(untitled) (JOURNAL OF ADVANCES IN CHEMISTRY, 2016)
Components used in chemical equipments are produced from forging, extrusion and casting processes with classic dimension tolerances due to its producing ability. So machining processes were introduced for close tolerance assembly and improve the product working efficiencies. At present,Â lot of machining processes are available for producing chemical equipments such as turning, milling, drilling and grinding etc.,. Milling operation is playing critical role on making the chemical equipmentâ€™s components with high accuracy and higher productivity. Face milling operation is one of the milling processes which is used for achieving higher flatness and surface finish of chemical equipmentâ€™s parts. Thiswork concentrates the parameters influence on Material Removal Rate (MRR) and Surface Roughness (SR) by using aluminium as work piece material. Actually, aluminium alloy has theÂ most significant in chemical industries because of its inherent properties such as, corrosive resistance , low weight to strength ratio. The milling parameters such as feed rate, spindle speed Â and depth of cut are selected as parameters for improving the quality and productivity. This work put together the link between input and response variables for developing the face milling performances. The Response Surface Methodology (RSM) is employ for making the link between dependent and independent variables. Building the empirical model by conducting regression analysis The performance of developed regression models are verified with experimental results. Verification results show the developed models have best agreement with experimental results. The developed models are used for achieving the best input parameters by using Genetic Algorithm (GA). Finally, the optimal parameters are evaluated by GA.