Increase of Surface Quality in Milling process by Taguchi Technique and Genetic Algorithm

this paper is proposed an experimental and numerical study for modeling and optimization of face milling process. The approach is based on mathematical modeling and statistical analysis on the experimental data gathered using Taguchi design of experiments (DOE). 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 roughness of AISI1045 steel milled parts have been studied. Various regression functions have been fitted on the data based on output characteristics, in order to establish the relations between the input and the output parameters. Analysis of variance (ANOVA) approach has been used to evaluate the significance of the process parameters on the quality characteristic of the process (SR). Then, statistical analysis and confirmation experiments have been carried out to compare the models derived and select the best and most fitted model. In the last step of this research, mathematical model has been developed for SR prediction using genetic algorithm (GA) on the basis of experimental results. The model developed for optimization has been validated by confirmation experiments. It has been found that the predicted SR using GA algorithm is in good agreement with the actual ones.