International Journal of Engineering and Technology

Materials are manufactured from casting, forging and extrusion processes have higher typical dimension tolerences due to its producing ability. So machining processes were introduced for close tolerence asssembly and improve the product working efficiencies. In response, now a day’s lot of machining processes are available such as turning, milling, drilling and grinding to overcome these problems. Milling operation is playing vital role on making the components with high accuracy and higher productivity. Subsequently, face milling operation is utilized for planning the surface of work material with improved surface texture.It is one of the important milling processes to achieve high flatness and low roughness. The work enlights the parameters influence on Material Removal Rate(MRR) and Surface Roughness (SR) in aluminium as a work piece material. In fact, aluminium alloy has the most significant in automobile and automation industries because of its inherent properties such as low weight to strength ratio. The selection of milling parameters such as spindle speed, feed rate and depth of cut are essential for improving the productivity and part quality.This work formulates the relationship between input and response variables for improving the face milling performances. The Response Surface Methodology (RSM) is utilized for making the relationship between independent and dependent variables. Finally, the selection of the best parameter is important to the manufacturing industries in order to improve the productivity and product quality through scientific approach. The performance of RSM models show the developed empirical relationship and it has the best agreement with experimental results. The Genetic Algorithm (GA) is used to select the optimal machining parameters.