INTERNATIONAL JOURNAL OF RESEARCH IN AERONAUTICAL AND MECHANICAL ENGINEERING ISSN (ONLINE): 2321-3051 Vol.3 Issue.4, April 2015. Pgs: 1-6 R. Arularasan, K.Sivasakthivel, M.Alagesan, R.Anand, K. Arshad Ahmed, S.Vimalan 61 OPTIMIZATION OF PROCESS PARAMETER IN TITANIUM ALLOY BY USING GENETIC ALGORITHM R. Arularasan 1 , K.Sivasakthivel 2 , M.Alagesan 3 , R.Anand 4 , K. Arshad Ahmed 5 , S.Vimalan 6 1,2 Faculty, 3,4,5,6 Students Dept. of Mechanical Engineering, University College of Engineering Arni, Thatchur, Tiruvannamali District- 632326, India E-Mail: vrarasan@gamil.com 1 , ksivasakthivel73@gmail.com 2 . Abstract Turning is one of the very important machining operations in engineering industries. Optimization of turning processes parameters still remains as one of the most challenging problems because of its high complexity and non-linearity. Hence, there is a need to apply most powerful optimization techniques to get desired accuracy of optimum solution. In this paper, non-conventional optimization techniques, genetic algorithm (GA) results were compared with Taguchi Optimization technique. The process variables considered for optimization are speed, feed, and depth of cut. The objective considered in the present work is surface finish subjected to the constraints. Keywords: Turning, Optimization, Process parameters, Taguchi Analysis, Genetic algorithm. 1. INTRODUCTION Process parameters composed of cutting speed, feed and depth of cut (for turning operation), have essential effects on the machining productivity and cost. The selection of cutting parameters has long depended on the skills and experience of machine tool operators or handbooks, and conservative cutting parameters are usually selected. This situation would cause significant productivity loses and lead to a costly machining operation. The determination of optimum cutting parameters is a combinatorial optimization problem and is usually realized by applying optimization algorithms. These algorithms include neural network, geometric programming, simulated annealing, genetic algorithm (GA), particle swarm optimization (PSO) etc. GA was considered as a suitable algorithm for solving any type of machining process optimization problem. In this paper, Process parameters optimization by using GA and Taguchi Method were discussed comprehensively. 2. LITERATURE REVIEW Tarng. Y.S , S.C. Juang and C.H. Chang [2] proposes the use of grey-based Taguchi methods for the optimization of the Submerged Arc Welding (SAW) process parameters in hard facing with considerations of multiple weld qualities. In this new approach, the grey relational analysis is adopted to solve the SAW process with multiple weld qualities. A grey relational grade obtained from the grey relational analysis is used as the performance characteristic in the Taguchi method.