AI-Driven Optimization of Helmet Material Design: Mitigating Traumatic Axonal Injuries through Innovative Constitutive Law Enhancement

Abstract

Sports helmets do not provide full protection against brain injuries. Our study aims to improve helmet liner efficiency by employing a novel approach that optimizes their properties. By exploiting a finite element model that simulates impacts, we developed deep learning models that predict the peak kinematics of a dummy head protected by various liner materials. The models exhibited a remarkable correlation coefficient of 0.99 within the training dataset, highlighting their predictive ability. Deep learning-based material optimization predicts a significant reduction in the risk of traumatic axonal injuries for impact energy ranging from 250 to 450 Joules. This result emphasizes the effectiveness of a sophisticated material design to mitigate sport-related brain injury risks. This research introduces promising avenues for optimizing helmet designs to enhance their protective capabilities.