A simulation approach for estimating flank wear and material removal rate in turning of Inconel 718

A simulation approach for estimating ?ank wear and material removal rate in turning of Inconel

718

Rajiv Kumar Yadav ?,Kumar Abhishek,Siba Sankar Mahapatra

Department of Mechanical Engineering,National Institute of Technology,Rourkela 769008,India

a r t i c l e i n f o Article history:

Received 7February 2014

Received in revised form 27November 2014Accepted 9December 2014

Keywords:Flank wear

Material removal rate Finite element analysis Analysis of variance Wear model

a b s t r a c t

The present work attempts to study the effect of important machining variables on perfor-mance characteristics such as material removal rate and tool wear in turning of Inconel 718using chemical vapour deposition (CVD)coated tungsten carbide (WC)tool.A three dimen-sional machining model using Lagrangian approach has been developed using DEFORM 3D.The machining simulation is carried out to predict the ?ank wear and material removal rate (MRR).Flank wear is calculated using Usui’s wear model in the simulation model.The results from simulation model are compared with experimental data generated by the use of Taguchi’s L 16orthogonal array for reducing the experimental runs.Analysis of variance (ANOVA)is performed to identify the most in?uencing variables for both the per-formance characteristics.It is found that simulation results are in good agreement with experimental results.A valid simulation models helps the tool engineers to gather relevant process related information without resorting to costly and time consuming experimentation.

ó2014Elsevier B.V.All rights reserved.

1.Introduction

Now-a-days,nickel-based super alloys are extensively used in aerospace industry particularly in making components for jet engines and gas turbines due to its high yield strength,excellent fatigue resistance and good corrosion endurance in severe conditions.Out of commercially available nickel base super-alloys,Inconel 718is mostly used in aircraft gas turbines,reciprocating engines,space vehicles,nuclear power plants,chemical and petrochemical industries and heat exchangers.The mechanical components are mainly produced to required shape using different machining operations.Therefore,it becomes essential to understand the material removal process in metal cutting and judicious selection of tool material to maintain desired dimensional accuracy and surface integrity of the ?nished product.Out of many conventional machining methods,turning operations is most commonly performed to modify shape,dimension,and surface of a work piece.Turning of Inconel 718is indeed a challenging task due to excessive tool wear resulting in poor surface ?nish and heat generation [1–12].Due to low machinability,turning of Inconel 718is a time and cost consuming effort.Therefore,many studies have been directed towards simulation of turning operation of Inconel 718using three-dimensional ?nite element method (FEM)approach [16–32].In fact,?nite element (FE)machining models plays an important role in understanding chip formation and tool wear analysis without resorting to costly and time consuming experimental approach.Generally,two dimensional orthogonal plane strain assumption or arbitrary damage criterion or remeshing techniques for chip formation are used in

https://www.360docs.net/doc/362262953.html,/10.1016/j.simpat.2014.12.004

1569-190X/ó2014Elsevier B.V.All rights reserved.

?Corresponding author.

E-mail addresses:rajiv.nitrkl@https://www.360docs.net/doc/362262953.html, (R.K.Yadav),krabhishek1987@https://www.360docs.net/doc/362262953.html, (K.Abhishek),mahapatrass2003@https://www.360docs.net/doc/362262953.html, (S.S.Mahapatra).

2R.K.Yadav et al./Simulation Modelling Practice and Theory52(2015)1–14

FE modelling needing more computational efforts.However,three dimensional computational models can be adopted to study the mechanism of material removal process together with its in?uence on the machined part and wear behaviour on the cutting tool for analyzing physical phenomenon in materials undergoing large elastic–plastic deformation.

