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.