Statistical analysis of sound and vibration signals for monitoring rolling element bearing condition

Study on prediction of surface quality in machining process

The relationship between the cutting force characteristics and tool usage (wear) in a micro-end-milling operation was studied for two different metals. Neural-network-based usage estimation methods are proposed that use force-variation- and segmental-averaging-based encoding techniques.

Tool wear estimation in micro-machining.: Part I: tool usage–cutting force relationship

Identification and online prediction of lifetime of cutting tools using cheap sensors is crucial to reduce production costs and downtime in industrial machines. In this paper, we use the acoustic emission from an embedded sensor for computation of features and prediction of tool wear. Acoustic sensors are cheap and nonintrusive, coupled with fast dynamic responses as compared with conventional force measurements using dynamometers. A reduced feature subset, which is optimal in both estimation and clustering least squares errors, is then selected using a new dominant-feature identification algorithm to reduce the signal processing and number of sensors required. Tool wear is then predicted using an Auto-Regressive Moving Average with eXogenous inputs model based on the reduced features. Our experimental results on a ball nose cutter in a high-speed milling machine show the effectiveness in predicting the tool wear using only the dominant features. A reduction in 16.83% of mean relative error is observed when compared to the other methods proposed in the literature.

Tool Wear Monitoring Using Acoustic Emissions by Dominant-Feature Identification

This paper presents in-process monitoring and control of surface roughness during machining processes via ultrasonic sensing. A newly developed system is utilized to measure the reflected intensity of ultrasonic beams from the surface and in-process measurement capability is evaluated under machine vibration and tool wear. The experimental results show that the system is able to detect the changes in surface roughness caused by tool wear and can possibly be used to monitor tool wear. Finally, in-process control of surface roughness using the ultrasonic system is demonstrated by adapting the system to a CNC machining center and performing geometric adaptive control. The results show it is possible to maintain surface roughness within 10% of the target value for tool wear up to 0.3 mm.

J.J. Liu

Papers

Correlating tool life, tool wear and surface roughness by monitoring acoustic emission in finish turning