Jitendra. M. Varma, Chirag. P. Patel / International Journal of Engineering Research andApplications (IJERA) ISSN: 2248-9622 www.ijera.comVol. 3, Issue 3, May-Jun 2013, pp.1011-10151011 | P a g eParametric Optimization of Hard turning of AISI 4340 Steel bysolid lubricant with coated carbide insert.Jitendra. M. Varma*, Chirag. P. Patel***Student,M.Tech, (Department of MechanicalEngineering, Ganpat University, Kherva-384012,)** Associate Prof., (Department of Mechanical Engineering, Ganpat University, Kherva-384012,)AbstractThis study investigated the optimizationof Hard turning of AISI 4340 steel by solidlubricant assisted environment using the Greyrelational analysis method. Hexagonal boronnitride (H-bn) has been selected as solidlubricant. All the experiment are carried out atdifferent cutting parameter (Cutting speed, Feedrate, Depth of cut and nose radius) in Dry, Wetand solid lubricant assisted environment.TwentySeven experiments runs based on an orthogonalarray of Taguchi method were performed. Eachnine experiments were carried out in Dry, Wetand Solid lubricant assisted environment. Surfaceroughness and cutting force selected asaresponsevariable.An optimal parametercombination of the turning operation wasobtained via Grey relational analysis. Byanalyzing the Grey relational grade matrix, thedegree of influence for each controllable processfactor onto individual quality targets can befound. The optimal parameter combination isthen tested for accuracy of conclusion with a testrun using the same parameters.Keywords: Optimization, Grey relational analysis,surface roughness, cutting force, feed force.1. IntroductionDuring the past few years, unprecedentedprogress has been made in the hard turning. Thegreatest advantage of using hard turning is thereduced machining time and complexity required tomanufacture metal parts.Before few years problemassociated with the hard turning was the generationof high temperature in cutting zone. This adverselyaffects the quality of the products produced. Cuttingfluids have been the conventional choice to deal withthis problem. But, the application of conventionalcutting fluids creates some techno-environmentalproblems like environmental pollution, biologicalproblems to operators, water pollution, Further; thecutting fluids also incur a major portion of the totalmanufacturing cost.Reduction of environmental pollution hasbeen the main concern in the present day metalcutting industry. The government has put pressureon industries to minimize the use of cutting fluids.Although the cutting fluid can be recycled.Recycling services in the United-state charge twiceprice for disposal and the cost is four times as muchin Europe. In addition to environmental and healthconcerns, the metal cutting industry continues toinvestigate ways to achieve longer tool life, highercutting speed, better work surface quality, less builtup edge, easier chip breaking, lower production cost,overall quality and productivity. Although drymachining eliminates the use of cutting fluid, itnegatively affects too life.Application of solid lubricant in drymachining has proved to be a feasible alternative tocutting fluid, if it can be applied properly.Advancement in modern tribology has identifiedmany solid lubricants like graphite, boric acid,hexagonal boron nitride (White graphite),molybdenum disulfide and tungsten disulfide, whichcan sustain and provide lubricity over a wide rangeof temperatures. If suitable solid lubricant can beapplied in a more refined and defined way.In machining operation, the quality ofsurface finish is an important requirement of workpieces and parameter in manufacturing engineering.During the turning operation, the cutting tool and themetal bar are subjected to a prescribed deformationas a result of the relative motion between the tooland work-piece both in the