Optimization of Cutting Parameters on Carbon Steel Using Taguchi-Grey Relation Method

Abstract:

In machining process, surface roughness and material removal rate have a vital importance since they affect mass production, consumption of energy, force, and tool life and product quality. In this study, Taguchi-Grey Relation Method (TGRM) is applied to AISI 1040 mild steels in the hardened form when machined with ceramic inserts using response surface methodology for multi-objective optimization. Grey-Relation Method and Pareto chart reveal that feed rate, depth of cut, speed besides square effect of speed/feed rate are effective parameters on the response. Among all eighteen experiments, trial twelfth provides the best multi-performance characteristics while the first experiment shows the worst performance. Optimal levels are determined at higher speed, higher feed rate associated with higher depth of cut. It is concluded that quadratic regression model and reduced quadratic regression model are developed. The correlation coefficients range from 98.3% to 96.89%, respectively. As a result, TGRM has an efficient to provide a good modelling in combination of surface roughness and metal removal rate.

• Periodical: Key Engineering Materials (Volume 1033)
• Pages: 19-34
• DOI: https://doi.org/10.4028/p-OEqN0i
• Online since: November 2025
• Authors: Yusuf Şahin*, Magd Abdel Wahab
• Keywords: Cutting Speed, Depth of Cut, Feed Rate, Machining, Metal Removal Rate, Mild Steel, Multi-Response Optimization, Surface Roughness, Taguchi-Grey Relation Analysis

References

[1] L. B. Abhang, M. Hameedullah, "Multi performance optimization in machining of EN-31 alloy using Taguchi-utility concept", Journal of Manufacturing Technology Research 2 (197) (2020) 265-270.

DOI: 10.1007/s42452-020-2011-1

[2] Y. Şahin, "Talas Kaldırma Prensipleri, Cilt 1 (Metal Cutting Principles Vol. 1), 2. Baskı (2nd Press)", Seçkin Yayın&Dağıtım, Ankara, Turkey, 2003.

[3] Y. Şahin, "Prediction of surface roughness when machining mild steel using statistical methods", Advances in Materials and Processing Technologies 3 (2023) 1-17. DOI: 10.1080/2374068X.2023. 2198822.

DOI: 10.1080/2374068x.2023.2198822

[4] K. Krishnaiah, P. Shahabudeen, "Applied design of experiments and Taguchi methods", Kindle Ed., PHI learning Private Limited, New Delhi-110001, India, 2012.

[5] R. Panneerselvem, "Research Methodology", Prentice-Hall of India, New Delhi, India, 2004.

[6] J. Ashish George, K. Lokesha, "Optimization and effect of tool rake and approach angle on surface roughness and cutting tool vibration", SN Applied Sciences 1 (2019) 1133-1138.

DOI: 10.1007/s42452-019-1175-z

[7] C. J. Rao, D. N. Rao, P. Srihari, "Influence of cutting parameters on cutting force and surface finish in turning operation", Proceedia Engineering 64 (2013) 1405–1415.

DOI: 10.1016/j.proeng.2013.09.222

[8] K. S. Geethanjali, C. M. Ramesha, B. R. Chandan, "Comparative studies on machinability of medium carbon low alloy steels EN19 and EN24 using Taguchi optimization techniques", Materials Todays: Proc. 5 (11) (2018) 25705-25712.

DOI: 10.1016/j.matpr.2018.11.012

[9] S. Khalilpourazary, P. M. Kashtiban, N. Payam, "Optimization of turning operation of St37 steel using grey relational analysis", Journal of Computational and Applied Research in Mechanical Engineering 3 (2) (2014) 135-144.

[10] P. Sahoo, "Optimization of turning parameters for surface roughness using RSM and GA", Advances in Production Engineering & Management 6 (3) (2011) 197-208.

[11] S. Kalyanakumar, S. T. Chandy, K. T. Adil Muhammed, P. S. Rohith, "Multi-response optimization of machining parameters of turning operation with green environment in EN24T using grey relational analysis in Taguchi method", Materials today: Proceedings 45 (7) (2020) 6193-6197.

