Performance studies of multilayer hard surface coatings (TiN/TiCN/Al2O3/TiN) of indexable carbide...

Measurement xxx (2012) xxx–xxx

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Measurement

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Performance studies of multilayer hard surface coatings(TiN/TiCN/Al2O3/TiN) of indexable carbide inserts in hard machining:Part-II (RSM, grey relational and techno economical approach)

Ashok Kumar Sahoo a,⇑, Bidyadhar Sahoo b

a School of Mechanical Engineering, KIIT University, Bhubaneswar, Odisha 751024, Indiab Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang, Odisha, India

a r t i c l e i n f o

Article history:Available online xxxx

Keywords:Flank wearSurface roughnessCoated carbideResponse surface methodologyGrey relationalEconomical

0263-2241/$ - see front matter � 2012 Elsevier Ltdhttp://dx.doi.org/10.1016/j.measurement.2012.09.02

⇑ Corresponding author. Tel.: +91 0674 6540805.E-mail address: [email protected] (A. Kumar S

Please cite this article in press as: A. Kumar Saof indexable carbide inserts in hard machininhttp://dx.doi.org/10.1016/j.measurement.2012

a b s t r a c t

This paper presents the mathematical modelling and parametric optimization on flankwear and surface roughness based on response surface methodology and grey-based Tagu-chi method in finish hard turning of AISI 4340 steel (HRC 47 ± 1) using multilayer coatedcarbide (TiN/TiCN/Al2O3/TiN) insert under dry environment. The economical feasibility ofutilizing multilayer TiN coated carbide insert has been described. Model adequacy has beenchecked using correlation coefficients. From main effect, it is evident that, cutting speed isthe most significant factor for flank wear followed by depth of cut and feed. Again, feed isthe most significant factor for surface roughness followed by cutting speed and depth ofcut. The coefficient of determination (R2) is more than 75% for RSM models developed,which shows the high correlation exist between the experimental and predicted values.The experimental vs. predicted values of flank wear and surface roughness (Ra and Rz)are also found to be very close to each other implying significance of models developed.The improvement of grey relational grade from initial parameter combination (d2–f3–v4) to the optimal parameter combination (d1–f1–v3) is found to be 0.3093 using grey rela-tional analysis coupled with Taguchi method for simultaneous optimization of responses.Flank wear (VBc) and surface roughness parameters (Ra and Rz) are decreased 1.9, 2.32 and1.5 times respectively considering optimal parametric combinations for multi-responses.The calculated total machining cost per part is only Rs. 3.17 due to higher tool life(47 min at their optimal level) of multilayer TiN coated carbide insert. It brings to thereduction of downtime and increases the savings.

� 2012 Elsevier Ltd. All rights reserved.

1. Introduction

Recent developments in engineering materials andpressing demands to achieve higher productivity in theglobal manufacturing arena have resulted in intensiveresearch in the field of cutting tool materials. To meet chal-lenges of higher cutting speeds to achieve high productionrates, carbides known as cemented or sintered carbideswere introduced to the industry in 1928. Due to its high

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ahoo).

hoo, B. Sahoo, Performancg: Part-II (RSM, grey rel.09.023

hardness over a wide range of temperature, high elasticmodulus and thermal conductivity, and low thermalexpansion, carbides became popular. In order to improvethe hardness and surface conditions, carbide tools arecoated with one or more thin layers of wear resistant hardmaterials by CVD or PVD process. Significant improvementof the cutting tool performance is noticed particularly withcoated carbide inserts in soft machining and 80% of allindexable tips used in turning are now coated and thatCVD is the dominant coating process [1]. Titanium basedcoatings are most commonly used due to improved wearresistance and ability to cut at higher cutting speeds.

e studies of multilayer hard surface coatings (TiN/TiCN/Al2O3/TiN)ational and techno economical approach), Measurement (2012),

http://dx.doi.org/10.1016/j.measurement.2012.09.023

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Nomenclature

d depth of cut (mm)f feed (mm/rev)v cutting speed (m/min)SS sum of squaresMS mean squareRa arithmetic surface roughness averageRz maximum peak-to-valley heightR2 coefficient of determinationP probability of significanceAISI American Iron and Steel InstituteCNC computerized numerical controlCVD chemical vapour depositionci grey relational gradeRSM response surface methodology�ci mean grey relational grade at optimal levelTc machining timeTd down timex cost of machine and labourV machined chip volume (cm3)ANOVA analysis of variance

OA orthogonal arrayDF degrees of freedomGRG grey relational gradeVBc flank wear at nose corner (mm)r nose radius (mm)FFD full factorial designR2 (adj) adjusted R2

F variance ratioHRC Rockwell hardnessS/N signal-to-noise ratiow distinguishing coefficientc estimated grey relational gradecm total mean grey relational gradeni(k) grey relational coefficientT tool lifeC total machining cost per party mean value single cutting edgePVD physical vapour deposition

2 A. Kumar Sahoo, B. Sahoo / Measurement xxx (2012) xxx–xxx

In recent years, hard machining of steel parts becamevery popular and effective technology replacing succes-sively grinding operations [2]. Substantial reduction ofmanufacturing cycles, manufacturing costs, decrease ofset up time, reduction of number of necessary machinetools, achievement of comparable surface finish, elimina-tion of part distortion caused by heat treatment, elimina-tion of environmentally harmful coolant, low capitalinvestment cost, low energy consumption observed in hardturning has made this a preferred choice over grinding inmany applications reported by Tönshoff et al. [3]. However,hard machining is usually performed by costly CBN andceramic tools. Searching for a cost effective cutting toolto meet the requirement of hard machining, author pro-poses multilayer coated carbide insert whose applicationis rarely being investigated. In finish hard turning, surfacequality is the most customer demand requirements and isadversely affected by tool wear. For successful applicationof this insert in hard machining, modelling and optimiza-tion of process parameter for surface roughness and flankwear is highly essential. At the same time, economical fea-sibility of multilayer coated carbide insert has to be stud-ied so that it can be successfully implemented in hardmachining effectively, efficiently and economically.

2. Literature review

In this section the findings of different authors relatedto modelling of responses and multi-objective optimiza-tion approach has been reported.

