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International Journal of Enhanced Research in Science, Technology & EngineeringISSN: 2319-7463, Vol. 5 Issue 1, January-2016

EFFECT OF CUTTING PARAMETER FORTURNING EN-31 MATERIAL USING RSM

Mahesh k. sharma1,Sanjay Singh2,Rakesh kumar3

1M.Tech Research Scholar, Mechanical Engineering Department Jagannath University, Rajasthan, India 2,3 Assistant Professor, Mechanical Engineering Department Jagannath University, Rajasthan, India

ABSTRACT

This paper presents the effect of process parameter in turning operation to predict surface roughness and material removal rate (MRR). The turning process by using banka 34 lathe machine. Applications of the turning process can be found in many industries ranging from large engine manufactures to small die shops. The parameters that affect the turning operation are vibration, tool wear, surface roughness, material removal rate etc. Among this surface roughness and material removal rate are an important factor that affects the quality in manufacturing process. The main objective of this paper is to predict the surface roughness and material removal rate on EN -31 alloy steel by using coated carbide tool. A mathematical model is developed using regression technique and optimization is carried out using Box-Behnken of response surface methodology. Therefore, this study attempts the application of response surface methodology to find the optimal solution of the cutting conditions for giving the minimum value of surface roughness and maximum MRR .

Keywords: Turning process,Surface roughness, MRR, ResponsemSurface method, DOE,ANOVA

1. INTRODUCTION

Metal cutting is one of the vital processes and widely used manufacturing processes in engineering industries .Highly competitive market requires high quality products at minimum cost. Industries in which the cost of raw material is a big percentage of the cost of finished goods, higher productivity can be achieved through proper selection and use of the materials. . To improve productivity with good quality of the machined parts is the main challenges of metal industry there has been more concern about monitoring all aspects of the machining process. Surface finish is an important parameter in manufacturing engineering and it can influence the performance of mechanical parts and the production costs. The ratio of costs and quality of products in each production phase has to be monitored and quick corrective actions have to be taken in case of deviation from desired output . Surface roughness measurement presents an important task in many engineering applications. .Carbide cutting tools are very popular in metal cutting industry for the cutting of various hard materials such as, alloy steels, die steels, high speed steels, bearing steels, white cast iron and graphite cast iron. In the past few decades there had been great advancements in the development of these cutting tools. Coating is also used on cutting tools to provide improved lubrication at the tool/chip and tool/work piece interfaces and to reduce friction, and to reduce the temperatures at the cutting edge.

2. EXPERIMENTION DETAILS 2.1 Work piece Material

The EN-31 is a high quality alloy steel giving good ductility and shock resisting properties combined with resistance to wear. This steel is basically known as bearing steel and used for bearing production in industrial sector. Application of this material with its properties are used to make axels, gears, camshafts, driving pinion and link components for transportation and energy products as well as many applications in general mechanical engineering. The chemical composition, mechanical properties and heat treatment properties properties of the work-piece materials are shown in table 1, table 2 and table 3

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Table 1: Chemical Composition Table 2: Mechanical Properties

Table 3: Heat Treatment of EN-31

2.2 Cutting Insert

The tool insert chosen was a coated carbide tool (Kennametal make) whose specifications are shown below. Coated carbide tools are found to perform better than uncoated. The chosen insert was a square type negative insert meaning that it was rotatable and reversible so that a total number of 8 cutting edges can be generated . The coating on the insert is TiAlN (Titanium Aluminium Nitride).

Table 4: Specification of Cutting Tool

Fig 1: Selected cutting tool insert

2.3 Experiment Machine

The turning of work piece in dry turning conditions were conducted on lathe BANKA 34 having following specifications: Power required : 2 HP, Spindle speed: 60-1025 rpm, Spindle nose loo type, spindle bore 38 mm, Swing over cross slide 190 mm, Swing over bed 350 mm.

