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

The present work addresses a statistical modeling andoptimization procedure for milling process of 7075-T6aluminum alloy using regression modeling, Taguchitechnique and simulated annealing algorithm(SAA). Theinput parameters are feed rate, cutting speed, axial-radialdepth of cut, and machining tolerance. The surfaceroughness has been considered as the performancecharacteristics of the process. The experimental data aregathered using Taguchi L27 design of experiments tominimize the process characteristic. Simulated AnnealingAlgorithm has also been employed to predict the cuttingvariables for minimizing the surface roughness. Aconfirmation experiment based on the optimal levels ofprocess parameters was carried out in order to compere theeffectiveness of the Taguchi method and SA algorithm inoptimization of the process. Taguchi method does not givethe best results frequently. In previous papers SA algorithmgives a better results than Taguchi, note that the SA givesrandomly answer. The advantages of this paper, Taguchimethod chose the best result for this example it will becompared with the SAA to know the power of thisalgorithm.