Surface roughness measurements in NFMQL assisted turning of titanium alloys: An optimization approach

Munish K. GUPTA; P. K. SOOD

doi:10.1007/s40544-017-0141-2

Friction 2017, 5(2): 155-170 https://doi.org/10.1007/s40544-017-0141-2

Research Article |

Open Access | Issue | Published: 31 March 2017

Surface roughness measurements in NFMQL assisted turning of titanium alloys: An optimization approach

Show Author's Information Hide Author's Information Munish K. GUPTA(

), P. K. SOOD

MED, NIT, Hamirpur (H.P.) 177005, India

Keywords:

surface roughness, optimization, nano-fluids, turning, titanium alloy

An erratum to this article is available online at https://doi.org/10.1007/s40544-022-0721-7

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GUPTA MK, SOOD PK. Surface roughness measurements in NFMQL assisted turning of titanium alloys: An optimization approach. Friction, 2017, 5(2): 155-170. https://doi.org/10.1007/s40544-017-0141-2

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Abstract

The prediction and optimization of surface roughness values remain a critical concern in nano-fluids based minimum quantity lubrication (NFMQL) turning of titanium (grade-2) alloys. Here, we discuss an application of response surface methodology with Box–Cox transformation to determine the optimal cutting parameters for three surface roughness values, i.e., R_a, R_q, and R_z, in turning of titanium alloy under the NFMQL condition. The surface roughness prediction model has been established based on the selected input parameters such as cutting speed, feed rate, approach angle, and different nano-fluids used. Then the multiple regression technique is used to find the relationship between the given responses and input parameter. Further, the experimental data were optimized through the desirability function approach. The findings from the current investigation showed that feed rate is the most effective parameter followed by cutting speed, different nano-fluids, and approach angle on R_a and R_q values, whereas cutting speed is more effective in the case of R_z under NFMQL conditions. Moreover, the predicted results are comparatively near to the experimental values and hence, the established models of RSM using Box-Cox transformation can be used for prediction satisfactorily.

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Surface roughness measurements in NFMQL assisted turning of titanium alloys: An optimization approach

Show Author's information Hide Author's Information Munish K. GUPTA(

), P. K. SOOD

MED, NIT, Hamirpur (H.P.) 177005, India

Abstract

Keywords: surface roughness, optimization, nano-fluids, turning, titanium alloy

References(35)

[1]

Routara B C, Bandyopadhyay A, Sahoo P. Roughness modeling and optimization in CNC end milling using response surface method: effect of work piece material variation. Int J Adv Manuf Technol 40:1166–1180(2008)

DOI Google Scholar

[2]

Davim J P, Gaitonde V N, Karnik S R. Investigations into the effect of cutting conditions on surface roughness in turning of free machining steel by ANN models. J Mater Process Technol 205:16–23(2007)

DOI Google Scholar

[3]

Bhardwaj B, Kumar R, Singh P K. Surface roughness (Ra) prediction model for turning of AISI 1019 steel using response surface methodology and Box-Cox transformation. Proc IMechE, Part B: J Eng Manuf 228(2):223–232(2013)

DOI Google Scholar

[4]

Sharma V S, Dogra M, Suri N M. Cooling techniques for improved productivity in turning. Int J Mach Tool Manu 49(6):435–453(2009)

DOI Google Scholar

[5]

Gupta M K, Sood P K, Sharma V S. Optimization of machining parameters and cutting fluids during nano-fluid based minimum quantity lubrication turning of titanium alloy by using evolutionary techniques. J Cleaner Production 135:1276–1288(2016)

DOI Google Scholar

[6]

Dureja JS, Singh R, Singh T, Singh P, Dogra M, Bhatti M S.. Performance evaluation of coated carbide tool in machining of stainless steel (AISI 202) under minimum quantity lubrication (MQL). Int J Precis Eng Manuf 2:123–129(2015)

DOI Google Scholar

[7]

Gupta M K, Sood P K, Sharma V S. Machining parameters optimization of titanium alloy using response surface methodology and particle swarm optimization under minimum quantity lubrication environment. Mater Manuf Process 31:1671–1682(2016)

DOI Google Scholar

[8]

Sharma VS, Singh G, Sorby K. A review on minimum quantity lubrication for machining processes. Mater Manuf Process 30(8):935–953(2015)

DOI Google Scholar

[9]

Shen B, Malshe A P, Kalita P, Shih A J. Performance of novel MOS₂ nano-particles based grinding fluids in minimum quantity lubrication grinding. Trans NAMRI/SME 36:357–364(2008)

Google Scholar

[10]

Ramesh S, Karunamoorthy L, Palanikumar K. Surface roughness analysis in machining of titanium alloy. Mater Manuf Process 23(2):174–181(2008)

DOI Google Scholar

[11]

Ramesh S, Karunamoorthy L, Palanikumar K. Fuzzy Modeling and analysis of machining parameters in machining titanium alloy. Mater Manuf Process 23(4):439–447(2008)

DOI Google Scholar

[12]

Ramesh S, Karunamoorthy L, Senthilkumar V S, Palanikumar K. Experimental study on machining of titanium alloy (Ti64) by CVD and PVD coated carbide inserts. Int J Manuf Technol Manag 17(4):373–385(2009)

DOI Google Scholar

[13]

Sridharan U, Malkin S. Effect of minimum quantity lubrication (MQL) with nano-fluid on grinding behavior and thermal distortion. Trans NAMRI/SME 37:629–636(2009)

Google Scholar

[14]

Kwon P, Drzal L T. Nanoparticle graphite-based minimum quantity lubrication method and composition. U.S. Patent 649:12–655, 2010.

