Characterization and selection of a skull surrogate for the development of a biofidelic head model.

This research paper explores the advancement of physical models simulating the human skull-brain complex, focusing on applications in simulating mild Traumatic Brain Injury (mTBI). Existing models, especially head forms, lack biofidelity in accurately representing the native structures of the skull, limiting the understanding of intracranial injury parameters beyond kinematic head accelerations. This study addresses this gap by investigating the use of additive manufacturing (AM) techniques to develop biofidelic skull surrogates. Materials such as Polylactic Acid (PLA), a bone-simulant PLA variant, and Hydroxyapatite-coated Poly(methyl methacrylate) (PMMA) were used to create models tested for their flexural modulus and strength. The trabecular bone regions were simulated by adjusting infill densities (30%, 50%, 80%) and print raster directions, optimizing manufacturing parameters for biofidelic performance. Among the tested materials, PLA and its bone-simulating variant printed at 80% infill density with a side (tangential) print orientation demonstrated the closest approximation to the mechanical properties of cranial bone, yielding a mean flexural modulus of 1337.2 MPa and a mean ultimate strength of 56.9 MPa. Statistical analyses showed that infill density significantly influenced the moduli and strength of the printed simulants. Digital Image Correlation (DIC) corroborated the comparable performance of the simulants, showing similar strain and displacement behaviors to native skull bone. Notably, the performance of the manufactured cortical and trabecular regions underscored their crucial role in achieving biofidelity, with the trabecular structure providing critical dampening effects when the native bone is loaded. This study establishes PLA, particularly its bone-simulant variant, as an optimal candidate for cranial bone simulants, offering significant potential for developing more accurate biofidelic head models in mTBI research.