Optimization and Modelling of End Milling Process Parameters by Using Taguchi Method
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2014, Vol 2, Issue 10
Abstract
Surface roughness is an index which determines the quality of machined products and is influenced by the cutting parameters. The aim is prediction of surface roughness by using artificial neural systems. The neural network model can be efficiently find the best cutting parameters value for a specific cutting condition in milling operation and achieve minimum surface roughness. In machining operations, achieving desired surface quality features of the machined invention, is really a exciting job. Because, these quality structures are highly correlated and are expected to be influenced directly or indirectly by the direct effect of process parameters or their interactive effects (i.e. on process environment). However, the amounts of major influence of the process parameters are different for different reactions. Therefore, optimization of surface roughness is a multi-factor, multi-objective optimization difficulty. Thus, to solve such a multi-objective optimization problem, it is sensed necessary to classify optimal parametric combination, following which all purposes could be optimized instantaneously. In this context, it is important to convert all objective functions into an equivalent single objective function or overall representative function to meet desired multi-quality features of the machined surface. The mandatory multi-quality features may or may not be contradictory in nature. The representative single objective function, thus designed, would be optimized finally. In the present work, Design of Experimentation (DOE) with Taguchi L9 Orthogonal Array (OA) has been explored. Finally, Taguchi method has been adopted for searching optimal process condition to yield desired surface quality. In the present work an experimental investigation of the end milling of AISI D2 steel with carbide tool by varying feed, speed and depth of cut and the surface roughness was measured using Surface Roughness Tester. The neural network design and development was done using MATLAB. The training data set has been used to train ANN model for data prediction.
Authors and Affiliations
Dimple Rani, Dinesh Kumar
Keywords
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