Literature DB >> 30031287

In situ characterization of low-viscosity direct ink writing: Stability, wetting, and rotational flows.

Leanne Friedrich1, Matthew Begley2.   

Abstract

HYPOTHESIS: Direct ink writing (DIW) of composites can be coupled with magnetic, electric, or acoustic fields to control spatial variations of microstructure that enhance performance. The use of such external fields often requires inks with lower viscosities than conventional DIW, which presents new challenges with regards to maintaining the stability of printed lines and targeted microstructures. In-situ monitoring of the print bead, combined with slot-die models, can be used to predict printing modality and guide printing protocols for low viscosity inks. EXPERIMENTS: Using videos of the nozzle-substrate gap, we systematically study how ink composition, stand-off distance, and nozzle and substrate surface coatings influence the printing process. We establish in-situ digital image analysis techniques to evaluate filament stability, nozzle wetting, and rotational flows in low viscosity composite inks.
FINDINGS: Variations in the fluid-substrate contact line position and angle can be used to evaluate stability on-the-fly, and lubrication theory can be used to predict filament-to-droplet transitions. To limit nozzle wetting and disruption of microstructures established in the nozzle, it is necessary to use low flow to stage speed ratios, in regimes close to those associated with instabilities. However, low viscosities, small stand-off distances, and functionalized nozzles can improve the print line's resilience against instabilities.
Copyright © 2018 Elsevier Inc. All rights reserved.

Keywords:  3D printing; Contact angle; Contact line; Direct ink writing; Lubrication theory

Year:  2018        PMID: 30031287     DOI: 10.1016/j.jcis.2018.05.110

Source DB:  PubMed          Journal:  J Colloid Interface Sci        ISSN: 0021-9797            Impact factor:   8.128


  1 in total

1.  Generalisable 3D printing error detection and correction via multi-head neural networks.

Authors:  Douglas A J Brion; Sebastian W Pattinson
Journal:  Nat Commun       Date:  2022-08-15       Impact factor: 17.694

  1 in total

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