Condition Monitoring for Indexable Carbide End Mill Using Acceleration Data

Abstract

In order to automate machining operations, it is necessary to develop robust tool condition monitoring techniques. In this paper, a tool monitoring strategy for indexable tungsten carbide end milling tools is presented based on the Fourier transform and statistical analysis of the vibrations of the tool during the machining operations. Using a low-cost, tri-axial piezoelectric accelerometer, the presented algorithm demonstrates the ability to accurately monitor the condition of the tools as the wear increases during linear milling operations. One benefit of using accelerometer signals to monitor the cutting process is that the sensor does not limit the machine's capabilities, as a workpiece mounted dynamometer does. To demonstrate capabilities of the technique, four tool wear life tests were conducted under various conditions. The indirect method discussed herein successfully tracks the tool's wear and is shown to be sensitive enough to provide sufficient time to replace the insert prior to damage of the machine tool, cutter, and/or workpiece.

Beamforming Applied to Ultrasound Analysis in Detection of Bearing Defects

“…Condition monitoring enables predictive maintenance, which can reduce downtime of equipment and overall maintenance cost. Furthermore, when applied to milling operations, e.g., the state of the mill, it could extend the lifespan of the inserts, reduce cost and make way for a new industry standard of quality and health control [1].…”