1Mechanical Engineering, SBAS Government Polytechnic College, Badbar, India
2Mechanical Engineering, National Institute of Technical Teachers Training and Research, Chandigarh, India
American Journal of Mechanical Engineering.
2014,
Vol. 2 No. 5, 130-142
DOI: 10.12691/ajme-2-5-2
Copyright © 2014 Science and Education PublishingCite this paper: H. S. Sidhu, S. S. Banwait. Analysis and Multi-objective Optimisation of Surface Modification Phenomenon by EDM Process with Copper-Tungsten Semi-sintered P/M Composite Electrodes.
American Journal of Mechanical Engineering. 2014; 2(5):130-142. doi: 10.12691/ajme-2-5-2.
Correspondence to: H. S. Sidhu, Mechanical Engineering, SBAS Government Polytechnic College, Badbar, India. Email:
hss1636@gmail.comAbstract
In the present experimentation work, attempts have been made to model, analyse and optimise the surface modification phenomenon in electrical discharge machining process using response surface methodology. The central composite second order rotatable design has been chosen for designing the experiments and response surface methodology was applied for developing the mathematical models. Efforts has been made to correlate the four input process parameters; peak discharge current, pulse-on time, pulse-off time and tool electrode powder compaction pressure with two output responses; surface deposition rate and surface roughness. Results obtained were presented in the form of three dimensional surface plots. Analysis of variance had been performed to check the adequacy of the developed mathematical models as well as significance of each term comprising the models. Statistical software was used to construct the plots to analyse the influence of individual input process parameter on output responses. Composite desirability function approach was used for multi-objective optimisation of the developed models. Optimal parameter combinations for achieving maximum surface deposition rate and minimum surface roughness have been observed and presented in the form of contour plots. The optimal predicted results were experimentally verified, matched well with the predicted results.
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