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    Effects of spatial and temporal offset during landing on mixing performance in intersecting-jets printing

    Year: 2022

    Journal: Addit. Manuf., Volume 55, JUL

    Authors: Sole-Gras, Marc; Christensen, Kyle; Chen, Yunxia; Liang, Changxiao; Mei, Renwei; Wallace, David B.; Huang, Yong

    Organizations: US National Science Foundation [CMMI-1634755]

    Keywords: Inkjetting; Intersecting jets; Mixing performance; Reactive material

    Intersecting-jets printing extends the inkjet printing capability to allow the deposition of different materials using a printing-then-mixing approach. This study focuses on the analysis of the robustness of mixing performance under spatial and temporal offset conditions during intersecting-jets printing and how this robustness is affected by the ink rheological properties and reactivity. Reactive sodium alginate and calcium chloride solutions have been used as the model materials. Shannon's entropy has been used to measure the mixing efficiency, and high-speed imaging and particle image velocimetry have been used to characterize the ink motion after intersecting collision and landing onto a receiving substrate to better understand its mixing dynamics. While optimal mixing performance (> 90%) can be obtained under ideal printing conditions, the presence of a spatial offset may result in a smaller post-landing velocity but it may not affect significantly the time required to reach an equilibrium state depending on ink materials used. The spatial offset may induce a negative effect on the mixing performance, and selecting a less viscous or low reactivity ink can largely attenuate the effects of spatial offset on the mixing performance. Due to the gelation of the first deposited droplet, even small temporal offsets, on the order of 0.5 - 2.0 ms, may still lead to inconsistencies in the mixing performance. While the intersecting-jets printing process is a complicated process that may involve complex fluids as inks, the mixing performance can still be satisfactorily predicted by using an artificial neural network-based modeling approach.
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