A MULTISENSOR STRATEGY FOR TOOL FAILURE-DETECTION IN MILLING

A multi-sensor monitoring strategy for detecting tool failure during the milling process is presented. In this strategy, both cutting forces and acoustic emission signals are used to monitor the tool condition. A feature extracting algorithm is developed based on a first order auto-regressive (AR) model for the cutting force signals. This AR(1) model is obtained by using average tooth period and revolution difference methods. Acoustic emission (AE) monitoring indices are developed and used in determining the setting threshold lever on-line. This approach was beneficial in minimizing false alarms due to tool runout, cutting transients and variations of cutting conditions. The proposed monitoring system has been verified experimentally by end milling Inconel 718 with whisker reinforced ceramic tools at spindle speeds up to 3000 rpm.