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Numerically controlled machine tools are commonly used in metalworking processes due to their precision and reproducibility. However, finding the appropriate cutting parameters and machining process using simple machining features is limited, as parts may have complex interacting machining features. This study contributes to solving this problem by integrating PSO-ANN hybrid algorithm and genetic algorithm, to predict and optimize roughness, cost, and energy consumption for interactive features. From the research carried out, it was found that the output variables were highly correlated, with coefficients above 0.97%. In addition, it was demonstrated that proper selection of machining techniques and sequences could lead to a significant reduction in energy consumption, with a 99.25% variation between minimum and maximum values. The genetic algorithm identified the optimum cutting parameters, namely Vc = 25.45 m/min, f = 0.111 mm/rev, and ap = 0.58 mm, which led to a considerable improvement in the results obtained.

A combination of PSO-ANN hybrid algorithm and genetic algorithm to optimize technological parameters during milling 2017A alloy