Abstract
ABSTRACT This paper describes a new method for finding optimal process parameter values of plastic gears based on a small experimental dataset. Taking a polyamide 66 gear as an example, the simulation study and the real experiment have been designed based on the Taguchi approach. Then, the Levenberg-Marquardt back propagation (LMBP) neural network is built based on the data generated by the real experiment. This network is also used as the fitness function of the particle swarm optimization algorithm. The results show that, by using optimal parameter values, the comprehensive score of the polyamide 66 gear increases by 8.71%.
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