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Parametric optimization of electrical discharge machining process on α–β brass using grey relational analysis

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Abstract

In the present work, a multi response optimization technique based on Taguchi method coupled with grey relational analysis is used for electrical discharge machining operations on duplex (α–β) brass. Stir casting technique was used to fabricate the duplex brass plates. The mechanical properties of the material are reported. Experiments were conducted with three machining variables such as current, pulse-on time and spark voltage and planned as per Taguchi technique. Material removal rate (MRR), electrode wear rate (EWR), and surface roughness (SR) are chosen as output parameters for this study. Results showed that, peak current and spark voltage were the significant parameters to affect MRR, EWR, and SR as per grey relational grade. The optimal combination parameters were identified as A3B3C2 i.e., pulse current at 14 A, pulse on-time at 200 µs, and voltage at 50 V. Analysis of variance was used for analyzing the results. The confirmation tests were performed to validate the results obtained by grey relational analysis and the improvement was achieved.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Vickram College of Engineering, Anna University Chennai, Sivagangai, 630 561, Tamilnadu, India

    S. Marichamy & G. Veerappan

  2. Department of Mechanical Engineering, Sri Subramanya College of Engineering and Technology, Anna University Chennai, Palani, 624 615, Tamilnadu, India

    M. Saravanan

  3. Department of Mechanical Engineering, Chendhuran College of Engineering and Technology, Anna University Chennai, Pudukkottai, 622 507, Tamilnadu, India

    M. Ravichandran

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  1. S. Marichamy
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  2. M. Saravanan
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  4. G. Veerappan
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Correspondence to S. Marichamy.

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Marichamy, S., Saravanan, M., Ravichandran, M. et al. Parametric optimization of electrical discharge machining process on α–β brass using grey relational analysis. Journal of Materials Research 31, 2531–2537 (2016). https://doi.org/10.1557/jmr.2016.213

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  • DOI: https://doi.org/10.1557/jmr.2016.213

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