Analysis of Spindle AE Signals and Development of AE-Based Tool Wear Monitoring System in Micro-Milling

Abstract

Acoustic emission (AE) signals collected from different locations might provide various sensitivities to tool wear condition. Studies for tool wear monitoring using AE signals from sensors on workpieces has been reported in a number of papers. However, it is not feasible to implement in the production line. To study the feasibility of AE signals obtained from sensors on spindles to monitor tool wear in micro-milling, AE signals obtained from the spindle housing and workpiece were collected simultaneously and analyzed in this study for micro tool wear monitoring. In analyzing both signals on tool wear monitoring in micro-cutting, a feature selection algorithm and hidden Markov model (HMM) were also developed to verify the effect of both signals on the monitoring system performance. The results show that the frequency responses of signals collected from workpiece and spindle are different. Based on the signal feature/tool wear analysis, the results indicate that the AE signals obtained from the spindle housing have a lower sensitivity to the micro tool wear than AE signals obtained from the workpiece. However, the analysis of performance for the tool wear monitoring system demonstrates that a 100% classification rate could be obtained by using spindle AE signal features with a frequency span of 16 kHz. This suggests that AE signals collected on spindles might provide a promising solution to monitor the wear of the micro-mill in micro-milling with proper selection of the feature bandwidth and other parameters.

Keywords

• AE signals
• micro-milling
• hidden Markov model (HMM)
• tool wear monitoring