ONLINE PCBN TOOL FAILURE MONITORING-SYSTEM BASED ON ACOUSTIC-EMISSIONSIGNATURES

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) insert s in milling high-chromium materials. In face milling of high-chromium materials, almost without exception, PCBN inserts fail due to fractur e on the nose or rake face. The change in the root mean square of AE s ignals (AE Delta RMS) was found to be the best indicator of the tool f ailure process for the subject tool-work combination. From the experim ents, the critical values of AE Delta RMS where the tool fractures, ca lled AE Delta RMS(c), were obtained. Regression analysis was performed to fit a model of \Delta RMS(c)\ as a function of feed rate and depth of cut. The regression model was then used to estimate the values of \Delta RMS(c)\ for conditions that were excluded from the experiment. The accuracy of the monitoring system was tested with simulated as wel l as actual experiments.