Although many works related to machinability studies of Inconel718have been carried out recently using experimental, statistical,and numerical approaches and optimization of machining parameters,3D simulation of?ank wear with Usui model in turning process is not adequately addressed.Simulation is a convenient way of predicting machining parameters that can optimize the performance characteristics such as cutting forces,temperature,residual stresses,material removal rate and surface roughness.The present study proposes a simulation approach to estimate in?uence of machining parame-ters such as spindle speed,feed and depth of cut on material removal rate and?ank wear using DEFORM3D.The principle of estimating?ank wear using Usui model from simulation model is highlighted.Experiments have been conducted using Taguchi’s L16experimental strategy.The aim of selecting Taguchi’s experimental plan lies in the fact that it can derive max-imum information related to the process with less number of experimental runs.The simulation results are validated with experimental data.Analysis of variance is conducted to identify signi?cant process parameters.It is found that simulation results are in good agreement with experimental results.

2.Literature review

Extensive research has been carried out using experimental,analytical and computational approaches in an effort to understand the effects of various process parameters during machining.Thakur et al.[1]have examined the behaviour of cutting parameters in machining of super-alloy Inconel718by cemented tungsten carbide(K20)insert tool.Thakur et al.

[2]have demonstrated correlation between cutting speed,feed,and depth of cut with cutting force,cutting temperature, and tool life in high speed turning of Inconel718using cemented tungsten carbide tool in an effort to identify the optimum combination of cutting parameters.Thakur et al.[3]have attempted to enhance the machinability characteristics in high speed turning of super-alloy Inconel718using quantity of lubricant,delivery pressure at the nozzle,frequency of pulses, direction of application of cutting?uid,cutting speed,and feed rate as the process parameters.It has been concluded that use of optimized minimum quantity lubrication parameters under pulsed jet mode leads to lower cutting force,cutting tem-perature,and?ank wear.Bhatt et al.[4]have conducted experimental investigation to study the wear mechanisms of uncoated tungsten carbide(WC),PVD and CVD tools in oblique?nish turning of Inconel718.Khidhir and Mohamed[5]have studied the effect of cutting speed on surface roughness and chip formation when machining nickel-based alloy.The study has also explored the types of wear caused due to cutting speed on coated and uncoated carbide inserts.Pawade and Joshi[6] have proposed a multi-objective optimization approach for simultaneous optimization of surface roughness and cutting forces in high-speed turning of Inconel718using Taguchi grey relational analysis(TGRA).The study reveals that depth of cut has most signi?cant effect on performance characteristics.Zhou et al.[7]have studied on surface quality generated under high speed?nish turning conditions of age-hardened Inconel718.The study also focusses on surface roughness,metallo-graphic analysis of surface layer and surface damages produced by machining with coated and uncoated cubic boron nitride (CBN)tools.Olovsjo et al.[8]have developed a model to examine the effect of parameters such as composition,grain size, hardness,heat treatment,temperature and strain rate on ductility,strain hardening,and yield strength in machining of Inco-nel718.Thakur et al.[9]have established the relationship among degree of work hardening and tool life as a function of cutting parameters like cutting speed,feed,depth of cut,untreated tungsten carbide and postcryogenic-treated tool in machining of Inconel718.Zhu et al.[10]have emphasized on study of tool wear in the machining of nickel-based super alloys.This study reveals the existing gaps in the knowledge on equilibrium thermodynamic condition,thermal–mechanical coupling,tool nose radius,and thermal diffusion layer in coated tools in machining of nickel-based super alloys.Cantero et al.[11]have analysed tool wear mechanisms in?nish turning of Inconel718,both in wet and dry condition,using cemen-ted carbides,ceramics and CBN tools.Fahad et al.[12]have investigated the cutting performance of tungsten carbide tools with multilayer CVD(chemical vapour deposition)coatings in dry turning of low carbon alloy steel(AISI/SAE-4140)over a wide range of cutting speeds(between200and879m/min).The study reveals that coating layouts and cutting tool edge geometry signi?cantly affect heat distribution in the cutting tool.

