Online Dimensional Controlling System for Drilling

The drilling is well known as one of the most common hole making processes in the industry. Due to close tolerance requirement for drilled holes in the most of work pieces, online controlling of the diameter of drilled holes seems to be necessary. In the current work, an online dimensional controlling system was developed for drilling process. Doing this, drilling process was executed in different cutting conditions (feed per tooth and cutting speed) and different flank wear of cutting edges. In each drilling test, axial force and diameter of drilled hole was recorded. According to the results obtained from analysis of variance (ANOVA), increase of flank wear in cutting edges increases the axial force and hole-diameter. In this way, the axial cutting force, as online measurable parameter, could be used for online estimation of the hole-diameter. Neural network (NN) was used to model the correlation between axial force and the hole-diameter. In this way, the obtained NN model estimates the maximum acceptable axial force by receiving cutting conditions and maximum acceptable hole-diameter. The drilling process has to be stopped as its axial force exceeds the estimated value for drill changing.