Multiparameter signal-to-noise ratio optimization for end milling cutting conditions of aluminium alloy 5083

Abstract

Surface integrity problems during selective material removal processes are a very common limitation for process productivity and part quality, especially in difficult-to-machine materials like 5083 aluminium alloy (AA), which is known for its remarkable performance in extreme environments. In general, tuning the cutting-part material properties with cutter geometry and cutting parameters can optimize surface texture, increase parts accuracy and resistance in corrosion, and eliminate process noise and energy waste. This work is an experimental study of surface parameter optimization during finish end milling of an AA5083 under a specific range of three cutting parameters with an optimized two-flute carbide cutter by previous work. So, twenty-seven experiments were run having varied the radial depth of cut (RDOC), feed rate (f), and cutting speed (S). Surface roughness parameters (Ra and Rt) were measured in the direction of cutting speed at three different distances by the upper edge. The signal-to-noise (SN) ratios have been calculated, and the process was optimized following the analysis of means. Then, additive models with linear interactions were fitted on SN ratios, and the analysis of variances and residual normality plots were utilized to validate the models’ goodness. The SN approach and analysis of means conclude that 0.5 mm RDOC, 6000 rpm speed, and 0.082 mm/tooth feed optimize the process and can effectively predict the Ra and Rt responses. The newly produced machinability data can benefit further applications of AA5083 in industrial applications such as shipbuilding and vehicle bodies.

A Comparative Study of Different Milling Strategies on Productivity, Tool Wear, Surface Roughness, and Vibration

“…On the other hand, they stated that increasing the cutting speed reduces the residual stresses installed on the surface, while an increase in the feed per tooth induces a higher level of residual stresses. Kechagias [33] carried out a generic study that encompassed several machining parameters, trying to optimize them when milling AA 5083 aluminum alloy, essentially aiming to improve tool life and the condition of the machined surface. On the other hand, Fountas et al [34] carried out a study more focused on the aeronautical industry, with a view to optimizing the planning of machining operations through programmable support functions, with a view to shortening and improving the connection between the design phases and machining operations in this industrial sector.…”