A Hybrid Active Balancing Strategy for Grinding Machine Spindles Based on an Influence Coefficient Stability Index

This paper proposes a hybrid strategy based on an influence coefficient stability index for the active balancing of grinding machine spindles. The analysis begins by investigating the reasons why the Auto-Tuning Adaptive Control method struggles to reduce vibration below the target threshold during the later stages of balancing, approaching the problem from both theoretical and methodological standpoints. Subsequently, the Nelder-Mead algorithm is introduced for the final balancing stage, leveraging its inherent advantages such as strong anti-interference capabilities. To determine the optimal switching point between the two methods, this work innovatively proposes an influence coefficient stability index, which is derived from the analysis of the aforementioned late-stage issues. The proposed strategy was validated through both numerical simulations using a rotor model and experimental verification on a laboratory rotor test rig. Results from both the simulations and experiments demonstrate that the proposed hybrid strategy reduces system vibration below the target threshold more rapidly and with greater stability than the conventional Auto-Tuning Adaptive Control method. This verifies its suitability for the online active balancing needs of industrial grinding machine spindles.