A robotic 3D printer for UV-curable thermosets: dimensionality prediction using a data-driven approach

IF 3.7 3区 工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS International Journal of Computer Integrated Manufacturing Pub Date : 2023-09-18 DOI:10.1080/0951192x.2023.2257652
Luis Velazquez, Genevieve Palardy, Corina Barbalata
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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].
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用于uv固化热固性材料的机器人3D打印机:使用数据驱动方法进行尺寸预测
摘要本文介绍了一种专门用于紫外线固化热固性材料的机器人3D打印机,该打印机的打印参数可通过预测建模策略进行选择。设计了专用挤出头,并与UR5e机械臂集成。开发了软件包,使挤出机头和机械臂之间的通信,并实施了控制系统来调节打印过程。提出了一种基于前馈神经网络(FNN)或卷积神经网络(CNN)的预测方法,用于基于工艺参数估计未来打印件的尺寸。这样可以为高质量的打印选择适当的参数。该策略旨在减少昂贵的材料和打印参数调整的试错活动。实验结果验证了机器人3D打印机的性能和预测方法的准确性。关键词:紫外光固化热固性机器人系统增材制造机器学习披露声明作者未报告潜在利益冲突。本研究得到了路易斯安那州校董会的支持[LEQSF-EPS(2022)-LAMDASeed-Track1B-11];路易斯安那州董事会[LEQSF-EPS(2021)-LAMDASeed-Track1B-01];综合活动办公室[OIA1946231]。
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来源期刊
CiteScore
9.00
自引率
9.80%
发文量
73
审稿时长
10 months
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.
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