Zhao et al.[13]have examined the effect of internal cooling on the?ank wear of cutting tool in orthogonal cutting.The study has also presented a?ank wear model for a cutting tool based on wear models considering the normal stress and ther-mal softening.Arsecularatne et al.[14]have studied wear mechanisms of cutting tools made of tungsten-carbide(WC),poly-crystalline cubic boron nitride(PCBN)and polycrystalline diamond(PCD)considering tool life and tool tip temperature.The study reveals that dominant tool wear mechanism for WC is diffusion wear whereas chemical wear for https://www.360docs.net/doc/362262953.html,ui et al.[15] have developed an analytical model for crater and?ank wear of tungsten carbide tool insert.Wear characteristics equation is ?rst derived theoretically and veri?ed experimentally.

Zebala and Slodki[16]have applied?nite element modelling(FEM)to analyse the effect of chip breakers during turning of Inconel718.Uhlmann et al.[17]have studied the formation of chips during high speed turning of Inconel718using dif-ferent software such as DEFORM and ABAQUS.Vaz et al.[18]have explained the complex physical phenomenon involved in chip formation using numerical modelling.Lorentzon and Jarvstrat[19]have developed an empirical model through a?nite element simulation to predict tool wear in machining of Inconel718.It has been shown that advanced friction model is nec-essary than Coulomb friction for accurate prediction of wear.Lorentzon et al.[20]have examined the effect of different

R.K.Yadav et al./Simulation Modelling Practice and Theory52(2015)1–143 fracture criteria on chip formation,particularly formation of segmented chips,by a?nite element model considering Inconel 718as work piece.It has been noticed that produced chips are long and continuous at low cutting speeds(below50m/min) whereas chips are segmented at high cutting speeds(above100m/min).Yue et al.[21]have proposed a?nite element model to estimate tool wear in turning of hardened steel with PCBN tool.The study implements Usuis tool wear model to study the relationship between tool life and cutting parameters.Attanasio et al.[22]have developed a tool wear model in turning of AISI1045steel bars with ISO P40uncoated tools using?nite element analysis.The study highlights in?uence of rake angle and tool stresses on tool.Li[23]presents a FEM simulation approaches for predicting tool wear and tool life in orthogonal cutting.Ceretti et al.[24]have used DEFORM3D to simulate cutting operations,particularly orthogonal cutting and oblique cutting operation.Yen et al.[25]have developed a methodology to forecast the tool wear and tool life of uncoated carbide tool in orthogonal cutting of carbon steel using FEM simulation.Pittala and Monno[26]have used DEFORM3D to predict cutting forces in milling of aluminium alloy by coated tungsten carbide(WC)-Cobalt(Co)tool insert.Inta et al.[27]have presented modelling of progressive tool wear in turning operation by integrating programming language Matlab code with commercial FEM code DEFORM2D Machining.Tanase et al.[28]have predicted cutting edge temperature considering appro-priate friction model at the tool-chip interface using DEFORM3D software package.Tamizharasan and Kumar[29]have studied the effect of tool geometry?ank wear,surface roughness and cutting forces while machining of AISI1045with uncoated carbide inserts.It is reported that wear depth and cutting force decrease as nose radius increases.Bhoyar and Kam-ble[30]have proposed a model to predict cutting forces,speci?c cutting energy and temperatures occurring at different points of chip/tool contact region and coating/substrate boundary for a wide range of tool materials using DEFORM3D.Ezi-larasan et al.[31]have explored the simulated turning of Nimonic C-263super alloy with cemented carbide cutting tool using DEFORM3D.Arrazola et al.[32]have compared experimental residual stresses with3D?nite element based simula-tion in machining of nickel-based alloy IN718.

