Tool Condition Monitoring in Turning Using Statistical Parameters of Vibration Signal

Abstract

In this study, the relationship between vibration and tool wear is investigated during high-speed dry turning by using statistical parameters. It is aimed to show how tool wear and the work piece surface roughness changes with tool vibration signals. For this purpose, a series of experiments were conducted in a CNC lathe. An indexable CBN tool and a 16MnCr5 tool steel that was hardened to 63 HRC were both used as material twins in the experiments. The vibration was measured only in the machining direction using an acceleration sensor assembled on a machinery analyzer since this direction has more dominant signals than the other two directions. In addition, tool wear and work piece surface roughness are measured at different cutting time intervals where the cutting speed, radial depth of cut, and feed rate are kept constant. The vibration signals are evaluated using statistical analysis. The statistical parameters in this study are the Root Mean Square (RMS), Crest Factor, and Kurtosis values. When the flank wear increases, the Kurtosis value and RMS also increase, but the Crest factor exhibited irregular variations. It is concluded that these statistical parameters can be used in order to obtain information about tool wear and work piece surface roughness.