KOLÁŘ, P., et al. Indirect drill condition monitoring based on machine tool control system data. MM Science Journal. 2022, 2022(3), 5905-5912. ISSN 1803-1269. DOI 10.17973/MMSJ.2022_10_2022119.
Automatic process monitoring, including tool wear monitoring, is a key aspect of improving the energy efficiency and cost of the machining process. The tool flank wear continuously increases during drilling operations. The intensity of tool wear may vary depending on the local properties of the material and the process settings. This paper shows the potential of drill condition monitoring based on machine tool control system data, namely spindle drive current and Z slide current. The workpiece vibration measurement is used as a reference method. Correlations of various features of monitored signals are evaluated. These features are shown to depend, in general, on the instantaneous drilling depth. Among the features investigated, the RMS signal has been shown to exhibit a significant correlation with tool wear. The results were compared for two values of cutting speed. The correlation of selected features is shown to be independent of the total lifetime of the tool, thus demonstrating the attractiveness of these features for tool wear prediction. Specifically, the root mean square of the vibration and spindle torque signals strongly correlate with flank wear near the bottom of the hole while the root mean square of the drive current of the drilling axis strongly correlates with flank wear near the middle of the hole.
eng
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
MM Science Journal
dc.relation.ispartof
MM Science Journal
dc.subject
tool wear monitoring
eng
dc.subject
spiral drill wear
eng
dc.subject
smart machine tools
eng
dc.subject
edge computing
eng
dc.subject
correlation analysis
eng
dc.title
Indirect drill condition monitoring based on machine tool control system data