Senthilkumaar et al.[33]have proposed the genetic algorithm coupled with arti?cial neural network(ANN)to optimize the machining parameters in turning of Inconel718.The study also examines the interaction effect of cutting speed,feed, and depth of cut on the performance measures such as surface roughness and?ank wear.Ezugwu et al.[34]have developed an arti?cial neural network model for analysis and prediction of the relationship between process parameters such as speed, feed rate,depth of cut,cutting time and coolant pressure with tangential force(cutting force,F z),axial force(feed force,F x), spindle motor power consumption,surface roughness,average?ank wear(VB),maximum?ank wear(VB max)and nose wear (VC)during high speed turning of Inconel718.

3.Simulation modelling

Finite element method has been proved to be an effective technique for analyzing chip formation process and predicting machining performance characteristics such as temperatures,forces,and stresses.In this work,?nite element method is used to simulate turning of Inconel718using CVD coated tungsten carbide tool.DEFORM3D software is used for?nite ele-ment analysis.DEFORM3D software is based on an implicit Lagrangian computational approach[35].For evaluation of?ank wear and MRR,it is necessary to determine the output tool-chip interface pressure,tool-chip interface temperature,sliding velocity and volume change in work piece material.At initial stage of simulation,the process parameters for work piece and tool insert have to be de?ned.During simulation an initial temperature of20°C,shear friction factor of0.6,heat transfer

Fig.1A.Simpli?ed model for work piece with mesh before turning.

range 1260–1335°C,yield strength 1185MPa,ultimate tensile strength 1435MPa and thermal expansion 0.000012m/m/°C

at temperature 93°C.The composition of the work piece in mass percentage of 50–55Ni,17–21Cr,2.8–3.3Mo,4.75–5.5Nb,0.35Mn,0.2–0.8Cu,0.65–1.15Al,1Co,0.3Ti,0.35Si,0.08C,0.015S,0.015P,0.006B and balance Fe.Inconel 718has a precipitation of d phase with morphology of needle shape surrounding Ni 2Nb phase.Ageing of Inconel 718caused increase in hardness due to precipitation of hard phases such as c 0and c 00clusters.A three layered CVD coated (TiN,Al 2O 3and TiCN)tungsten carbide tool is used due to its excellent build-up edge resistance,high hardness capable of cutting hard materials,high stability at high speeds and temperature and good wear resistance characteristics [36,37].The thermal barrier effect of the coating,especially Al 2O 3layer,appears to signi?cantly resist tool deformation at initial stage of cutting of hard materials.Vicker’s hardness of TiN coating is 2200as compared to 1580of tungsten carbide.Similarly,melting point of TiCN coating is 3070°C in comparison to 2785°C of tungsten carbide.

Johnson–cook model is suitable for modelling material because high strain,strain rate,strain hardening and non-linear material properties are involved in turning process [17,31].The material model is given in Eq.(1).It is de?ned in DEFORM 3D material library.The model constants are shown in Table 1[17].

r ??A tB e n ?

1tC ln _ e _ e

0 !"

#?1àT àT r

T m àT r m e1T

where

Fig.1B.Work piece at step 1.

Fig.1C.Work piece in deformed shape at step 3000.

Table 1

Johnson–Cook model constants for Inconel 718[17].A (MPa)B (MPa)n C m _ e 0(1/s)T r (°C)T m (°C)450

1700

0.65

0.017

1.3

0.001

20

1297

r

?Flow stress e ?Equivalent plastic strain A =Yield stress constant _ e

?Strain rate B =Strain hardening coef?cient

_ e

0?Reference strain rate n =Strain hardening exponent

T =Temperature

C =Strain rate dependence coef?cient T r =Room temperature m =Temperature dependence coef?cient

T m =Melting temperature

During machining process,friction plays an important role along cutting tool and work piece contact.Friction affects chip geometry,built-up edge formation,cutting temperature and tool wear.The friction force is de?ned by Coulomb’s law shown in Eq.(2).The shear friction factor of 0.6is considered in this work [31].

f s ?lr t

e2T

where f s =Fictional stress,l =Shear friction factor,r t ?Interface pressure

The tool insert is de?ned from DEFORM 3D library.The meshed tool insert is shown in Fig.2.During turning simulation,+Y axis is the cutting direction,àX axis is the feed direction and +Z axis is the depth of cut direction.

