Engineering ceramics, which have interest of grinding of advanced ceramics over the last two decades, are widely used in several areas for example in the aerospace, petrochemical, marine, electrical, automobile and manufacturing industry. The machining technology of engineering ceramics use laser, electro discharge machining (EDM), ultrasound, plasma technology, cutting, grinding and turning in order to improve efficiency and reduce expenses. They are typical because of their high hardness, high strength, and brittleness.
Therefore, the optimization of cutting parameters such as cutting speed, feed speed, cutting depth, and tool cutting edge angle should be determined before experiments were carried out. Moreover, the optimal results are that materials removal rate ? is relatively large and the cutting tool wear rate ? sis relatively small. Multi-objective optimization was made to optimize cutting parameters for prediction models using response surface methodology.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.