Analysis of machining parameters for EN24 material in CNC vertical machining center by Ti-N coated HSS tool using GRG reinforced RSM technique

Murugan R; Senthilkumar G; Vinodkumar V; Rathinasabapathi G; Dhanasakkaravarthi B; Sivaraman V

doi:10.54392/irjmt2543

Authors

Murugan R Department of Mechanical Engineering, Panimalar Engineering College, Chennai, India. Author https://orcid.org/0000-0002-9313-9436
Senthilkumar G Department of Mechanical Engineering, Panimalar Engineering College, Chennai, India. Author
Vinodkumar V Department of Mechanical Engineering, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, India. Author
Rathinasabapathi G Department of Mechanical Engineering, Panimalar Engineering College, Chennai, India. Author
Dhanasakkaravarthi B Department of Mechanical Engineering, Agni College of Technology, Chennai, India. Author
Sivaraman V Department of Mechanical Engineering, Sri Muthukumaran Institute of Technology, Chennai, India. Author

DOI:

https://doi.org/10.54392/irjmt2543

Keywords:

Response Surface Methodology, Grey Relational Grade, Cutting Force, Material Removal Rate, Surface roughness

Abstract

The goal of the present study is to evaluate the optimum machining parameters for EN24 material to achieve an increased material removal rate (MRR), low cutting force (CF), and surface roughness (SR). The total number of trial experiments for the three control parameters spindle speed (SS), feed rate (FR), and depth of cut (DOC) varying at three levels are designed based on the Taguchi L27 orthogonal array. To optimize the multiple machining parameters for EN24 steel based on the three experimentally measured responses such as MRR, CF, and SR, a contemporary and hybrid multi-objective optimization technique, grey relational grade (GRG) reinforced response surface methodology (RSM) is employed in the present study. Based on the results obtained from the integrated multi-objective optimization technique, the predicted optimum machining conditions for SS, FR, and DOC are 1383.47 rpm, 106.5 mm/min, and 0.79 mm respectively. The responses MRR – 21.56 mm3/min, CF – 195.56N, and SR- 0.53µm are obtained at optimum level. The percentage of improvement achieved in the preferred responses for milling the EN 24 material after implementing the GRG reinforced RSM technique are MRR - 5.19%, CF - 15.42%, and SR- 19.69%.

References

S. Patil, P.S. Rao, M.S. Prabhudev, M.Y. Khan, G. Anjaiah, Optimization of cutting parameters during CNC milling of EN24 steel with Tungsten carbide coated inserts: A critical review. Materials Today: Proceedings, 62, (2022), 3213–3220. https://doi.org/10.1016/j.matpr.2022.04.217

A. Abraham Eben Andrews, J. Jensin Joshua, Gnanadurai Ravikumar Solomon, B. Gokul, K. Srinath, Optimizing machining parameters of carbon steel EN24 to minimize surface roughness and tool wear. Materials Today: Proceedings, 33 (7), (2020). 3902-3906. https://doi.org/10.1016/j.matpr.2020.06.246

K.Y. Benyounis, A.G. Olabi, Optimization of different welding processes using statistical and numerical approaches–A reference guide. Advances in engineering software, 39 (6), (2008) 483-496. https://doi.org/10.1016/j.advengsoft.2007.03.012

G. Senthilkumar, R. Murugan, G. Gnanakumar, N. Nithyanandan, Fuzzy logic modelling of machining characteristics for CNC milling of EN24 using Ti-N coated tool. Materials Today: Proceedings, 82, (2023) 137-144. https://doi.org/10.1016/j.matpr.2022.12.111

S.A.A. Daniel, R. Pugazhenthi, R. Kumar, S.Vijayananth, Multi-objective prediction and optimization of control parameters in the milling of aluminium hybrid metal matrix composites using ANN and Taguchi-grey relational analysis. Defence Technology, Vol. 15 (4), (2019) 545-556. https://doi.org/10.1016/j.dt.2019.01.001

M. Santhanakumar, R. Adalarasan, M. Rajmohan, Experimental modelling and analysis in abrasive waterjet cutting of ceramic tiles using grey-based response surface methodology. Arabian Journal for Science and Engineering, 40, (2015) 3299-3311. https://doi.org/10.1007/s13369-015-1775-x

M. Santhanakumar, R. Adalarasan, S. Siddharth, A. Velayudham, An investigation on surface finish and flank wear in hard machining of solution treated and aged 18% Ni maraging steel. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 39, (2017) 2071-2084. https://doi.org/10.1007/s40430-016-0572-0

R. Kumar, M. Balasubramanian, Application of response surface methodology to optimize process parameters in friction welding of Ti–6Al–4V and SS304L rods. Transactions of Nonferrous Metals Society of China, 25 (11), (2015) 3625-3633. https://doi.org/10.1016/S1003-6326(15)63959-0

R. Kadaganchi, M.R. Gankidi, H. Gokhale, (2015) Optimization of process parameters of aluminum alloy AA 2014-T6 friction stir welds by response surface methodology. Defence Technology, 11(3), 209-219. https://doi.org/10.1016/j.dt.2015.03.003

