Monitoring tool wear and surface roughness in the face milling process of high-strength compacted graphite cast irons

The identification and monitoring state variables in the machining process are one of the best methods to increase machinability. This study evaluated the use of different signal analysis parameters for a vertical machining center's electric current input during the milling process of three different high-strength compacted graphite cast irons. The machinability was initially assessed in relation to the tool life and workpiece surface roughness, for two levels of the surface condition of the workpiece (solid and with pre-holes), machining atmospheres (dry and MQL), and tool coatings (TiN and TiAlN). The output variables (tool life and surface roughness) were then correlated with the electric current using the signal variance, mean square deviation, skewness and kurtosis. Despite the amplitude signal parameters presenting the best performance regarding the monitoring of the process, the use of alternative parameters such as skewness and kurtosis proved to be effective to identify the workpiece surface condition.