International Research Journal of Engineering and Technology
(IRJET) e-ISSN: 2395-0056 Volume: 02 Issue: 04 |
July-2015www.irjet.netp-ISSN: 2395-0072 2015, IRJET.NET-All
RightsReserved Page 1453 Effect of Cutting Parameters on Surface
Quality of AISI 316 Austenitic Stainless Steel in CNC Turning
Prajwalkumar M. Patil 1, Rajendrakumar V. Kadi 2,Dr. Suresh T.
Dundur3,Anil S. Pol 4 1Research Scholar, Department of Product
Design and Manufacturing, VTU, Belagavi, Karnataka, India 2 Asst.
Professor, Department of Mechanical Engineering, TCE, Gadag,
Karnataka, India 3Professor, Department of Industrial and
Production Engineering, BEC, Bagalkot, Karnataka, India 4 Asst.
Professor, Department of Product Design and Manufacturing, VTU,
Belagavi, Karnataka, India
---------------------------------------------------------------***--------------------------------------------------------------AbstractSurfacequalityisoneoftheprime
requirementsofcustomersformachinedparts.The
presentworkdealswiththestudyofeffectofcutting
parametersonsurfaceroughnessandhardnessofAISI
316austeniticstainlesssteelinCNCturningunder
conventionalcoolingcondition.Taguchimethodhas
beenemployedintheoptimizationofcutting
parameters-suchasspeed,feedanddepthofcut.The
turningexperimentsunderconventionalcoolingwere
plannedasperTaguchisL9orthogonalarray(O.A.)
whichisdesignedwiththreelevelsofturning
parameters.TheAnalysisofMeans(ANOM)and
AnalysisofVariance(ANOVA)werecarriedoutto
determinetheoptimalparameterlevelsandobtainthe
levelofimportanceofthecuttingparameters,
respectively.Validationtestswithoptimallevelsof
parameterswereperformedtodemonstratethe effectiveness of Taguchi
optimization. The optimization
resultsrevealedthatfeedisthesignificantparameter for minimizing the
surface roughness, whereas depth of
cutplaysimportantroleinmaximizingthehardness.
Alsoacomprehensiveanalysishasbeenmadeto
understandthenatureofdeformationbeneaththe
turnedsurfaceandthicknessofmachiningaffected zone (MAZ).
KeywordsAusteniticStainlessSteelAISI316,CNC
Turning,Conventionalcoolingcondition,TaguchiMethod, Surface
Roughness, Surface Hardness, ANOVA. 1. INTRODUCTION
Austeniticstainlesssteelisoneofthemost
importantengineeringmaterialswithwidevarietyof
applications.Thismaterialisattractivebecauseofits
propertiessuchashighhardness,toughness,yield
strength,excellentductility,superiorresistanceto corrosion and
oxidation, compatibility in high temperature and high vacuum. But
these materials are very difficult to machine than carbon and low
alloy steels because of their
highstrength,poorthermalconductivityandahigher degree of ductility
andwork hardenability [1,2, 4,5].The
problemssuchaspoorsurfacefinishandhightoolwear
arecommonwhilemachiningthesematerials[1].
Therefore,attemptshavebeenmadetoimprovethe
machinabilityofausteniticstainlesssteelbyaddingfree
machiningelementslikelead,sulfur,telluriumand
selenium[7].Inthemachiningprocess,oneofthemost
noteworthymechanicalrequirementsofthecustomersis
surfacefinish.Toimprovethefatiguestrength,corrosion
resistance,aestheticappealandtribologicalpropertiesof
theproduct;asensiblygoodsurfacefinishisrequired.