R. Adalarasan, M. Santhanakumar, S. Thileepan, Selection of optimal machining parameters in pulsed CO2 laser cutting of Al6061/Al2O3 composite using Taguchi-based response surface methodology (T-RSM). The International Journal of Advanced Manufacturing Technology, 93, (2017) 305-317. https://doi.org/10.1007/s00170-016-8978-5

S. Singh, M. Doddamani, S. Powar, Multi-objective optimization of machining parameter in laser drilling of glass micro balloon/epoxy syntactic foams. Journal of materials research and technology, 23, (2023) 3869-3879. https://doi.org/10.1016/j.jmrt.2023.02.025

E. Posti, I. Nieminen, Influence of coating thickness on the life of TiN-coated high speed steel cutting tools. Wear, 129(2), (1989) 273-283. https://doi.org/10.1016/0043-1648(89)90264-0

S. Zhang, W. Zhu, TiN coating of tool steels: A review, Journal of Materials Processing Technology, 39(1–2), (1993) 165-177. https://doi.org/10.1016/0924-0136(93)90016-Y

M.I. Equbal, M. Shamim, R.K. Ohdar, A grey-based Taguchi method to optimize hot forging process. Procedia Materials Science, 6, (2014) 1495-1504. https://doi.org/10.1016/j.mspro.2014.07.129

O.O. Daramola, I. Tlhabadira, J.L. Olajide, I.A. Daniyan, E.R. Sadiku, L. Masu, L.R. Van Staden, Process Design for Optimal Minimization of Resultant Cutting Force during the Machining of Ti-6Al-4V: Response Surface Method and Desirability Function Analysis. Procedia CIRP, 84, (2019) 854–860. https://doi.org/10.1016/j.procir.2019.04.185

N. Senthilkumar, J. Sudha, V. Muthukumar, A grey-fuzzy approach for optimizing machining parameters and the approach angle in turning AISI 1045 steel. Advances in Production Engineering & Management, 10(4), (2015) 195-208. http://dx.doi.org/10.14743/apem2015.4.202

G. Senthilkumar. R. Ramakrishnan, A comparative study of predicting burn-off length in continuous drive solid state friction welding for ASTM A516 Steel by regression analysis, fuzzy logic analysis, and finite element analysis. Journal of Applied Science and Engineering, 24(3), (2021) 359-366. https://doi.org/10.6180/jase.202106_24(3).0011

G. Senthilkumar. T. Mayavan, R. Ramakrishnan Optimization of friction welding input factors to maximize tensile strength and minimize axial shortening in ASTM A516 grade 70 steel rods. Journal Applied Science and Engineering, 25(5), (2021) 773-784. https://doi.org/10.6180/jase.202210_25(5).0008

G. Senthilkumar, R. Ramakrishnan, Design of optimal parameter for solid state welding of EN 10028-P355 GH Steel using grey incidence reinforced surface response methodology. Arabian Journal of Science and Engineering, 46, (2021) 2613-2628. https://doi.org/10.1007/s13369-020-05169-z

N. Natarajan, R.M. Arunachalam, Optimization of micro-EDM with multiple performance characteristics using Taguchi method and Grey relational analysis. Journal of Scientific & Industrial Research, 70, (2011) 500-505.

A. Jankovic, G. Chaudhary, F. Goia, Designing the design of experiments (DOE) – An investigation on the influence of different factorial designs on the characterization of complex systems. Energy and Buildings, 250, (2021) 111298. https://doi.org/10.1016/j.enbuild.2021.111298

D.K. Naik, K. Maity, Application of desirability function based response surface methodology (DRSM) for investigating the plasma arc cutting process of sailhard steel. World Journal of Engineering, 15(4), (2018) 505-512. https://doi.org/10.1108/WJE-06-2017-0125

M. Srivastava, S. Maheshwari, T.K. Kundra, S. Rathee, Multi-response optimization of fused deposition modelling process parameters of ABS using response surface methodology (RSM)-based desirability analysis. Materials Today: Proceedings, 4(2), (2017) 1972-1977. https://doi.org/10.1016/j.matpr.2017.02.043

S. Ramesh, L. Karunamoorthy, K. Palanikumar, Fuzzy modeling and analysis of machining parameters in machining titanium alloy. Materials and Manufacturing Processes, 23(4), (2008) 439-447. https://doi.org/10.1080/10426910801976676

S.T. Alam, A.A. Tomal, M.K. Nayeem, High-speed machining of Ti–6Al–4V: RSM-GA based optimization of surface roughness and MRR. Results in Engineering, 17, (2023) 100873. https://doi.org/10.1016/j.rineng.2022.100873

A. Ercetin, K. Aslantaş, O. Ozgün, M. Perçin, M.P.G. Chandrashekarappa, Optimization of Machining Parameters to Minimize Cutting Forces and Surface Roughness in Micro-Milling of Mg13Sn Alloy. Micromachines, 14(8), (2023) 1590. https://doi.org/10.3390/mi14081590

F. Lai, A. Hu, K. Mao, Z. Wu, Y. Lin, Effect of milling processing parameters on the surface roughness and tool cutting forces of T2 pure copper. Micromachines, 14(1), (2023) 224. https://doi.org/10.3390/mi14010224