Due to technical and economical factors, hard turning is competing successfully with the grinding process in the industries. However, due to the large number of variables and their interactions affecting the hard turning process, the process control becomes complex. So, the selection of optimal machining conditions for good surface quality, in hard turning, is of great concern in the manufacturing industries these days. In the present work, experimental investigation has been conducted to study the effect of the tool geometry (effective rake angle and nose radius) and cutting conditions (cutting speed and feed) on the surface roughness during the hard turning of the bearing steel with mixed ceramic inserts. Central composite design was employed for experimentation. The first and the second order mathematical models were developed in terms of machining parameters by using the Response Surface Methodology (RSM) on the basis of the experimental results. Results show that all the factors and their interactions were significantly influencing the surface roughness. Analysis of Variance (ANOVA) indicated that the second order surface roughness model was significant. Further, the surface roughness prediction model has been optimized by using genetic algorithms (GA). The genetic algorithm program gives minimum values of surface roughness and their respective optimal machining conditions (cutting conditions and tool geometry).
Surface roughness prediction model and experimental results based on multi-wavelength fiber optic sensors
