Optimisation of Electrical Discharge Machining of Al-LM-6/ SiC/ B4C Composite: A Grey Relational Approach

2018 ◽  
Vol 5 (9) ◽  
pp. 19147-19155
Author(s):  
Manish Shukla ◽  
S.K. Dhakad
2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
S. Suresh Kumar ◽  
M. Uthayakumar ◽  
S. Thirumalai Kumaran ◽  
P. Parameswaran ◽  
E. Mohandas

The goal of the present experimental work is to optimize the electrical discharge machining (EDM) parameters of aluminum alloy (Al 6351) matrix reinforced with 5 wt.% silicon carbide (SiC) and 10 wt.% boron carbide (B4C) particles fabricated through the stir casting route. Multiresponse optimization was carried out through grey relational analysis (GRA) with an objective to minimize the machining characteristics, namely electrode wear ratio (EWR), surface roughness (SR) and power consumption (PC). The optimal combination of input parameters is identified, which shows the significant enhancement in process characteristics. Contributions of each machining parameter to the responses are calculated using analysis of variance (ANOVA). The result shows that the pulse current contributes more (83.94%) to affecting the combined output responses.


In this paper, a compelling methodology, Taguchi grey relational analysis, was employed to the test results of wire-cut electrical discharge machining on Titanium Grade - 5 material with the consideration of multiple performance characteristics of the output response variables. The methodology merges the orthogonal array design of experiment with grey relational analysis. The primary target of this examination is to accomplish the maximization of material removal rate, minimization of both Surface roughness and kerf width. Grey relational theory is implemented to assess the optimal process parameters that improve the response measures. The test was finished by utilizing Taguchi's orthogonal array L18. Each test has been performed under various states of input parameters. The response table and grey relational grade for each level of the machining parameters have been established. From 18 tests, the best mix of parameters was identified. The results of test verify that the suggested technique in this investigation adequately develops the machining performance of Wire cut EDM process.


Author(s):  
Anshuman Kumar Sahu ◽  
Siba Sankar Mahapatra

Titanium and its alloys are a class of metallic materials having high strength to weight ratio with excellent properties of resistance to temperature, corrosion and oxidation. These properties increase their use in aerospace, chemical and biomedical industries. Electrical discharge machining (EDM), a non-conventional machining process, is the most suitable process for the machining of titanium and its alloys. Generally, tool electrode for EDM application is prepared through various conventional and non-conventional machining processes. The cost of production of EDM electrodes accounts for more than 50% of the cost of the final product. Therefore, additive manufacturing (AM) technology can be suitably applied for direct manufacturing of the complex EDM electrodes. Selective laser sintering (SLS) is one of the appropriate AM processes for preparation of EDM tool electrode. In the present work, machining performance of the AlSi10Mg tool electrode produced through AM process along with copper and brass tool electrodes have been studied considering titanium alloy (Ti6Al4V) as work piece material and commercial grade EDM 30 oil as dielectric fluid. In addition to the tool electrodes, two more EDM parameters such as pulse-on-time (Ton) and discharge current (Ip) have been considered. Four performance measures like material removal rate (MRR), tool wear rate (TWR), average surface roughness (Ra) and surface crack density (SCD) are used to assess the machining performance. In order to reduce the number of experiments, design of experiment (DOE) approach like Taguchi’s L9 orthogonal array is used. Since the performance measures are conflicting in nature, grey relational analysis (GRA) is used to convert four performance measures into an equivalent single performance measure. The best parametric condition is reported for optimal grey relational grade.


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