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چکیده
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The aim of this paper is to investigate and optimize surface quality and geometrical characteristics in drilling process of AISI H13 steel, because they are critical items for precision manufacturing. After conducting the experiments, two regression models are developed to extensively evaluate the effect of drilling parameters on process outputs. After that, evolutionary multi-objective optimization algorithm is employed to find the optimal drilling conditions. Non-dominated Sorting Genetic Algorithm (NSGA-ІІ) is developed and regression functions are taken into account as objective functions of algorithm to simultaneously optimize the surface roughness and deviation of circularity. The optimization results are successfully in agreement with experimental findings and finally the set of optimal drilling conditions is reported that can be selected by process engineer according to the priority and application. It is shown that, an increase in Cutting speed and liquid coolant intensity decreases the surface quality, while higher depth of cut, tool diameter and reed rate improve it. It is also found that tool diameter and depth of cut are the most effective input parameters on deviation of circularity. Finally, it can be concluded that, the implemented approach in this research provides an efficient method for other manufacturing processes to increase the performance and reduction of production costs.
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