cutting speed directionand feed direction. As a response to the prescribeddeformation, the tool is subjected to thermal loadson those faces that have interfacial contact with thework-piece or chip. Usually the material removaloccurs in a highly hostile environment with hightemperature and pressure, in the cutting zone.The ultimate objective of the science ofmetal cutting is to solve practical problemsassociated with efficient material removal in themetal cutting process. Turning is by far the mostcommonly used operation in a lathe. In this the workheld in the spindle is rotate while the tool is fed pastthe work piece in a direction parallel to the axis ofrotation. The surface generated is the cylindricalsurface.Lathe turning parameter like cutting speed,feed rate, depth of cut and nose radius are optimizewith multi response optimization method. By usingthis method, complicated multiple responses can beconverted into normalized response known as GreyRelational Coefficient (GRC). Grey relationalanalysis was performed to combine the multipleresponses into one numerical score, rank these
Jitendra. M. Varma, Chirag. P. Patel / International Journal of Engineering Research andApplications (IJERA) ISSN: 2248-9622 www.ijera.comVol. 3, Issue 3, May-Jun 2013, pp.1011-10151012 | P a g escores, and determine the optimal cutting parametersettings. Confirmation tests were performed by usingexperiments.2. Experimental details2.1 Work piece materialAISI 4340 has been selected as work pieceMaterial .It widely used for aircraft landing gear,power transmission gear, shaft and other parts. Thediameter and Length of work piece was 47 mm and200 mm respectively. Chemical composition of thismaterial is Carbon 0.38 to 0.48%, Chromium 0.7 to0.9%, Manganese 0.6 to0.8, Molybdenum 0.2 to 0.3,Nickel 1.65 to 2%, Phosphorus 0.035 max, Silicon0.15 to0.30%, Sulphur 0.04max.2.2 Cutting insertsAll the experiments were performed byusing coated carbide insert of PR series (TNMG-PR1125) of different geometry. It is hard TiAlNbase PVD coated super micro-grain carbide inserthaving superior toughness and heat resistance. It isused for Finishing and light interrupted cutting ofstainless steel. Right hand side cutting tool holder ofGRADE-MTJNR 2020 K16 has been used.2.3 Cutting fluidIn order to perform the experiment in wet cuttingenvironment, soluble oil (KOOLKUT-40, a productof Hindustan Petroleum, Mollifiable oil, Emulsionstrength 5–10%) has been used as a cutting fluid. HPKOOLKUT-40 is general-purpose emulsifiable oiland forms milky white emulsion. Suitable for allmetals for machining operations where emulsifiableoil is normally used. The recommendedconcentration is between 5 to 10 percent dependingon the severity of machining. Sometimes usedsuccessfully for even grinding operations.2.4 Solid lubricantIn order to perform the experiment in solidlubricant cutting environment, Hexagonal boronnitride powder has been selected.Boron Nitride is aceramic powder lubricant. The most interestinglubricant feature is its high temperature resistance of1200°C service temperature in an oxidizingatmosphere. Furthermore, boron has a high thermalconductivity. Boron is available in two chemicalstructures, i.e. cubic and hexagonal where the last isthe lubricating version. The cubic structure is veryhard and used as an abrasive and cutting toolcomponent. Another advantage of h-BN overgraphite is that its lubricity does not require water orgas molecules trapped between the layers. Therefore,h-BN lubricants can be used even in vacuum, e.g. inspace applications. It doesn’t affect the environmentand human health. Hence, the lubricating propertiesof fine-grained h-BN are used in cosmetics, paints,dental cements.2.5Experimental apparatusThe hard turning of work piece isconducted on Latheturning center havingfollowingSpecifications:Height of center: 175 