Optimization of Gear Hobbing Process Parameters NSGA-ⅡBased on GA Optimized BP Model
Studied under the condition of high speed gear hobbing process parameter setting and optimization,the new non domi-nant genetic algorithm NSGA-Ⅱ design,the optimization mathematical model was optimized to achieve the lowest energy con-sumption as well as the longest tool life,again with GABP neural networks as the goal set up the forecasting model and establish the fitness function,Pareto optimal conditions matching gear hobbing process were obtained after iterative optimization.The results show that the mean square error of the prediction model in this paper is equal to 10-5 after five cycles of calculation,and the optimal value of 0.000425 is obtained.It is concluded that the above network meets good stability.Compared with the latter,the tool life er-ror is reduced by 16％,and the energy loss is reduced by 36％.It is found that the GABP algorithm has better convergence ability.The performance of Pareto solution set is better than that of similar machining sample set,so the multi-objective optimization model can ensure the optimal state of machining energy consumption and tool life simultaneously.The research has a good practical appli-cation value to improve the machining parameters and machining efficiency.
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