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Helical milling is an alternative to conventional drilling which, applied to the molds and dies industry, greatly impacts its competitiveness once it guarantees high levels of surface and geometrical quality. Excellent surface quality and the ability to drill holes with just one tool are some of the advantages of this process. Concerning the machining of hardened materials, its low machinability is another aspect that compromises the competitiveness of the mold segment. Faced with the challenges of hard machining, the present research aims the robust multi-objective optimization of AISI H13 hardened steel at high speeds to achieve competitive levels of surface quality and increase the productivity of the helical milling process. Experimental planning, robust parameter design with a focus on the crossed array, response surface methodology, and enhanced normalized normal constraint method were employed in the conduction of experiments, analysis, modeling, and optimization of the responses of interest. The responses evaluated were the mean roughness Ra, the total circularity Ront, and the material removal rate MRR. The control variables used were the axial feed per tooth fza, the tangential feed per tooth fzt, and the cutting speed vc. For the robust parameter design, the tool overhang length lto and borehole depth lb were considered as noise variables. The possibility of increasing the productivity of helical milling in a hardened material, maintaining the quality of the holes, was concluded. It was verified to the total circularity robustness about the two noise variables, and the average of the confirmation runs is equal to the mean model. [ABSTRACT FROM AUTHOR]

Helical milling is an advanced hole-making process and different approaches considering controllable variables have been presented addressing modeling and optimization of machining forces in helical milling. None of them considers the importance of the noise variables and the fact that machining forces components are usually correlated. Exploring this issue, this paper presents a multivariate robust modeling and optimization of cutting forces of the helical milling of the aluminum alloy Al 7075. For the study, the tool overhang length was defined as noise variable since in cavities machining, there are specific workpiece geometries that constrain this variable; the controllable variables were axial feed per tooth, tangential feed per tooth, and cutting speed. The cutting forces in the workpiece coordinate system were measured and the components in the tool coordinate system, i.e., the axial and radial forces, were evaluated. Since these two outcomes are correlated, the weighted principal component analysis was performed together with the robust parameter design to allow the multivariate robust modeling of the mean and variance equations. The normal boundary intersection method was used to obtain a set of Pareto robust optimal solutions related to the mean and variance equations of the weighted principal component. The optimization of the weighted principal component through the normal boundary intersection method was performed and the results evaluated in the axial and radial cutting force components. Confirmation runs were carried out and it was possible to conclude that the models presented good fit with experimental data and that the Pareto optimal point chosen for performing the confirmation runs is robust to the tool overhang length variation. Finally, the cutting force models were also presented for mean and variance in the workpiece coordinate system in the time domain, presenting low error regarding the experimental test, endorsing the results. [ABSTRACT FROM AUTHOR]