Nonlinear Hyperelastic Characterization of the PolyJet Digital Anatomy Material Library and Selection Methodology for Tissue-Mimicking Anatomical Models.

Abstract

Additive manufacturing of anatomical prototypes increasingly relies on multi-material polymer systems designed to approximate the mechanical behavior of biological tissues for medical training and device testing, providing reproducible patient- and pathology-specific alternatives to cadavers and animal models. This study provides the first systematic experimental nonlinear constitutive characterization of nearly the entire Stratasys Digital Anatomy material library and a reference dataset for predictive modeling of PolyJet-printed prototypes in finite element simulations. The nonlinear elastic responses of 99 Stratasys Digital Anatomy PolyJet materials were characterized in tension, and a framework was developed to identify materials that best match target tissue behavior. Four hyperelastic constitutive models (neo-Hookean, Mooney-Rivlin, Ogden, and Holmes-Mow) and three data consolidation approaches were compared to determine an optimal method for reporting representative model coefficients across multiple specimens. In addition to Mooney-Rivlin coefficients, a model-independent comparison of tangent modulus at 2% strain is provided to enable stiffness-based material selection. Four exemplar tissues were matched to PolyJet materials to demonstrate the framework. Material labels were not always reliable indicators of optimal matches. The Digital Anatomy materials did not reproduce the full stiffness range of tissues, and their near-linear elasticity limited their ability to capture nonlinear tissue behavior across strain levels.