Analysis of surface roughness and material removal rate in machining of AISI 1040 steel using CNC turning process

Authors

  • Saadat Ali Rizvi * Faculty member in University Polytechnic, Jamia Millia Islamia, New Delhi, India
  • Wajahat Ali Department of Mechanical Engineering, SCRIET (CCS University), Meerut, India

DOI:

https://doi.org/10.59615/ijie.1.3.8

DOR:

https://dorl.net/dor/20.1001.1.27831906.2021.1.3.2.8

Keywords:

Turning, grey-relation analysis (GRA), Taguchi technique, optimization, ANOVA, Surface roughness, MRR

Abstract

The present study investigates the effect of turning process parameters on the surface roughness and metal removal rate (MRR) of the AISI 1040 steel. Twenty-seven ( L27) runs based on an orthogonal array of Taguchi techniques were performed and the grey relational analysis method was later applied to determine a most favourable turning parameters setting. Apart from this, the analysis of variance (ANOVA) was conducted to statically identify the effect of the most significant parameters. Cutting speed, feed, depth of cut, and nose radius were carefully selected as input parameters, while surface roughness and material removal rate (MRR) was output respectively. Various plots and curves have been drawn to identify the effect of various parameters on surface roughness, material removal rate, and grey relational grade. From the result, it was observed that the Taguchi-based grey relational analysis approach can be effectively used as a structured method to optimize the parameters. The results of the range analysis show that the cutting speed has the most significant effect, followed by the feed rate and depth of cut.

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International Journal of Innovation in Engineering

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Published

2021-10-07

How to Cite

Rizvi, S. A., & Ali, W. . (2021). Analysis of surface roughness and material removal rate in machining of AISI 1040 steel using CNC turning process. International Journal of Innovation in Engineering, 1(3), 8–19. https://doi.org/10.59615/ijie.1.3.8

Issue

Vol. 1 No. 3 (2021): Summer

Section

Original Research

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Copyright (c) 2021 Saadat Ali Rizvi, Wajahat Ali

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This work is licensed under a Creative Commons Attribution 4.0 International License.