Abstract: Electrical discharge machining (EDM), a non-conventional machining process, finds wide applications for shaping difficult-to-cut alloys. Process modeling of EDM is required to exploit the process to the fullest. Process modeling of EDM is a challenging task owing to involvement of so many electrical and non-electrical parameters. This work is an attempt to model the EDM process using artificial neural network (ANN). Experiments were carried out on die-sinking EDM taking Inconel 825 as work material. ANN modeling has been performed using experimental data. The prediction ability of trained network has been verified experimentally. Results indicate that ANN can predict the values of performance measures of EDM satisfactorily.
Abstract: Haynes 25 alloy (also known as L-605 alloy) is cobalt
based super alloy which has widely applications such as aerospace
industry, turbine and furnace parts, power generators and heat
exchangers and petroleum refining components due to its excellent
characteristics. However, the workability of this alloy is more
difficult compared to normal steels or even stainless. In present work,
an experimental investigation was performed under cryogenic
cooling to determine cutting tool wear patterns and obtain optimal
cutting parameters in turning of cobalt based superalloy Haynes 25.
In experiments, uncoated carbide tool was used and cutting speed (V)
and feed rate (f) were considered as test parameters. Tool wear
(VBmax) were measured for process performance indicators.
Analysis of variance (ANOVA) was performed to determine the
importance of machining parameters.