In this case genetic algorithms were usually used to optimize cutting parameters. Moreover in the following investigation the reaction surface approach was used to develop into regression model cutting force by manipulating experimental measurements from these cutting forces. The regression model was then combine with genetic algorithm to establish optimum end mill process parameter. The cutting speed was the dominant factor, followed by the cutting feed rate, and the axial depth of cut. Genetic algorithms was used to get regression equations between material removal rate, surface roughness, and input parameters such as cutting speed, feed rate, and depth of cut, etc. The genetic algorithm-based approach yielded maximum value of material removed rate (MRR). In addition, materials removal rate and cutting tool wear rate were predicted by the least squares method. Finally, the principal objective optimization of cutting parameters was obtained from genetic algorithms, and those parameters will be explained in detail in the following paragraphs.


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