Nowadays,fabricatingcommercialventuresare
particularlyconcernedwithdimensionalprecisionand surface
completion. Our main objective is to study effect of
cuttingparametersonAISI316austeniticstainlesssteel
workpiecesurfaceroughnessandhardnessbyemploying
designofexperimentsviaTaguchimethodsandAnalysis
ofVariance(ANOVA)usingtungstencarbidetoolonCNC lathe under wet
environment. Austenitic grade of stainless
steelisoneofthevastlyconsumedsteel(70percentage) universally [4,
8]. The austenitic alloys used most often are
thoseoftheAISI300series.Grade316isthestandard
molybdenum-bearinggrade.Molybdenumgives316
preferablecorrosionresistancepropertiesovercrevice
corrosioninchlorideenvironment.Ithasexcellent
formingandweldingcharacteristics.AISI316austenitic
stainlesssteelhaswiderangeofapplicationssuchasitis
usedinchemicalprocessingequipment;aerospace
components;forfood,dairyandbeverageindustries;for
surgicalembedsinsideofthethreateningenvironmentof
thebody;indeckcomponentsforboatsandshipsin marine environment; and
for heat exchangers [3, 4]. 1.1 Taguchi Parameter Design In the
early 1950s, Dr. Genichi Taguchi, the father
ofQualityEngineering,introducedtheconceptofoff-line
QualitycontroltechniquesknownasTaguchiparameter
design[9].Off-lineQualitycontroltechniquesarethose
activitiesperformedduringtheproduct(orprocess)
designanddevelopmentphases.Taguchiparameter
designisbasedontheconceptoffractionalfactorial
design[10].Taguchidesignisapowerfulmethodology
designedforfindingtheoptimumlevelsofthecontrol
processparameterstomaketheproductorprocess
impervioustothenoisefactors[11,12].TheTaguchi
methodisbasedonmatrixexperiments,andthese International Research
Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 02 Issue: 04 | July-2015www.irjet.netp-ISSN: 2395-0072
2015, IRJET.NET-All RightsReserved Page 1454
experimentalmatricesarespecialorthogonalarrays
(OAs),whichallowthesimultaneouseffectsofseveral process parameters
to be studied [11, 12]. The purpose of conducting an orthogonal
experimentation is to determine
themostfavourablelevelforeverysingleprocess
parameterandtodeterminetherelativesignificanceof
individualparameteronperformancecharacteristic[11,
12].Classicalexperimentaldesignmethodsaretoo
intricate,time-consumingandnotsimpletouse.Alarge
numberoftestshavetobeperformedwhenmore
parametersareinvolved.Toresolvethisproblem,the
Taguchiprincipleusesaspecialpurposedesignof orthogonal arrays (OAs)
to study the complete parameter
spacewithonlyalessernumberofexperimentations.
Taguchiconveysthatsignal-to-noise(S/N)ratioisthe
mainobjectivefunctionfororthogonalmatrixtrials[11,
12].Thesignal-to-noiseratioisusedtomeasure
performancecharacteristicsandshowsthedegreeof
expectableperformanceintheexistenceofnoisefactors.
TaguchicategorizestheS/Nratiointotypessmallerthe
better,largerthebetterandnominalthebestbased
onthenatureofobjectivefunction.TheAnalysisof
Means(ANOM)establishedontheS/Nratioisusedto
decidethebestlevelsoftheprocessparametersin
Taguchisdesignofexperiment.Theoptimallevelfora
processparameteristhelevelofoutcomesinthe
maximumvalueofsignal-to-noiseratiointhe
experimentalregion.TheAnalysisofVariance(ANOVA)
inTaguchiconstraintdesigncreatesthecomparative
significanceofprocessparametersandwasperformedon
theS/Nratiotofindthecontributionofeachprocess parameter [11, 12].
