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Powder mixed electric discharge machining of high-speed steel T1 grade: Optimize through grey relational analysis

Kanwal Jit Singh (Department of Mechanical Engineering, Giani Zail Singh Campus College of Engineering and Technology, Bathinda, Bathinda, India)

Multidiscipline Modeling in Materials and Structures

ISSN: 1573-6105

Article publication date: 14 June 2019

Issue publication date: 14 June 2019

Abstract

Purpose

The purpose of this paper is to represent the innovative process of powder-mixed electrical discharge machining of high-speed steel T1 grade and to conduct experimental investigation to optimize the machining parameters associated with multiple performance characteristics using grey relational analysis. The machining of high-speed steel T1 grade via conventional machining is a difficult process. However, it can be easily machined by powder-mixed electric discharge machining.

Design/methodology/approach

Carefully selected machining parameters give the optimum output results. For experimentation, the following input parameters have been used: pulse on-time, discharge current, tool material and powder concentration. The effects of input parameters, namely, material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR), have been investigated in this research.

Findings

Grey relational analysis and analysis of variance have been performed to optimize the input parameters for better output response. Optimized results show increment of TWR, MRR and SR, which is 63.24, 52.18 and 42.49 per cent, respectively.

Originality/value

This research paper will be beneficial for the industrial application. The GRA result gives the better output response.

Keywords

Acknowledgements

There is no special financial support for this research work from the funding agency.

Citation

Singh, K.J. (2019), "Powder mixed electric discharge machining of high-speed steel T1 grade: Optimize through grey relational analysis", Multidiscipline Modeling in Materials and Structures, Vol. 15 No. 4, pp. 699-713. https://doi.org/10.1108/MMMS-03-2018-0039

Publisher

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Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited