RCPM—A new method for robust chatter prediction in milling

The reliability of conventional chatter prediction algorithms is limited by the inaccuracy of machining system dynamic models. In this paper, a new probabilistic algorithm for a robust analysis of stability in milling, which performs the stability analysis on an uncertain dynamic milling model, is presented. In this approach, model parameters are considered as random variables, and robust analysis of stability is carried out in order to estimate system's probability of instability for a given combination of cutting parameters. By doing so, probabilistic instead of deterministic stability lobes are obtained, and a new criterion for system stability based on level curves and gradient of the probabilistic lobes can be applied to identify optimal robust stable cutting conditions. Experimental validation consisted of different phases: firstly, machining system dynamics were estimated by means of pulse tests. Secondly, cutting force coefficients were determined by performing cutting tests. Eventually, chatter tests were performed and the experimental stability lobes were compared with the predicted robust stable regions.