Wire electrical discharge machining of gamma titanium aluminide for optimum process criteria yield in single pass cutting operation

Selection of optimum machining parameter combinations for obtaining higher cutting efficiency and accuracy is a challenging task in WEDM due to the presence of large number of process variables and complicated stochastic process mechanism. In the present research, single pass wire electrical discharge machining (WEDM) of γ TiAl alloy has been studied. This paper attempts to develop an appropriate machining strategy for maximum process criteria yield in WEDM. A cascade-forward back-propagation neural network based on Bayesian regularisation is developed to model the machining process. The three most important parameters, cutting speed, surface roughness and wire offset, have been considered as measure of process performance. The model is capable of predicting the response parameters (cutting speed, surface roughness and wire offset) as a function of different control parameters (pulse on time, pulse off time, peak current, wire tension, dielectric flow rate and servo reference voltage). Verification experiments have been carried out to check the validity of the developed model and then optimal parametric combinations were searched out using an advanced optimisation strategy.