Optimization of machining process for the electro discharge machining using non-traditional algorithms

Abstract

In non-traditional machining, considerable amount of material is removed from the raw material to get the desired profile. This fact leads metal removal, a more expensive process when compared to other manufacturing processes. So cost consciousness is very much expected in producing a component. In today s competitive manufacturing environment, the manufacturing systems should be designed not only to increase the production rate and quality of the component, but also to decrease time and cost involved in manufacturing. So there is a need to develop a system that can ensure the quality of the component at minimum machining time. In order to achieve these two great objectives, it is necessary for the process planner to use computers for the selection of appropriate cutting parameters for any machining. In the existing methods, the desired surface finish is achieved by the selection of cutting parameters either by experience of the process planner or from the machining handbook. Optimization of machining parameters is done using Artificial Neural Network (ANN) and Taguchi method and the output parameters are analyzed. Non-traditional algorithms are used for optimization and the result shows the input parameters like pulse on time, work piece material and current shows as the most influencing parameters. Composite electrodes performances are good and the composite electrode wear rate is less when compared to bare electrode like copper. Scanning electron microscope images are analyzed and ovality and taperness increased with increase in current. Various software (MATLAB, ANSYS AND SYSTAT) are used to analyze the experimental data and their results are trained using ANN. By using the Non-traditional algorithm and conducting experiments in nontraditional machines like Electro Discharge machine (EDM), Wire-EDM the output parameters are optimized.