Silicone 3D printing: Experimental validation of a reduced-order numerical model for optimal layer deposition

Silicone 3D printing holds a significant potential in soft robotics, stretchable electronics, and custom medical devices. However, challenges such as inconsistent material deposition continue to limit printing precision. This study presents a reduced-order numerical model integrating material rheology, process parameters and nozzle geometry to optimize silicone extrusion. A support vector regression algorithm fine-tunes printing parameters to generate an inlet pressure profile that minimizes over- and under-extrusion and ensures precise layer contouring. Full three-dimensional simulations of non-Newtonian silicone deposition coupled with experimental validation confirm the model's accuracy in 3D printing a 90-degree rounded corner shape. The proposed approach yields more stable printing paths and reduces layer width variability to 4.21%, compared to 15.02% obtained with constant inlet pressure. Finally, the proposed model is applied to fabricate wavy and multi-strand paths, demonstrating enhanced accuracy and consistency.