Reproducing viscoelastic properties of soft tissues in 3D printed silicone models by two-phase infill tuning

Abstract

Anatomical models are essential tools for teaching, patient education, or training. Recent developments in 3D printing enabled the production of customised models based on individual imaging data. Although most 3D printing processes can accurately reproduce anatomical structures geometrically, they lack similarity in haptic properties. Therefore, in this study, we investigated the influence of highly viscous silicone oil injections in 3D printed silicone samples on enhancing viscoelastic behaviour. For this, 72 specimens with 3 different infill densities (20 %, 30 %, 40 %) were printed and tested using stress relaxation tests. Afterwards, they were filled using 3 different high viscous silicone oils (1 kPa$\cdot$s, 5 kPa$\cdot$s, 10 kPa$\cdot$s) and retested. The material properties of the silicone infill/silicone oil combination were extracted from the structural properties of the tested samples using an optimisation strategy based on a finite element model to get the material response for the infill only. Alongside the infill density, the storage modulus increases from 28.0 to 52.3 kPa for empty samples. By adding high viscous silicone oil the loss modulus is increased from 3.3–5.6 kPa up to 12.0–20.0 kPa. The resulting loss tangent increases from 0.10–0.12 to 0.28–0.29 for the different infill densities. With this range of possible viscoelastic properties, several different biological soft tissues can be modelled. It could be proven that a silicone oil injection is a promising way to increase the loss moduli of 3D printed silicone samples, greatly increasing the design space of possible printable viscoelastic properties.