Modeling of material removal rate and surface roughness in CNC milling of AISI 1040 steel plate using grey-fuzzy algorithm

In this thesis, the surface roughness and material removal rate in CNC (ComputerNumerical Control) face milling of AISI 1040 steel were optimized using the Grey Relational Analysis with Taguchi L16 orthogonal array. In the study, the test pieces were initially machined on a CNC machine using an HSS Ø20 mm end mill according to the determined cutting parameters. The process parameters were cutting speed of 47.10, 62.80, 78.50, 94.20 mm/min, cutting depth of 0.5, 0.75, 1, 1.25 mm, cutting method of zig-zag, morph spiral, one way, zig, and spindle speed of 750, 1000, 1250, 1500 rpm. In the conducted experiments, avarage surface roughness value (Ra) and material remove rate (Mrr) were measured. These values were determined as the input parameters of the Grey fuzzy algorithm GRG (grey relation grade) was used as the output parameter of Ra and Mrr. Input and output values are expressed as mathematical sets in the form of membership functions. These subset were named as "lowest-low-medium-high-higher-highest". Each subset of the input and output variables was fuzzifed using Mamdani inference model in a fuzzy toolbox, and then the obtained output values were defuzzified according to the centroid method and converted into mathematical expressions. The effects of variations in the parameters on surface roughness and material removal rate was observed. The relationship between material removal rate and surface roughness was analyzed using ANOVA. According to the results, cutting depth had a 39.47% effect, cutting speed had a 24.73% effect, cutting strategy had a 17.21% effect, and spindle speed had a 4.76% effect. Accordingly to the experimantal finding of this study, the best avarege surface roughness value was measured as 2.76 µm in experiment number 14. According to the output data of variance analysis, it was found that the ideal surface roughness could be achieved spindle speed of 1250 rpm, cutting speed of 1.45 mm/min, depth of cut of 1.25 mm and zig cutting strategy. As a result, it was determined that the optimum surface roughness varies depending on the depth of cut and cutting speed, and that the cutting speed and cutting depth are the parameters that have the most significant affect on the surface roughness. Keywords: CNC Milling, surface roughness, Gray Blurred modeling, Taguchi Optimization.