mm, Swing over bed: 350mm,Swing over slide: 190mm,Swing in gap:550mm,Width of bed: 240mm, Spindle speed NO. 8:60 To 1025 RPM, Cross slide travel: 200mm, Topslide travel: 125mm, Net weight: 700 kg, Leadscrew: 4 TPI, Power required: 1.5W/2H.P, Bedlength: 4’.5”/600mm2.6 Measurement of surface roughness andcutting forcesThe surface roughness of the turnedsamples was measured with Mitutoyo make Surfaceroughness tester (SJ-110) and the cutting forcesmeasured with the help of 2-D dynamometer.2.7Design of experimentIn this study, four controllable variables,namely, cutting speed, feed rate, depth of cut andnose radius has been selected. In the machiningparameter design, three levels of the cuttingparameters were selected, shown in Table 1.Table1.Factors with levels valueFactors Level 1 Level 2 Level 3Cutting speed(m/min)67.1 100.8 151.3Feed rate (mm/rev) 0.4 0.8 1.0Depth of cut (mm) 0.3 0.4 0.5Nose radius (mm) 0.4 0.8 1.2As per table 1, L9 orthogonal array of“Taguchi method” has been selected for theexperiments in MINITAB 15. Each 9 experimentswill carry out in dry, wet and solid lubricant assistedenvironment. Surface roughness, cutting force andfeed force has been selected as response variable.Allthese data are used for the analysis and evaluation ofthe optimal parameters combination.Experimentresult as shown in Table2.Table2. Experimental results.Sr.noV.(m/min)FR.(mm)DOC(mm)NR(mm)SR(µm)Fc(Kg)Ff(Kg)DRY1 67.1 0.8 0.4 0.8 2.033 49.0 68.6
Jitendra. M. Varma, Chirag. P. Patel / International Journal of Engineering Research andApplications (IJERA) ISSN: 2248-9622 www.ijera.comVol. 3, Issue 3, May-Jun 2013, pp.1011-10151013 | P a g e2 67.1 1.0 0.5 1.2 3.308 107.9 176.53 100.8 0.8 0.5 0.4 2.489 78.4 117.74 151.3 0.4 0.5 0.8 0.901 49.0 78.45 151.3 0.8 0.3 1.2 1.987 29.4 39.26 151.3 1.0 0.4 0.4 2.469 68.6 107.97 67.1 0.4 0.3 0.4 1.358 39.2 49.08 100.8 0.4 0.4 1.2 1.423 58.8 68.69 100.8 1.0 0.3 0.8 2.121 29.4 49.0WET10 67.1 0.8 0.4 0.8 1.967 39.2 58.811 67.1 1.0 0.5 1.2 3.316 88.2 166.712 100.8 0.8 0.5 0.4 2.444 68.6 98.113 151.3 0.4 0.5 0.8 0.842 39.2 58.814 151.3 0.8 0.3 1.2 1.919 39.2 39.215 151.3 1.0 0.4 0.4 2.429 49.0 78.416 67.1 0.4 0.3 0.4 1.348 29.4 39.217 100.8 0.4 0.4 1.2 1.402 49.0 58.818 100.8 1.0 0.3 0.8 2.097 19.6 49.0SOLID LUBRICANT19 67.1 0.8 0.4 0.8 2.123 29.4 68.620 67.1 1.0 0.5 1.2 3.256 68.6 166.721 100.8 0.8 0.5 0.4 2.376 58.8 107.922 151.3 0.4 0.5 0.8 0.866 29.4 58.823 151.3 0.8 0.3 1.2 1.826 19.6 49.024 151.3 1.0 0.4 0.4 2.531 39.2 88.225 67.1 0.4 0.3 0.4 1.369 39.2 49.026 100.8 0.4 0.4 1.2 1.389 19.6 78.427 100.8 1.0 0.3 0.8 1.842 19.6 58.83. Methodology3.1 Grey relational analysis methodIn the grey relational analysis, experimentalresults were first normalized and then the greyrelational coefficient was calculated from thenormalized experimental data to express therelationship between the desired and actualexperimental data. Then, the grey relational gradewas computed by averaging the grey relationalcoefficient corresponding to each process response(3 responses). The overall evaluation of the multipleprocess responses is based on the grey relationalgrade. 3.2 Data preprocessingIn grey relational generation, thenormalized data corresponding to Lower-the-Better(LB) criterion can be expressed as:𝑋𝑖 𝑘 =max 𝑦𝑖 𝑘 −𝑦𝑖 (𝑘)max 𝑦𝑖 𝑘 −min 𝑦𝑖(𝑘)(1)For Higher-the-Better (HB) criterion, the normalizeddata can be expressed as:𝑋𝑖 𝑘 =𝑦𝑖 𝑘 −min 𝑦𝑖 (𝑘)max 𝑦𝑖 𝑘 −min 𝑦𝑖(𝑘)(2)Where xi (k) is the value after the grey relationalgeneration, min yi (k) is the smallest value ofyi (k)for the kth response, and max yi (k) is the largestvalue of yi (k) for the kthresponse.An ideal sequence is x0(k) (k=1, 2) for tworesponses. The definition of the grey relational gradein the grey relationalanalysis is to show the relational degree between thetwenty-seven sequences (x0(k) and xi(k), i=1, 2, . . ., 27; k=1, 2). The