Experimental investigation on surface roughness

5th International & 26th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12th–14th, 2014, IIT Guwahati, Assam, India

Experimental investigation on surface roughness characteristics in hard turning of EN31 steel using coated carbide insert: Taguchi and mathematical modeling approach Panda, A1, Dutta, S.K2, Sahoo, A.K3*, Rout, A.K4, Routra, B.C5 1,3,4,5

School of Mechanical Engineering, KIIT University, Bhubaneswar-751024, Odisha, India *[email protected], [email protected], [email protected], 5 [email protected] 2 KIIT Polytechnic, Bhubaneswar-24, Odisha, India, 2 [email protected] Abstract Now-a-days turning of hardened steel over 45 HRC is an emerging technology replacing grinding operation for finishing the components. Multilayer coated carbide insert being an inexpensive cutting tool material has less researched in machining hardened components. Thus, the objective of the present work has been set to have a study on hard turning of EN 31 steel (55HRC) using TiN/TiCN/Al2O3 multilayer coated carbide inserts through Taguchi L16 orthogonal array design and investigates surface roughness under dry environment. The mathematical model has been developed for better prediction of responses using response surface methodology and correlated for its significance. The mathematical model presented high correlation coefficients (higher R2 value) and fitted well. Feed is found to be most dominant parameter for affecting the surface roughness. A Taguchi technique has been utilized for parametric optimization of surface roughness. From the study, the potential and effectiveness of multilayer coated carbide insert has been noticed while turning hardened steels under dry environment. Keywords: Hard turning, Surface roughness, Coated carbide, RSM, Taguchi

1. Introduction Turning of hardened steel above 45 HRC is known as Hard Turning. It brings revolutionary in manufacturing industries especially bearing, automotive, aero space and die and mold sectors. Now- a- days, it suitably replaces the costly grinding process. It has several benefits over conventional grinding process. Although grinding is known to produce good surface finish at relatively high feed rates, hard turning can produce as good or better surface finish at significantly higher material removal rates. Although the process is performed with in small depths of cut and feed rates, estimates of reduced machining time are as high as 60% for conventional hard turning. In particular, it reduces manufacturing costs, decreases production time, improves overall product quality and eliminates

coolant application. The availability of super hard tool materials like CBN and ceramic replaces slow traditional grinding process in machining hardened steels to produce desired quality. Many researchers have tried by utilizing CBN and ceramic tool in hard machining. But the potential of using multilayer grade of coated carbide in hard turning is lacking. Thus the current research on hard turning using coated carbide insert will be worthy.

2. Literature review Zhou et al. (2004) performed hard turning of AISI 5140 steel using separately conventional ceramic tool and wiper geometry ceramic tool, wiper tool geometry gives comparable good bearing properties to the machined surface at a double federate. Grezesik (2008) carried out hard turning of AISI 5140 (DIN 41Cr4) steel hardened to 60±1 HRC

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Experimental investigation on surface roughness characteristics in hard turning of EN31 steel using coated carbide insert: Taguchi and mathematical modeling approach

with conventional and wiper ceramic tools at variable feed rate and its changes originated from tool wear. Suresh et al. (2012) performed studies on hard turning of AISI 4340 steel using multilayer coated carbide tool. In the present study, performance of multilayer hard coatings (TiC/TiCN/Al2O3) on cemented carbide substrate using chemical vapor deposition (CVD) for machining of hardened AISI 4340 steel was evaluated. Here the cutting parameters are analyzed on machinability aspect using Taguchi technique. Response surface plots were generated for the study of effects of cutting conditions on machinability factors. The analysis of the result revealed that, the optimal combination of low feed rate and low depth of cut with high cutting speed is beneficial for reducing machining forces. Higher values of feed rate are necessary to minimize the specific cutting force. Cora Lahiff et al. (2007) uses polycrystalline cubic boron nitride (PCBN) cutting tools in hard turning applications. This area of study was focuses on the continuous turning of hardened steels. Also here primary wear mechanism and modes of PCBN tool failure was discussed. This paper also provides knowledge regarding critical factors, those influence the PCBN tool behavior in hard turning and method of optimize the tool performance. Ozel and Karpat (2005) uses neural network modeling to predict tool wear and surface roughness patterns seen in finish hard turning processes. Decrease in the feed rate resulted in better surface roughness but slightly faster tool wear development, and increasing cutting speed resulted in significant increase in tool wear development but resulted in better surface roughness. This paper also concluded that increase in the work piece hardness resulted in better surface roughness but higher tool wear. Overall, CBN inserts with honed edge geometry performed better both in terms of surface roughness and tool wear development. Pavel et al. (2005) studied the effect of tool wear on surface finish for a case of continuous and interrupted hard turning. The paper presents the results of an experimental study focused on the influence of tool wear on surface finish in interrupted and continuous OD hard turning. The outcome was in the case of continuous cutting, the Ra, Rz and Rpk tend to increase significantly with tool wear, while in the case of interrupted cutting the opposite effect is recorded. In interrupted cutting a special care should be given to burr formation, which can damage the adjacent surfaces. D.I.Lalwani et al. (2008) attempted to investigate the effect of cutting parameters (cutting speed, feed rate and depth of cut) on cutting forces (feed force, thrust force and cutting force) and surface roughness in finish hard turning of MDN250 steel (equivalent to 18Ni (250) maraging steel) using coated ceramic tool through response surface methodology (RSM) and sequential approach using face centered central composite design. The results show that cutting forces and surface

