The Side-Cutter Position Adjustment Method for Enhancing Milled Rotor ProfileAccuracy using ANN and NSGA-II

Enhancing rotor profile accuracy is an attention issue of researchers because the rotor profile accuracy is of great significance for enhancing the pump performance. This issue can be solved by modifying or changing the cutter position because the machining error has certain stability and repeatability. Nevertheless, the first method approach demands more time and financial resources. Therefore, this study proposes the pre-compensation method for improving rotor profile error using the position adjustment of the side-cutting tool by applying an Artificial neural network and NSGA-II. The mathematical model of the side-cutting tool and the milling model of the screw rotor on a multi-axis CNC milling machine is established. An artificial neural network is designed to evaluate the effect of the cutting tool adjustment parameters on the rotor profile error. The multi-objective optimization method named NSGA-II is applied to solve a set of optimal adjustment parameters for minimizing the rotor profile error. In the presented examples, the regression function of rotor profile error on the parameters of side-cutter adjustment positions is investigated, and generated the regression function with an R-value of 0.99917. The machining accuracy is enhanced by 85.26% when using the optimization set of tool adjustment positions.