Lalwani et al. [4] studied the effect of cutting parame-ters on cutting forces and surface roughness in finish hardturning using coated ceramic tool applying RSM andsequential approach using face centred CCD. A linear mod-el fitted well to the variation of cutting forces and a non-linear quadratic model found suitable for the variation of

Please cite this article in press as: A. Kumar Sahoo, B. Sahoo, Performancof indexable carbide inserts in hard machining: Part-II (RSM, grey relhttp://dx.doi.org/10.1016/j.measurement.2012.09.023

surface roughness with significant contribution of feedrate. Depth of cut was significant to the feed force. Forthe thrust force and cutting force, feed rate and depth ofcut contributed more. Jenn-Tsong et al. [5] developedRSM model using CCD in the hard turning using uncoatedAl2O3/TiC mixed ceramics tool for flank wear and surfaceroughness. Flank wear was influenced principally by thecutting speed and the interaction effect of feed rate withnose radius of tool. The cutting speed and the tool cornerradius affected surface roughness significantly. Sahin andMotorcu [6] indicated that the feed rate was found out tobe dominant factor on the surface roughness, but it de-creased with decreasing cutting speed, feed rate, and depthof cut in turning AISI 1050 hardened steels by CBN cuttingtool. The RSM predicted and experimental surface rough-ness values were found to be very close. Zhang et al. [7]investigated the surface integrity of hardened bearing steelin hard turning using Taguchi method and optimized theparameter. Ozel and Karpat [8] developed regression andANN models in finish hard turning of AISI H13 steel for sur-face roughness and tool wear. Decrease in the feed rate,better was the surface roughness but slightly faster toolwear development. Increasing cutting speed resulted insignificant increase in tool wear development but resultedin better surface roughness. Increase in the workpiecehardness resulted in better surface roughness with highertool wear. Basak et al. [9] carried out the optimization ofa finish hard turning process for the machining of D2 steelwith ceramic tools using neural network models to predictthe surface roughness and tool wear as functions of cuttingspeed, feed, and machining time and found suitable tochoose the appropriate process parameters. Singh andVenkateswara Rao [10] described the effect of the toolgeometry and cutting conditions on the surface finish dur-ing hard turning of bearing steel with mixed ceramic insertby developing first and second-order mathematical models



A. Kumar Sahoo, B. Sahoo / Measurement xxx (2012) xxx–xxx 3

by RSM. The model was found to correlate very well withthe experimental results. Cemal Cakir et al. [11] developedmathematical model (1st order, 2nd order and exponen-tial) for surface roughness in hard turning of AISI P20 steel[52–54 HRC] using CVD coated carbide [TiCN/Al2O3/TiN]and PVD TiAlN coated carbide inserts. Higher feed ratesproduced higher surface roughness values, whereas cuttingspeed had a contrary effect and cutting depth did not sig-nificantly effect. Second order model was the most appro-priate one with its total average error being the smallest.Chien and Tsai [12] developed a predictive model (back-propagation neural network) for the tool flank wear andGA optimization model in turning 17-4PH stainless steel(38–40 HRC) using TiN coated tool. The predictive modelwas capable of predicting the tool flank wear. Bhattacharyaet al. [13] employed Taguchi approach to study the effectsof cutting parameters on finish and power consumptionduring high speed machining of AISI 1045 steel (50 HRC)by coated carbide tool (TiC/TiN). Cutting speed was thesignificant parameter on surface roughness and powerconsumption, while feed and depth of cut did not substan-tially affect the responses. Aggarwal et al. [14] investigatedthe power consumption in hard turning of AISI P-20 toolsteel (32–36 HRC) using TiN coated carbide insert withTaguchi’s and RSM technique. Cryogenic environmentwas the most significant factor in minimizing power con-sumption followed by cutting speed and depth of cut.The effects of feed rate and nose radius were found to beinsignificant. RSM technique was found to be better thanTaguchi’s method. Paiva et al. [15] studied alternative hy-brid approach combining RSM and principal componentanalysis (PCA) to optimize multiple correlated responsesin turning of AISI 52100 steel with mixed ceramic tool.The approach was adequate where the multiple responsesexhibited some correlation. A second-order model provedmore appropriate. Aslan et al. [16] optimized the cuttingparameters for flank wear and surface roughness usingTaguchi orthogonal array when turning hardened steelwith mixed ceramic tool. The relationship between theparameters and the responses were determined using mul-tiple linear regression analysis. Davim and Figueira [17]employed orthogonal array and ANOVA to investigate themachinability in hard turning of cold work tool steel withceramic tools.

The orthogonal array (OA) and Taguchi’s S/N is verypopular for the optimization of single objective function.Recently, Taguchi methodology coupled with grey rela-tional analysis has been implemented for solving multi re-sponse optimization problems. Lin [18] optimized turningparameters with multiple performance characteristicsusing grey based Taguchi method. It is shown that the re-sponses such as tool life, cutting force and surface rough-ness are greatly improved. Noorul Haq et al. [19] utilizedTaguchi orthogonal array with grey relational analysis forthe optimization of drilling parameters in drilling Al/SiCmetal matrix composite with multiple responses and is im-proved effectively through this approach. Aggarwal et al.[20] optimized the multiple performance characteristics(tool life, cutting force, surface roughness and power con-sumption) in turning of AISI P20 tool steel (32–36 HRC)with TiN coated carbide tool using PCA. It could be


concluded that middle level of cutting speed(160 m/min), nose radius (0.8 mm), lower level of feed(0.1 mm/rev) and depth of cut (0.2 mm) provided optimalresult. Udrea and Vizireanu [21] proposed a precise andefficient quantized multiple sinusoids signal estimationalgorithm and found to be computationally efficient.Vizireanu and Halunga [22] proposed a simple, fast andaccurate amplitude estimation algorithm of sinusoidal sig-nals for DSP based instrumentation. Vizireanu and Halunga[23] obtained an analytical formula for amplitude estima-tion errors as effects of sampling period deviation. Vizire-anu and Halunga [24] presented a general method todetermine all the parameters of a single wave carrier sig-nal, i.e. the frequency, phase, amplitude and DC compo-nent, based on four different samples. The analyticalformulas are derived for arbitrary samples and finally par-ticularized for equally spaced samples. Vizireanu [25] de-scribed the principles for the development of portableinstrumentation equipment (multimetres, frequencymetres, etc.) using quantized sine signals, with a commonunknown fixed frequency, and an estimation algorithmimplemented on digital signal processors (DSPs). The algo-rithm stops when quantization conditions are satisfied.Vizireanu [26] proposed a simple and precise instanta-neous frequency estimation method of single sinusoid sig-nals, using four equal spaced samples, for portable DSPbased instrumentation. An analytical formula is obtained.Four-point instantaneous frequency estimator is sensitiveto deviation of the sampling rate and quantization noise.A DSP based instantaneous frequency metre was builtand tested with satisfactory performance. Vizireanu [27]presented a simple and accurate method to estimatetime-varying frequency for single-phase electric powersystems, based on three equally spaced samples. A sinusoi-dal voltage signal model, without dc offset, with time-varying frequency was assumed and analytical formulasare derived. The method shows good estimation accuracyover a real world wide range of frequency changes.

