Prediction of Surface Roughness for Milling Operation Using Artificial Neural Network
Rasmy, M., Soliman, O., Gadallh, M., El- Sayed, R. (2010). Prediction of Surface Roughness for Milling Operation Using Artificial Neural Network. EKB Journal Management System, 14(14th International Conference on Applied Mechanics and Mechanical Engineering.), 1-15. doi: 10.21608/amme.2010.37649
Mohamed H. Rasmy; Omar S. Soliman; Mohamed H. Gadallh; Reda El- Sayed. "Prediction of Surface Roughness for Milling Operation Using Artificial Neural Network". EKB Journal Management System, 14, 14th International Conference on Applied Mechanics and Mechanical Engineering., 2010, 1-15. doi: 10.21608/amme.2010.37649
Rasmy, M., Soliman, O., Gadallh, M., El- Sayed, R. (2010). 'Prediction of Surface Roughness for Milling Operation Using Artificial Neural Network', EKB Journal Management System, 14(14th International Conference on Applied Mechanics and Mechanical Engineering.), pp. 1-15. doi: 10.21608/amme.2010.37649
Rasmy, M., Soliman, O., Gadallh, M., El- Sayed, R. Prediction of Surface Roughness for Milling Operation Using Artificial Neural Network. EKB Journal Management System, 2010; 14(14th International Conference on Applied Mechanics and Mechanical Engineering.): 1-15. doi: 10.21608/amme.2010.37649

Prediction of Surface Roughness for Milling Operation Using Artificial Neural Network

The International Conference on Applied Mechanics and Mechanical Engineering
Article 33, Volume 14, 14th International Conference on Applied Mechanics and Mechanical Engineering., May 2010, Page 1-15  XML PDF (390.44 K)
Document Type: Original Article
DOI: 10.21608/amme.2010.37649
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Authors
Mohamed H. Rasmy1; Omar S. Soliman1; Mohamed H. Gadallh2; Reda El- Sayed1
1Department of DS, faculty of computer and Information Cairo University Giza, Egypt.
2Institute of statistical studies and Research Cairo University Giza, Egypt.
Abstract
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).
Keywords
Milling – feed forward – radial base – generalized regression – surface roughness
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