DOI: 10.1016/j.matpr.2020.10.508

[12] A. K. Sahoo, B. Sahoo, "Surface roughness model and parametric optimization in finish turning using coated carbide insert: Response surface methodology and Taguchi approach", International Journal of Industrial Engineering Computations 2 (4) (2011) 819-830.

DOI: 10.5267/j.ijiec.2011.06.001

[13] P. Sharma, K. Bhambri, "Multi-response optimization by experimental investigation of machining parameters in CNC turning by Taguchi based grey relational analysis", Materials Science 2 (5) (2012) 1594-1602.

DOI: 10.1016/j.proeng.2014.12.242

[14] E. Nas, N. A. Özbek, "Optimization of the machining parameters in turning of hardened hot work tool steel using cryogenically treated tool", Surface Review and Letters 27 (5) (2020) 1950177.

DOI: 10.1142/S0218625X19501774

[15] M. Özdemir, M. Rafighi, M. A. Awadh, "Comparative evaluation of coated carbide and CBN inserts performance in dry hard-turning of AISI 4140 steel using Taguchi-based grey relation analysis", Coatings 13 (6) (2023) 979-983.

DOI: 10.3390/coatings13060979

[16] H. Gürbüz, Y. Emre Gönülaçar, "Optimization and evaluation of dry and minimum quantity lubricating methods on machinability of AISI 4140 using Taguchi design and ANOVA", Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering&Science 235 (7) (2021) 1211-1227.

DOI: 10.1177/0954406220939609

[17] K. Safi, M. Yallese, S. Belhadi, T. Mabrouki, S.  Chihaoui, "Parametric study and multi-criteria optimization during turning of X210Cr12 steel using the desirability function and hybrid Taguchi-WASPAS method", ARCHIVE Proceedings of the Institution of Mechanical Engineers, Part C:Journal of Mechanical Engineering Science 236 (15) (2022) 8401–8420.

DOI: 10.1177/09544062221086171

[18] S. K. Shihab, Z. A. Khan, A. Mohammad, A. N. Siddiquee, "Investigations on the effect of CNC dry hard turning process parameters on surface integrity: a multi-performance characteristics optimization", Journal for Manufacturing Science and Production 14 (1) (2013) 23-30.

DOI: 10.1515/jmsp-2013-0019

[19] S. Kumar, I. Saravanan, L. Patnaik, "Optimization of surface roughness and material removal rate in milling of AISI 1005 carbon steel using Taguchi approach", Materials Today: Proceedings 22 (3) (2020) 654–658

DOI: 10.1016/j.matpr.2019.09.039

[20] S. Shankar, T. Mohanraj, S. K. Thangarasu, "Multi-response milling process optimization using the Taguchi method coupled to grey relational analysis", Materials Testing 58 (5) (2016) 462-470.

DOI: 10.3139/120.110869

[21] A.P. Markopoulos, N.E. Karkalos, M. Mia, D.Y. Pimenov, M.K. Gupta, H. Hegab, N. Khanna, V.A. Balogun, S. Sharma, "Sustainability assessment, investigations, and modelling of slot milling characteristics in eco-benign machining of hardened steel", Metals 10 (12) (2020) 1650-1657.

DOI: 10.3390/met10121650

[22] D. Y. Pimenov, A. T. Abbas, M. K. Gupta, I. Erdakov, M. S. Soliman, M. M. El Rayes, "Investigations of surface quality and energy consumption associated with costs and material removal rate during face milling of AISI 1045 steel", International Journal of Advanced Manufacturing Technology 107 (2020) 3511–3525.

DOI: 10.1007/s00170-020-05236-7

[23] A. T. Abbas, D. Y. Pimenov, I. N. Erdakov, T. Mikolajczyk, M. S. Soliman, M. M. El Rayes, "Optimization of cutting conditions using artificial neural networks and the Edgeworth-Pareto method for CNC face-milling operations on high-strength Grade-H steel", International Journal of Advanced Manufacturing Technology 105 (2019) 2151–2165.

