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Optimization of machining variables in Electric Discharge Machining using stainless steel 317 in full factorial method

Nandhakumar S., Sathish Kumar S., Sakthivelu K.

Mechanics and Mechanical Engineering

2018

Vol. 22, nr 1

105--117

Electric Discharge Machining (EDM) is a non-conventional machining process and has a larger extent of application in manufacturing industry due to its accuracy. EDM simply uses electrical spark between the tool and workpiece in presence of dielectric medium to erode the workpiece in controlled manner. Improving the material removal rate and decreasing the tool wear rate (TWR), achieving higher surface finish, reducing machining time and enhancing dimensional accuracy are the major areas of focus in electrical discharge machining (EDM) process of SS 317 grade steel. In this research work effort to reduce the tool wear rate is concentrated by comparing the machining performance of two distinct electrodes namely copper and brass. Each electrode has their unique machining capabilities and the experimental results were compared in-terms of tool wear rate (TWR), Metal Removal Rate (MRR) and Machining Time (TM). Input variables were optimized based on the experimental output responses to achieve optimal level of input variables.

An Influence of Parameters of Micro-electrical Discharge Machining on Wear of Tool Electrode

Puthumana G.

Archive of Mechanical Engineering

2017

Vol. LXIV, nr 2

149--163

To achieve better precision of features generated using the micro-electrical discharge machining (micro-EDM), there is a necessity to minimize the wear of the tool electrode, because a change in the dimensions of the electrode is reflected directly or indirectly on the feature. This paper presents a novel modeling and analysis approach of the tool wear in micro-EDM using a systematic statistical method exemplifying the influences of capacitance, feed rate and voltage on the tool wear ratio. The association between tool wear ratio and the input factors is comprehended by using main effect plots, interaction effects and regression analysis. A maximum variation of four-fold in the tool wear ratio have been observed which indicated that the tool wear ratio varies significantly over the trials. As the capacitance increases from 1 to 10 nF, the increase in tool wear ratio is by 33%. An increase in voltage as well as capacitance would lead to an increase in the number of charged particles, the number of collisions among them, which further enhances the transfer of the proportion of heat energy to the tool surface. Furthermore, to model the tool wear phenomenon, a regression relationship between tool wear ratio and the process inputs has been developed.