Design of experiment and statistical methods havecommonly been used for analysis, prediction and optimi-zation in machining. In actual production, tool wear andsurface roughness are considered to be the most importantparameters for judging machinability. Hence seeking amulti response optimization technique to predict a set ofoptimum process parameters to obtain minimum toolwear and better surface finish simultaneously will be wise.It is observed from literature that the use of multi-response optimization approach like grey based Taguchimethod has rarely been adopted in hard machining appli-cations. Further, relatively few works related to themachining of hardened steels (AISI 4340) based on the toolwear or surface roughness prediction models using multi-layer coated carbide inserts have been reported. The pro-cess modelling by RSM using design of experiments isnot only an efficient modelling tool but also gives the re-quired information about the direct and interaction effectsof process parameters [28]. Grey relational analysis cou-pled with Taguchi method has been implemented in thepresent work for simultaneous optimization of responses.At the same time, economical feasibility study should alsobe carried out at optimal parametric level to find the



Table 1Process parameters and their levels.

Parameters Notation Unit Levels of factors

Level1

Level2

Level3

Level4

Depth ofcut

d mm 0.2 0.3 0.4 0.5

Feed f mm/rev

0.05 0.1 0.15 0.2

Cuttingspeed

v m/min

60 90 120 150


suitability of multilayer coated carbide inserts in hardmachining applications. Hence, this paper is organized asfollows:

1. Conduct the turning experiment on hardened AISI 4340steels (47 ± 1 HRC) using multilayer coated carbideinsert TiN/TiCN/Al2O3/TiN (TiN being top layer) in drycutting environment based on four3 full factorial design(FFD). Flank wear (VBc), arithmetic surface roughnessaverage (Ra) and maximum peak-to-valley heightroughness (Rz) are considered as the machinabilityindices.

2. Develop the flank wear and surface roughness mathe-matical model (second order) using main cuttingparameters such as cutting speed, feed and depth ofcut using response surface methodology. The adequacyof models has been checked by their correlationcoefficients.

3. Optimize the process parameters for multiple perfor-mance characteristics using Taguchi parameter designcombination with grey relational analysis. Finally, con-firmation trials have been carried out to verify theimprovement of optimal results in hard turningprocess.

4. Determine the tool life and analyze the economical fea-sibility of multilayer coated carbide inserts at their opti-mal parametric level in hard turning.

3. Experimental details

AISI 4340 medium carbon low alloy high strength steel(47 ± 1 HRC) was taken in the form of round bars of diam-eter 45 mm and 100 mm long. The machine tool used wasa high rigid CNC lathe having spindle speed of 3500 rpm(maximum) and power of 16 KW with sinumeric control-ler. In tests, multilayer coated carbide insert (TiN/TiCN/Al2O3/TiN) of ISO designation CNMG 120408 (80� diamondshaped insert) mounted on a PCLNR2525M12 tool holderhas been employed for experimentation. The cuttingparameters and their levels are shown in Table 1. Threefactors with four levels each comprises 64 experimentalruns has been designed based on full factorial design. Theresponses (surface roughness and flank wear) are mea-sured by Taylor Hobson (Surtronic 25) surface roughnesstester, Nikon profile projector. The images of tool tip aftermachining have been taken by Stereo zoom microscope,Radical instruments, India. To measure roughness of thesurface formed while processing the workpiece, the cutofflength and assessment length was fixed as 0.8 mm and4 mm respectively. Two surface roughness parameterssuch as arithmetic surface roughness average (Ra) andmaximum peak-to-valley height (Rz) were measured. Theinstrument was calibrated using a standard calibrationblock prior to the measurements. The measurement wastaken at four locations (90� apart) around the circumfer-ence of the workpieces and repeated twice at each pointon the face of the machined surface and the average valuesare recorded. The criteria of flank wear and surface rough-ness is taken as VBc = 0.3 mm and Ra = 1.6 lm [29,30]. Themachining length was fixed as 600 mm for each run. Theexperimental results at each run are shown in Table 2.


4. Response surface model

Response surface methodology is a collection of mathe-matical and statistical techniques for the modelling andanalysis of problems based on statistical design of experi-ments and least square error fitting. The response is influ-enced by several input variables and the objective is to findthe correlation between the response and the variablesinvestigated [28]. A regression is performed on the datacollected wherein the observed variable is approximatedbased on a functional relationship between the estimatedvariable and one or more input variables. The second orderresponse surface representing the output (Y) can be ex-pressed as a function of cutting parameters such as d, fand v. The relationship between the output and machiningparameters has been expressed as follows:

Y ¼ b0 þ b1ðdÞ þ b2ðf Þ þ b3ðvÞ þ b4ðd2Þ þ b5ðf 2Þ

þ b6ðv2Þ þ b7ðdf Þ þ b8ðfvÞ þ b9ðdvÞ ð1Þ

where Y is the corresponding response, b0 is constant calledthe intercept of the plane and b1,b2, . . . ,b9 are regression co-efficient that depends on main effects. The b coefficientsused in Eq. (1) can be calculated using least square tech-niques. The terms d, f and v are the input variables, d2, f2

and v2 are the square terms and df, fv and dv are interactionterms respectively for input variables. Second order modelis normally used when the response function is not knownor non-linear. To test the fit of the model, the regressionequation and determination coefficient (R2) were calculatedand acceptance was based on high to very high coefficientsof correlation (R2). It also signifies that how much variationin the response is explained by the model.

4.1. Flank wear model

The second order (quadratic) model for flank wear hasbeen developed at 95% confidence level considering fullfactorial experimental design data set. Using the responsesurface methodology, flank wear model (uncoded) isshown in the following equation:

VBc ¼ 0:95967� 1:54597d� 1:85979f � 0:01235v

þ 0:47969d2 � 0:34375f 2 þ 0:00004v2

þ 2:8985df þ 0:01259dvþ 0:01399fv ð2Þ

R2 ¼ 85%; R2 ðadjÞ ¼ 82:4%



Table 2Experimental results.

Run d f v VBc Ra Rz

1 0.2 0.05 60 0.103 0.612 3.3752 0.2 0.05 90 0.124 0.335 2.2253 0.2 0.05 120 0.128 0.32 2.2254 0.2 0.05 150 0.132 0.29 1.8755 0.2 0.1 60 0.115 0.722 5.056 0.2 0.1 90 0.128 0.43 2.2757 0.2 0.1 120 0.13 0.39 2.258 0.2 0.1 150 0.143 0.325 2.059 0.2 0.15 60 0.119 0.757 5.15

