Abstract: In order to solve the problem of excessive weight of the mining helmet, carbon fiber reinforced composite (CFRP) material was devised to substitute for the conventional ABS plastic to realize the lightweight design of the mining helmet. The finite element model of steel ball-helmet for collision simulation was established. The mechanical properties of ABS and CFRP helmet shell were compared and analyzed in simulation. The strength and stiffness of CFRP helmet shell are superior to ABS casings, and have better protective properties. The input-output relationship between CFRP lamination parameters and helmet mechanical properties was simulated by BP neural network. And the global optimization of helmet mechanical properties was realized by particle swarm optimization algorithm. The optimization results indicate that the optimal lamination parameters consist ofa total number of 6 layers of CFRP, with each layer having a thickness of 0.2 mm and arranged in the following angle sequence . The collision simulation results show that the optimized CFRP helmet’s top experiences a maximum deformation of 19.601 mm, while the headform endures a maximum force of 4.891 kN. The optimization configuration can meet the requirements of relevant national standards and achieve a lightweight design with 49.3% weight reduction compared with the ABS helmet shell.

Key words: carbon fiber reinforced plastics, finite element analysis, BP neural network, particle swarm optimization, lightweight design