MODELING THE DRILLING PROCESS OF SOME AL-MG-CU ALLOYS AND AL-MG-CU/SIC COMPOSITES USING ARTIFICIAL NEURAL NETWORK

Year 2011, Volume: 1 Issue: 1, 18 - 24, 23.07.2016

Mohammed T. Hayajneh Adel Mahamood Hassan Ahmad Turki Mayyas Abdalla Alrashdan

Abstract

Machining of metal matrix composites (MMC's) is very important process and has been a major problem that attracts many researchers to study of characteristics of MMC's during machining process like turning, milling and drilling. This paper concerns with the potential of using feed forward backpropagation neural network in prediction of torque and thrust force during dry drilling of aluminum-copper/silicon carbide composites produced by stir casting method. The effect of the addition of copper as alloying element and silicon carbide as reinforcement particles to Al-4wt.% Mg metal matrix has been investigated by using artificial neural networks. The mean absolute relative errors between experimental and predicted values from network were 2.03% for torque, and 3.46% for thrust force. Therefore, it is suggested that by using ANN outputs, it is possible to predict the results of cutting parameters in drilling process which will be in a good agreement with the experimental ones

Keywords

Aluminum , Artificial Neural Network (ANN) , Casting , Drilling , Metal matrix Composites (MMCs)

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