Luis Velazquez, Genevieve Palardy, Corina Barbalata
{"title":"A robotic 3D printer for UV-curable thermosets: dimensionality prediction using a data-driven approach","authors":"Luis Velazquez, Genevieve Palardy, Corina Barbalata","doi":"10.1080/0951192x.2023.2257652","DOIUrl":null,"url":null,"abstract":"ABSTRACTThis paper presents a robotic 3D printer specifically designed for ultraviolet (UV)-curable thermosets, whose printing parameters can be selected by using a predictive modeling strategy. A specialized extruder head was designed and integrated with a UR5e robotic arm. Software packages were developed to enable the communication between the extruder head and the robotic arm, and control systems were implemented to regulate the printing process. A predictive approach using either a feedforward neural network (FNN) or convolution neural network (CNN) is proposed for estimating the dimensions of future prints based on the process parameters. This enables selection of the appropriate parameters for high-quality prints. This strategy aims to decrease expensive trial-and-error campaigns for material and printing parameter tuning. Experimental results demonstrate the capabilities of the robotic 3D printer and the accuracy of the predictive approach.KEYWORDS: UV-curable thermosetsrobotic systemadditive manufacturingmachine learning Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the Louisiana Board of Regents [LEQSF-EPS(2022)-LAMDASeed-Track1B-11]; Louisiana Board of Regents [LEQSF-EPS(2021)-LAMDASeed-Track1B-01]; Office of Integrative Activities [OIA1946231].","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Computer Integrated Manufacturing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/0951192x.2023.2257652","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
ABSTRACTThis paper presents a robotic 3D printer specifically designed for ultraviolet (UV)-curable thermosets, whose printing parameters can be selected by using a predictive modeling strategy. A specialized extruder head was designed and integrated with a UR5e robotic arm. Software packages were developed to enable the communication between the extruder head and the robotic arm, and control systems were implemented to regulate the printing process. A predictive approach using either a feedforward neural network (FNN) or convolution neural network (CNN) is proposed for estimating the dimensions of future prints based on the process parameters. This enables selection of the appropriate parameters for high-quality prints. This strategy aims to decrease expensive trial-and-error campaigns for material and printing parameter tuning. Experimental results demonstrate the capabilities of the robotic 3D printer and the accuracy of the predictive approach.KEYWORDS: UV-curable thermosetsrobotic systemadditive manufacturingmachine learning Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the Louisiana Board of Regents [LEQSF-EPS(2022)-LAMDASeed-Track1B-11]; Louisiana Board of Regents [LEQSF-EPS(2021)-LAMDASeed-Track1B-01]; Office of Integrative Activities [OIA1946231].
期刊介绍:
International Journal of Computer Integrated Manufacturing (IJCIM) reports new research in theory and applications of computer integrated manufacturing. The scope spans mechanical and manufacturing engineering, software and computer engineering as well as automation and control engineering with a particular focus on today’s data driven manufacturing. Terms such as industry 4.0, intelligent manufacturing, digital manufacturing and cyber-physical manufacturing systems are now used to identify the area of knowledge that IJCIM has supported and shaped in its history of more than 30 years.
IJCIM continues to grow and has become a key forum for academics and industrial researchers to exchange information and ideas. In response to this interest, IJCIM is now published monthly, enabling the editors to target topical special issues; topics as diverse as digital twins, transdisciplinary engineering, cloud manufacturing, deep learning for manufacturing, service-oriented architectures, dematerialized manufacturing systems, wireless manufacturing and digital enterprise technologies to name a few.