Chatter prediction based on operational modal analysis and an adaptive complex Morlet filter

Abstract

Chatter that occurs between a cutting tool and a workpiece greatly reduces the surface quality and production efficiency. Therefore, it is of great importance to predict and avoid chatter so as to guarantee the stability of the manufacturing process. To realise the accurate prediction of the stability boundary of machine tools, operational modal analysis (OMA) is increasingly receiving attention due to its adequate consideration of variations in working conditions in the industrial environment. However, because of the influence of harmonic components in the response signals, the accuracy in identifying the modal parameters is seriously compromised. In this paper, an adaptive complex Morlet filter (ACMF) is presented to remove the harmonic components by adaptively adjusting the centre frequency and bandwidth according to the local character of the ambient environment in a specific frequency range and filtering out harmonic components that are not strict integer multiples of the fundamental frequency owing to non-rigid periodic motion of the machine tool spindle. In order to show the effectiveness of the proposed method, milling experiments are carried out and experimental modal analysis (EMA) is utilised to make comparisons with the proposed method. Moreover, comparisons between the ACMF and two other typical filtering methods are made. The results indicate that the proposed method performs well in modal parameter recognition for machine tools.