OBRABOTKAMETALLOV MATERIAL SCIENCE Vol. 26 No. 1 2024 a b c Fig. 1. Machining Setup (a), Temperature calibration setup (b), Work materials (c) Ta b l e 2 Process parameters and experimental levels Parameters/Levels L 1 L 2 L 3 L 4 L 5 Vc (m/min) 140 190 240 290 340 f (mm/rev) 0.08 0.12 0.16 0.20 0.24 doc (mm) 0.6 0.7 0.8 0.9 1.0 Results and discussion The central composite design of the response surface method was used for the main experiments. Table 3 shows the experimental results. The objective of the experimental analysis was to identify the signifi cant factor that has a greater infl uence on the response variables and to develop a generalized empirical model to predict surface roughness and generated temperature using Buckingham’s π theorem. Statistical analysis of surface roughness and temperature rise was carried out using RSM. The main objective of this paper is to develop semi-empirical formulae using the Levenberg-Marquardt method to predict the surface roughness and temperature of various materials. Using the values from Table 2, individual regression equations were constructed and the full factorial values were extracted from the regression. These full factorial values are used to derive the semi-empirical formula. The regression equations for surface roughness of materials are given below. 2 0.60 0.00018 2.7 1.37 0.000003 19.03 a c c SSR V f d V f = + + - - + + 2 0.79 0.0050 0.00050 1.87 ; c c d V xf V xd fxd - + + (I) 2 2 0.31 0.00202 10.01 1.20 0.00005 31 61 a c c SAER V f d V c f = - + - - + - 2 0.11 0.2604 0.00908 5.1 ; c c d V xf V xd fxd - + - (II) 2 2 3.135 0.01331 9.76 1.09 0.000023 59.66 a c c ENR V f d V f = - - - + + + 2 0.670 0.00312 0.00125 0.31 ; c c d V xf V xf fxd - + + (III) 2 2 14.32 0.0478 12.4 12.97 0.000093 53.7 a c c AlR V f d V df = - - - + + + 2 7.97 0.0444 0.0027 16.6 . c c d V xf V xd fxd - - + (IV)
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