Tool path optimization of globoidal cam flank milling based on spatial linear-regression analysis

Abstract

In order to tackle the incongruous error of the normal vector of globoidal cam in non-equal diameter machining, the tool axis vector with minimum machining error is preliminarily fitted through the principle of ruled surface generation by using spatial linear-regression algorithm. The vector error of the initial tool axis is gradually reduced by linear regression iteration. The improved tool axis is used as the generatrix of NURBS ruled surface to reconstruct the theoretical tool axis surface. Furthermore, the least square method is carried out to optimize the tool path, and the optimization model of flank milling error of the tool axis path is constructed. Subsequently, a real-coded artificial immune algorithm for solving the optimization model is proposed. The validity of the algorithm is verified by the results of simulation and calculation of tool axis vector of globoidal cam machining.