Tool wear detection in dry high-speed milling based upon the analysis of machine internal signals

Abstract

Unattended machining plants require intelligent monitoring systems, able to detect the different events that can happen during the machining process. In particular, the control of tool wear is an important objective of a monitoring system when dry machining is applied. This work evaluates the suitability of a tool wear monitoring system based on machine tool internal signals. It presents a sensorless monitoring procedure for the dry and high-speed milling of aerospace aluminium alloys. Dry high-speed experiments were performed using aluminium Al 7075-T6 workpieces. The sensor data from internal signals were compared and analyzed, assessing the deviation in representative variables in time and frequency domains. The signal analysis confirmed the relevance of cutting force signals for tool wear monitoring in the high-speed milling of aluminium alloys.