Volume 7, 2020
Special Issue - The emerging materials and processing technologies
|Number of page(s)||13|
|Published online||23 September 2020|
Multi-optimization of process parameters for machining of a non-conductive SiC ceramic composite by non-conventional machining method
S ‘O’ A University, Iter, BBSR-751030, Odisha, India
* e-mail: Pallavi.firstname.lastname@example.org
Accepted: 15 August 2020
The objective of the study is to predict the optimized set of the input parameters for the machining of non-conductive silicon carbide (SiC) by electric discharge machining (EDM). The insulated SiC ceramic composite machining was performed with 4 volumes (by percentage) of carbon nano (CNT) the SiC, which makes it electrically conductive. SiC has very good mechanical properties due to its widespread application in the aerospace, MEMS, and bio-sensor industries. This application requires a highly precise machining hole with a good surface quality that can be processed by machining processes such as EDM. The input parameters in this study are differing by three levels and the experimentation has been done by L27 orthogonal array. Four output parameters such as material removal rate (MRR), plasma flushing efficiency (PFE), surface-roughness (SR) and recast layer thickness (Rlt) for has been calculated for the detailed experimental analyses. In this research, a comparative analysis between the Multi-attribute management mechanisms (MADM) i.e. WPCA, MOORA & MOORA, and WPCA was conducted. The statistical analysis was also conducted to determine the impact of input parameters on performance measures. The study concluded that by integrating MOORA 's method with a PCA, the highest MRRs of 2.56 mm3/min & 78% PFE, lowest SR 2.1 µm, and Rlt 2.56 µm were obtained, with an experimental testing error of 5 percent.
Key words: Electrical discharge machining / non-conducting ceramic / multi-optimization technique / weighted principal component analysis / material removal rate
© P. Chaudhury and S. Samantaray, Published by EDP Sciences 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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