Optimization of WEDM process parameters of γ-TiAl alloy using SVM method

Abstract

Wire electrical discharge machining (WireEDM) process is a widely used method to produce precision tools. There are many parameters that have influence on the process such as pulse on time, pulse off time, voltage, wire tension, wire diameter, material etc. In certain cases values of the parameters (e.g. cutting speed and surface roughness) conflict with each other. Usually surface quality decreases while cutting speed increases, and vice versa. It is difficult to improve both properties simultaneously. Due to the complexity of process, it is convenient to use some stochastic methods to find optimal process parameters. In this study, Wire-EDM process of gamma titanium aluminide alloy was optimized by support vector machines (SVM) method. To achieve this goal, Wire-EDM experimental results of the alloy were used as training set, and then predictions were made using this set. Obtained results were submitted as graphs and Pareto optimal points were determined among predicted points. Lastly, an optimum point was selected according to desired surface roughness value using multi-objective optimization methodology. Results showed that using SVM is effective as much as traditional prediction methods like Artificial Neural Networks (ANN).