Abstract:

On the base of traditional simulation and combined with deep learning， a rapid prediction method of seat’s anti-whiplash injury performance is proposed. Firstly， a series material level， component level， subassembly level and seat level static and dynamic physical experiments are carried out on a Shanghai VW’s vehicle seat. Then using the results of experiments to calibrate the existing simulation model， resulting in the effectiveness of the simulation model verified. Next， a simulation on all factors affecting the seat’s whiplash performance is conducted by using full-factor method， and based on simulation results and using deep learning method， a long- and short-term memory （LSTM） neural network model is established to rapidly predict the whiplash injury response of dummy. The results show that the dummy response curve obtained from prediction by LSTM neural network model agrees well with simulated curve， so can be used in subsequent seat’s whiplash performance optimization.

Key words: seat anti-whiplash performance, deep learning, neural network, finite element simulation, BioRIDII, Pam-Crash