Abstract
We introduce a mesoscale approach for the simulation of multicomponent flows to model the direct-writing printing process, along with the early stage of ink deposition. As an application scenario, alginate solutions at different concentrations are numerically investigated alongside processing parameters, such as apparent viscosity, extrusion rate, and print head velocity. The present approach offers useful insights on the ink rheological effects upon printed products, susceptible to geometric accuracy and shear stress, by manufacturing processes such as the direct-writing printing for complex photonic circuitry, bioscaffold fabrication, and tissue engineering.
Highlights
In the last decade, 3D printing processes have gained enormous attention as tool for additive manufacturing in many fields of science and engineering
In order to assess the quality of the print process with respect to tunable parameters, we introduce a Parameter Optimization Index (POI) following[14]: P OI = accuracy/theoretical shear stress
It was found that the shear stress can be minimised by manipulating printing parameters[14], since it is proportional to the inlet pressure p and inversely proportional to the nozzle diameter d
Summary
3D printing processes have gained enormous attention as tool for additive manufacturing in many fields of science and engineering. The vast potential of additive manufacturing requires, an unprecedented control over several aspects of the soft materials involved in the 3D printing process Their dynamics, composition, structure, function and rheology are all key elements, which severely affect the features of the produced parts and structures. We open a new route for predicting 3D printability, developing the regularised version of the Colour Gradient (CG) Lattice Boltzmann (LB) model[8,9,10] to account for the non-Newtonian rheological behaviour, typical of 3D printed pseudo-plastic inks These systems endure a largely varying apparent viscosity, depending on the shear rate[12]. The printing accuracy is discussed in terms of a Parameter Optimization Index (POI)[14], which is predicted in terms of the numerical inputs
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