Google Scholar

[15]

Nam J S, Lee P H, Lee S W. Experimental characterization of micro-drilling process using nano-fluid minimum quantity lubrication. Int J Mach Tool Manuf 51(7–8):649–652(2011)

DOI Google Scholar

[16]

Samuel J, Rafiee J, Dhiman P, Yu Z Z, Koratkar N. Graphene colloidal suspensions as high performance semi-synthetic metal-working fluids. J Phys Chem C 115(8):3410–3415(2011)

DOI Google Scholar

[17]

Park K H, Ewald B, Kwon P Y. Effect of nano-enhanced lubricant in minimum quantity lubrication balling milling. J Tribol 133:031803(2011)

DOI Google Scholar

[18]

Vasu V, Reddy P K G. Effect of minimum quantity lubrication with Al₂O₃ nanoparticles on surface roughness, tool wear and temperature dissipation in machining Inconel 600 alloy. Proc Inst Mech Eng Part N J Nanoeng and Nanosyst 225:3–16(2011)

DOI Google Scholar

[19]

Ramesh S, Karunamoorthy L, Palanikumar K. Measurement and analysis of surface roughness in turning of aerospace titanium alloy (gr5). Measurement 45:1266-1276(2012)

DOI Google Scholar

[20]

Khandekar S, Sankar M R, Agnihotri V, Ramkumar J. Nano- cutting fluid for enhancement of metal cutting performance. Mater Manuf Process 27(9):963–967(2012)

DOI Google Scholar

[21]

Kalita P, Malshe A P, Arun Kumar S, Yoganath V G, Gurumurthy T. Study of specific energy and friction coefficient in minimum quantity lubrication grinding using oil-based nanolubricants. J Mater Process Technol 14:160–166(2012)

DOI Google Scholar

[22]

Nguyen T K, Do I, Kwon P. A tribological study of vegetable oil enhanced by nano-platelets and implication in MQL machining. Int J Prec Eng Manuf 13(7):1077–1083(2012)

DOI Google Scholar

[23]

Amrita M, Srikant R, Sitaramaraju A, Prasad M, Krishna P V. Experimental investigations on influence of mist cooling using nanofluids on machining parameters in turning AISI 1040 steel. Proc Inst Mech Eng Part J J Eng Tribol 227:1334–1346(2013)

DOI Google Scholar

[24]

Paul P S, Varadarajan A S. Performance evaluation of hard turning of AISI 4340 steel with minimal fluid application in the presence of semi-solid lubricants. Proc Inst Mech Eng Part J J Eng Tribol 227:738–748(2013)

DOI Google Scholar

[25]

Srikiran S, Ramji K, Satyanarayana B, Ramana S. Investigation on turning of AISI 1040 steel with the application of nano- crystalline graphite powder as lubricant. Proc Inst Mech Eng Part C J Mech Eng Sci 228:1570–1580(2014)

DOI Google Scholar

[26]

Amrita M, Srikant R R, Sitaramaraju A V. Performance evaluation of nanographite-based cutting fluid in machining process. Mater Manuf Process 29:600–605(2014)

DOI Google Scholar

[27]

Sharma P, Sidhu B S, Sharma J. Investigation of effects of nanofluids on turning of AISI D2 steel using minimum quantity lubrication. J Cleaner Production 108:72–79(2015)

DOI Google Scholar

[28]

Su Y, Gong L, Li B, Liu Z, Chen D. Performance evaluation of nanofluid MQL with vegetable-based oil and ester oil as base fluids in turning. Int J Adv Manuf Technol 83(9):2083–2089(2015)

DOI Google Scholar

[29]

Barzani M M, Sarhan A A D, Farahany S, Ramesh S, Maher I. Investigating the machinability of Al–Si–Cu cast alloy containing bismuth and antimony using coated carbide insert. Measurement 62:170–178(2015)

DOI Google Scholar

[30]

Barzani M M, Zalnezhad E, Sarhan A A D, Farahany S, Ramesh S. Fuzzy logic based model for predicting surface roughness of machined Al–Si–Cu–Fe die casting alloy using different additives-turning. Measurement 61:150–161(2015)

DOI Google Scholar

[31]

Unune DR, Barzani MM, Mohite SS, Mali HS. Fuzzy logic-based model for predicting material removal rate and average surface roughness of machined Nimonic 80A using abrasive-mixed electro-discharge diamond surface grinding. Neural Computing and Applications, in press, (2016)

DOI Google Scholar

[32]

Oudjene M, Ben-Ayed L, Delamézière A, Batoz J L. Shape optimization of clinching tools using the response surface methodology with Moving Least-Square approximation. J Mater Process Technol 209:289–296(2009)

DOI Google Scholar

[33]

Hewidy M S, El-Taweel T A, El-Safty M F. Modelling the machining parameters of wire electrical discharge machining of Inconel 601 using RSM. J Mater Process Technol 169:328–336(2005)

DOI Google Scholar

[34]

Montgomery D C. Design and Analysis of Experiments. New York: Wiley, 2001.

[35]

Gupta M K, Sood P K, Sharma V S. Investigations on surface roughness measurement in minimum quantity lubrication turning of titanium alloys using response surface methodology and Box–Cox transformation. J Manuf Sci Product 16:75–88(2016)

DOI Google Scholar

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Acknowledgements

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Publication history

Received: 19 September 2016

Revised: 26 October 2016

Accepted: 11 December 2016

Published: 31 March 2017

Issue date: June 2017

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Acknowledgements

The authors are extremely grateful to Dr. Vishal S. Sharma, NIT Jalandhar for providing the research facilities. Authors also acknowledge the MHRD, Govt. of India and Central Workshop NIT Hamirpur (H.P.) for the financial support.

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