End-of-Life Prediction for Milling Cutters Based on an Online Vibro-Acoustic System

Improving the capabilities of online condition monitoring systems, able to detect arising of catastrophic wear on cutting tools, has been an important target to be pursued for the metal cutting industry. Currently, different systems have been proposed, moved by the rising need of part quality improvements and production cost control. Despite this, cutter wear development, being related to several process variables and conditions, is still really difficult to be predicted accurately. This paper presents a detection wear method based on the time-domain analysis of vibro-acoustic signals. Specifically, cutter wear monitoring, using sound signals of a milling process, was performed at a laboratory level in a well-isolated working room. Sound signals were recorded at fixed main machining parameters, i.e., cutting speed, feed rate and depth of cut. The tests were carried out starting with a new set of inserts with significant wear conditions for the investigated process configuration. Results showed a consistent overlapping between the beginning of the catastrophic wear and an evident increment in the trend of the root mean square of the monitored acoustic signal, showing the potential of the methodology in detecting a suitable time to stop the milling process and to change the worn-out cutters.