Prediction of pose- and position-dependent tool-tip dynamics in high-speed dry gear hobbing

Abstract

High-speed dry gear hobbing is an advanced and efficient machining technology for automotive gears. Frequent changes in the hob mounting angle (tilt angle) and shifting position result in variations in the tool-tip frequency response function (FRF), which inevitably modify the hobbing stability boundary. To construct an accurate stability boundary for high-speed dry gear hobbing, the primary objective is to predict the tool-tip FRF related to different combinations of hob tilt angles and shifting positions, that is, pose- and position-dependent tool-tip FRF. This study proposes a novel tool-tip FRF prediction strategy that combines multi-output Gaussian Process regression (MOGPR) and Receptance Coupling Substructure Analysis (RCSA). First, the FRFs at the tips of the hob-mounting base were estimated through a series of impact tests under a limited combination of hob tilt angles and shifting positions, and the corresponding modal parameters were identified. The MOGPR model was then leveraged to reveal the mapping relationships between the modal parameters, hob tilt angles and shifting positions. Based on this, the hob-mounting base pose- and position-dependent FRFs can be obtained using the modal fitting technique. Subsequently, the pose-and position-dependent tool-tip FRFs were obtained by coupling the receptances of the hob-toolbar assembly with the fitted hob-mounting base FRFs through the RCSA. Finally, a series of impact tests were conducted to verify the effectiveness of the proposed method. The prediction of pose- and position-dependent tool-tip dynamics can provide a foundation for accurately modeling hobbing stability.