Despite widespread helmet usage in high-speed sports, the incidence of brain injuries continues to rise, indicating some deficiencies in current helmet technologies. Existing helmet safety standards only address linear impacts, failing to account adequately for rotational accelerations, a contributor to severe brain injuries such as traumatic axonal injury (TAI). These injuries induce healthcare challenges and highlight the need for enhanced helmet design capable of mitigating rotational forces to improve safety outcomes. This thesis aims to develop a solution that reduces the risk of brain injury upon impact and improves helmet capabilities to lower the risk of TAI. To reach this goal, this thesis was divided into four studies.

First, a biomechanical analysis was performed using a finite element model to simulate head impacts in an AI-driven approach. Deep learning models were trained to predict peak head kinematics based on material properties and impact energy. The optimization framework enabled the identification of ideal compressive stress-strain profiles tailored to specific impact energies, showing a potential TAI risk reduction of up to 65% compared to conventional expanded polystyrene foams (EPS).

In parallel with the biomechanical analysis, a database was created integrating current protective technologies such as EPS. To complete this database, novel materials were developed and characterized for their impact absorbing potential. A formulation of a hybrid iono-organogel was identified with low shear modulus and high dissipation capabilities. The gel was obtained by the co-polymerization of acrylic acid and DMAPS inside a hybrid solvent composed of an ionic liquid and an oligomer. Its network structure, featuring reversible hydrogen bonding and ion-dipole interactions, efficiently absorbs and dissipates energy under shear and compression during successive impacts. Loss factors exceeded 0.5 under various dynamic conditions. Additionally, six different bioinspired 2D lattice structures were produced via 3D printing and characterized under quasi-static and impact conditions. Surrogate modeling and multi-objective optimization were employed to identify designs and parameters that maximize energy absorption while minimizing buckling stress.

Finally, optimized stress-strain profiles for three target impact energies, derived from the biomechanical analysis, guided material selection for ski helmet liners. Experimental stress-strain curves were compared to ideal profiles using a scoring algorithm evaluating peak stress, specific energy absorption, and curve similarity. FEM simulations demonstrated that hexagonal and beetle-inspired lattices outperformed EPS by reducing rotational and linear acceleration by up to 36% and 63%, respectively, thereby lowering the risk of TAI for users. These materials were then combined into layered structures tailored for different impact severities and tested under compressive and 45° impacts, confirming the improved energy absorptions.

Overall, this thesis presents a transferable and integrative approach to materials design and selection, combining artificial intelligence, FEM, experimental mechanics and advanced manufacturing. The resulting methodology provides a generalizable framework for improving the performance of protective equipment. Though focused on ski helmets, the findings hold relevance for broader applications in sports, automotive safety and biomedical devices.