Dynamics modeling of gear honing machine based on parameter identification

Gear honing is an important gear finishing process that can effectively improve tooth surface texture and reduce transmission noise. However, in the actual gear honing process, chatter often occurs, which seriously affects the machining accuracy of the machine tool and the quality of the workpiece. Therefore, accurately characterizing the dynamic characteristics of the gear honing machine is crucial for avoiding chatter and improving machining accuracy. To address this issue, this paper proposes a novel method for establishing a dynamic model of the gear honing machine under operating conditions. This method takes the honing force as the excitation source, combines with the vibration signal of the machine tool, and uses the particle swarm optimization algorithm to identify the dynamic parameters of the machine tool, thereby realizing the construction of the machine tool’s dynamic model. The correlation between the model-predicted vibration signals and the actual signals and the root-mean-square (RMS) errors of the amplitude are comparatively analyzed through the experimental validation of gear honing processing. The experimental results show that the model predicted signals are highly compatible with the actual signals, the correlation coefficients in the orthogonal directions are all stably higher than 0.77 ( p <0.01), and the RMS errors of the amplitudes of the two vibration signals are controlled to be within 9%, which fully proves the accuracy of the established dynamic model. The modeling method proposed in this paper can establish the dynamic model of the machine tool under operating conditions without special design or operation and without conducting destructive vibration tests, providing an effective method for improving the vibration performance of the machine tool.