Effects of Process Parameters on Surface Roughness and MRR in the hard turning of EN 36 steel

Authors(2) :-Amritpal Singh, Dr. Rakesh Kumar

In the present study, Experimental investigation of the effects of various cutting parameters on the response parameters in the hard turning of EN36 steel under the dry cutting condition is done. The input control parameters selected for the present work was the cutting speed, feed and depth of cut. The objective of the present work is to minimize the surface roughness to obtain better surface finish and maximization of material removal rate for better productivity. The design of experiments was done with the help of Taguchi L9 orthogonal array. Analysis of variance (ANOVA) was used to find out the significance of the input parameters on the response parameters. Percentage contribution for each control parameter was calculated using ANOVA with 95 % confidence value. From results, it was observed that feed is the most significant factor for surface roughness and the depth of cut is the most significant control parameter for Material removal rate.

Authors and Affiliations

Amritpal Singh
M.Tech Student, IKGPTU, Kapurthala, Department of Mechanical Engineering, North West Group of Institutions, Dhudike, Moga, Punjab, India
Dr. Rakesh Kumar
Department of Mechanical Engineering, North West Group of Institutions, Moga, Punjab, India

Hard turning, EN36, Surface roughness, Material removal rate, HRC

  1. Ashok Kumar Sahoo, (2014), Application of taguchi regression analysis on surface roughness in machining of hardened AISI D2 steel, International Journal of Industrial Engineering, pp. 295-304. 
  2. Gaurav Bartarya, S.K Chaudhury, (2012), Effects of cutting parameters on cutting force and surface roughness during finish hard turning AISI 52100 grade steel, 5th CIRP Conference on High Performance, pp.651-656.
  3. H Aouici, B Fnides, M Elbah, S Benlabmidi and M.A Yallese, (2016), surface roughness evaluation of various cutting materials in hard turning of H11, International Journal of Industrial Engineering Computations, pp.339-352.
  4. Hessainia Zahia, Yellese Mohamed Athmane, Bouzid Lakhdar and Mabrouki Tarek, (2015), On the application of Response surface methodology for predicting and optimizing surface roughness and cutting forces in hard turning by PVD coated Insert, International Journal of Industrial Engineering Computations, pp.267-284.
  5. I.Daniel Lawrence, M.Pandiarajan, G.Kaviprakash, (2015), Prediction of machining parameters in turning on EN36, International Journal of Applied Engineering Research, ISSN: 0973-4562, vol.10, No.55, pp.4070-4075.
  6. Mikell P. Groover, (2010), Fundamentals of modern manufacturing, 3rd ed. Wiley Asia.
  7. R.Suresh, S.Basavarajappa, (2014), Effect of process parameters on tool wear and surface roughness during turning of hardened steel with coated ceramic tool, International Conference on Advances in Manufacturing and Material Engineering, No.5, pp. 1450-1459.
  8. S.B Salvi, R.R Deshmukh, Dr. S.D Deshmukh, (2013) Analysis of surface roughness in hard turning by using Taguchi method, International Journal of Engineering and Technology, Vol.5, No.02, ISSN: 0975-5462, pp.365-370.
  9. Waleed Bin Rashid, Saurav Goel, J Paulo Davim, Shrikrishna Joshi, (2015), Parametric optimization of Hard turning of AISI 4340 Stainless steel, International Journal of Manufacturing Technology.
  10. Y. Karpat, “Predictive modelling and optimization in hard turning, (2007), Ph.D. Dissertation, Rutgears University-New Brunswick, New Jersey.

Publication Details

Published in : Volume 4 | Issue 11 | November-December 2018
Date of Publication : 2018-12-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 102-110
Manuscript Number : IJSRSET11841126
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Amritpal Singh, Dr. Rakesh Kumar, " Effects of Process Parameters on Surface Roughness and MRR in the hard turning of EN 36 steel, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 11, pp.102-110, November-December-2018. Available at doi : https://doi.org/10.32628/IJSRSET11841126
Journal URL : http://ijsrset.com/IJSRSET11841126

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