2. EXPERIMENTAL DETAILS 2.1 Materials and Methods Used
Turningisapopularmaterialremovalprocessin
whichacuttingtoolremovesunwantedouterlayerof
materialfromtherotatingcylindricalworkpiece.The
ComputerNumericalControlled(CNC)machineplaya critical function in
currentmachining industry to improve an item quality and
profitability [13]. Theworkpiecematerialselectedforinvestigation
isAISI316austeniticstainlesssteelrod.Inthepresent work, we have
used a round workpiece of dimensions 360
mmlengthand30mmdiameter.Thechemical
composition,mechanicalandphysicalpropertiesofAISI
316austeniticstainlesssteelareshowninTable-1and Table-2
respectively. TurningexperimentswereperformedusingCNC Ace Turn Mill
Fanuc, lathe type LT-2XLMMC. The lathe is
equippedwithmaximumspindlespeedof4000rpmand
11KW.AtoolholderwithageneralspecificationGCLNR
2020MK12wasusedinthisexperiment.Thecoated
carbideinsertofISOgeometryCNMG120416withchip breaker were used
throughout the experiment. The
insertshaveCVDcoatingofTiNoncementedcarbidesubstrate,
whichconsistsofthick,moderatetemperature,chemical
vapordeposition(MT CVD)of TiNforheatresistanceand
lowcoefficientoffriction.Theturningexperimentswere
conductedunderconventionalcoolingconditionwhere soluble oil (1:20)
was used as a coolant. Theexperimentsareconductedwiththree
controllable3-levelfactorsandtworesponse
variables.Table-3presentsthreecontrolledfactorsofthe
cuttingspeed(i.e.,A(m/min)),thefeedrate(i.e.,B
(mm/rev)),andthedepthofcut(i.e.,C(mm))withthree
levelsforeachfactor.Asperfullfactorialdesigns,33 design can be
expressed as a {3 x 3 x 3 = 33} design, where
totalof27runsareneeded.Turningexperimentsare
conductedfor27setsofcuttingparameterswhichare
recordedasperL27orthogonalarray.Afterturning,two
qualityobjectivesoftheworkpiecesareselected,that
includesthesurfaceroughness( and(m))and micro-hardness (H (HV)).
Typically, small values of surface
roughnessandtargetvaluesofmicro-hardnessare
desirableforthesurfaceintegrityinturningoperations.
For27turnedpartssurfaceroughnesstestiscarriedout.
Table-4shows27setofcuttingparameterslistedasper
L27orthogonalarraywithcorrespondingsurface
roughnessvalues.Ninetrialrunsbasedontheorthogonal array L9 are
necessary. Nine cutting experimental runs are
selectedaccordingtoL9O.A.(orthogonalarray)table.
Wirecuttingwascarriedoutforonly9turnedportions
amongst27referringtoL9O.A.Thiswasdonetoreduce the cost and time of
experimentation. 2.2 Measuring Apparatus
Thesurfaceroughnessofthe27turnedsurfaces
wasmeasuredwithaMitutoyoSurftestmodelSJ-201P,a
portablesurfaceroughnessinstrument.Allthe measurements were carried
out with a cutoff length of 0.8
mm,andineachcasetheaverageoffivereadingswas
used.Thesurfaceroughnesswasmeasuredtoassessthe
qualityoftheturnedsurfacequantitatively.Tomeasure
thehardness,thefirststepistowirecuttheturned
surfaceoftheroundbar.Andthenmeasurethehardness
ofthewirecutspecimenusinghardnesstester.The
surfacehardnessof9wirecutspecimenswasmeasured
alongthelengthandaroundtheperipherybyapplyinga
loadof0.3kgfromthesurfaceofthespecimentothe
depthofthespecimen.Theaveragevaluesofsix
measurementsforeachspecimenwasrecorded.The
hardnessvaluewasusedtoexplaintheeffectofsurface
modificationcausedbytheturningprocess.Figure-1and
Figure-2exhibitthephotographsofsurfaceroughness measuring device
and Vickers hardness tester employed in
thepresentwork.Thecalculatedvaluesofsurface roughness and hardness
are tabulated in Table-5. International Research Journal of
Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 02
Issue: 04 | July-2015www.irjet.netp-ISSN: 2395-0072 2015,
IRJET.NET-All RightsReserved Page 1455 Fig-1:Mitutoyo Surf Test
Model SJ-201P Fig -2: Vickers Hardness TesterTable-1: Chemical
Composition of Austenitic Stainless Steel (AISI 316)
ElementsCSiMnSPCrNiMoCuCoTiVNbFe Wt.