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Element ObjectiveTensile strength

750 N/mm2

Reduction of area

45%

Yield stress 450 N/mm2

Elongation 30%Modulus of elasticity

215000 N/mm2

Density 7.8 kg/m3

Hardness 63HRC

Element Chemical Compositi on(wt%)

C 1.08%Si 0.25%Mn 0.53%S 0.015%P 0.022%Ni 0.33%Cr 1.46%Mo 0.06%

Element ObjectiveHardening

Temperature802˚- 860˚ C

Quenching Medium OilTempering

Temperature180˚- 225˚ C

Brinell-Rockwell Hardness

59-65

ISO CatalogNumber

ANSI CatalogNumber

Grade Dimensions

D Smm in mm in

SNMG 120408

SNMG 432MS

KCU25

12.7 0.5

0.5 4.76


Fig 2: Banka 34 lathe machine

2.4 Roughness MeasurementSurface roughness has been precisely measured with the help of a portable stylus-type profile-meter, Talysurf (Taylor Hobson, Surtronic 3+, UK). Measurements were taken at different locations and the average was reported for each run.

Fig 3: Setup of Taylor hobson for measurement of Surface Roughness

2.5 Process variables and their levelThe working ranges of parameters for subsequent design of experiment based on Response Surface Methodology have been selected. In the present experimental work, spindle speed, feed rate and depth of cut have been considered as main process variables. The process variables with their units (and notations) are listed in Table 5

Table 5: Factors and Levels of Process Parameters

3. RESULTS AND DISCUSSION

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Factor Level-1 Level-2 Level-3Cutting Speed (m/min)

90 146 202

Feed (mm/rev)

0.06 0.10 0.16

Depth of cut (mm)

0.1 0.2 0.3


Table 6: Results of main experiments for MRR & average surface roughness value RaFactor I Factor II Factor III Response I Response II

StdRun A:Speed

B:Feed C:Depth of cut

Ra(µm) MRR(mm3/sec)

15 1 146 0.11 0.2 1.2 287.2316 2 146 0.11 0.2 1.2 281.633 3 90 0.16 0.2 2.41 210.5314 4 146 0.11 0.2 1.1 225.634 5 202 0.16 0.2 1.4 284.842 6 202 0.06 0.2 1.4 375.6813 7 146 0.11 0.2 1.3 270.368 8 202 0.11 0.3 1.8 395.412 9 146 0.16 0.3 1.4 345.656 10 202 0.11 0.1 1.3 24811 11 146 0.06 0.3 1.5 265.3210 12 146 0.16 0.1 1.7 206.869 13 146 0.06 0.1 0.7 145.2417 14 146 0.11 0.2 0.99 276.977 15 90 0.11 0.3 2.9 210.165 16 90 0.11 0.1 2.2 190.291 17 90 0.06 0.2 2.1 107.43

ANOVA For RaANOVA for response Ra is given in Table 7

Source Total sum of squares

DF Mean square

Total F Value

p-valueprob>F

Remarks

Model 4.88 9 0.54 22.24 0.0002 significantA-speed 1.72 1 1.72 70.61 < 0.0001B-feed 0.18 1 0.18 7.51 0.0289C-doc 0.36 1 0.36 14.83 0.0063

AB 0.024 1 0.024 0.99 0.3538AC 0.010 1 0.010 0.41 0.5421BC 0.30 1 0.30 12.42 0.0097A2 2.05 1 2.05 84.01 < 0.0001B2 3.242E-003 1 3.242E-003 0.13 0.7260C2 0.16 1 0.16 6.55 0.0375

Residual 0.17 7 0.024Lack of Fit 0.12 3 0.038 2.78 0.1742 not significantPure Error 0.055 4 0.014Cor Total 5.05 16Std. Dev. 0.16 R-Squared 0.9662

Mean 1.56 Adj R-Squared 0.9228C.V. % 9.98 Pred R-Squared 0.6174PRESS 1.93 Adeq Precision 17.458

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ANOVA For MRRANOVA for response MRR is given in Table 8

Source Total sum of squares

DF Mean square

Total F Value

p-valueprob>F

Remarks

Model 82081.42 6 13680.24 15.03 0.0002 significantA-speed 42852.75 1 42852.75 47.09 < 0.0001B-feed 2972.59 1 2972.59 3.27 0.1008C-doc 22699.41 1 22699.41 24.94 0.0005

