A shape-predictive model for the spreading of photo-curable polymers in material extrusion additive manufacturing

Recently, there is a growing interest in material extrusion to print thermoset resins to manufacture large format and high-performance parts. However, the fidelity and mechanical integrity of the printed parts are limited by challenges such as uncontrolled spreading of individual beads (filament/droplet) after deposition and during cure. There is a considerable lack of experimental and theoretical studies on the spreading of reactive beads on solid substrates. In this work, we studied the simultaneous spreading and photo-curing of the photopolymerizable thermoset beads via experiment and numerical simulations. We used a novel experimental setup to track the spreading of droplets and filaments during photopolymerization and validate a moving mesh computational fluid dynamics (CFD) model. The CFD model was used to develop an approach (predictive model) to accurately predict the final spreading coefficient of cured resin beads without the need for full numerical simulations. The predictive model combines the generalized theory of a Newtonian spreading filament with a characteristic viscosity μave and time to gelation, τgel. Interestingly, μave is shown to be a material parameter that does not depend on processing conditions, but only on the material’s chemorheology. The predictive model is tested against a wide range of chemorheology and cure kinetic parameters and found to be in excellent agreement with the full numerical CFD simulations. This work will be very useful in estimating the final shape of beads during the material extrusion printing process, as well as a model to successfully parameterize extrusion-based 3D printers to control the shape of printed beads a-priori.