After simulation,Usui model is used for calculation of ?ank wear (Eq.(3)).The parameters such as interface pressure,interface temperature and sliding velocity are noted down from each simulation run [15].

dw ?A r t V exp àB

e3T

where w =Flank wear,r t =Interface pressure,V =Sliding velocity and h =Interface temperature 4.Experimental details

A series of experiment has been carried out in CNC lathe (Model No.Sprint 16TC manufactured by Batliboi Ltd.India)to

assess the behaviour of machining parameters (spindle speed,feed rate and depth of cut)on the material removal rate and tool wear.A bar of Inconel 718having diameter 30mm and length 150mm has been used as work piece material.Here,chemical vapour deposition (CVD)coated tool of TNMA 332_KC9025manufactured by Kennametal (three coatings:TiN-1l m,Al 2O 3-9.5l m and TiCN-4l m)is used as tool material having tool signature (10–10–7–7–5–5–0.8).Base material of coated tool is tungsten carbide (WC).DTGNL tool holder is used for this tool insert.Fig.3shows the dimensions of tool TNMA 332:IC =9.52mm,T =4.76mm,R =0.8mm,B =13.49mm and H =3.81mm where IC =theoretical diameter of the insert inscribed circle,T =thickness of the tool insert,R =nose radius of tool insert,B =cutting edge length and H =inner hole diameter.

Fig.2.The meshed tool insert.

R.K.Yadav et al./Simulation Modelling Practice and Theory 52(2015)1–145

6R.K.Yadav et al./Simulation Modelling Practice and Theory52(2015)1–14

Table2

Domain of experimentation.

Sl.no.Factors Symbols Unit Level1Level2Level3Level4

1Spindle speed N RPM4216057871020 2Depth of cut d mm0.40.60.81 3Feed rate f mm/rev0.080.120.160.20

Table3

Experimental data.

Sl.no.Levels of factors Experimental responses

N d f Flank wear(l m)MRR(mm3/s)

14210.40.08211.2523.91 24210.60.12224.4530.81 34210.80.16241.8234.24 442110.2288.1846.22 56050.40.12231.9428.45 66050.60.08247.333.71 76050.80.2257.4634.78 860510.16317.4348.78 97870.40.16232.4638.36 107870.60.2246.4143.91

Flank wear for spindle speed at421RPM,depth of cut0.6mm,feed rate

Tool wear is caused due to relative motion between the cutting tool and the work piece or the cutting tool and chip on machining area during turning.Tool wear is measured experimentally by optical microscope (SteREO Dis-covery V20manufactured by Carl Zeiss MicroImaging Inc.having eyepiece magni?cation:10?and objective magni-?cation: 2.5?to 100?)as shown in Figs.4A and 4B .Maximum damage length of ?ank face has been considered for assessing the ?ank wear of tool insert.Experimental data of material removal rate and tool wear are listed in Table 3.

As material removal rate (MRR)and tool wear have dominant effect on economics of the machining operations,estimation of these machining characteristics is important and necessary in metal cutting for maintaining both produc-tivity and quality.Material removal rate is de?ned as volume of work piece material that can be removed per time unit.

MRR ?

eV i àV f Tt m

e4T

where V i =initial volume of work-piece,V f =?nal volume of work-piece and t m =machining time.

Flank wear for spindle speed 605RPM,depth of cut 0.6mm,feed rate 0.08Fig.5a.Tool interface temperature in machining of Inconel 718at spindle speed 605RPM,depth of cut 0.4mm and feed rate 0.12mm/rev.

Fig.5b.Tool interface pressure in machining of Inconel718at spindle speed605RPM,depth of cut0.4mm and feed rate0.12mm/rev.

