A Comparative Study for Modeling and Optimization of Surface Roughness in Milling Process Using Taguchi Technique and Simulating Annealing Algorithm

Abstract

The present work addresses a statistical modeling and optimization procedure for milling process of 7075-T6 aluminum alloy using regression modeling, Taguchi technique and simulated annealing algorithm(SAA). The input parameters are feed rate, cutting speed, axial-radial<br><br>depth of cut, and machining tolerance. The surface roughness has been considered as the performance<br><br>characteristics of the process. The experimental data are gathered using Taguchi L27 design of experiments to minimize the process characteristic. Simulated Annealing Algorithm has also been employed to predict the cutting variables for minimizing the surface roughness. A confirmation experiment based on the optimal levels of process parameters was carried out in order to compere the<br><br>effectiveness of the Taguchi method and SA algorithm in optimization of the process. Taguchi method does not give the best results frequently. In previous papers SA algorithm gives a better results than Taguchi, note that the SA gives randomly answer. The advantages of this paper, Taguchi<br><br>method chose the best result for this example it will be compared with the SAA to know the power of this algorithm.