(%)0.0580.3491.0800.0190.01316.53610.7692.0860.5590.0790.0090.0140.02068.319
Table-2: Mechanical and Physical Properties of Austenitic Stainless
Steel (AISI 316) Table-3:Control factors and their levels TABLE-4:
O.A. L27 of the experimental runs with measured surface roughness
PropertyValue Yield strength (Mpa)290 Tensile strength (Mpa)580
Hardness (HB)140-160 Density(g/cm)8 Poissons Ratio0.25 Elongation
at break50% Modulus of elasticity (Gpa)193 CodeControl Factor Level
123 ASpeed, (m/min)120150180 BFeed, (mm/rev)0.200.250.30 CDepth of
Cut, (mm)0.51.01.5 International Research Journal of Engineering
and Technology (IRJET) e-ISSN: 2395-0056 Volume: 02 Issue: 04 |
July-2015www.irjet.netp-ISSN: 2395-0072 2015, IRJET.NET-All
RightsReserved Page 1456 Trail No. Speed, A (m/min) Feed, B
(mm/rev) Depth of Cut, C (mm) Surface Roughness Ra (m)Rz(m)
11200.200.51.376.394 21200.201.01.4346.700 31200.201.51.4426.798
41200.250.52.019.372 51200.251.02.039.458 61200.251.51.898.790
71200.300.52.1710.075 81200.301.02.2910.706 91200.301.52.310.700
101500.200.51.255.766 111500.201.01.4666.852 121500.201.51.356.330
131500.250.51.687.746 141500.251.01.778.176 151500.251.51.486.876
161500.300.52.1529.934 171500.301.01.868.712 181500.301.51.989.230
191800.200.51.095.030 201800.201.01.185.478 211800.201.51.4466.058
221800.250.51.797.918 231800.251.01.839.612 241800.251.51.829.912
251800.300.51.978.986 261800.301.02.29610.024 271800.301.52.089.608
TABLE-5:Orthogonal array L9 of the experimental runs withMeasured
Responsesand Corresponding S/N Ratios Sl. no. Speed (m/min) Feed
(mm/rev) DOC (mm) Ra (m) Rz (m) Hardness H (Hv) S/N Ratio for (dB)
S/N Ratio for (dB) S/N Ratio for Hardness (dB) 11200.20.5
1.376.394208-2.73441-16.115546.3613 21200.251.0
2.039.458224-6.14992-19.516047.0050 31200.301.5
1.898.790266-5.52924-18.879848.4976 41500.21.0
1.4666.852239-3.32268-16.716347.5680 51500.251.5
1.486.876248-3.40523-16.746747.8890 61500.300.5
2.1529.934206-6.65685-19.942546.2773 71800.21.5
1.4466.058267-3.20337-15.646648.5302 81800.250.5
1.797.918212-5.05706-17.972346.5267 91800.301.0 2.29610.024
221-7.21944-20.020846.8878 3. RESULTS AND DISCUSSIONS 3.1 ANOM and
ANOVA International Research Journal of Engineering and Technology
(IRJET) e-ISSN: 2395-0056 Volume: 02 Issue: 04 |
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RightsReserved Page 1457 Inthepresentwork,thegoalistoreducethe
surfaceroughnessandtoincreasethehardnessof
turningprocess.Hence,smallerthebettertype
classificationforsurfaceroughnessandlargerthe
bettertypeclassificationforhardnesshavebeen
selected.TheS/Nratioconnectedwiththetarget
capacitiesforeachtrialoftheorthogonalarrayisgiven by: = 10 log10 (
)(1) = 10 log10 ( )(2) = 10 log10 ( )(3)
TheS/Nratiosforeachtrialoforthogonal
arrayweredeterminedusingEqs.(1),(2)and(3)and are presented in
Table-5. TheAnalysisofMeans(ANOM)basedonthe
S/Nratiowasusedtodeterminetheoptimallevelsof process parameters
