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

• Tác giả: Pierre-Etienne Bourban, Pezhman Eghbali, Jean-Yves Fournier, Naser Nasrollahzadeh, Dominique P Pioletti, Vincent Varanges
• Ngôn ngữ: eng
• Ký hiệu phân loại: 641.36 Meat
• Thông tin xuất bản: England : Communications engineering , 2025
• Bộ sưu tập: NCBI
• ID: 167302

Tóm tắt

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