10 0.2 0.15 90 0.13 0.747 4.42511 0.2 0.15 120 0.131 0.61 4.37512 0.2 0.15 150 0.149 0.64 3.6513 0.2 0.2 60 0.123 1.18 5.38714 0.2 0.2 90 0.14 1.082 4.9515 0.2 0.2 120 0.147 1.05 4.916 0.2 0.2 150 0.19 0.711 4.1517 0.3 0.05 60 0.126 0.615 2.1518 0.3 0.05 90 0.127 0.33 1.919 0.3 0.05 120 0.129 0.312 1.720 0.3 0.05 150 0.158 0.3 1.721 0.3 0.1 60 0.127 0.822 3.522 0.3 0.1 90 0.14 0.772 3.423 0.3 0.1 120 0.143 0.66 3.2524 0.3 0.1 150 0.16 0.505 2.67525 0.3 0.15 60 0.129 1.02 5.8526 0.3 0.15 90 0.145 0.92 3.8527 0.3 0.15 120 0.145 0.81 3.42528 0.3 0.15 150 0.243 0.742 3.3529 0.3 0.2 60 0.133 1.14 6.62530 0.3 0.2 90 0.146 1.08 4.431 0.3 0.2 120 0.149 0.852 4.2532 0.3 0.2 150 0.31 0.81 3.5533 0.4 0.05 60 0.13 0.63 2.834 0.4 0.05 90 0.138 0.452 2.4535 0.4 0.05 120 0.141 0.29 1.6536 0.4 0.05 150 0.282 0.22 1.62537 0.4 0.1 60 0.132 0.897 3.9538 0.4 0.1 90 0.155 0.787 3.839 0.4 0.1 120 0.195 0.44 2.340 0.4 0.1 150 0.431 0.31 1.77541 0.4 0.15 60 0.149 1.1 4.62542 0.4 0.15 90 0.158 0.98 3.9543 0.4 0.15 120 0.203 0.847 3.444 0.4 0.15 150 0.439 0.78 2.67545 0.4 0.2 60 0.157 1.34 546 0.4 0.2 90 0.175 1.22 4.447 0.4 0.2 120 0.215 1.08 3.62548 0.4 0.2 150 0.508 0.9 3.22549 0.5 0.05 60 0.138 0.557 3.97550 0.5 0.05 90 0.153 0.51 2.72551 0.5 0.05 120 0.184 0.4 2.3552 0.5 0.05 150 0.285 0.31 1.77553 0.5 0.1 60 0.139 0.76 4.154 0.5 0.1 90 0.17 0.755 3.8555 0.5 0.1 120 0.19 0.58 2.856 0.5 0.1 150 0.432 0.39 2.17557 0.5 0.15 60 0.15 0.96 4.32558 0.5 0.15 90 0.177 0.86 4.1559 0.5 0.15 120 0.248 0.762 3.72560 0.5 0.15 150 0.654 1.475 6.0561 0.5 0.2 60 0.167 1.51 6.47562 0.5 0.2 90 0.207 1.43 6.32563 0.5 0.2 120 0.254 1.4 6.27564 0.5 0.2 150 0.745 1.69 6.675


The statistical significance and adequacy of model hasbeen checked using an analysis of variance depending on


F-value and P-value. It is commonly used to summarizethe test of the regression model, test of significance factorsand their interactions. In ANOVA table, the sum of squaresis calculated to estimate the square of deviation from thegrand mean. Mean squares are obtained by dividing thesum of squares by degrees of freedom [31]. If P value ofmodel is less than 0.05 (95% confidence level), significanceof corresponding term is established and the model has asignificant effect on the response [32]. In general, the R2

measures percentage of the variation of data that is ex-plained by the regression equation. The adjusted R2 valueis particularly useful when comparing models with differ-ent number of terms. When R2 approaches to unity, the re-sponse model fits the actual data effectively. The modelpresented high determination coefficient (R2 = 0.85)explaining 85% of the variability in the response whichindicates the goodness of fit for the model and high statis-tical significance of the model. It shows the high correla-tion exist between the experimental and predictedvalues. Also, the adjusted R2 (82.4%) value is very close tothe predicted R2 that shows that the unnecessary termsare not added in the model. Adjusted R2 indicates that82.4% of the total variability is explained by the modelafter considering the significant factors. From model ANO-VA (Table 3), analyzing of variance shows that the regres-sion model is significant as the values of probability lessthan 0.05. The normal probability plot of the residuals(i.e. error = predicted value from model–actual value) ofRSM model shows that the residuals lie reasonably closeto a straight line implying that errors are distributed nor-mally and giving support that the terms mentioned inthe model are significant (Fig. 1). A main effects plot is aplot of the means of the response variable for each levelof a factor. From main effect plot (Fig. 2), cutting speed isfound to be the most significant contribution taking flankwear into consideration followed by depth of cut and feed.Again from interaction plot (Fig. 3), cutting speed–depth ofcut and cutting speed–feed interaction has significance onflank wear. Interaction effect of feed–depth of cut has beenfound to be very minimal. The experimental vs. predictedvalues of flank wear is shown in Fig. 4 and found to be veryclose to each other implying the significance of the modeldeveloped. Thus, the model developed using response sur-face methodology can be utilized to predict accurate pre-diction of the flank wear in machining hardened AISI4340 steels using multilayer TiN coated carbide inserts.Furthermore, contour plots have been analyzed to findthe cutting conditions for which desired flank wear valuecan be achieved. This helps in the prediction of flank wearat any zone of the experimental domain. The contour plotof flank wear in cutting speed–feed plane and feed–depthof cut plane are shown in Figs. 5 and 6 respectively. Bothhave curvilinear profile in accordance to the quadraticmodel fitted. Figs. 5 and 6 clearly show that minimumflank wear can be achieved for low level of cuttingspeed–feed and depth of cut conditions.

When regression technique is utilized applying the leastsquare method to the experimental data, the following 1storder linear model for flank wear (Eq. (3)) is obtained.

VBc ¼ �0:249þ 0:4738dþ 0:5378f þ 0:002v ð3Þ



Table 3Results of ANOVA for flank wear model.

Source DF Seq SS Adj SS Adj MS F P Remarks

Regression 9 0.81943 0.81943 0.091048 33.81 0.000 SignificantLinear 3 0.530946 0.158758 0.052919 19.65 0.000Square 3 0.080833 0.080833 0.026944 10 0.000Interaction 3 0.207651 0.207651 0.069217 25.7 0.000Residual error 54 0.145433 0.145433 0.002693

Total 63 0.964863

Fig. 1. Normal probability plot of the residuals for flank wear (VBc).

Fig. 2. Main effects plot for flank wear (VBc).


R2 ¼ 55%; R2 ðadjÞ ¼ 52:8%

The model presented low determination coefficient(R2 = 0.55) explaining only 55% of the variability in theresponse which indicates the poor fit for the model


compared to 2nd order model. It is clear that, the responsesurface model (2nd order) represents most consistent andappropriate one with its coefficient of correlations beingthe highest for better prediction of flank wear in hardturning.



Fig. 3. Interaction plot for flank wear (VBc).

0 10 20 30 40 50 60 700.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Flan

k w

ear,

VBc

(mm

)

Observations

Experimental Predicted

Fig. 4. Experimental vs. predicted values of flank wear (VBc).

Fig. 5. Flank wear contour in cutting speed (v) and feed (f) plane.