[11]; the results of ANOM for surface roughness(
),surfaceroughness( )andhardness
(H)arerepresentedinTables6,7and8respectively. The parameter level
that corresponds to highest value of
S/Nratioisthebestlevelofcombination.Theideal parameter setting is
found to be A2, B1, C3 for minimum surfaceroughness( and
);andA3,B1,C3for maximum hardness. TABLE-6:ANOM forvalues based on
S/N Ratio Parameter Code LevelsOptimum Level 123 A
-4.805-4.462-5.1602 B -3.087-4.871-6.4691 C -4.816-5.564-4.0463
TABLE-7:ANOM for Rzvalues based on S/N Ratio Parameter Code
LevelsOptimum Level 123 A -18.17-17.80-17.882 B -16.16-18.08-19.611
C -18.01-18.75-17.093 TABLE-8:ANOM for Micro Hardness based on S/N
Ratio Parameter Code LevelsOptimum Level 123 A 47.2947.2447.313 B
47.4947.1447.221 C 46.3947.2248.313
TheAnalysisofVariance(ANOVA)basedon
S/Nratiohasbeenemployedtostudytheeffectsof
turningprocessparametersquantitatively[11,12].The
summaryofANOVAresultsofsurfaceroughness( ), surface roughness ( )
and hardness are givenTables 9,
10and11,respectively.Itcanbeobservedfromthe
ANOVAtablesthatfeed(75.76%)incaseofandfeed
(77.43%)incaseofmakemajorcontributionsin
minimizingthesurfaceroughness;whereasspeedand
depthofcuthaveleasteffectsinminimizingthesurface roughness. The
depth of cut (90.93%) play major role in maximizing the hardness,
whereas speed and feed do not show noticeable effects in
controlling the hardness. TABLE-9: ANOVA for Ra values based on S/N
Ratio Parameter Code DFAdj SSAdj MS % Contribution A2
0.73170.36583.23 B2 17.17108.585575.76 C2 3.45701.728515.25 Error8
1.30500.65255.76 Total26 22.664711.3323100.00 TABLE-10: ANOVA for
Rz values based on S/N Ratio Parameter Code DFAdj SSAdj MS %
Contribution A20.22630.11320.97 B217.97788.988977.43
C24.14942.074717.87 Error80.86320.43163.73
Total2623.216711.6084100.00 TABLE-11:ANOVA for Micro Hardness based
on S/N Ratio Parameter Code DFAdj SSAdj MS % Contribution A2
0.007510.003760.12 B2 0.196910.098463.20 C2 5.588262.7941390.93
Error2 0.353050.176525.75 Total8 6.145733.07287100.00 3.2. Main
Effect Plots Analysis Theanalysisismadewiththeassistanceof
softwarepackageMINITAB-16[14].Themaineffectof
theplotisshowninFig.3,4and5.Itdemonstratesthe variation of every
single response with three parameters
i.e.speed,feedrateanddepthofcutdistinctly.Inthe plot, x-axis
signifies the value of each process parameter
andy-axissignifiestheresponsevalue.Themeanofthe
responseisindicatedbyhorizontalline.Themaineffect
plotsareutilizedtodeterminetheoptimaldesign
conditionstogettheidealsurfacefinishandhardness. As indicated by
main effect plots, the ideal conditions for leastsurfaceroughness(
)arespeedatlevel2 (150m/min),feedrateatlevel1(0.20mm/rev)and
depthofcutatlevel3(1.5mm);theidealconditionsfor
mostextremehardnessarespeedatlevel3(180 m/min), feedrate at level 1
(0.20 mm/rev) and depth of cut at level 3 (1.5mm). International
Research Journal of Engineering and Technology (IRJET) e-ISSN:
