Thursday, April 6, 2017

New M.Tech. Thesis Submitted from Production


Ball end milling operation is commonly used for producing flat and contoured surfaces of manufacturing dies and moulds etc. due to its high quality and productivity nature. It is very sensitive
manufacturing operation because of complex tool geometry and involvement of large number of process parameters. The energy consumption, surface quality and Material Removal Rate (MRR) play a vital role for any manufacturing process to be sustainable. The present work presents an analytical approach to predict energy consumption, surface roughness and MRR during ball end milling operation. The modeling procedure begins with analytical prediction of uncut and cut chip geometry followed by the evaluation of the associated angles and further determination of the cutting forces. These cutting forces, cutting speed and milling time were used to calculate the energy consumption during the operation. In analytical model of surface roughness, the cusp height was calculated from topography of the surface produced by the ball end mill tool geometry which was further used to calculate the theoretical average surface roughness (��). The analytical models of MRR were developed from geometry and the impressions left by the ball end mill on the work surface. Experiments were performed using 6 mm diameter ball end mill on CNC Vertical Milling Center on EN8 material work piece for different speeds (1000, 1500, 2000 RPM), different feed rates (400, 600, 800 mm/min) and different depth of cuts (0.10, 0.15, 0.25 mm) to validate the proposed analytical models. The radial depth of cut was kept 1 mm during all experiments. The proposed analytical model of energy consumption shows comparatively higher error than the error found by calculating energy consumption based upon cutting forces model available in literature (Sonawane and Joshi, 2010) for EN8 material whereas it shows comparatively less error for Inconel 718 material. An error of 94.044 % to 122.793 % and -52.593 % to -28.705 % had been observed in predicting surface roughness and MRR respectively by the proposed analytical models. The results predicted by the proposed analytical models of surface roughness and MRR matches exactly with the expected results of surface roughness and MRR presented in Table 1 in the research paper by Quintana et al. (2010) but the final equations for these models by them not give satisfactory results. The effect of the various machining variables on the different input and output parameters predicted by the proposed models comes out to be of the same trend as observed in the literature.