Modal Analysis of Tool Wear Based on Random Subspace Identification

Abstract

This article proposes a work mode analysis method based on the random subspace method for monitoring tool wear status during cutting processes. This method extracts the characteristic frequency, dam** ratio, and vibration mode of the tool workpiece system through vibration response signals. Firstly, the basic principles and steps of commonly used working mode analysis methods such as data-driven random subspace identification (Data-SSI) and stability maps were introduced. Then, a three degree of freedom discrete dynamic model was established and its accuracy in identifying frequency and dam** ratio was verified under different contact stiffness conditions. Subsequently, cutting experiments were conducted under micro lubrication conditions and corresponding vibration signals were collected. The free modal parameters and working modal parameters are calculated by the finite element analysis (FEA) method and the Data SSI method respectively, and are compared and analyzed. Finally, by analyzing the vibration response under different wear values, it was proven that Data SSI can effectively identify the changes in tool modal parameters during the cutting process.