Detection of instabilities and transition in milling operation using wavelets

Abstract

Machining operations are an intrinsic part of the manufacturing industry and, as a result, they are under constant scrutiny for development and technological enhancements. Mechanical instabilities that occur during machining processes are damaging and undesirable due to their detrimental effects. Chatter is one such type of instability and is characterized by the violent relative vibration between the workpiece and tool. The objective of this research is to detect the onset of instabilities in milling operation and to detect the transition stage from stable to unstable operation using Wavelet Analysis. A single degree of freedom model is utilized to represent the end milling process and the stability criterion in the form of the stability lobe diagram for the process has been developed. The force data obtained from the experiments performed on a milling machine was processed using Wavelet Transform at different spindle speed ranges for a particular axial depth of cut. These results were used to identify and establish the state of the process as stable or unstable or in transition. This research establishes Wavelet Transform as an effective tool, and presents a new and different perspective to detect the changes in the state of the milling operation. This method is suitable and consistent in detecting the state of the metal cutting process in real-time and can be effectively used to detect the onset of instabilities and the transition of the system deteriorating from a stable to unstable state. This new approach for detection of the onset of instabilities can be effectively used as a monitoring tool and process optimization.