2395-0056 Volume: 02 Issue: 04 | July-2015www.irjet.netp-ISSN:
2395-0072 2015, IRJET.NET-All RightsReserved Page 1458 Fi g-3:
Effect of turning parameters on Surface Roughness ( ) Fig-4: Effect
of turning parameters on Surface Roughness ( ) Fig-5: Effect of
turning parameters on Hardness 3.3 Machining Affected Zone (MAZ)
Micro-hardnessismeasuredbytaking6micro-indentations diagonally
along the cross section of turned
surface.ThisdistinguishesbetweenMAZandthebulk
materialandasteepmicro-hardnessgradienthasbeen observed. Fig. 6
shows the typical micro-hardness profile
ofoneofthesamplesusedintheexperiment.Micro-hardnessofallthespecimenvariesinbetween268to
160 and depth of MAZ lies between 100 to 120 m. Fig-6: Micro
indentation along MAZ 3.4. Verification Test of Optimal Result
Afterselectingtheoptimallevelofprocess
parameters,thelaststepistopredictandconfirmthe
performancecharacteristics.Thepredictedoptimum value of S/N ratio
(is given by [10] : (4) where istheS/Nratioofoptimumleveliof
parameterj,mistheoverallmeanofS/Nratioandpis
thenumberofparametersthataffectthemachinability characteristics.
Inordertojudgetheclosenessofthe experimentalvalueofS/Nratio(
)withthatofthe predictedvalue),theconfidenceinterval(CI)of (
)fortheoptimumprocessparameterlevel
combinationat95%levelisdetermined.TheCIisgiven by [10, 11]: (5)
whereistheFvaluefor95%confidenceinterval;
isthedegreesoffreedomforerror;isthemean square of error; =,N =
Total trial number in orthogonalarrayand=Degreesoffreedomofp
factors;is the confirmatory test trial number. International
Research Journal of Engineering and Technology (IRJET) e-ISSN:
2395-0056 Volume: 02 Issue: 04 | July-2015www.irjet.netp-ISSN:
2395-0072 2015, IRJET.NET-All RightsReserved Page 1459 Here, the
best combination values of the process parameters obtained through
Taguchi optimization were set, and the workpieces of the identical
lots were turned. TheexperimentalvalueofS/Nratio( )and
predictedvalueofS/Nratio( )werecompared.
Table-12givestheconfirmatorytestresults,andfrom
thetableweseethatthepredictionerror,i.e., (
)iswithintheCIvalue,indicatingthe
adequacyofthesurfaceroughnessandhardness
additivemodels.Thebestcombinationsofprocess
parametersforminimizingsurfaceroughnessandfor
maximizinghardness,alongwiththecorresponding optimal values are
given in Table-13. TABLE-12:Results of Confirmatory Tests
Performance measures RaRzHardness Levels (A, B, C)2, 1, 32, 1, 33,
1, 3 Experimental value 1.35 m6.330 m267 Hv S/N experimental (
),dB-2.60668-16.028148.5465 S/N predicted ( ),dB -1.977-15.1548.53
Prediction error, dB( ) 0.629680.8781-0.0165 Confidence interval
value (CI), dB +3.6629+2.9791+1.9052 TABLE-13:Optimal
Parametersetting and the Corresponding Optimal Values Response
Optimal process parameter setting Speed (m/min) Feed (mm/rev) DOC
(mm) Optimal value Surface roughness (Ra) 1500.201.51.35 m Surface
roughness (Rz) 1500.201.56.330m Hardness1800.201.5267 Hv 4.
