Dynamic models of machining vibrations, designed for classification of tool wear

Abstract

The goal of this dissertation is to develop a machining tool-wear classification system which uses features drawn from accelerometers that respond to machining vibrations. Specifically, we use features from wide band accelerometer signals in a two stage dynamic classifier estimating the flank wear on end mills cutting notches in either steel or titanium workpieces. We introduce an experimental paradigm which incorporates new evaluation metrics not previously used in tool-wear monitoring.Our experiments also show that within an individual cutting pass the wear process changes as the cutter moves into and out of "regions of interest" which effect the sensor features used in classification. We select features which are sensitive to the dynamics at these various time scales. We demonstrate a single-rate dynamic classifier which models the dynamics of wear both within an individual cutting pass and also over the cutting life of the tool.To improve the modeling of the rapidly varying discrete wear events that last several milliseconds, we extend the single-rate dynamic classifier to a multi-rate classifier. We demonstrate that coupling the two classifiers during classification gives better performance than combining the outputs of the separate classifiers in a second stage.We demonstrate a method of using both labeled and unlabeled data to train model parameters demonstrate feature processing which allows us to generalize to a limited range of cutting conditions including the use of features drawn from accelerometers with different response characteristics.We present the information from the classifier in several different formats to assist the machinist in making an informed decision. Our system estimates the wear on the primary cutting edge at the end of each cutting pass. In addition to this estimate, we provide a measure of the confidence in the cutter wear having exceeded a predefined level considered to constitute the end of the cutter's useful life. We incorporate the actual cutting behavior seen for the particular cutter in use resulting in a more accurate prediction than is possible with a simple average.The accuracy of our single-rate classifier is 90% to 97% when classifying the wear on cutters milling steel. Even on the more difficult problem of classification when cutting titanium, our multi-rate classifier achieves accuracy of 94%. (Abstract shortened by UMI.)