Five Axis Nc Machining And The Screw Rotorpump

Screw rotor is the core component of the dry vacuum pump, its precision directly affects the efficiency of the screw rotor. Traditional screw rotor machining is generally used molding method with knives and gear hanging processed in ordinary milling machines, machining using this method is difficult and inefficient. Since the screw rotor geometry of the complex, which belongs to shaft parts, so the presence of geometric programming error, processing distortion error is difficult to select and process parameters and other issues in the process. How to choose the right type of line, parametric modeling and reliable method to achieve high efficiency screw rotors finishing an important task. Therefore, further study of helical axis NC machining parameter optimization method and process in order to effectively improve the machining accuracy and efficiency is a helical rotor machining urgent need to address the problem.In this paper, the basic principle of a helical surface point of view, through mathematical derivation to obtain a single pitch trapezoid concave top rotor profile equation, according to the number of pumping area utilization coefficient and theoretical, comparative analysis of the parameters affect the dry vacuum pump spiral rotor, compared to a vacuum pump measured data spiral rotor, derivation and validation parameters are correct. Combined with NC simulation technology, the use of UG parameters modeling and simulation tool path generation, using VERICUT simulation axis NC machining principle to build the machine, in order to verify the feasibility of the tool path and program.On this basis, using MATLAB genetic algorithm to milling speed, the amount of feed per tooth, axial depth of cut and radial depth of cut four parameters are variable, with the highest productivity and lowest cost of production and processing of surface roughness of screw rotors the objective function of process parameters to the main cutting force, the machine rated output power, feed rate, spindle speed and tool parameters this amount is five constraints. First we were determined in five constraints to meet each objective function value of the variable, and then calculated the five constraint satisfaction at this multi-objective function value of the variable. The value of the input variables obtained VERICUT program, the simulation process. Proved that the use of multi-objective optimization genetic algorithm derived from the results of feasibility.