Adaptive data-driven modular control approach to computer aided process planning for manufacturing spiral bevel and hypoid gears

Abstract

Machine setting modification has been an increasingly important access to the accurate flank manufacturing geometric accuracy control for spiral bevel and hypoid gears. More recently, machine setting driven integration of the theoretical design and the actual gear manufacturing is gaining more and more attention. In this paper, the traditional machine setting modification is extended to the case when higher-order component of the prescribed ease-off flank topography is investigated in form of high-order polynomial expression. Moreover, the actual gear manufacturing and general measurement are integrated into an adaptive data-driven high-order machine setting modification. In particular, this modification method is used to perform adaptive modular control for computer aided process planning (CAPP). Here, a data-driven operation and optimization is developed for adaptive high-order modification. It mainly includes: (i) Polynomial fitting and its optimization by using overall interpolation based on energy method, (ii) Data-driven ease-off flank parametrization based on the fastest descent Newton iteration method, (iii) adaptive control strategy by considering the sensitivity analysis, and (iv) Levenberg-Marquardt (L-M) based approximation for high-order machine setting modification. Given numerical test can verify the proposed method.