Resumo:
Super duplex stainless steels have a mixed microstructure consisting of ferrite and austenite phases and are used especially in the oil and gas industry, but have low machinability. The helical milling process consists of rotating the cutter around its own axis combined with the helical feed, it presents greater processing efficiency and lower cost compared to the conventional drilling process and allows obtaining holes of different diameters. The process also has advantages such as high levels of surface quality, greater dimensional accuracy and shape quality, lower levels of cutting efforts and greater smoothness in the machining operation. This work consists of a robust multi-objective optimization of helical milling of the super duplex stainless steel UNS S32760. The experimental design was carried out with a central composite arrangement, considering as control factors the axial feed per tooth, the tangential feed per tooth and the cutting speed. Mean roughness responses, axial thrust force and circularity deviations were evaluated. The robust parameter design was used, the response surface methodology for conducting the experiments, analysis and modeling of the responses of interest, and multi-objective optimization was performed through the methods of optimization of multi-objective particle swarm with agglomeration distance (MOPSO-CD) and non-dominated sorting genetic algorithm (NSGA-II). For the robust parameter design, the overhang length of the cutter (lto), the measured height of the machined hole (lb) and the cutting fluid flow rate (Q) were considered as noise variables. Robust multi-objective evolutionary optimization allows the evaluation of process factor levels. Different attributions for the objective functions were analyzed in order to obtain optimal solutions on the Pareto Frontier for the evaluated responses.
