S. Sepehrinia - Mechanical Eng. Department, Islamic Azad University, Shiraz Branch, Shiraz, Iran
A.R. Tahavvor - Mechanical Eng. Department, Islamic Azad University, Shiraz Branch, Shiraz, Iran

Information of the drilling hole temperature, is important in drilling quality and tools life aspects. In the present study drilling hole temperature is determined by using artificial neural networks according to certain points temperature of the work piece and three parameters, drill diameter, work piece thickness and ambient temperature with considering convection. Using the three-dimensional CFD simulations, temperature in nods of the work piece specified. Results obtained from CFD are used for training and testing the ANN approach. Using reverse engineering and setting the parameters as input data, drilling hole temperature that determined by neural network is presented as output data. the hole temperature for different conditions and parameters obtained experimentally in previous studies and a comparison is performed among the soft programming ANN, CFD results and experimental data and it is observed that ANN soft programming code can be used more efficiently to determine hole temperature in drilling process.

Drilling; Hole temperature; Artificial neural networks; Experimental