Fig.5c.Tool sliding velocity in machining of Inconel718at spindle speed605RPM,depth of cut0.4mm and feed rate0.12mm/rev.

5.Results and discussion

Simulations of turning process for all experimental runs shown in Table3are carried out using https://www.360docs.net/doc/362262953.html,ui’s tool wear model has been used to evaluate?ank wear.During simulation,the length of work piece that is machined is kept

constant as 60mm.In Usui’s tool wear model,the ?ank wear is a function of interface pressure,relative velocity and absolute temperature.The constants of the wear model are taken as A =0.0000000078and B =5302[25,29].In order to determine ?ank wear,it is necessary to observe tool tip temperature.The tool tip temperature arises due to interface friction between the tool tip and work piece.It has been noticed that temperature may vary from 950°C to 1100°C in turning simulation of Inconel 718.Figs.5a–5c exhibit tool interface temperature,tool interface pressure and tool sliding velocity respectively for assessing the ?ank wear.Hardness is not a single cause for plastic deformation of tool and ?ank wear.Other causes of plastic deformation are listed as:high heat generation,friction between tool and workpiece,large stress developed during turning and abrasion and adhesion of tool material.Flank wear is measured in accordance with ISO 3685[4]using standard optical microscope (SteREO Discovery V20).To measure the ?ank wear,insert has been removed from the tool holder,the image of the wear portion is taken under optical microscope and analysed the ?ank wear.The turning process is stopped when any of the following criteria are reached in accordance with ISO

3685:

Fig.6.Change in volume in machining of Inconel 718at spindle speed 605RPM,depth of cut 0.4mm and feed rate 0.12mm/rev.

Table 4

ANOVA for ?ank wear.Source Degree of freedom Sum of squares Adjusted S S Adjusted M S F -value P -value Spindle speed 313635.013635.04545.0154.360.000Depth of cut 314075.014075.04691.6656.110.000Feed rate

3430.0430.0143.32 1.71

0.263

Residual Error 6501.7501.7

83.61

Total

15

28641.7

Table 5

ANOVA for MRR.Source Degree of freedom Sum of squares Adjusted S S Adjusted M S F value P value Spindle speed 31599.021599.02533.008260.280.000Depth of cut 31451.511451.51483.837236.270.000Feed rate

392.2792.2730.75615.02

0.003

Residual Error 612.2912.29

2.048

Total

15

3155.09

10R.K.Yadav et al./Simulation Modelling Practice and Theory52(2015)1–14

(a)average?ank wear of300l m

(b)maximum?ank wear of600l m.

Material removal rate is evaluated using the values of change in volume obtained after the simulation through DEFORM 3D.The relation for calculation of MRR is shown in Eq.(4).Fig.6shows the change in volume for a speci?c experimental run.

Experimental data are collected as per experimental plan of Taguchi’s L16orthogonal array.Lower-the-better(LB)crite-rion is used for?ank wear whereas higher-the-better(HB)criterion is used for MRR.Analysis of variance(ANOVA)is a method of partitioning observed variance into components of different explanatory variables to identify signi?cance of each parameter[2,3].Tables4and5present the ANOVA tables for?ank wear and material removal rate respectively.It is to be noted that spindle speed and depth of cut are signi?cant factors in?uencing on?ank wear at signi?cance level0.05.Similarly, the factors like spindle speed,depth of cut and feed rate are signi?cant factors in?uencing on MRR at signi?cance level0.05. The optimal parametric setting can be obtained from the factorial plots.Fig.7a reveals that spindle speed,depth of cut and

feed rate should be maintained at 421RPM,0.4mm and 0.08mm/rev respectively to minimize ?ank wear.Similarly,Fig.7b indicates that spindle speed,depth of cut and feed rate should be maintained at 1020RPM,1mm and 0.12mm/rev respec-tively to maximize MRR.

