HARD MACHINING OF P235GH STEEL WITH DIFFERENT PARAMETERS USING GSRS MODELLING
Keywords:
hard machining, nanofluid, P235GH steel, grey response surface
Abstract
The high-strength and heat-resistant P235GH steel finds wide applications in pressure vessels and heat exchangers. It is a difficult-to-handle material, which reduces the life of the tool during machining, hence spoiling the finish of machined surface. Cutting fluids can improve the life of tools and surface finish. The current investigation observes the effect of machining parameters (revolution and feed rate) and different cutting environments (SAE-40 oil with 5 % boric acid, soybean oil and nanofluid) on the surface finish and flank wear in hard turning of P235GH steel. Machining trials are designed using an L18 orthogonal array with replications. A hybrid approach of grey system based response surface (GSRS) modelling is employed to study the effects of various parameters and their interactions. The chip morphology and flank wear are analysed using scanning electron microscopic images and the predicted optimal machining condition for a better surface finish and reduced tool wear is also validated.
References
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20 H. Bouchelaghem, M. A. Yallese, T. Mabrouki, A. Amirat, J. F. Rigal, Experimental investigation and performance analyses of CBN insert in hard turning of cold work tool steel (D3)’, Mach. Sci. Technol., 14 (2010) 4, 471-501, doi: 10.1080/10910344.2010.533621.
2 S.R. Das, A. Kumar, D. Dhupal, Experimental Investigation on Cutting Force and Surface Roughness in Machining of HardenedAISI-52100 Steel Using C-B-N Tool, Int. J. Mach. Mach. Mater., 18 (2016) 5-6, 501-521, doi: 10.1504/IJMMM.2016.078997
3 Y. Yang, S. Guo, L. Si, T. Liu, Y. Dai, C. Yan, C. Zhang, Investigation of a new water-based cutting fluid for machining of titanium alloys, J. Manuf. Process., 71 (2021), 398-406, doi: 10.1016/j.jmapro.2021.09.046
4 M.A. Makhesana, K. M. Patel, Performance assessment of vegetable oil-based nanofluid in Minimum Quantity Lubrication (MQL) during machining of Inconel 718, Adv. Mater. Process. Technol., (2021) doi: 10.1080/2374068X.2021.1945305
5 S. Kumar, D. Singh, N. S. Kalsi. Experimental Investigations of Surface Roughness of Inconel 718 under different Machining Conditions, Mater. Today: Proc., 4 (2017) 2, 1179-85, doi: 10.1016/j.matpr.2017.01.135
6 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, J. Braz. Soc. Mech. Sci. Eng., 39 (2016) 6, 2071-2084, doi: 10.1007/s40430-016-0572-0
7 N. S. Reddy, M. Nouari, M. Yang, Development of electrostatic solid lubrication system for improvement in machining process performance, Int. J. Mach. Tools Manuf., 2010 Sep 1;50 (2010) 9, 789-97, doi: 10.1016/j.ijmachtools.2010.05.007
8 A. Davoudinejad, M.Y. Noordin, Effect of cutting edge preparation on tool performance in hard-turning of DF-3 tool steel with ceramic tools, J. Mech. Sci. Technol., 28 (2014) 11, 4727–4736, doi: 10.1007/s12206-014-1039-9
9 M. Dogra, V.S. Sharma, Machinability and surface quality issues in finish turning of hardened steel with coated carbide and CBN tools, Mater. Manuf. Process., 27 (2012) 10, 1110–1117, doi: 10.1080/10426914.2011.654164
10 A. Batish, A. Bhattacharya, M. Kaur, M.S. Cheema, Hard turning: Parametric optimization using genetic algorithm for rough/finish machining and study of surface morphology, J. Mech. Sci. Technol., 28 (2014) 5, 1629–1640, doi:10.1007%2Fs12206-014-0308-y
11 H. Aouici, M.A. Yallese, B. Fnides, K. Chaoui, T. Mabrouki, Modeling and optimization of hard turning of X38CrMoV5-1 steel with CBN tool: machining parameters effects on flank wear and surface roughness, J. Mech. Sci. Technol., 25 (2011) 11, 2843–2851, doi: 10.1007/s12206-011-0807-z
12 Y. Kazancoglu1, U. Esme, M.K. Kulekci, F. Kahraman, R. Samur, A. Akkurt, M.T. Ipekci, Application of a Taguchi-based neural network for forecasting and optimization of the surface roughness in a wire-electrical-discharge machining process, Mater. Technol., 46 (2012) 5, 471-476, doi: 621.9.025.5:620.191.35
13 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), Int. J. Adv. Manuf. Technol., 93 (2016) 1-4, 305-317, doi: 10.1007/s00170-016-8978-5
14 N. A. Ozbek, Optimization of flank wear and surface quality in the turning of 1.2343 tool steel using carbide tools coated via different methods, Surf. Topogr.: Metrol. Prop,, 9 (2021) 2, 025028, doi:10.1088/2051-672X/abfd06
15 M.Santhanakumar, R. Adalarasan, Application of Grey Taguchi based Response Surface Methodology (GT-RSM) in Injection Moulding of Polypropylene/E-glass Composite, Int. J. Manuf. Mater. Mech., 5 (2015) 1, 35-48, doi: 10.4018/ijmmme.2015010103
16 R. Adalarasan, M. Santhanakumar, M. Rajmohan, Application of Grey Taguchi-based response surface methodology (GT-RSM) for optimizing the plasma arc cutting parameters of 304L stainless steel, Int. J. Adv. Manuf. Technol., 78 (2015) 5-8, 1161-1170, doi: 10.1007%2Fs00170-014-6744-0
17 R. Adalarasan, M. Santhanakumar, A. Shanmuga Sundaram, Optimization of weld characteristics of friction welded AA6061-AA6351 joints using grey principal component analysis (G-PCA), J. Mech. Sci. Technol., 28 (2014), 301-307, doi: 10.1007/s12206-013-0963-4
18 M.S. Vijayanand, M. Ilangkumaran, Optimization of Micro-EDM Parameters using Grey-Based Fuzzy Logic Coupled with the Taguchi Method, Mater. Technol., 51 (2017) 6, 989-995, doi:10.17222/mit.2017.048
19 K. Sobiyi, I. Sigalas, Chip Formation Characterisation and TEM Investigation of Worn PcBN Tool During Hard Turning, Mach. Sci. Technol., 19 (2014) 3, 479-498, doi:10.1080/10910344.2015.1051542
20 H. Bouchelaghem, M. A. Yallese, T. Mabrouki, A. Amirat, J. F. Rigal, Experimental investigation and performance analyses of CBN insert in hard turning of cold work tool steel (D3)’, Mach. Sci. Technol., 14 (2010) 4, 471-501, doi: 10.1080/10910344.2010.533621.
Published
2022-06-03
How to Cite
1.
Nelson EstherEP, Ramalingam A, Muthuvel S. HARD MACHINING OF P235GH STEEL WITH DIFFERENT PARAMETERS USING GSRS MODELLING. MatTech [Internet]. 2022Jun.3 [cited 2025Feb.11];56(3):255–261. Available from: https://mater-tehnol.si/index.php/MatTech/article/view/373
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