grey relational coefficient ξi(k)can be calculated as:𝜉𝑖 𝑘 =𝑚𝑖𝑛 ∆+𝛳𝑚𝑎𝑥 ∆∆𝑖 𝑘 +𝛳𝑚𝑎𝑥 ∆(3)Where Δi = | X0 (k) – Xi (k) | = difference of theabsolute value x0 (k) and xi (k); θ is thedistinguishing coefficient 0 ≤ θ ≤ 1; minΔ = ∀j min ϵi∀kmin= | X0 (k) – Xi (k) | = the smallest value ofΔ0i; and maxΔ = ∀jmax ϵ i∀kmax = largest value ofΔ0i. After averaging the grey relational coefficients,the grey relational grade γi can be computed as𝑦𝑖 =1𝑛𝜉𝑖 (𝑘)𝑛𝑘=1 (4)Where n = number of process responses. The highervalue of grey relational grade corresponds to intenserelational degree between the reference sequence x0(k) and the given sequence xi (k). The referencesequence x0 (k) represents the best processsequence. Therefore, higher grey relational grademeans that the corresponding parameter combinationis closer to the optimal. 4. Results and discussionsA level average analysis was adopted tointerpret the results. This analysis is based oncombining the data associated with each level foreach factor. The difference in the average results forthe highest and lowest average response is themeasure of the effect of that factor. The greatestvalue of this difference is related to the largesteffects of that particular factor. Data preprocessingof each performancecharacteristic and theexperimental results for the grey relational accordingto formulas (1),(2),(3) and (4) are given in Table 3and 4.Table 3 Normalize value of SR, Fc and Ff for dry,wet and solid lubricant environment.ExNO.Normalizevalue of SRNormalizevalue of FcNormalizevalue of FfDRY1 0.5297 0.7500 0.78572 0 0 03 0.3403 0.3750 0.42864 1 0.7500 0.71435 0.5488 1 16 0.3486 0.5000 0.50007 0.8101 0.8750 0.92868 0.7831 0.6250 0.78579 0.4931 1 0.9286WET10 0.5453 0.7143 0.846211 0 0 0
Jitendra. M. Varma, Chirag. P. Patel / International Journal of Engineering Research andApplications (IJERA) ISSN: 2248-9622 www.ijera.comVol. 3, Issue 3, May-Jun 2013, pp.1011-10151014 | P a g e12 0.3525 0.2857 0.538513 1 0.7143 0.846214 0.5647 0.7143 115 0.3585 0.5714 0.692316 0.7955 0.8571 117 0.7736 0.5714 0.846218 0.4927 1 0.9231SOLID LUBRICANT19 0.4741 0.8000 0.833320 0 0 021 0.3682 0.2000 0.500022 1 0.8000 0.916723 0.5983 1 124 0.3033 0.6000 0.666725 0.7895 0.6000 126 0.7812 1 0.750027 0.5916 1 0.9167Table 4 Calculation of GRC and GRDEx.NoGRCof SRGRCof CFGRCof FFGRG GradeNo.DRY1 0.5153 0.6667 0.7000 0.6273 62 0.3333 0.3333 0.3333 0.3333 93 0.4311 0.4444 0.4667 0.4474 84 1 0.6667 0.6364 0.7677 45 0.5257 1 1 0.8419 16 0.4343 0.5000 0.5000 0.4781 77 0.7247 0.8000 0.8750 0.7999 28 0.6974 0.5714 0.7000 0.6562 59 0.4966 1 0.8750 0.7905 3WET10 0.5237 0.6364 0.7648 0.6416 611 0.3333 0.3333 0.3333 0.3333 912 0.4357 0.4118 0.5200 0.4558 813 1 0.6364 0.7648 0.8004 214 0.5346 0.6364 1 0.7236 415 0.4380 0.5384 0.6190 0.5318 716 0.7097 0.7777 1 0.8219 117 0.6883 0.5384 0.7648 0.6638 518 0.4964 1 0.8667 0.7877 3SOLID LUBRICANT19 0.4874 0.7143 0.7500 0.6505 620 0.3333 0.3333 0.3333 0.3333 921 0.4418 0.3846 0.5000 0.4421 822 1 0.7143 0.8572 0.8571 123 0.5545 1 1 0.8515 224 0.4178 0.5556 0.6000 0.5244 725 0.7037 0.5556 1 0.7531 526 0.6956 1 0.6667 0.7874 427 0.5504 1 0.8572 0.8025 3In grey relational analysis higher the greyrelational grade of experiment says that thecorresponding experimental combination is optimumcondition for multi objective optimization and givesbetter product quality. Also form the basis of thegrey relational grade, the factor effect can beestimated and the optimal level for each controllablefactor can also be determined. From Table 4. It isfound that experiment 22 has the best multipleperformance characteristic among 27 experiments,because it has the highest grey relational grade of0.8571.The main effects plot of grey relationalgrade vs. process parameter can generated byMinitab 15 statistical software to find out optimumparameter combination, is shown in graph 1.
