Cutting parameters optimization method based on improved genetic algorithm
Aiming at the optimization problem of metal cutting parameters,a multi-objective optimization model based on improved genetic algorithm is proposed.Considering the requirements of different processing stages,the objective function based on maximum production efficiency and minimum surface roughness was constructed,and then the improved NSGA-Ⅱ genetic algorithm was utilized to solve the objective function.The simulation results show that in rough machining,when the spindle speed is 6 904.3 r/min,the feed rate is 2 670.4 mm/min,the milling depth is 4.0mm and the milling width is 17.8 mm,the removal rate of aero A17050 alloy material is the best.In finish machining,when the spindle speed is 7 344.6r/min,the feed rate is 2 815.6 mm/min,the milling depth is 1.0mm and the milling width is 4.0mm,the obtained surface roughness is the best.The actual milling shows that the optimal parameter combination after optimization has little difference with the actual value obtained in the test.The error between the measured surface roughness and the optimal value is 5.92％in rough machining and 3.12％in fine machining.Therefore,the optimal parameters obtained by solving can be used in the actual production and processing,and give certain guidance to metal cutting.
metal cuttingparameters optimizationobjective functionconstraint conditionnon-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)
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