A Review of Research on Improvement and Optimization of Performance Measures for Electrical Discharge Machining
Electrical Discharge Machining (EDM) is a non conventional machining method which can be used to machine electrically conductive work pieces irrespective of their shape, hardness and toughness. High cost of non conventional machine tools, compared to conventional machining, has forced us to operate these machines as efficiently as possible in order to reduce production cost and to obtain the required reimbursement. To achieve this task, machining parameters such as pulse on time, pulse off time, discharge current, gap voltage, flushing pressure, electrode material, etc. of this process should be selected such that optimal value of their performance measures like Material Removal Rate (MRR) Surface Roughness (SR) Electrode/Tool Wear Rate (EWR/TWR) dimensional accuracy, etc. can be obtained or improved. In past decades, intensive research work had been carried out by different researchers for improvement and optimization of EDM performance measures using various optimization techniques like Taguchi, Response Surface Methodology (RSM) Artificial Neural Network (ANN) Genetic Algorithm (GA) etc. This paper reviews research on improvement and optimization of various performance measures of spark erosion EDM and finally lists down certain areas that can be taken up for further research in the field of improvement and optimization for EDM process.