Modelling the Dynamics of a CNC Spindle for Tool Condition Identification Based on On-Rotor Sensing

Abstract

Cutting tool plays an important role in modern manufacturing industry, however, tool wear is unavoidable during machining which could reduce the efficiency. Aiming at studying an appropriate and efficient tool condition monitoring method to improve the accuracy of finished parts, the roughness of the turned surface, a novel On-Rotor Sensing (ORS) is installed on the rotating workpiece to obtain vibration signals. To get an in-depth understand of the vibration data, a multi-degree-of-freedom (MDOF) system consisted of spindle, chuck and workpiece is established and its multi-mode natural frequency is obtained by finite element model (FEM) method. It is found that the dynamic response of the spindle rotor determines machining accuracy in the turning process and shows that the first several modes in the frequency range within 2000 Hz are the main responses of the system, which can be effectively captured by the ORS. Especially, the spring stiffness is calibrated based on the FEM results and the accuracy of the dynamic modal responses of this model are verified when the mass of the workpiece decreases during the turning process. According to the results, two frequency bands are advocated for ORS based online monitoring of tool wear conditions.