DOI: 10.1007/s00170-019-04327-4

[24] M. Shnfir, O. A. Olufayo, W. Jomaa, V. Songmene, "Machinability study of hardened 1045 steel when milling with ceramic cutting inserts", Materials (MDPI) 12 (23) (2019) 3974–3981. DOI: 10. 3390/ma12233974.

DOI: 10.3390/ma12233974

[25] Y. Fedai, F. Kahraman, K. K. Akın, G. Basar, "Optimization of machining parameters in face milling using multi-objective Taguchi technique", Tehnički glasnik 12 (2) (2018) 104–108.

DOI: 10.31803/tg-20180201125123

[26] V. Parashar, R. Purohit, "Investigation of the effects of the machining parameters on material removal rate using Taguchi method in end milling of steel grade EN19", Materials Today: Proceedings 4 (2017) 336–341

DOI: 10.1016/j.matpr.2017.01.030

[27] I. Ramu, P. Srinivas, K. Vekatesh, "Taguchi based grey relational analysis for optimization of machining parameters of CNC turning steel 316 R", in 2018 IOP Conference Series: Materials Science and Engineering 377 (2018) 012078.

DOI: 10.1088/1757-899X/377/1/012078

[28] 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) 756–761.

DOI: 10.30574/wjarr.2023.17.1.0080

[29] K. Chandrasekaran, P. Marimuthu, K. R. Raja, A. Manimaran, "Multi response optimization of machining parameters for turning stainless steel using coated tools", Applied Mechanics and Materials 573 (2014) 644-648.

DOI: 10.4028/www.scientific.net/AMM.573.644

[30] S.R. Sundara Bharathi, D. Ravindran, A. A. Marcel Moshi, "Multi-response optimization of CNC turning parameters of austenitic stainless steel 303 using Taguchi-based grey relational analysis", Transactions of the Canadian Society for Mechanical Engineering 44 (4) (2020) 110-113.

DOI: 10.1139/tcsme-2019-0254

[31] M. Kunto ˘glu, A. Aslan, D. Y. Pimenov, K. Giasin, T. Mikolajczyk, S. Sharma, "Modeling of cutting parameters and tool geometry for multi-criteria optimization of surface roughness and vibration via response surface methodology in turning of AISI 5140 steel", Materials 13 (19) (2020) 4242-4247.

DOI: 10.3390/ma13194242

[32] S.K. Rajbongshi, D.K. Sharma, "Process parameters optimization using Taguchi's orthogonal array and grey relational analysis during hard turning of AISI D2 steel in forced air-cooled condition", in 2019 IOP Conference Series: Materials Science, Engineering 491 (012032) (2019) 1-6.

DOI: 10.1088/1757-899X/491/1/012032

[33] M. Elbah, M. A. Yallese, H. Aouici, T. Mabrouki, J. C. Rigal, "Comparative assessment of wiper and conventional ceramic tools on surface roughness in hard turning AISI 4140 steel", Measurement 46 (9) (2013) 3041–3056

DOI: 10.1016/j.measurement.2013.06.018

[34] Y. Şahin, D. Akbar, "Multi-response optimization in cutting mild steels", Applied Chemical Engineering 7 (1) (2024) 1-10.

DOI: 10.24294/ace.v7i1.2599

[35] H. Yaka, L. Uğur, H. Akkuş, "Investigation of surface roughness with multiple regression in turning of AISI 1040 steel", Afyon Kocatepe University Journal of Sciences and Engineering 16, (035901) (2016) 770-775.

DOI: 10.5578/fmbd.34280

[36] Y. Şahin, "Prediction of surface roughness on hardened bearing steel machined by ceramic cutting tool", SAE International Materials & Manufacturing 6 (3) (2023) 1-9.

DOI: 10.4271/05-16-03-0021