A new method for fast and accurate identification of tool-workpiece relative vibration based on machining surface topography

Abstract

This study utilizes the improved fast and adaptive bidimensional empirical modal decomposition (IFABEMD) method to study the impact of relative vibration between the tool and the workpiece on surface topography. The sieving stop condition is determined based on the difference in standard deviation, effectively addressing the issue of modal mixing in the traditional bidimensional empirical modal (BEMD) method. Additionally, a novel boundary extension method is proposed using the Gerchberg algorithm. The decomposition example demonstrates that the IFABEMD method can effectively handle modal mixing and boundary effects. Finally, the relative vibration frequencies between the tool and the workpiece are identified by analyzing the spatial spectrum of the feature surface with vibration information. Simulation and experimental surface morphology decomposition results validate the effectiveness of the IFABEMD method in identifying relative vibration between the tool and the workpiece.