Diagnosis of a Cutting Tool in a Machining Center

The successful performance of machining centers involves selection, control and monitoring of a large number of parameters. Cutting tool condition is one of the most important variables due to the strong influence on dimensional accuracy and surface finish of the product. However, the cutting tool condition is difficult to measure online. A new proposal for online monitoring of the cutting tool condition based on hidden Markov models is presented. The generated vibration signals between the cutting tool and the workpiece are used for classification of the tool condition in different states (new, half-new, half-worn, and worn cutting tool). Feature vectors were obtained from these vibration signals by applying a triangular filter bank and computing the Mel frequency cepstrum coefficients. The proposal includes a training step using the Baum-Welch algorithm and a diagnosis step exploiting the Viterbi algorithm. A Monte Carlo simulation validates a successful performance using experimental data in a face milling process. A comparison with classical approaches such as artificial neural network is discussed.