Abstract—Nowadays numerical and Artificial Neural Networks (ANN) methods are widely used for both modeling and optimizing the performance of the manufacturing technologies. Optimum machining parameters are of great concern in manufacturing environments, where economy of machining operation plays a key role in competitiveness in the market.
In this paper, the selection of optimal machining parameters (i.e., spindle speed, depth of cut and feed rate) for face milling operations was investigated in order to minimize the surface roughness and to maximize the material removal rate. Effects of selected parameters on process variables (i.e., surface roughness and material removal rate) were investigated using Response Surface Methodology (RSM) and artificial neural networks. Optimum machining parameters were carried out using RSM and compared to the experimental results. The obtained results indicate the appropriate ability of RSM and ANN methods for milling process modeling and optimization.
Index Terms—Milling operations, Optimization, Modeling, Response Surface Methodology, Artificial Neural Network
Cite: M.R. SOLEYMANI YAZDI and A. KHORRAM, "Modeling and Optimization of Milling Process by using RSM and ANN Methods," International Journal of Engineering and Technology vol. 2, no. 5, pp. 474-480, 2010.