Online PCBN tool failure monitoring system based on acoustic emission signatures

The paper describes an online tool failure monitoring system based on acoustic emission (AE) signatures. The system was developed primarily to detect failure of polycrystalline cubic boron nitride (PCBN) inserts in milling high-chromium materials. In face milling of high-chromium materials, almost without exception, PCBN inserts fail due to fracture on the nose or rake face. The change in the root mean square of AE signals (AE ΔRMS) was found to be the best indicator of the tool failure process for the subject tool-work combination. From the experiments, the critical values of AE ΔRMS where the tool fractures, called AE ΔRMS_c, were obtained. Regression analysis was performed to fit a model of |ΔRMS_c| as a function of feed rate and depth of cut. The regression model was then used to estimate the values of |ΔRMS_c| for conditions that were excluded from the experiment. The accuracy of the monitoring system was tested with simulated as well as actual experiments