An In-process thin-walled workpiece dynamics update model using a microphone

Material removal-induced variations in the in-process thin-walled workpiece dynamics are crucial to develop a vibration-free machining process. Therefore, monitoring varying vibration modes to adjust the cutting parameters along the toolpath is imperative. This study aims at identifying the natural frequencies of thinwalled parts during milling using a non-contact sensor. An output-only Autoregressive (AR) model with cutting sound signals and an Autoregressive with eXogenous inputs (ARX) model using the measured cutting forces and cutting sound are developed to predict the in-process dominant mode frequencies of a flexible part as material is removed. The natural frequencies obtained by the developed models are compared with the experimental modal analysis results. The comparisons showed that the developed AR and ARX models can respectively predict the dominant in-process workpiece mode frequencies with maximum errors of 8.6% and 7.8%. These results reveal the potential of the proposed methodology for monitoring the machining of thinwalled parts to improve part quality and process productivity

Mots clés

• thin-walled part machining
• in-process dynamics identification
• non-contact measurement