Jitendra. M. Varma, Chirag. P. Patel / International Journal of Engineering Research andApplications (IJERA) ISSN: 2248-9622 www.ijera.comVol. 3, Issue 3, May-Jun 2013, pp.1011-10151015 | P a g eGraph 1. Mean effect plot of grey relational gradVs. cutting speed, feed rate, depth of cut and noseradius.From the graph 1. It is conclude that theoptimum condition for better surface finish ismeeting at cutting speed (A3), feed rate (B1), depthof cut (C1) and nose radius (D2).The final step in the experiment is to doconfirmation test. The purpose of the confirmationruns is to validate the conclusion drawn during theanalysis phases. In addition, the confirmation testsneed to be carried out in order to ensure that thetheoretical predicted parameter combination foroptimum results using the software was acceptableor not. The parameters used in the confirmation testare suggested by grey relational analysis. Theconfirmation test with optimal process parameters isperformed in solid lubricant environment at levelsA3 (151.3 m/min Cutting speed), B1 (0.4 mm/revFeed), C1 (0.3 mm Depth of cut) and D2 (0.8 mmnose radius) of process parameter takes for 10 mmlength of cut and gives 0.721 Ra value.5. ConclusionIt is concluded that the application of solidlubricant in dry machining has proved to be afeasible alternative to cutting fluid, if it can beapplied properly. There is a considerableimprovement in surface roughness and quality ofproduct produced.6. AcknowledgmentIt is a privilege for me to have beenassociated with Mr. C. P. Patel, Asso. Prof. ofMechanical Engg.Dept. at U.V Patel College ofengineering, Kherva.During this paper work. I havebeen greatly benefited by his valuable suggestionsand ideas. It is with great pleasure that I express mydeep sense of gratitude to him for his valuableguidance and constant encouragement throughoutthis work.References. APS Gaur, Sanjay Agarwal“Investigationto Study the Applicability of SolidLubricant in Turning AISI 4340Steel”,Procedia CIRP 1 (2012) 243 – 248.. PatilDeepak Kumar h, Dr. M. Sadaiah,“investigations on finish turning of AISI4340 steel in different cutting environmentsby CBN insert”ISSN : 0975-5462, Vol. 3No.10 October 2011.. Dilbag Singh, P. VenkateswaraRao,“Improvement in Surface Quality withSolid Lubrication in HardTurning”International Journal of MaterialsScience and Engineering, Vol. 2, No. 1-2,January-December 2011.. L B Abhang, M. Hameedullah,“Experimental investigation of minimumquantity lubricants in alloy steelturning”,International Journal ofEngineering Science and Technology, Vol.2(7), 2010, 3045-3053.. P. Vamsi Krishna, D. NageswaraRao, “Theinfluence of solid lubricant particle size onmachining parameters inturning”,International Journal of MachineTools & Manufacture 48 (2008) 107–111.. N.R. Dhar, M. Kamruzzaman, MahiuddinAhmed, “Effect of minimum quantitylubrication (MQL) on tool wear and surfaceroughness in turning AISI-4340steel”.Journal of Materials ProcessingTechnology 172 (2006) 299–304. PatrikDahlman, Fredrik Gunnberg, MichaelJacobson, “The influence of rake angle,cutting feed and cutting depth on residualstresses in hard turning”, Journal ofMaterials Processing Technology 147(2004) 181–184. R.F. Avila,A.M. Abrao, “The effect ofcutting fluids on the machining of hardenedAISI 4340 steel”Journal of MaterialsProcessing Technology 119 (2001) 21–26. Mohd H.I. Ibrahim, NorhamidiMuhamad ,Khairur R. Jamaludin , Nor H.M. Nor andSufizar Ahmad, Optimization of MicroMetal Injection Molding with MultiplePerformance Characteristics using GreyRelational Grade, Chiang Mai J. Sci. 38(2) :231-241, (2011). Ramanujam R., Raju R., Muthukrishnan N., Optimization Of Machining ParametersFor Turning Al-Sic (10p) Mmc UsingTaguchi Grey Relational Analysis,International Journal Of IndustrialEngineering, 18(11), 582-590, (2011). J.L. Deng, “Introduction to Grey system,”J. Grey Syst., vol. 1, pp. 1–24, 1989.