CONCLUSIONS Thefollowingaretheconclusionsdrawnbased
ontheexperimentalinvestigationconductedonturning
AISI316austeniticstainlesssteelusingCarbideinsert
underconventionalcoolingconditionsatthreelevelsby
employingTaguchitechniquetodeterminetheoptimal level of process
parameters. Analysisofvariance(ANOVA)demonstratesthatthe
feedratehasthehighestinfluenceonsurface
roughness.RaisinfluencedbyfeedratewithPCRof
75.76%.RzisinfluencedbyfeedratewithPCRof
77.43%.Nevertheless,thedepthofcutandcutting
speedhavenegligibleinfluenceonthesurface roughness at the
reliability level of 95%. ANOVAdemonstratesthatthedepthofcuthasthe
highest influence on hardness. Hardness is influenced
bydepthofcutwithPCRof90.93%.Cuttingspeed
andfeedratehavenegligibleinfluenceonthe
hardness.Theoptimalcombinationprocessparametersfor
minimumsurfaceroughness(RaandRz)isobtained at 150 rpm, 0.2 mm/rev
and 1.5mm. Theoptimalcombinationofprocessparametersfor maximum
hardness is obtained at 180 m/min cutting speed, 0.3 mm/rev feed,
1.5 mm depth of cut. Thehardnessvaluesofthemicro-hardnessprofile
revealedthatthehardnessoftheturnedspecimen
goesondecreasingfromthesurfacetothedepthof the specimen.
Theverificationtestshavededucedthattheresults obtained are accurate
up to 95% of confidence level.
ItisalsopredictedthatTaguchimethodisabest
methodforoptimizationofvariousmachining parameters as it reduces
the number of experiments. ACKNOWLEDGMENT
Theauthorswouldliketoexpresstheir
gratitudetoMr.Adivesh.K.ofKarnatakaOriginal
EquipmentManufacturers(KOEM),Gadag,Karnataka, India for extending
their laboratory facilities to carry out
theexperimentsandfortheirvaluablesuggestions during its
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2 BIOGRAPHIES Mr.PrajwalkumarM.Patilcompleted
B.E.(Mechanical)degreein2013 fromTontadaryaCollegeof
Engineering,Gadag,Karnataka, IndiaandM.Tech.(ProductDesignand
Manufacturing)degreein2015from VisveswarayaTechnological
University, Belagavi, Karnataka, India. Mr.RajendrakumarV.
KadicompletedB.E.(Industrialand productionengineering)degreein
1996andM.Tech.(Production Management)degreein2003from
BEC,Bagalkot,Karnataka,India.Heis currentlyworkingasanAsst.
ProfessorintheDepartmentof MechanicalEngineering,Tontadarya
CollegeofEngg.,Gadag,Karnataka, India.Hehas12yearsofteaching
experience.Hehaspublishedmany researchpapersininternationaland
national journals. Dr.SureshT.DundurcompletedB.E.
(Mechanical)degreein1984from BEC,Bagalkot,Karnataka,Indiaand
M.Sc.(Engg.)inM/cDesignand AnalysisfromREC,Rourkelain1990.
HeisalsoawardedPh.D.inMetal Machining,SliplineFieldTheoryfrom
NIT,Rourkelain2006.Heisalsoa Lifetime Member in ISTAM, ISTE and a
Fellow Member in IE. He is currentlya Professor and Head in
theDepartment ofIndustrialandProduction Engineering, BEC, Bagalkot,
Karnataka, India.Hehaspublishedmanyresearch
papersininternationalandnational journals.
Mr.AnilS.PolcompletedB.E. (Mechanical)degreein2010from
KLEsMSSCET,Belagavi,Karnataka, IndiaandM.Tech(ProductDesignand
Manufacturing)in2012fromVTU, Belagavi,Karnataka,India.Heis
currently working as anAsst. Professor
intheDepartmentofProductDesign andManufacturing,VTU,Belagavi,
Karnataka,India.Hehas2yearsof teachingexperience.Hehaspublished 2
research papers in national journals.
