An Improved Height Difference Based Model of Height Profile for Drop-on-Demand 3D Printing With UV Curable Ink

Author(s):  
Yumeng Wu ◽  
George Chiu
Author(s):  
Yumeng Wu ◽  
George Chiu

Abstract This paper proposes a height profile model for drop-on-demand printing of UV curable ink. Existing models include superposition of single drops, numerical models, and graphic-based model. They are either too complicated or over simplified. Graphic model intends to find a sweet spot in between, however, accuracy is marginally improved from superposition model while it demands more computation. The proposed model aims to achieve the same as graphic model by introducing volume and area propagation matrices to reflect the localized ink flow from higher location to the lower, while avoiding the detailed physics behind it. This model assumes a constant volume and area propagation of subsequent drop due to height profile difference. It is validated with experiments on single drop, 2-drop and 3-drop line printing. Stability of this model is analyzed.. Using root mean square (RMS) error as benchmark, proposed model achieves 6.6% along the center row and 7.4% overall, better than existing models.


2018 ◽  
Vol 7 (2.23) ◽  
pp. 68 ◽  
Author(s):  
Anton V. Mironov ◽  
Aleksandra O. Mariyanac ◽  
Olga A. Mironova ◽  
Vladimir K. Popov

Present work describes the results of the development of the universal system, which capable to utilize varies 3D printing methodologies. The main goal of the study is to provide cheap, versatile and easy expandable equipment for multiple purpose research in the field of material science. 3D printing system was experimentally validated for fused deposition modeling, hydrogel, liquid dispensing and drop-on-demand printing, as well as 3D photopolymerisation by UV laser and/or LED light using different types of materials.  


2017 ◽  
Vol 135 (9) ◽  
pp. 45933 ◽  
Author(s):  
Zhiwei Jiao ◽  
Fei Li ◽  
Liyang Xie ◽  
Xiaojun Liu ◽  
Baihong Chi ◽  
...  

2002 ◽  
Vol 758 ◽  
Author(s):  
W. Voit ◽  
K. V. Rao ◽  
W. Zapka

ABSTRACTWe demonstrate drop-on-demand inkjet printing technique to be a high throughput method for the patterned deposition of UV-curable epoxy materials. Different multi-nozzle printheads have been used to produce epoxy droplets with controlled volume in the range from 15 to 180 pl, and to apply the droplets with high placement accuracy. For a large dot grid pattern, which was printed by addressing 126 individual ink channels, standard deviations of σx = 2.3 μm and σy = 2.6 μm have been achieved for the error in dot placement. The deposited epoxy dots were found to form planar convex lenses with a focal length of 142 μm. In addition, we have successfully printed magnetic nanoparticles in a carrier fluid with the drop-on-demand printheads, as a step towards the production of composites.


2020 ◽  
Vol 32 ◽  
pp. 101016 ◽  
Author(s):  
Elham Davoodi ◽  
Haniyeh Fayazfar ◽  
Farzad Liravi ◽  
Elahe Jabari ◽  
Ehsan Toyserkani

2019 ◽  
Vol 2019 (1) ◽  
pp. 89-93 ◽  
Author(s):  
Nick Jackson ◽  
Wolfgang Voit ◽  
Renzo Trip ◽  
Angus Condie ◽  
Xaar Plc

2021 ◽  
Vol 1 (3) ◽  
Author(s):  
Yumeng Wu ◽  
George Chiu

Abstract This paper proposes an improved model of height profile for drop-on-demand printing of ultraviolet curable ink. Unlike previous model, the proposed model propagates volume and covered area based on height difference between adjacent drops. Height profile is then calculated from the propagated volume and area. Measurements of two-drop and three-drop patterns are used to experimentally compute model parameters. The parameters are used to predict and validate height profiles of four and more drops in a straight line. Using the same root-mean-square (RMS) error as benchmark, this model achieves 5.9% RMS height profile error on four-drop lines. This represents more than 60% reduction from graph-based model and an improvement from our previous effort.


2018 ◽  
Vol 166 (3) ◽  
pp. A5059-A5064 ◽  
Author(s):  
Ido Ben-Barak ◽  
Yosef Kamir ◽  
Svetlana Menkin ◽  
Meital Goor ◽  
Inna Shekhtman ◽  
...  

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