Performance modeling and multi-objective optimization during turning AISI 304 stainless steel using coated and coated-microblasted tools

OBRABOTKAMETALLOV MATERIAL SCIENCE Том 23 № 3 2021 EQUIPMEN . INSTRUM TS Vol. 5 No. 4 2023 T h e E n d Ta b l e 1 2 Optimum parameters Optimum responses Desirability Single desirability (DM) Fc (N) Ff (N) Fr (N) Ra (µm) T (min) DFc DFf DFr DRa DT [230, 0.055, 0.1] 17.60 15.13 9.13 0.36 34.15 0.97 0.94 0.94 0.88 0.82 0.91 [310, 0.05, 0.1] 13.77 12.71 8.09 0.27 28.99 0.99 0.98 0.98 0.96 0.66 0.91 [230, 0.05, 0.12] 19.46 15.19 8.94 0.37 34.08 0.96 0.94 0.95 0.87 0.81 0.90 [240, 0.055, 0.1] 17.18 14.92 9.06 0.35 33.07 0.97 0.94 0.94 0.89 0.78 0.90 [320, 0.05, 0.1] 13.53 12.57 8.04 0.26 28.31 0.99 0.98 0.99 0.96 0.64 0.90 [240, 0.05, 0.12] 19.00 14.97 8.88 0.36 33.01 0.96 0.94 0.95 0.88 0.78 0.90 [250, 0.055, 0.1] 16.80 14.71 9.00 0.34 32.06 0.98 0.95 0.94 0.90 0.75 0.90 Ta b l e 1 3 Family of optimal solutions [V (m/min), f (mm/rev), d (mm)] for MTCVD-TiCN/Al2O3 coated tools Optimum parameters Optimum responses Desirability Single desirability (DM) Fc (N) Ff (N) Fr (N) Ra (µm) T (min) DFc DFf DFr DRa DT [200, 0.05, 0.1] 21.20 14.23 15.48 0.37 51.14 0.97 0.99 0.93 0.91 1.00 0.96 [210, 0.05, 0.1] 20.70 14.17 15.37 0.36 48.90 0.97 0.99 0.93 0.92 0.95 0.95 [220, 0.05, 0.1] 20.24 14.11 15.27 0.35 46.86 0.98 0.99 0.94 0.92 0.90 0.94 [230, 0.05, 0.1] 19.81 14.05 15.17 0.34 44.98 0.98 0.99 0.94 0.93 0.86 0.94 [240, 0.05, 0.1] 19.40 13.99 15.08 0.33 43.26 0.98 0.99 0.95 0.94 0.82 0.93 [200, 0.05, 0.12] 24.61 15.49 15.70 0.41 48.20 0.95 0.97 0.92 0.88 0.93 0.93 [200, 0.055, 0.1] 23.17 15.47 16.42 0.39 48.39 0.96 0.97 0.88 0.89 0.94 0.93 [250, 0.05, 0.1] 19.02 13.94 14.99 0.32 41.67 0.98 0.99 0.95 0.94 0.78 0.93 [210, 0.05, 0.12] 24.03 15.42 15.59 0.40 46.09 0.96 0.97 0.92 0.89 0.88 0.92 [210, 0.055, 0.1] 22.62 15.40 16.30 0.38 46.27 0.96 0.97 0.89 0.90 0.89 0.92 [260, 0.05, 0.1] 18.66 13.89 14.91 0.32 40.20 0.98 0.99 0.96 0.95 0.74 0.92 [220, 0.05, 0.12] 23.50 15.35 15.49 0.39 44.17 0.96 0.97 0.93 0.90 0.84 0.92 [220, 0.055, 0.1] 22.12 15.33 16.20 0.37 44.34 0.97 0.97 0.89 0.91 0.84 0.91 [270, 0.05, 0.1] 18.33 13.84 14.83 0.31 38.83 0.98 0.99 0.96 0.95 0.71 0.91 [230, 0.05, 0.12] 23.00 15.29 15.39 0.38 42.40 0.96 0.97 0.93 0.90 0.80 0.91 [230, 0.055, 0.1] 21.65 15.27 16.09 0.36 42.57 0.97 0.97 0.90 0.92 0.80 0.91 [280, 0.05, 0.1] 18.00 13.79 14.76 0.30 37.56 0.99 0.99 0.96 0.96 0.68 0.91 [240, 0.05, 0.12] 22.53 15.23 15.30 0.37 40.78 0.96 0.97 0.94 0.91 0.76 0.90 [200, 0.05, 0.14] 27.92 16.63 15.89 0.44 45.85 0.94 0.95 0.91 0.85 0.88 0.90 [240, 0.055, 0.1] 21.20 15.21 16.00 0.35 40.94 0.97 0.97 0.90 0.92 0.76 0.90 [290, 0.05, 0.1] 17.70 13.75 14.68 0.30 36.37 0.99 0.99 0.97 0.96 0.65 0.90 Validatory experiments are conducted under optimal cutting conditions for the different tools considered in the present study. Table 14 depicts that the predicted results of cutting forces at optimal cutting conditions for different tools using developed mathematical models are in good agreement with the experimental results. The error in the predicted and experimental results is less than 15 % for cutting forces and less than

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