ISSN 2466-4677; e-ISSN 2466-4847
Journal Menu
Archive
Last Edition
Journal Menu
Archive
Optimization of machining parameters in turning to steel using grey relational analysis
Authors:
Wisam Naji Hasan1
,
, Dhyai H. Jawad Aljashaami1
Dhyai H. Jawad Aljashaami1
1Department of Automobile Engineering, Al-Musayyib College of Engineering, University of Babylon, Hilla, Al-Musayab, Babylon, Iraq
Received: 27 April 2025
Revised: 11 June 2025
Accepted: 19 June 2025
Published: 30 June 2025
Abstract:
The present study investigated the multi-response optimization of turning using nanofluids as coolants to determine the best parametric combination for surface roughness, flank wear, and material removal rate (MRR) by employing the Taguchi method and Grey relational analysis. Eighteen experimental runs were carried out using an orthogonal array of the Taguchi method within the defined experimental domain to derive and optimize the goal functions. The selected objective functions related to the turning process parameters included the volume fraction of nanoparticles (0.04%, 0.08%), cutting speed (110, 170, and 230 m/min), feed rate (0.125, 0.15, and 0.175 mm/rev), type of nanoparticles (MoS2, multi-walled carbon nanotubes (MWCNT), and SiO2), and depth of cut (0.3, 0.6, and 0.9 mm). The multi-response optimization problem was addressed using the Taguchi approach in conjunction with Grey relational analysis. The significance of the factors affecting the overall quality characteristics in the Minimum Quantity Lubrication (MQL) turning of AISI 4340 with nanofluid was quantitatively evaluated through Signal-to-Noise ratio (S/N) analysis and Analysis of Variance (ANOVA) to determine the contribution of each parameter to performance outcomes. The cutting speed was identified as the most significant parameter. Verification experiments were conducted to validate the optimal results. These findings demonstrated the effectiveness of the Taguchi technique and Grey relational analysis in continuously improving product quality in the manufacturing sector.
Keywords:
Turning process, Nanofluid, Material removal rate, Grey relation analysis, Taguchi method
References:
[1] M. Sayuti, O.M. Erh, A.A.D. Sarhan, M. Hamdi, Investigation on the morphology of the machined surface in end milling of aerospace AL6061-T6 for novel uses of SiO₂ nanolubrication system. Journal of Cleaner Production, 66, 2014: 655–663. https://doi.org/10.1016/j.jclepro.2013.11.058
[2] A.K. Sharma, A.K. Tiwari, A.R. Dixit, R.K. Singh, Investigation into performance of SiO₂ nanoparticle based cutting fluid in machining process. Materials Today: Proceedings, 4(2), 2017: 133–141.
https://doi.org/10.1016/j.matpr.2017.01.006
[3] D.G. Subhedar, B.M. Ramani, A. Gupta, Experimental investigation of heat transfer potential of Al₂O₃/water-mono ethylene glycol nanofluids as a car radiator coolant. Case Studies in Thermal Engineering, 11, 2018: 26–34. https://doi.org/10.1016/j.csite.2017.11.009
[4] F. Puh, Z. Jurković, M. Perinić, M. Brezočnik, S. Buljan, Optimization of machining parameters for turning operation with multiple quality characteristics using grey relational analysis. Tehnički Vjesnik, 23(2), 2016: 377–382. https://doi.org/10.17559/TV-20150526131717
[5] M. Nawaz, U. Nazir, An enhancement in thermal performance of partially ionized fluid due to hybrid nano-structures exposed to magnetic field. AIP Advances, 9(8), 2019: 085024.
https://doi.org/10.1063/1.5120455
[6] L.B. Abhang, M. Hameedullah, Optimization of machining parameters in steel turning operation by Taguchi method. Procedia Engineering, 38, 2012: 40–48. https://doi.org/10.1016/j.proeng.2012.06.007
[7] E. Nas, N.A. Özbek, Optimization of the machining parameters in turning of hardened hot work tool steel using cryogenically treated tools. Surface Review and Letters, 27(5), 2019: 1950177.
https://doi.org/10.1142/S0218625X19501774
[8] S. Roy, A. Ghosh, High speed turning of AISI 4140 steel using nanofluid through twin jet SQL system. Proceedings of the ASME 2013 International Manufacturing Science and Engineering Conference collocated with the 41st North American Manufacturing Research Conference, Vol.2, 10–14 June 2013, Madison, Wisconsin, USA, V002T04A002. https://doi.org/10.1115/MSEC2013-1067
[9] S.K. Nayak, J.K. Patro, S. Dewangan, S. Gangopadhyay, Multi-objective optimization of machining parameters during dry turning of AISI 304 austenitic stainless steel using grey relational analysis. Procedia Materials Science, 6, 2014: 701–708. https://doi.org/10.1016/j.mspro.2014.07.086
[10] M.S. Surya, M. Shalini, A. Sridhar, Multi-Response Optimization on EN19 Steel Using Grey Relational Analysis Through Dry & Wet Machining. Materials Today: Proceedings, 4(2), 2017: 2157–2166.
