Study of Surface Roughness and AE Signals while Machining Titanium Grade-2 Material using ANN in WEDM

Abstract Wire Electrical Discharge Machining (WEDM) is a specialized thermal machining process capable of accurately machining parts with varying hardness or complex shapes, which have sharp edges that are very difficult to be machined by the main stream machining processes. It is widely recognized that Acoustic Emission (AE) is gaining ground as a monitoring method for health diagnosis on rotating machinery. The advantage of AE monitoring over vibration monitoring is that the AE monitoring can detect the growth of subsurface cracks whereas the vibration monitoring can detect defects only when they appear on the surface. This study outlines the development of model and its application to estimation of machining performances using Artificial Neural Network (ANN). Each experiment has been performed under different process parameters of pulse-on time, pulse-off time, current and bed speed. Among different process parameters voltage and flush rate were kept constant. Molybdenum wire having diameter of 0.18 mm was used as an electrode. Estimation and comparison of responses like surface roughness and AE signals was carried out using ANN.