Fig. 6. Flank wear contour in feed (f) and depth of cut (d) plane.


4.2. Surface roughness model

Similarly applying RSM with the experimental data, thesurface roughness model (uncoded) for Ra and Rz are pre-sented in Eqs. (4) and (5) respectively.

Ra ¼ 1:5276� 1:9883d� 1:8439f � 0:0128v

þ 0:9172d2 þ 8:7687f 2 þ 0:0000v2

þ 10:2735df þ 0:0083dvþ 0:0106fv ð4Þ

R2 ¼ 87%; R2 ðadjÞ ¼ 84:6%

Rz ¼ 9:5221� 24:8351dþ 10:4626f � 0:0639v

þ 30:0578d2 þ 3:3562f 2 þ 0:0002v2

þ 16:7085df þ 0:0305dvþ 0:0139fv ð5Þ

R2 ¼ 81%; R2 ðadjÞ ¼ 77:5%

From model ANOVA Tables 4 and 5, it is seen that theregression model is significant as the values of probabilityless than 0.05. The model is adequate as F calculated value


is greater than F-table value at 95% confidence level. Insurface roughness model, the adjusted R2 value is also veryclose to the predicted R2. The model presented high deter-mination coefficient (R2 = 0.87 and 0.81) explaining 87%



Table 4Results of ANOVA for Ra model.


Regression 9 6.72788 6.72788 0.747543 39.46 0.000 SignificantLinear 3 6.30357 0.18106 0.060352 3.19 0.031Square 3 0.07257 0.07257 0.024191 1.28 0.292Interaction 3 0.35174 0.35174 0.117248 6.19 0.001Residual error 54 1.02295 1.02295 0.018943

Total 63 7.75083

Table 5Results of ANOVA for Rz model.


Regression 9 98.308 98.3075 10.92306 25.16 0.000 SignificantLinear 3 89.459 10.3246 3.44154 7.93 0.000Square 3 7.268 7.2681 2.42271 5.58 0.002Interaction 3 1.58 1.5804 0.52681 1.21 0.314Residual error 54 23.445 23.4455 0.43418

Total 63 121.753


and 81% of the variability in the response (Ra and Rz)which indicates the goodness of fit for the model and highsignificance of the model. This analysis showed that theprediction model sufficiently explains the relationship be-tween surface roughness and the independent variables.The normal probability plot of residuals for surface rough-ness parameters such as Ra and Rz are shown in Figs. 7 and13. It clearly shows that the residuals lie reasonably closeto a straight line implying that errors are distributed nor-mally. This indicates that surface roughness model (Ra andRz) proposed is adequate. The main effect plot (Figs. 8 and14) and interaction plot (Figs. 9 and 15) are helpful in visu-alizing the effect of process parameters and their interac-tions on surface roughness. From main effect plot, it isevident that, feed is the significant factor affecting surfaceroughness followed by cutting speed and depth of cut.Figs. 9 and 15 clearly show the significance interaction ofdepth of cut and feed. The experimental vs. predicted val-

Fig. 7. Normal probability plo


ues of surface roughness (Ra and Rz) are shown in Figs. 10and 16 and found to be very close to each other implyingthe significance of the model developed. Thus, the modeldeveloped using response surface methodology can be uti-lized to predict accurate prediction of the surface rough-ness parameters (Ra and Rz) in machining hardened AISI4340 steels using multilayer TiN coated carbide inserts.The contour plot of Ra and Rz in cutting speed–feed planeand feed–depth of cut plane are shown in Figs. 11 and 12and Figs. 17 and 18 respectively. Both have curvilinearprofile in accordance to the quadratic model fitted. Con-tour plots clearly show that minimum surface roughnesscan be achieved for high level of cutting speed, low levelof feed and nearly low or high level of depth of cutconditions.

When regression technique is utilized applying the leastsquare method to the experimental data, the following 1storder linear model for Ra and Rz are obtained.

t of the residuals for Ra.



Fig. 8. Main effects plot for Ra.

Fig. 9. Interaction plot for Ra.


Ra ¼ 0:1596þ 0:8141dþ 5:0551f � 0:003v ð6Þ

R2 ¼ 81:3%; R2 ðadjÞ ¼ 80:4%

Rz ¼ 2:4643þ 1:4993dþ 18:6139f � 0:016v ð7Þ

R2 ¼ 73:5%; R2 ðadjÞ ¼ 72:1%

The above 1st order model presented low determina-tion coefficient (R2 = 0.813 and R2 = 0.735) explaining only81.3% and 73.5% of the variability in the response whichindicates the poor fit compared to 2nd order model. It isclear that, the response surface model (2nd order)represents most consistent and appropriate one with itscoefficient of correlations being the highest for better pre-diction of Ra and Rz in hard turning.


5. Multi-response parametric optimization using greyrelational analysis

Traditional Taguchi design of experiment is commonand popular for single response optimization problem. Inhis methodology, a loss function is required to calculatethe deviation of experimental value and desired value.The loss function is further transformed to a S/N ratio. Thenthe optimal parametric combination is selected based onhigher S/N ratio. However, multi-response parametricoptimization is different from that of single response opti-mization for performance characteristics. The higher sig-nal-to-noise ratio for one performance characteristicsmay correspond to a lower S/N ratio for another. To over-come this problem, the grey relational analysis coupledwith Taguchi method is proposed in this paper for para-metric optimization of multi-responses, i.e. to yield low



0 10 20 30 40 50 60 700.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8Su

rfac

e ro

ughn

ess,

Ra

(mic

rons

)

Observations

Experimental Predicted

Fig. 10. Experimental vs. predicted values of Ra.

V

f

Contour Plot of Ra vs f, d

Fig. 11. Ra contour in cutting speed (v) and feed (f) plane.

Fig. 12. Ra contour in feed (f) and depth of cut (d) plane.


value of flank wear (VBc) and surface roughness (Ra andRz) simultaneously in hard turning. A suitable orthogonal


array based on Taguchi (L16) is chosen and experiment isconducted as per Taguchi orthogonal array layout(Table 6). In grey relational analysis, the experimental dataare first normalized from 0 to 1 called relational genera-tion. Next, grey relational coefficient is calculated basedon normalized experimental data to represent the correla-tion between the desired and actual experimental data. Alower-the-better criterion is used for both responses toget grey relational generation [16]. This is expressed as:

xiðkÞ ¼max yiðkÞ � yiðkÞ

max yiðkÞ �min yiðkÞð8Þ

Here xi(k) is the value after the grey relational generation,min yi(k) is the smallest value of yi(k) for the kth response,and max yi(k) is the largest value of yi(k) for the kth re-sponse. An ideal sequence is [x0(k) (k = 1, 2, 3, 4, 5)] forthe responses. The grey relational coefficient ni(k) can beexpressed as:

niðkÞ ¼Dmin þ wDmax

DoiðkÞ þ wDmaxð9Þ

where D0i = kx0(k) � xi(kk is the difference of the absolutevalue between x0(k) and xi(k), Dmin and Dmax are the min-imum and maximum values of the absolute differences(D0i) of all comparing sequences. w is a distinguishingcoefficient (0 6 w 6 1) and in the present study, w = 0.5 isused. Then overall grey relational grade is calculated byaveraging the grey relational coefficient corresponding toselected responses. The objective is to show the degree ofrelation between the sequences [x0(k) and xi(k),i = 1,2,3, . . . ,16]. The grey relational grade ci is expressedas follows:

ci ¼1n

Xn

k¼1

niðkÞ ð10Þ

where n is the number of process responses. The highergrey relational grade, stronger is the relational degree be-tween the ideal sequence x0(k) and the given sequence xi