https://doi.org/10.1016/j.matpr.2017.02.062
[11] N.M.I. Elsiti, M.H.S. Elmunafi, Optimization of machining parameters for turning process by using grey relational analysis. World Journal of Advanced Research and Reviews, 17(1), 2023: 755-761.
https://doi.org/10.30574/wjarr.2023.17.1.0080
[12] P. Jadhav, S. Kumar, A. Bongale, Optimization of cutting forces by cryogenic treatment on tungsten carbide inserts during dry turning of the P 20 tool steel. Materials Today: Proceedings, 28, 2020: 2485–2493. https://doi.org/10.1016/j.matpr.2020.04.798
[13] M.S. Surya, Optimization of machining parameters while turning AISI316 stainless steel using response surface methodology. Scientific Reports, 14, 2024: 30083. https://doi.org/10.1038/s41598-024-78657-z
[14] M. Mia, N.R. Dhar, Optimization of surface roughness and cutting temperature in high-pressure coolant-assisted hard turning using Taguchi method. The International Journal of Advanced Manufacturing Technology, 88, 2017: 739–753. https://doi.org/10.1007/s00170-016-8810-2
[15] I. Ramu, P. Srinivas, K. Venkatesh, Taguchi based grey relational analysis for optimization of machining parameters of CNC turning steel 316. IOP Conference Series: Materials Science and Engineering, 377, 2018: 012078. https://doi.org/10.1088/1757-899X/377/1/012078
[16] S.S. Mulugundam, G. Prasanthi, A.K. Kumar, V.K. Sridhar, S.K. Gugulothu, Optimization of cutting parameters while turning Ti-6Al-4V using response surface methodology and machine learning technique. International Journal on Interactive Design and Manufacturing, 15(3), 2021: 453–462.
https://doi.org/10.1007/s12008-021-00774-0
[17] M. Fnides, S. Amroune, B. Mohamad, B. Fnides, B. Toufik, Optimization of surface roughness and MRR when milling 25CrMo4-25 steel using Taguchi design. Academic Journal of Manufacturing Engineering, 22(4), 2024: 118-125.
[18] C.Y. Chang, R. Huang, P.C. Lee, T.L. Weng, Application of a weighted Grey-Taguchi method for optimizing recycled aggregate concrete mixtures. Cement and Concrete Composites, 33(10), 2011: 1038–1049. https://doi.org/10.1016/j.cemconcomp.2011.06.005
[19] H. Ha, S. Müller, R.-P. Baumann, B. Hwang, Peakforce quantitative nanomechanical mapping for surface energy characterization on the nanoscale: A mini-review. Facta Universitatis, Series: Mechanical Engineering,
21(1), 2024: 001–012. https://doi.org/10.22190/FUME221126001H
[20] H. Makri, C. Saib, S. Amroune, S. Zergane, B. Mohamad, M. Benarioua, K. Necib, Elaboration and characterization of Cu–Zn–Al shape memory alloys. Pollack Periodica, 20(1), 2025: 117–124.
https://doi.org/10.1556/606.2024.01122
© 2025 by the authors. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)
Volume 10
Number 4
December 2025
Last Edition
Volume 10
Number 4
December 2025
How to Cite
W.N. Hasan, D.H.J. Aljashaami, Optimization of Machining Parameters in Turning to Steel Using Grey Relational Analysis. Applied Engineering Letters, 10(2), 2025: 90-99.
https://doi.org/10.46793/aeletters.2025.10.2.3
More Citation Formats
Hasan, W.N., & Aljashaami, D.H.J. (2025). Optimization of Machining Parameters in Turning to Steel Using Grey Relational Analysis. Applied Engineering Letters, 10(2), 90-99.
https://doi.org/10.46793/aeletters.2025.10.2.3
Naji Hasan, Wisam and Dhyai H. Jawad Aljashaami. “Optimization of Machining Parameters in Turning to Steel Using Grey Relational Analysis.“ Applied Engineering Letters, vol. 10, no. 2, 2025, pp. 90-99.
https://doi.org/10.46793/aeletters.2025.10.2.3
Naji Hasan, Wisam and Dhyai H. Jawad Aljashaami. “2025. Optimization of Machining Parameters in Turning to Steel Using Grey Relational Analysis.“ Applied Engineering Letters, 10 (2): 90-99.
https://doi.org/10.46793/aeletters.2025.10.2.3
Hasan, W.N., and Aljashaami, D.H.J. (2025). Optimization of Machining Parameters in Turning to Steel Using Grey Relational Analysis. Applied Engineering Letters, 10(2), pp. 90-99.
doi: 10.46793/aeletters.2025.10.2.3.