Abstract: In this work different types of artificial neural networks (ANN) models are developed comparing between them for the prediction of best surface roughness (Ra) values in (AL) alloy after milling machine process. The feed forward neural network (FFNN) with different training functions, radial base (RBNN) and generalized regression (GRNN) networks were selected and the data used for training these networks were derived from experiments conducted using CNC milling machine. The Taguchi design of experiments was applied to reduce the time and cost of the experiments. The six inputs (radial depth of cut, axial depth of cut, cutting speed , feed rate, tool diameter and machine tolerance) selected for the network with the selected output (surface roughness).
Rasmy, M., Soliman, O., Gadallh, M., & El- Sayed, R. (2010). Prediction of Surface Roughness for Milling Operation Using Artificial Neural Network. The International Conference on Applied Mechanics and Mechanical Engineering, 14(14th International Conference on Applied Mechanics and Mechanical Engineering.), 1-15. doi: 10.21608/amme.2010.37649
MLA
Mohamed H. Rasmy; Omar S. Soliman; Mohamed H. Gadallh; Reda El- Sayed. "Prediction of Surface Roughness for Milling Operation Using Artificial Neural Network". The International Conference on Applied Mechanics and Mechanical Engineering, 14, 14th International Conference on Applied Mechanics and Mechanical Engineering., 2010, 1-15. doi: 10.21608/amme.2010.37649
HARVARD
Rasmy, M., Soliman, O., Gadallh, M., El- Sayed, R. (2010). 'Prediction of Surface Roughness for Milling Operation Using Artificial Neural Network', The International Conference on Applied Mechanics and Mechanical Engineering, 14(14th International Conference on Applied Mechanics and Mechanical Engineering.), pp. 1-15. doi: 10.21608/amme.2010.37649
VANCOUVER
Rasmy, M., Soliman, O., Gadallh, M., El- Sayed, R. Prediction of Surface Roughness for Milling Operation Using Artificial Neural Network. The International Conference on Applied Mechanics and Mechanical Engineering, 2010; 14(14th International Conference on Applied Mechanics and Mechanical Engineering.): 1-15. doi: 10.21608/amme.2010.37649
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