PREDICTION OF THE TEMPERATURE OF THE HOLE DURING THE DRILLING PROCESS USING ARTIFICIAL NEURAL NETWORKS

Information about the temperature of drilling hole during the drilling process is important in work-piece quality and tools life aspects. In this study temperature of the drilling hole is determined using Artificial Neural Networks according to certain points’ temperature of the work piece and two parameters, drill diameter and ambient temperature. To achieve this aim, two-dimensional model of work piece is provided; then by Computational Heat Transfer simulations based on Finite Volume Method, temperature in different nodes of the work piece is specified. Obtained results are used for training and testing the neural network. Temperature of specified points, drill diameter and ambient temperature are selected as inputs of the network and temperature of drilling hole is considered as an output data. Also, for comparison, temperature is obtained experimentally. Comparison between numerical results and experimental data shows that neural network can be used more efficiently to determine temperature of hole in a drilling process.