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.