TAILIEUCHUNG - 0 The Optimization of the Electro-Discharge Machining Process Using Response

(BQ) In this paper, working current, working voltage, oil pressure, spark gap Pulse On Time and Pulse Off Time on Material Removal Rate (MRR) and Surface Finish (Ra) has been studied. Empirical models for MRR and Ra have been developed by conducting a designed experiment based on the Grey Relational Analysis. Genetic Algorithm (GA) based multi-objective optimization for maximization of MRR and minimization of Ra has been done by using the developed empirical models. Optimization results have been used for identifying the machining conditions. For verification of the empirical models and the optimization results, focused experiments have been conducted in the rough and finish machining regions. | Available online at Procedia Engineering 38 (2012) 3941 – 3950 International Conference on Modelling, Optimization and Computing (ICMOC - 2012) Al alloy The Optimization of the Electro-Discharge Machining Process Using Response Surface Methodology and Genetic Algorithms and M. Dev Anandb a Assistant Professor b Professor & Deputy Director Academic Affairs, Department of Mechanical Engineering, Noorul Islam Centre for Higher Education,Kumaracoil - 629180 Kanyakumari District, Tamilnadu, India Abstract Electric Discharge Machining (EDM) is a thermo-electric non-traditional machining process in which material removal takes place through the process of controlled spark generation between a pair of electrodes which are submerged in a dielectric medium. Due to the difficulty of EDM, it is very complicated to determine optimal cutting parameters for improving cutting performance. So, optimization of operating parameters is an important action in machining, particularly for unconventional electrical type machining procedures like EDM. A proper selection of machining parameters for the EDM process Since for an arbitrary desired machining time for a particular job, they do not provide the optimal conditions. To solve this task, multiple regression model and modified Genetic Algorithm model are developed as efficient approaches to determine the optimal machining parameters in electric discharge machine. In this paper, working current, working voltage, oil pressure, spark gap Pulse On Time and Pulse Off Time on Material Removal Rate (MRR) and Surface Finish (Ra) has been studied. Empirical models for MRR and Ra have been developed by conducting a designed experiment based on the Grey Relational Analysis. Genetic Algorithm (GA) based multi-objective optimization for maximization of MRR and minimization of Ra has been done by using the developed empirical models. Optimization results have been used for identifying the machining conditions. For

• Cơ khí - Chế tạo máy
• Electro Discharge Machining
• Grey Relational Analysis
• Genetic Algorithm
• Regression Model
• Taguchi Method
• The EDM process
• Experimental investigation of coated in electro discharge machining process
• Uncoated tools used in electro discharge machining process
• Machining of TI6AL4V
• The machining performance of Ti6Al4V
• Experimental investigation of various process parameters
• Various process parameters of powder mixed electro discharge machining process
• Machining of Al si alloy
• Material removal rate
• Tool wear rate
• Design of Experiments
• Response Surface Methodology
• Desirability Function Approach
• Process Modeling
• Multi Objective Optimization
• Polynomial model
• Neuro fuzzy model
• Non conventional machining
• Process parameters
• Electro erosion
• Taguchi grey multi responses optimization
• Electrical discharge machining
• Energy distribution
• Sinking electro discharge machining
• Mathematical model
• Ultrasonic vibration
• Dry EDM
• Powder additives
• EDM in water
• Surface roughness
• Hybrid model Optimization
• Artificial neural network
• Multi response optimization
• Powder mixed electrical discharge machining
• Surface micro hardness
• Pulse on time
• Pulse off time
• Electrical process parameters
• State of art in EDM process