(k). The grey analysis coupled with Taguchi methodconverts a multi-response optimization problem into a sin-gle response optimization called the overall grey relationalgrade (GRG). Thus, the higher grey relational grade impliesthat the corresponding parameter combination is closer tothe optimal. The mean response for grey relational gradewith its grand mean and the main effect plot of grey rela-tional grade are very important because optimal paramet-ric combination can be obtained from this plot. Analysis ofvariance has been conducted to judge the significance ofparameter on multi-responses. After identifying the opti-mal parameter settings, the next step is to verify theimprovements of quality characteristics through confirma-tion experiment.

5.1. Evaluation of optimal parameters for hard turning process

In the present study, optimal parametric combinationfor surface roughness and flank wear has been evaluatedusing Taguchi’s L16 orthogonal array [33] with greyrelational analysis. The experimental layout is shown inTable 6. The experimental data are shown in Table 7. The



Fig. 13. Normal probability plot of the residuals for Rz.

Fig. 14. Main effects plot for Rz.


normalized experimental data (grey relational generation)is shown in Table 8. For both the flank wear and surfaceroughness, lower-the-better criterion was selected. Greyrelational coefficients for each performance characteristicshave been calculated and shown in Table 9. These greyrelational coefficients for each response were accumulatedto evaluate grey relational grade. Table 9 shows the exper-imental results for the grey relational grade and their or-der. Thus, the multi-response optimization problem hasbeen reduced to a single objective function (grey relationalgrade) utilizing Taguchi approach coupled with grey rela-tional analyses. The mean of the grey relational grade foreach level of cutting parameters, and the total mean ofthe grey relational grade is summarized in Table 10. Opti-mal value is selected from higher value of the grey rela-tional grade. Fig. 19 shows the grey relational grades(GRGs) graph represented graphically in main effect plot.


With the help of mean grey relational grade and Fig. 19,the optimal parametric combination becomes d1–f1–v3,i.e.

Depth of cut: 0.2 mm.Feed: 0.05 mm/rev.Cutting speed: 120 m/min.

5.2. ANOVA for grey relational grade

The purpose of ANOVA is to identify which hard turningparameters significantly affect the multi responses. TheANOVA has been carried out taking data of grey relationalgrade and shown in Table 11. Feed is found to be the mostsignificant factor from ANOVA study considering flankwear and surface roughness simultaneously into accountas their P-value is less than 0.05.



Fig. 15. Interaction plot for Rz.

0 10 20 30 40 50 60 70

1

2

3

4

5

6

7

Peak

-to-v

alle

y he

ight

, Rz

(mic

rons

)

Observations

ExperimentalPredicted

Fig. 16. Experimental vs. predicted values of Rz.

Fig. 17. Rz contour in cutting speed (v) and feed (f) plane.

Fig. 18. Rz contour in feed (f) and depth of cut (d) plane.

Table 6Taguchi L16 (43) orthogonal array design.

d f v

1 1 11 2 21 3 31 4 42 1 22 2 12 3 42 4 33 1 33 2 43 3 13 4 24 1 44 2 34 3 24 4 1


Please cite this article in press as: A. Kumar Sahoo, B. Sahoo, Performance studies of multilayer hard surface coatings (TiN/TiCN/Al2O3/TiN)of indexable carbide inserts in hard machining: Part-II (RSM, grey relational and techno economical approach), Measurement (2012),http://dx.doi.org/10.1016/j.measurement.2012.09.023


Table 7Experimental data for VBc, Ra and Rz.

Run no. Process parameters Experimental results

d f v VBc Ra Rz

1 0.2 0.05 60 0.103 0.612 3.3752 0.2 0.1 90 0.128 0.43 2.2753 0.2 0.15 120 0.131 0.61 4.3754 0.2 0.2 150 0.19 0.711 4.155 0.3 0.05 90 0.127 0.33 1.96 0.3 0.1 60 0.127 0.822 3.57 0.3 0.15 150 0.243 0.742 3.358 0.3 0.2 120 0.149 0.852 4.259 0.4 0.05 120 0.141 0.29 1.65

10 0.4 0.1 150 0.431 0.31 1.77511 0.4 0.15 60 0.149 1.1 4.62512 0.4 0.2 90 0.175 1.22 4.413 0.5 0.05 150 0.285 0.31 1.77514 0.5 0.1 120 0.19 0.58 2.815 0.5 0.15 90 0.177 0.86 4.1516 0.5 0.2 60 0.167 1.51 6.475

Table 8Data preprocessing of each performance characteristics (grey relationalgeneration).

Run no. VBc Ra RzSmaller-the-better

Smaller-the-better

Smaller-the-better

Idealsequence

1 1 1

1 1 0.736 0.64242 0.9237 0.8852 0.87043 0.9146 0.7377 0.43524 0.7347 0.6549 0.48185 0.9268 0.9672 0.94816 0.9268 0.5639 0.61657 0.5731 0.6295 0.64768 0.8597 0.5393 0.46119 0.8841 1 1

10 0 0.9836 0.97411 0.8597 0.336 0.383412 0.7804 0.2377 0.4313 0.4451 0.9836 0.97414 0.7347 0.7622 0.761615 0.7743 0.5327 0.481816 0.8048 0 0


5.3. Confirmatory test

A confirmatory test needs to be carried out in order topredict and verify the improvement of quality characteris-tics using the optimal parametric combination. The esti-mated grey relational grade, c, using the optimal level ofthe process parameters can be calculated as [34]:

c ¼ cm þXo

i¼1

ð�ci � cmÞ ð11Þ

where cm is the total mean grey relational grade, �ci is themean grey relational grade at the optimal level, and o isthe number of the main design parameters that significantlyaffect the roughness characteristics of ground surfaces.

