Estimation of machinability performance in wire-EDM on titanium alloy using neural networks

The impact of process factors on wire-cut electrical discharge machining (WEDM) performance is complex and nonlinear. In the present work, initially, the WEDM tests were conducted on titanium alloy (Ti-6Al-4V) with eight input factors and four machinability performance parameters. Later, an artificial neural network (ANN) model was established to estimate the WEDM performance. The ANN model with 8-5-5-4 architecture produced a least mean squared error (MSE) and average prediction error (AE) for both training and test data sets. The precision of the ANN model was assessed by relating model predictions with the experimental values. The combined effect of WEDM variables on the machinability performance was illustrated with the help of visual graphs. The R-value (correlation coefficient) of 0.9995 among WEDM test values and ANN estimated values shows the robustness of the developed ANN model in establishing the link between WEDM process factors and machinability parameters. The proposed model helps in minimizing the time for fixing the process parameter values, thereby increasing production and process efficiency.

Automated summary

The impact of process factors on wire-cut electrical discharge machining (WEDM) performance was tested and modeled in this study, with the combined effect of these factors on machinability performance illustrated through visual graphs. An artificial neural network (ANN) model was established to accurately estimate WEDM performance, contributing to increased production and process efficiency in practice.