AB 9403.18 1 9403.18 10.33 0.0093AC 4065.98 1 4065.98 4.47 0.0607BC 87.52 1 87.52 0.096 0.7628

Residual 9100.54 10 910.05Lack of Fit 6664.39 6 1110.73 1.82 0.2919 not significantPure Error 2436.15 4 609.04Cor Total 91181.96 16Std. Dev. 30.17 R-Squared 0.9002

Mean 254.54 Adj R-Squared 0.8403C.V. % 11.85 Pred R-Squared 0.6363PRESS 33161.10 Adeq Precision 15.461

3.1 Regression models:-The regression equations for the response characteristics as a function of input process parameters are given below in both coaded and actual factor.The insignificant coeffiecients (investigated from ANOVA) are omitted from the total equations. The developed statistical model for surface roughness and material removal rate areRoughness ==+1.16-0.46*A+0.15*B+0.21*C-0.078*AB-0.050*AC-0.27*BC+0.70*A2-0.028*B2+0.19*C2

Roughness=+5.07811-0.068373*speed+20.50807* feed+1.68857* doc-0.027679* speed* feed-8.92857E-003* speed * doc-55.00000* feed * doc+2.22337E-004*speed2-11.10000* feed2+19.47500* doc2

MRR=+254.54+73.19*19.28*B+53.27*C-48.49*AB+31.88*AC+4.68*BCMRR=176.48457+2.07305*speed+2726.57143*feed-401.45232*doc-17.31607*speed*feed+5.69330*speed*doc +935.50000*feed*doc

3.2 Discussions of Results of Main Experiment

Single Factor Effect on Ra: Fig. 4 show the effect of three process inputs i.e., speed, feed and depth of cut of turning on average surface roughness value.Effect of speed: From the main effect plots based on the fig. 4(a), it has been observed that whenever spindle speed is increased from 90 RPM to 152RPM the value of Ra is decrease and when again speed increased from 152RPM to 202 RPM, the value of Ra is increased .Effect of feed: From the main effect plots based on the fig. 4(b), it has been observed that when feed rate is increased from 0.06mm/min to 0.16 mm/min, theincreased of Ra.So result shows that feed gives the main effect on Ra.Effect of depth of cut: From the main effect plots based on the fig. 4(c), it has been observed that whenever depth of cut is increased from 0.1mm to 0.3mm the value of Ra is very slightly increase.

Single Factor Effect on MRR: Fig. 5 show the effect of three process inputs i.e., speed, feed and depth of cut of turning on material removal rate.Effect of speed: From the main effect plots based on the fig. 5(a) , it has been observed that whenever spindle speed is increased from 90 RPM to 202 RPM the value of MRR is increased. Effect of feed: From the main effect plots based on the fig. 5(b) it has been observed that when feed rate is increased from 0.06 mm/min to 0.16 mm/min, the increased of MRR. Effect of depth of cut: From the main effect plots based on the fig. 5(c), it has been observed that whenever depth of cut is increased from 0.1mm to 0.3mm the value of MRR is very slightly increase.

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Figure 4: Effect of (A) Speed (B) Feed (c) depth of cut on Ra

Figure 5: Effect of (A) Speed (B) Feed (c) depth of cut on MRR

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(a) (b) (c)

(d) (e) (f)

Figure 6:Ra for (a)contour plot(b) response surface(c)Interaction plot at feed of 0.11(d) Normal probability plot of residuals(e)Actual Vs predicted values(f) residual Vs run

(a) (b) (c)

(d) (e) (f)

Figure 7: MRR for (a)contour plot(b) response surface(c)Interaction plot at feed of 0.11(d) Normal probability plot of residuals(e)Actual Vs predicted values(f)residual Vs run

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Figure 6: a-b-c shows the contour plot, 3D response surface and Interaction Graph for the response Ra in terms of speed and depth of cut at a feed of 0.11mm.Contour plot plays a very important role in the study of response surface method with generating contour plot using Design of expert software for the response surface analysis, it is simple to characterize the shape of surface and locate the optimum with reasonable precision. By the examination of the contour plot and response surface, it is observed that SR decrease from 2.2 to 1.6 with increase in speed from 90 RPM to 202 RPM with increase of depth of cut from 0.1mm to 0.3 mm at a feed 0.11mm. Figure 6: d-e-f displays the normal probability plot of residuals, predicted versus actual plots and residual Vs run for Ra. It is observed that the residuals generally fall on the straight line implying that errors are normal distributed. The outlier points are then verified by checking for any points lying outside the red lines. It is evident from the fig. 6(f), all points lie inside the red lines, which indicates that the model fit well

