Method for early detection of the regenerative instability in turning

Nowadays, approaches in chatter detection and control are based on chatter prediction, by using a machining system dynamic model, or on chatter detection by different techniques, but after chatter onset. They are not efficient because the models are complicated and specific (in the first case) respectively because of chatter unwanted consequences occurrence (in the second case). This paper presents a method for early detection of the process regenerative instability state (as a specific process current dynamical state), based on cutting force monitoring. Using the cutting force records, the process current dynamical state is assessed. Appropriate cutting force signal features are defined, based on signal statistic processing, signal chaotic modeling or signal harmonic analysis, and used on this purpose. The process dynamical state evolution is modeled aiming the features values prediction. Two types of models were used in this purpose: linear and neural. The instability regenerative mechanism is identified by using either dedicated features or input variable selection. The method was conceived and experimentally implemented in the case of turning process. The results show the method reliability and the possibility of using it in developing an intelligent system for stability control.