Pascal Becker, N. Spielbauer, A. Rönnau, R. Dillmann
{"title":"Real-Time In-Situ Process Error Detection in Additive Manufacturing","authors":"Pascal Becker, N. Spielbauer, A. Rönnau, R. Dillmann","doi":"10.1109/IRC.2020.00077","DOIUrl":null,"url":null,"abstract":"The economic importance of additive manufacturing utilizing Fused Deposition Modeling (FDM) 3D-printers has been on the rise since key patents on crucial parts of the technology ran out in the early 2000s. Altough there have been major improvements in the materials and print quality of the printers used, the process is still prone towards various errors. At the same time almost none of the printers available use build in sensors to detect errors and react to their occurrence. This work outlines a monitoring system for FDM 3D-printers that is able to detect a multitude of severe and common errors through the use of optical consumer sensors. The system is able to detect layer shifts and stopped extrusion with a high accuracy. Furthermore additional sensors and error detection methods can be easily integrated through the modular structure of the presented system. To be able to handle multiple printer without the same amount of sensors, the sensor was added to the tool center point (TCP) of a robot.","PeriodicalId":232817,"journal":{"name":"2020 Fourth IEEE International Conference on Robotic Computing (IRC)","volume":"160 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 Fourth IEEE International Conference on Robotic Computing (IRC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IRC.2020.00077","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
The economic importance of additive manufacturing utilizing Fused Deposition Modeling (FDM) 3D-printers has been on the rise since key patents on crucial parts of the technology ran out in the early 2000s. Altough there have been major improvements in the materials and print quality of the printers used, the process is still prone towards various errors. At the same time almost none of the printers available use build in sensors to detect errors and react to their occurrence. This work outlines a monitoring system for FDM 3D-printers that is able to detect a multitude of severe and common errors through the use of optical consumer sensors. The system is able to detect layer shifts and stopped extrusion with a high accuracy. Furthermore additional sensors and error detection methods can be easily integrated through the modular structure of the presented system. To be able to handle multiple printer without the same amount of sensors, the sensor was added to the tool center point (TCP) of a robot.