Optimization of metal milling parameters based on simulated annealing algorithm and improved genetic Algorithm
To improve the quality of metal milling,a metal milling parameter optimization method based on simulated annealing algorithm and improved genetic algorithm is proposed.The method determines the optimization parameters for metal milling as cutting speed,axial cutting depth,radial cutting width,and feed rate per tooth,and constructs a multi-objective optimization model with the lowest burr size,lowest milling force,highest material removal rate,and lowest roughness.Finally,the multi-objective optimization model is solved using a genetic algorithm improved by a simulated annealing algorithm with added disturbances,achieving the optimization of metal milling parameters.The simulation results show that the proposed method achieves optimization of metal milling parameters,and the burr size of TC4 metal material obtained by milling is 20.07 μm.The milling force in the x,y,and z directions is 0.033,0.028,0.017N,and the roughness is 0.055,respectively μm.The material removal rate is 6.22 mm3/min;Compared with genetic algorithm,ant colony algorithm,particle swarm optimization algorithm,and simulated annealing algorithm,it can improve the quality of metal milling,reduce the size of metal burrs,milling force,and roughness obtained during milling,and improve material removal rate.
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维普
