Bayesian and regression approaches to on-line prediction of residual tool life

In this paper, two statistical approaches to on-line prediction of cutting tool life are presented and discussed. A Bayesian approach utilizes in-process information about the cutting tool state and constitutes a valuable basis for improved prediction. A second approach is based on the cutting forces and facilitates a prediction of the tool life with an uncertainty of 15% after 1.5–2.0 cutting minutes. Traditional tool condition monitoring can be improved by increased reliability of tool life predictions, increased utilization of the cutting tools together with reduced need for pre-process data and calibrating procedures.