Analysis of Phase-Space and Psychoacoustic Measures for Condition Monitoring of Milling Tools

This article presents a thorough analysis and evaluation of condition monitoring (CM) of a precision computer numerical control (CNC) mill based on acoustic emissions (AEs). Two separate techniques are used to analyze the data: the first computes metrics after representing the data in the phase space, in contrast to classical approaches that operate in the time and frequency domains. The second approach uses psychoacoustic metrics. Through the analysis of data in the phase domain, which is linked to the angular position of the milling tool, high angular resolution for the acoustic tool monitoring is achieved, enabling the evaluation of each cutting edge of the milling tool individually. The obtained results are consistent with those of direct measurement methods, in this case based on microscopic photographs. On the other hand, since the AE data are audible, the use of psychoacoustic metrics in CM is also investigated, with the goal to create a link to human perception of anomalies based on hearing the AEs. In order to evaluate the AE data, two relative metrics for wear and accumulated tool damage are defined in the phase space; additionally, several psychoacoustic metrics are investigated. The methods were evaluated on large datasets acquired during the production of a series of parts and by using different tools. The relative metrics in the phase space provide robust and meaningful results and are performing better than the psychoacoustic metrics since they effectively embed a priori knowledge about the tool geometry into the evaluation.