Figure 7: a-b-c shows the contour plot, 3D response surface and Interaction Graph for the response MRR in terms of speed and feed at a doc = 0.2 mm. Contour plot plays a very important role in the study of response MRR with generating contour plot by Design of expert software for the response surface method. By the examination of the contour plot and response surface, it is observed that MRR increases from 200 mm3/sec to 250 mm3/sec with increase in speed from 146 RPM to 202 RPM with increase of depth of cut from 0.1mm to 0.3 mm at a feed 0.11mm.

Figure 7: d-e-f displays the normal probability plot of residuals,predicted versus actual and residual Vs run plots for MRR . It is observed that the residuals generally fall on the straight line implying that errors are normal distributed. The outlier points are then verified by checking for any points lying outside the red lines. It is evident from the fig. 7(f), all points lie inside the red lines, which ensures that the model fit well.

Table 9: Solutions for optimum of process input for confirmation experimentExp no. Speed Feed Doc Ra MRR Desirability

1 146 0.16 0.3 1.41375 331.764 0.725

A:speed = 146

146

90 202

B:feed = 0.16

0.06 0.16

C:doc = 0.3

0.1 0.3

roughness = 1.41375

0.7 2.9

MRR = 331.764

107.43 395.4

Desirability = 0.725

Figure 8:Multi response optimization results for maximum MRR and minimum Ra with ramp diagrams.

Figure 9:Contour plot for results of Ra and MRR(at speed=146 rpm, feed rate=0.16 mm/rev,depth of cut=0.3mm)

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Once the optimal level of the process inputs is selected, the final step is to predict and verifying the improvement of the performance characteristics using the optimal level of the machining parameters. Experiments performed to machine and verify the Turning at the above optimal input parametric setting for MRR and surface roughness were compared with optimal response values. The observed MRR and surface roughness of the experimental results are 331.764 mm3/sec and 1.41375 µm respectively. Table 10 shows the error percentage for experimental validation of the developed models for the responses with optimal parametric setting during Turning of EN-31. From the analysis of Table10, it can be observed that the calculated error is small. The error between experimental and predicted values for surface roughness and MRR lies within 4.18% and 0.70% respectively. Obviously, this confirms the excellent reproducibility for the experimental conclusions.

Table 10: main Experimental validation of developed models with optimal parameter settings.

Responses Predicted Experimental Error

Surface roughness 1.41 1.40 0.70%

MRR 331.65 345.65 4.18%

4. CONCLUSION

In this study, the surface roughness and MRR in the surface finishing process of EN24 alloy steel were modeled and analyzed through RSM. Spindle speed, feed and depth of cut have been employed to carry out the experimental study. Summarizing the main features, the following conclusion can be drawn.[1]Analysied with ANOVA the experimental results showed that the speed (the most significant factor) contributed 34.05 %, where as the depth of cut and feed rate contribution were 7.12 % and 3.56 % for Ra.

[2]The experimental results with ANOVA analysis showed that the speed (the most significant factor) contributed 47 %, where as the depth of cut and feed rate contribution was 25 % and 3.26 % for MRR.

[3]The predicted values of R2 are 0.6174 for surface roughness and 0.6363 for MRR are reasonably well. Its value greater than 60% and closest to one is the best value for fit the model.

[4]The error between experimental and predicted values at the optimal combination of parameter setting for Ra and MRR lie with in 0.70 % and 4.18 % respectively. Obviosly,this confirms excellent reproducibility of the experimental conclusions.

[5]From the multi response optimization, we obtain the optimal combination of parameters settings are speed of 146 rpm, feed rate 0.16 mm/rev. and depth of cut 0.3 mm for achieving the required minimum surface roughness and maximum MRR.

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