Good agreement exists between the estimated value(0.9156) and experimental value (0.876) which is noticedfrom Table 12. The improvement of grey relational grade


from initial parameter combination (d2–f3–v4) to the opti-mal parameter combination (d1–f1–v3) is 0.3093. If theoptimal setting with a depth of cut 0.2 mm, feed of0.05 mm/rev and cutting speed of 120 m/min is used, theflank wear gives 0.128 mm and surface roughness (Raand Rz) gives 0.32 and 2.225 lm respectively which iscomparative lower than the initial factor settings. It isinteresting to note that, based on confirmation run, theflank wear (VBc) and surface roughness (Ra and Rz) are de-creased 1.9, 2.32 and 1.5 times respectively consideringoptimal parametric combinations for multi-responsesusing multilayer coated carbide inserts.

Hence, grey based Taguchi technique finds its suitabilityfor parametric optimization of multi-responses in the hardturning of AISI 4340 steel using multilayer TiN coated car-bide inserts and greatly improved. Therefore, there is aneed to investigate the economical feasibility of coatedcarbide inserts in hard turning at their optimal parameterrange to judge its performance. Such an analysis has beendone in subsequent section.

6. Evaluation of tool life and cost analysis at optimallevels in hard turning

Machining cost plays a vital role in manufacturing aproduct. During process planning the cost estimation formachining is essential. The objective of any such processhas always been to maximize throughput at minimum costinput. The machining cost estimation has three importantconstituents, machining cost, tool changing cost and thetool cost associated with the operation. A shorter tool lifeadds to tool cost and tool changing cost since it involvesresharpening cost and tool indexing costs. Tool life is di-rectly associated with tool wear. The wearing out durationcan be prolonged thus enhancing tool life through selec-tion of process parameters at a lower range. This shall de-crease the production rate and add to machining cost. Inorder to strike a balance among these opposing trends, adetailed economic analysis is required which has been pre-sented as follows.

To assess the economical feasibility using multilayer TiNcoated carbide insert at optimum parametric level in hardturning, evaluation of tool life is essential. For this, anexperiment has been conducted at successive machiningtime to evaluate the tool life insert at their optimal levelof cutting parameter considering flank wear criteria,VBc = 0.3 mm in hard turning. The optimal cuttingparameter for TiN coated carbide insert has been found tobe d1–f1–v3 (d = 0.2 mm, f = 0.05 mm/rev and v =120 m/min) in turning AISI 4340 work-piece (47 ± 1 HRC)under dry environment. The experimental results of flankwear and images of cutting edges at successive machiningtime at optimal level have been shown in Table 13 andFig. 20 respectively. The growth of flank wear is plotted inFig. 21. It is observed that the growth of flank wear wassteady for TiN coated carbide insert before wear criterionhas been reached and measured tool life has been foundto be 47 min at their optimal level. The cutting edge micro-graphs (Fig. 20) reveal no chipping or fracture of insertswhen machining is carried out under optimal parametric



Table 9Evaluation of D0i, grey relational coefficient with w = 0.5 and GRG for each of the responses.

Run no. Evaluation of D0i Grey relational coefficient with w = 0.5 Grey relational grade (GRG) Rank

VBc Ra Rz VBc Ra Rz

Ideal sequence 1 1 1 1 1 11 0 0.264 0.3576 1 0.6544 0.583 0.7458 62 0.0763 0.1148 0.1296 0.8676 0.8132 0.7941 0.8249 33 0.0854 0.2623 0.5648 0.8541 0.6559 0.4695 0.6598 84 0.2653 0.3451 0.5182 0.6533 0.5916 0.491 0.5786 115 0.0732 0.0328 0.0519 0.8722 0.9384 0.9059 0.9055 26 0.0732 0.4361 0.3835 0.8722 0.5341 0.5659 0.6574 97 0.4269 0.3705 0.3524 0.5394 0.5743 0.5865 0.5667 128 0.1403 0.4607 0.5389 0.7808 0.5204 0.4812 0.5941 109 0.1159 0 0 0.8118 1 1 0.9372 1

10 1 0.0164 0.026 0.3333 0.9682 0.9505 0.7506 511 0.1403 0.664 0.6166 0.7808 0.4295 0.4477 0.5526 1412 0.2196 0.7623 0.57 0.6948 0.3961 0.4672 0.5193 1513 0.5549 0.0164 0.026 0.4739 0.9682 0.9505 0.7975 414 0.2653 0.2378 0.2384 0.6533 0.6776 0.6771 0.6693 715 0.2257 0.4673 0.5182 0.6889 0.5169 0.491 0.5656 1316 0.1952 1 1 0.7192 0.3333 0.3333 0.4619 16

Table 10Response table for the mean grey relational grade.

Factors Mean grey relational grade Max–Min Rank

Level 1 Level 2 Level 3 Level 4

d 0.7022 0.6809 0.6899 0.6235 0.0787 3f 0.8465 0.7255 0.5861 0.5384 0.3081 1v 0.6044 0.7038 0.7151 0.6733 0.1107 2

Total mean grey relational grade = 0.6741.


levels. Due to extended tool life at optimal levels,multilayer TiN coated carbide insert can be successfullyimplemented in hard turning applications under dry envi-ronment. It can be revealed that, taking flank wear criteriaVBc = 0.3 mm, the tool cutting edge could produce 56.4 cm3

of machined chip volume during 47 min using multilayerTiN coated carbide insert (V = v � f � d � T cm3).

Fig. 19. Main effect


Next, the cost per piece has been calculated takingmachining length (L) of 100 mm, diameter (D) of 40 mm,AISI 4340 work-piece (47 ± 1 HRC) and at their optimumparametric level [30,35]. The total cost of the machineand labour (x) is taken as Rs. 250 per hour (4.16 min�1).The cost of coated carbide insert is taken as Rs. 220 perpiece and every piece of insert have four cutting edges.Therefore, the mean value of a single cutting edge (y) isfound to be Rs. 55 for multilayer coated carbide inserts.The calculated total machining cost per part using multi-layer TiN coated carbide inserts is found to be Rs. 3.17shown in Table 14. It is clear that the machining cost formultilayer TiN coated carbide insert is lower under optimalparametric conditions. It is basically due to the higher toollife of multilayer TiN coated carbide insert which brings tothe reduction of downtime and increases the savings. Thisclearly depicts the economical benefits of carrying out fin-ish hard turning with multilayer TiN coated carbide insert

plot for GRG.



Table 11ANOVA for grey relational grade.


d 3 0.01457 0.01457 0.00485 1.76 0.255 Insignificantf 3 0.23397 0.23397 0.07799 28.21 0.001 Significantv 3 0.02967 0.02967 0.00989 3.58 0.086 InsignificantError 6 0.01658 0.01658 0.00276

Total 15 0.29481

Table 12Results of confirmatory experiment.

