Online Dimensional Controlling System for Drilling

Subject Areas : journal of Artificial Intelligence in Electrical Engineering

Reza Farshbaf Zinati ¹ , Ahmad Habibi Zad navin ² , Mohammad Reza Razfar ³

1 - Department of Mechanical Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
2 - Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
3 - Department of Mechanical Engineering, AmirKabir University of Technology, Tehran, Iran.

Received: 2016-11-03 Accepted : 2016-11-03 Published : 2016-06-01

Keywords: Neural network, analysis of variance, Drilling, axial cutting force, diameter tolerance,

Abstract :

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, onlinecontrolling of the diameter of drilled holes seems to be necessary. In the current work, an onlinedimensional controlling system was developed for drilling process. Doing this, drilling processwas executed in different cutting conditions (feed per tooth and cutting speed) and different flankwear 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 incutting edges increases the axial force and hole-diameter. In this way, the axial cutting force, asonline measurable parameter, could be used for online estimation of the hole-diameter. Neuralnetwork (NN) was used to model the correlation between axial force and the hole-diameter. Inthis way, the obtained NN model estimates the maximum acceptable axial force by receivingcutting conditions and maximum acceptable hole-diameter. The drilling process has to bestopped as its axial force exceeds the estimated value for drill changing.

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