The proposed approach is based on statistical analysis on the experimental data gathered using Taguchi design matrix.Surface roughness (SR)is the most important performance characteristics of the face milling process. In this study the effect of input face milling process parameters on surface roughnessof AISI1045 steelmilled parts have been studied. The input parameters are cutting speed (v), feed rate (fz) and depth of cut (ap). The experimental data are gathered using Taguchi L9 design matrix.In order to establish the relations between the input and the output parameters, various regression functions have been fitted on the data based on output characteristics. The significance of the process parameters on the quality characteristics of the process was also evaluated quantitatively using the analysis of variance (ANOVA) method. Then, statistical analysis and validation experiments have been carried out to compare and select the best and most fitted models. In the last section of this research,mathematical model has been developed for Surface roughness prediction using simulated annealing (SA) algorithm on the basis of experimental results. The model developed for optimization has been validated by confirmation experiments. It has been found that the predicted roughness using SA algorithm is in good agreement with the actual surface roughness.

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