The objective of this study is to present an experimental framework for identifying the dynamic characteristics of a spindle–tool joint using the virtual point transformation (VPT) method and inverse receptance coupling substructure analysis (IRCSA)...
The objective of this study is to present an experimental framework for identifying the dynamic characteristics of a spindle–tool joint using the virtual point transformation (VPT) method and inverse receptance coupling substructure analysis (IRCSA). In order to reconstruct joint responses whose direct measurements are limited, particularly rotational degrees of freedom, translational receptances measured at multiple locations were transformed using the VPT method to obtain joint receptances including both translational and rotational components. Based on the reconstructed receptances, the spindle, tool, and joint were treated as separate substructures, and the joint receptance was estimated using IRCSA.
Because the joint receptance estimated by IRCSA can be affected by measurement noise and experimental uncertainty, an additional optimization procedure, with initial parameters determined by receptance coupling substructure analysis (RCSA) was employed to refine the joint receptance matrix. In the optimization procedure, equivalent stiffness and damping parameters of the joint were estimated assuming frequency-independent behavior. These parameters were determined by minimizing the difference between the experimentally obtained receptance of the assembled structure and the receptance reconstructed through substructure synthesis.
Experiments were conducted under various clamping force conditions, and the identified joint parameters revealed that increasing the clamping force led to an increase in joint stiffness, while the damping increased up to a certain level and subsequently decreased. These results demonstrate that the clamping force is a dominant factor that determines the dynamic behavior of spindle–tool joint. The proposed experimental identification framework can be applied to dynamic characterization and frequency-response prediction of machine tool joints, and it provides a basis for future studies on cutting stability analysis and joint design optimization.