Influence of process parameters on dimensional inaccuracies in µ-EDM of Ti–6Al–4V

The inherent mechanism of material removal in µEDM also removes material from the tool electrode, making it difficult to achieve the required dimensional accuracy. Therefore, the current study investigates the influence of voltage and depth of the µ-holes in achieving the required depth of µ-holes on T­i–6Al–4V with tungsten carbide (WC) as the tool electrode. A full factorial experimental design was adapted to investigate the dimensional inaccuracies. In order to minimize dimensional inaccuracies, material removal rate (MRR), electrode wear rate (EWR), and surface roughness ( S a ) are investigated. A maximum of dimensional inaccuracies of 43.26% was observed while machining a µ-hole with a depth of 400 µm at a voltage of 100 V. A minimum dimensional inaccuracies of 16.27% was observed while machining a hole of 200 µm with 80 V due to the efficient flushing of the debris with maximum MRR of 34.5 × <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msup> <mml:mn>10</mml:mn> <mml:mn>5</mml:mn> </mml:msup> </mml:math> µm 3 /min, EWR of 12.64 × <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msup> <mml:mn>10</mml:mn> <mml:mn>5</mml:mn> </mml:msup> </mml:math> µm 3 /min and improved S a of 2.21 µm compared to other µ-holes machined. In addition, the machined µ-hole surface was examined through SEM, EDX and XRD to understand the morphology and chemical composition and understand that a significant amount of carbide, oxides, and tungsten in various phases are deposited on the surfaces. Additionally, a model for an artificial neural network was developed to predict the process output responses (MRR, EWR and S a ) in relation to the input parameters (voltage and target depth). The minimum percentage of error between experimental and predicted values for MRR, EWR and S a are 9.21%, 11.56% and 8.43%, respectively.