Figs.8a–8c show the micrographs of the tool after turning at spindle speed of 421RPM,feed rate of 0.20mm/rev and depth of cut of 1mm obtained from scanning electron microscope (JEOL JSM 6480LV).Flank wear is the main wear in the turning operation.The high ?ank wear is observed when spindle speed =1020RPM,depth of cut =1mm and feed rate =0.08mm.

The results obtained from simulation and experimental runs need to be compared to check the accuracy of the model by determining relative percentage error using Eq.(5).

Relative Error e%T?

j M i àY i j

Y i

?100e5T

where M i and Y i are the simulation and experimental response value for run number i .

Table 6shows the relative error for ?ank wear and MRR.Figs.9and 10show the comparison between the results obtained from simulation and experimental approach for ?ank wear and material removal rate respectively [29,31].The average rel-ative error is found to be 4.18%and 6.68%for ?ank wear and MRR respectively.Therefore,it can be concluded that simulation of turning process results in good agreement with experimental outputs.

of CVD coated WC insert spindle speed of 421RPM,depth of cut of 1mm and WC insert at spindle speed of 421RPM,depth of cut of 1mm and feed rate of

WC insert at spindle speed of421RPM,depth of cut of1mm and feed rate of

Table6

Simulation results and corresponding relative error.

Sr.no.Simulation results Experimental results Relative error in% Flank wear(l m)MRR(mm3/s)Flank wear(l m)MRR(mm3/s)Flank wear MRR

1219.2525.85211.2523.91 3.798.11 2230.8432.78224.4530.81 2.85 6.39 3236.0936.68241.8234.24 2.377.13 4294.0048.18288.1846.22 2.02 4.24 5225.1731.14231.9428.45 2.929.46 6233.9536.15247.333.71 5.407.24 7260.6837.43257.4634.78 1.257.62 8294.3152.02317.4348.787.28 6.64 9253.9736.44232.4638.369.25 5.01 10250.5747.21246.4143.91 1.697.52 11243.6556.51255.6752.04 4.728.59 12332.9666.86320.2768.63 3.96 2.58 13273.8939.36280.3742.96 2.318.38 14299.7355.23312.5751.05 4.128.19 15307.5665.55337.5364.848.88 1.1 16365.3279.43350.7673.02 4.158.78 Average relative error 4.18 6.68

https://www.360docs.net/doc/362262953.html,parison between simulation and experimental results of?ank wear.

6.Conclusions

This paper presents numerical simulation of tool wear and material removal rate in turning of Inconel 718and validation by experimental data.Effect of process variables on tool wear and material removal rate have been examined using a sta-tistical design of experiment approach for experimental data collection.The simulation approach enables to predict the tool wear and material removal rate at any given cutting time as cutting tool geometry is constantly updated in FEM simulation through DEFORM https://www.360docs.net/doc/362262953.html,parison of simulation data for tool wear and material removal rate with experimental data shows that both are in good agreement.Thus,it can be concluded that simulation of machining process using DEFORM 3D can pre-dict the performance characteristics with reasonable accuracy quickly and appreciably save the time for running additional set of experiments.The following conclusions are drawn from the study:

The percentage relative error between experimental and simulation predicted value of ?ank wear and MRR by is below 7%.In most instances,simulation predicts higher value for ?ank wear and MRR than experimental values.However,sim-ulation can appreciably save costly experimental time,cost,and resources.

In experiments,the value of ?ank wear varies from 211.25l m to 350.76l m whereas it varies from 219.25l m to 365.32l m in simulation.

During turning process,the maximum temperature at tool tip-work piece interface is 1100°C at spindle speed of 1020RPM,depth of cut of 1mm and feed rate of 0.08mm/rev.

Analysis of variance suggests that the most in?uencing factors are spindle speed,depth of cut and feed rate in case of MRR whereas spindle speed and depth of cut are the most in?uencing factors in case of ?ank wear.

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