In-process Monitoring of Electrohydrodynamic Inkjet Printing using Machine Vision

被引:7
|
作者
Qin, Hantang [1 ,3 ]
Zhang, Xiao [1 ]
Singh, Rahul [2 ]
Zhang, Zhan [3 ]
Chen, Yongxin [2 ]
机构
[1] Iowa State Univ, Dept Ind & Mfg Syst Engn, Ames, IA 50011 USA
[2] Georgia Inst Technol, Daniel Guggenheim Sch Aerosp Engn, Atlanta, GA 30332 USA
[3] Iowa State Univ, Ctr Nondestruct Evaluat, Ames, IA 50011 USA
关键词
SYSTEM; NANOSCALE;
D O I
10.1063/1.5099808
中图分类号
TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
Electrohydrodynamic inkjet printing (e-jet printing) is one type of micro/nano scale 3D printing techniques that automatically deposit functional materials to form 3D structures on the substrate. Unlike traditional thermal or acoustic inkjet printing, e-jet printing utilizes high electrical forces for the ink to overcome surface tension at the tip of micro needles. The droplets and filaments coming out from the needle have dimensions much smaller than the dimension of the needle, thus to print geometries in micro and nano scale. E-jet printing process parameters can affect the final quality attributes of fabricated constructs. Currently, assessment of these critical geometries and attributes information must be performed offline using optical microscopy or scanning electron microscopy. This drawback affected the efficiency of micro/nano printing from translation to industrial practice. The research in this paper focused on fundamental research to enable in situ monitoring of e-jet printing using a real-time images characterization technique. In conclusion, the study in this paper investigated using machine vision for real-time monitoring of micro/nano scale 3D printing. The method worked well for micro-filament detection in e-jet printing, and may be further implemented into feedback control system of complicated e-jet printing. However, the optical machine vision was limited to micro scale detection. One of the future research topic is to develop nano scale in situ detection mechanism for e-jet printing.
引用
收藏
页数:8
相关论文
共 50 条
  • [41] Accelerated deep-learning-based process monitoring of microfluidic inkjet printing
    Kim, Seong Jae
    Choi, Eunsik
    Won, Dong Yeon
    Han, Gyuhyeon
    An, Kunsik
    Kang, Kyung-Tae
    Kim, Sanha
    CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY, 2023, 46 : 65 - 73
  • [42] Drop dynamics in the inkjet printing process
    Wijshoff, Herman
    CURRENT OPINION IN COLLOID & INTERFACE SCIENCE, 2018, 36 : 20 - 27
  • [43] MODEL CALIBRATION IN INKJET PRINTING PROCESS
    Zuniga-Navarrete, Christian
    Zhou, Chi
    Sun, Hongyue
    Segura, Luis Javier
    PROCEEDINGS OF ASME 2023 18TH INTERNATIONAL MANUFACTURING SCIENCE AND ENGINEERING CONFERENCE, MSEC2023, VOL 1, 2023,
  • [44] In-process detection of grinding burn using machine learning
    Sauter, Emil
    Sarikaya, Erkut
    Winter, Marius
    Wegener, Konrad
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2021, 115 (7-8): : 2281 - 2297
  • [45] In-process detection of grinding burn using machine learning
    Emil Sauter
    Erkut Sarikaya
    Marius Winter
    Konrad Wegener
    The International Journal of Advanced Manufacturing Technology, 2021, 115 : 2281 - 2297
  • [46] In-Process Monitoring of Surface Roughness of Internal Channels Using
    Sun, Zeqing
    Zuo, Peng
    Pavlovic, Mato
    Ang, Yi Feng
    Fan, Zheng
    EUROPEAN WORKSHOP ON STRUCTURAL HEALTH MONITORING (EWSHM 2022), VOL 3, 2023, : 782 - 789
  • [47] In-process measurement of surface roughness using machine vision with sub-pixel edge detection in finish turning
    Mohan Kumar Balasundaram
    Mani Maran Ratnam
    International Journal of Precision Engineering and Manufacturing, 2014, 15 : 2239 - 2249
  • [48] In-process Monitoring of Arc Welding Process
    Asai S.
    Yosetsu Gakkai Shi/Journal of the Japan Welding Society, 2019, 88 (08): : 597 - 608
  • [49] Automated Process Monitoring in 3D Printing Using Supervised Machine Learning
    Delli, Ugandhar
    Chang, Shing
    46TH SME NORTH AMERICAN MANUFACTURING RESEARCH CONFERENCE, NAMRC 46, 2018, 26 : 865 - 870
  • [50] In-process tool condition monitoring in compliant abrasive belt grinding process using support vector machine and genetic algorithm
    Pandiyan, Vigneashwara
    Caesarendra, Wahyu
    Tjahjowidodo, Tegoeh
    Tan, Hock Hao
    JOURNAL OF MANUFACTURING PROCESSES, 2018, 31 : 199 - 213