Optimization of Milling Parameters of Gun Metal Using Fuzzy Logic and Artificial Neural Network Approach. (November 2019) - Sasindran, Visnu; Vignesh, M; Arvind Krishna, S; Madusudhanan, A; Gokulachandran, J
Abstract: Growing importance is dedicated towards effective pollution control in industries and much attention has been focused on industrial applications and practices that consume large amounts of energy and release carbon emissions into the atmosphere. Hence there is an increasing demand for the introduction of green manufacturing techniques into industries, to reduce carbon usage and improve resource utilization. Besides this, industries are looking for effective analysis to reduce costs, improving productivity and product quality. This report proposes a systematic approach to analyse milling parameters of gunmetal using Computer Numeric Controlled (CNC) vertical milling machine. Milling is one of the most advanced and widely used manufacturing processes. Hence an effective analysis to optimize milling process parameters would have a significant positive impact on manufacturing productivity. In this study two soft computing models have been developed using the results obtained from the conduction of the experiments on gunmetal. The experiment is designed using Taguchi Method and the model development is done using Fuzzy Logic and Artificial Neural Network (ANN) approach. The results obtained from both the methods are compared and the error is tabulated. The more suitable model developer is suggested to be used in industries to determine optimum machining parameters for better surface finish, minimal power consumption and carbon emission.