On-Line Identification and Suppression of Time Varying Machining Chatter in Turning Via Dynamic Data System (DDS) Methodology
The time varying stability of the machining process necessitates a technique of on-line chatter identification and control. The Dynamic Data System (DDS) methodology which has been applied in the field of manufacturing and proved itself as a powerful tool to identify the machining process under working conditions is implemented for this purpose.
Mathematical models of the machining process with an inherent stochastic nature were developed as discrete ARMA (n,n-1) models. Based on off-line analysis, the peak of power spectral density corresponding to the dynamic mode of workpiece fundamental natural frequency served as a simple and reliable index of stability for on-line chatter identification. Using this criterion, the influence of speed and feed on stability were studied and the strategy of changing speed and feed incremently to find stable cutting conditions without sacrificing productivity was proposed. Due to the current capability of microcomputer, a simple and fast adaptive modeling technique was adopted for the on-line machining process identification. A forecasting control of chatter scheme was implemented to predict the generation of chatter. In addition, the vibration signal was purified from the results of the dynamic analysis of the chuck-workpiece-tailstock system, and a self-learning scheme was established to determine the threshold level of stability in each cutting process.
A chatter suppression controller was designed and interfaced to the PT15 CNC lathe. Cutting tests demonstrated the successful use of the theoretical and technical development by the DDS approach.
KeywordsMachine Tool Power Spectral Density Machine Process Control Chart Vibration Signal
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