Helmet material design for mitigating traumatic axonal injuries through AI-driven constitutive law enhancement

Sports helmets provide incomplete protection against brain injuries. Here we aim to improve helmet liner efficiency by employing a novel approach that optimizes their properties. By exploiting a finite element model that simulates head impacts, we developed deep learning models that predict the peak rotational velocity and acceleration of a dummy head protected by various liner materials. The deep learning models exhibited a remarkable correlation coefficient of 0.99 within the testing dataset with mean absolute error of 0.8 rad.s−1 and 0.6 krad.s−2 respectively, highlighting their predictive ability. Deep learning-based material optimization demonstrated a significant reduction in the risk of brain injuries, ranging from −5% to −65%, for impact energies between 250 and 500 Joules. This result emphasizes the effectiveness of material design to mitigate sport-related brain injury risks. This research introduces promising avenues for optimizing helmet designs to enhance their protective capabilities. Varanges and colleagues present a deep learning-based approach to optimize helmet liner materials for brain injury prevention. This method highlights the potential of AI-driven material design tailored for specific applications.