Initial factorsetting

Optimal cutting factors

Prediction Experiment

Level d2–f3–v4 d1–f1–v3 d1–f1–v3VBc 0.243 0.128Ra 0.742 0.32Rz 3.35 2.225Grey relational

grade0.5667 0.9156 0.876

Improvement in GRG = 0.3093.


in hard turning. This reveals that machining hardened AISI4340 steel (47 ± 1 HRC) with TiN/TiCN/Al2O3/TiN coatedcarbide insert could be adopted for any manufacturingprocesses.

7. Conclusions

The study focuses extensively on the aspects related tomathematical modelling of flank wear and surface rough-ness using RSM, parametric optimization through greyrelational analysis, evaluation of tool life and economicalfeasibility in hard turning of AISI 4340 steel (47 ± 1 HRC)using multilayer (TiN/TiCN/Al2O3/TiN) coated carbide in-serts. Therefore based from the findings of the researchon hard turning, following conclusions may be drawn.

1. Mathematical model output revealed that the RSMmodel proposed are statistically significant and ade-quate for all the three environmental conditionsbecause of higher R2 value (0.85 for flank wear,0.87 for Ra and 0.81 for Rz). It shows high correla-tions between the experimental and predicted val-ues of machinability characteristics. Also, theresiduals lie reasonably close to a straight lineimplying that errors are distributed normally. Thisindicated that the models are very well explainedthe variation around the mean. The experimental

Table 13Experimental results of flank wear with successive machining time at optimal lev

Optimal cutting parameters Flank wear (VBc)criteria

Inserts

v = 120 m/min, f = 0.05 mm/rev andd = 0.2 mm

0.3 Multilayer Tcarbide


vs. predicted values for flank wear and surfaceroughness are also found to be very close to eachother implying significance of models developed.

2. From main effect plot and interaction plot, it is evi-dent that, feed is the dominant factor affecting sur-face roughness followed by cutting speed anddepth of cut. The interaction of depth of cut and feedare found be more significant.

3. From main effect plot, it is evident that, cuttingspeed is the most significant factor affecting toolwear followed by depth of cut and feed. The toolwear decreases when all the parameters are takenat their low levels. From interaction plot, it is shownthat, the interaction of cutting speed–depth of cutand feed-cutting speed are found to be significant.

4. Hence, the proposed RSM models are consideredreliable for future tool wear and surface roughnesspredictions within the limits of the factor investi-gated. Contour plots can be used for selection of cut-ting conditions for desired flank wear and surfaceroughness.

5. A grey relational analysis of the experimental resultsof surface roughness and flank wear converts multi-optimization performance characteristics into singleoptimization characteristic called the grey relationalgrade. The optimal parametric combination becomesd1–f1–v3, i.e. depth of cut of 0.2 mm, feed of0.05 mm/rev and cutting speed of 120 m/min.

6. The improvement of grey relational grade from ini-tial parameter combination (d2–f3–v4) to the opti-mal parameter combination (d1–f1–v3) is found tobe 0.3093.

7. Based on confirmation run, the flank wear (VBc) andsurface roughness parameters (Ra and Rz) aredecreased 1.9, 2.32 and 1.5 times respectively con-sidering optimal parametric combinations.

8. It is clearly shown that the multiple performancecharacteristics (surface roughness and flank wear)in the hard turning process are greatly improvedusing grey based Taguchi method using multilayercoated carbide insert.

els in hard turning.

Flank wear at successive machining time (Tc) in min

5 min 14 min 26 min 31 min 35 min 47 min

iN coated 0.08 0.128 0.19 0.22 0.25 0.3



Fig. 20. Images of cutting edge at optimal levels in hard turning aftermachining times (a) 14 min, (b) 26 min (c) 35 min and (d) 47 min.

0 5 10 15 20 25 30 35 40 45 500.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

d = 0.2 mmf = 0.05 mm/revv = 120 m/min

Growth of flank wear at optimal level

Flan

k w

ear,

VBc

(mm

)

Machining Time, Tc (min)

Fig. 21. Growth of flank wear vs. machining time at optimal level in hardturning, i.e. depth of cut (d) = 0.2 mm, feed = 0.05 mm/rev and cuttingspeed (v) = 120 m/min.

Table 14Machining costs per part for TiN coated carbide insert in hard turning.

No. Costs Multilayer TiN coatedcarbide

1 Value of machine and operator (x),Rs. 250/h

Rs. 4.16 min�1

2 Machining cost per part (x � Tc) Rs. 2.293 Tool life for single edge (T) 47 min4 Tool changing cost per part [xTd(Tc/

T)]Rs. 0.24

5 Mean value of single cutting edge(y)

Rs. 55

6 Tool cost per part [y(Tc/T)] Rs. 0.647 Total machining cost per part (C),

(2 + 4 + 6)Rs. 3.17

Cutting conditions: TiN coated carbide (v = 120 m/min, f = 0.05 mm/rev,d = 0.2 mm), L = 100 mm, D = 40 mm, VBc = 0.3 mm, Tc = 0.55 min,Td = 5 min, W/P = AISI 4340 (47 ± 1 HRC).



9. Feed is the most significant factor considering flankwear and surface roughness simultaneously intoaccount as its P-value is less than 0.05.

10. The measured tool life has been found to be 47 minusing multilayer TiN coated carbide insert in hardturning at their optimal levels. The calculated totalmachining cost per part is only Rs. 3.17 due to highertool life of insert that brings to the reduction ofdowntime and increases the savings. It clearlydepicts the economical benefits of carrying out fin-ish hard turning with multilayer TiN coated carbideinsert in hard turning.

11. From the extensive study, the potential and effec-tiveness of TiN/TiCN/Al2O3/TiN multilayer coatedcarbide insert has been noticed to be efficient andeconomical while turning hardened steels underdry environment without the necessity for cylindri-cal grinding operations.

e studies of multilayer hard surface coatings (TiN/TiCN/Al2O3/TiN)ational and techno economical approach), Measurement (2012)
,


From the experimental investigation and observationsin hard turning of AISI 4340 steel, mathematical modelling,parametric optimization and economical study in part-II, ithas been concluded that the multilayer TiN coated carbideinsert out performs over the range of parameters chosen.The proposed mathematical models are considered ade-quate. The cost analysis clearly depicts the potential ofmultilayer TiN coated carbide insert in hard turning atoptimal parametric levels. This reveals that machininghardened AISI 4340 steel with TiN/TiCN/Al2O3/TiN coatedcarbide insert could be adopted for manufacturing process.

Future work may be extended to analyze the effects ofsome additional variables, such as cutting tool nose radius,cutting time and increased hardness of work materials onhard turning to investigate the machinability characteris-tics. Study of effects of minimum quantity lubrication oncoated carbide tool performance in hard turning must beexplored.

Acknowledgements

The author would like to thank Central Tool room andTraining centre, Bhubaneswar, India and KIIT University,Bhubaneswar, India for providing their facilities to carryout the research work.

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Performance studies of multilayer hard surface coatings (TiN/TiCN/Al2O3/TiN) of indexable carbide...

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