r−0.90 < >−0.16),因此通过对焊接速度的简单p控制来实现闭环控制。该方法可实现多轴沉积路径的多层多芯零件的制造。
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
自引率
0.00%
发文量
0
期刊最新文献
Customer Satisfaction in Software Development Projects A Lightweight Development of Outbreak Prevention Strategies Built on Formal Methods and xDSLs An Exploratory Teaching Proposal of Greek History Independence Events based on STEAM Epistemology, Educational Robotics and Smart Learning Technologies Merging Live Video Feeds for Remote Monitoring of a Mining Machine Incorporating energy efficiency measurement into CI\CD pipeline
×
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