roughness do not vary much with experimental cutting speed in the range of 55–93 m/min. A linear model best fits the variation of cutting forces with feed rate and depth of cut. Depth of cut is the dominant contributor to the feed force, accounting for 89.05% of the feed force whereas feed rate accounts for 6.61% of the feed force. In the thrust force, feed rate and depth of cut contribute 46.71% and 49.59%, respectively. In the cutting force, feed rate and depth of cut contribute 52.60% and 41.63% respectively, plus interaction effect between feed rate and depth of cut provides secondary contribution of 3.85%. More et al. (2006) studied the performance of CBN–TiN coated carbide inserts and PCBN compact inserts in turning AISI 4340 hardened steels. It was found that flank wear occurs mainly due to abrasive actions of the martensite present in the hardened AISI 4340 alloy. The crater wear of the CBN-TiN coated inserts was found to be less than that of the PCBN inserts because of the lubricity of TiN capping layer on the CBN–TiN coating. PCBN shows greater tool life. Thus, the objective of the present work has been set to have a systematic study to evaluate the performance of multilayer coated carbide tools. 1. Assessment of cutting performance of multilayer coated carbide cutting tools (TiN/TiCN/Al2O3) of grade K10-25 in hard turning of EN31 steel (55 HRC) under varying process parameters such as cutting speed, feed and depth of cut with respect to surface roughness (Ra and Rz) using Taguchi L16 orthogonal array design. 2. Develop the mathematical model for responses (Ra and Rz) using response surface methodology (RSM) and checked for its accuracy. 3. Optimize the process parameters by Taguchi approach.

3. Experimental procedure To comply with the objectives of the research, the work-piece specimens were taken in the form of round bars of diameter 40 mm, EN31 steel (55 HRC). The machine tool used was a high rigid CNC lathe having spindle speed of 3500 rpm (maximum) and power of 16 KW with sinumeric controller described earlier. In tests, coated carbide inserts (TiN / TiCN / Al2O3) of ISO designation CNMG 120408 (800 diamond shaped insert) mounted on a PCLNR2525 M12 tool holder has been employed for experimentation. The heat treated samples were cleaned by removing the hardened outer skin by machining to get the required diameter. The end faces were turned. The cutting parameters and their levels are shown in Table. All 16 experimental runs (based on L16 orthogonal array design) have been conducted with new cutting edge each under dry cutting environment. The responses

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(surface roughness) are measured by Taylor Hobson (Surtronic 25) surface roughness tester, The measurement was repeated four times and average value was reported. Also analysis of variance (ANOVA) has been performed to study the significant factors affecting surface roughness in hard turning. The machining time was fixed as 3 minute for each run.

4. Results and discussions Table 1 Experimental results. d (dept h of cut) Ru n no. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

f (feed)

v (Cuttin g speed)

mm

mm/re v

m/min

0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4

0.04 0.08 0.12 0.16 0.04 0.08 0.12 0.16 0.04 0.08 0.12 0.16 0.04 0.08 0.12 0.16

70 110 150 190 110 70 190 150 150 190 70 110 190 150 110 70

(Ra) µm 1.09 0.34 0.51 1.15 0.3 0.52 1.01 1.33 0.2 0.78 0.56 1.2 0.4 0.46 0.49 1.2

(Rz) µm 3.38 3.12 5.72 6.05 1.9 2.77 5.2 6.74 1.5 4.34 3.18 6.12 3.24 3.45 3.89 6

increasing cutting speed up to 110 m/min and then the surface roughness increased with increase of cutting speed. In addition, at higher cutting speed up to 110 m/min, there was expected a relative stability of the machining system and improves the surface finish. An increase of feed deteriorated the surface finish with feed noted as a detrimental factor and highly significant noticed from ANOVA study (Table 2 and 3). The surface roughness increased as the feed rate increases because the surface roughness is proportional to the square of the feed rate. In addition, the dynamic instability came up at higher feed. These prove detrimental to the surface quality and result was seen in terms of increasing Ra values. Interestingly, in spite of varying feed, the resulting surface quality obtained with multilayer coated carbide tool remained within the acceptable limit of 1.6 microns, which was comparable to the surface quality that can be obtained through grinding. The main effect plots show that the surface roughness parameters (Ra and Rz) were bit higher at low depth of cut compared to that at higher depth of cut. However, the difference of surface roughness value (Ra and Rz) was very marginal and found to be insignificant from the ANOVA study. From ANOVA analysis, feed was found to the most significant variable for both surface roughness parameters (Ra and Rz) as their P value is less than 0.05. Cutting speed and depth of cut were found to be insignificant for surface roughness. From the extensive study, the potential and effectiveness of TiN/TiCN/Al2O3 multilayer coated carbide insert were noticed to be efficient while turning hardened steels under dry environment even during high speed operations.

Main Effects Plot (data means) for Ra d

f

1.2 1.0

4.1. Surface roughness Mean of Ra

It is evident from the tabulated data (Table 1) that, the surface roughness parameters i.e. Ra (0.21.33 microns) and Rz (1.5-6.74 microns) of work piece machined using coated carbide insert were quite less at all conditions of cutting selected. Particularly, when machined by coated carbide insert, the arithmetic surface roughness average (Ra) value was within the recommendable limit of 1.6 microns. This revealed the potential of multilayer coated carbide insert in hard turning instead of grinding operation. Better surface quality generated using coated carbide insert even 0.2 microns at run 9 justify its application during hard machining. Fig 1 and 2 shows the influence of cutting speed, feed and depth of cut as main effect plot on the average surface roughness and maximum peakto-valley height during turning of hardened EN31 steel. The surface quality appeared better with

0.8 0.6 0.4 0.1

0.2

0.3

0.4

150

190

0.04

0.08

0.12

v 1.2 1.0 0.8 0.6 0.4 70

110

Figure 1 Main effect plot of Ra.

0.16


surface roughness (Ra and Rz) and machining parameters were expressed as follows:

Main Effects Plot (data means) for Rz d

f

6 5

Mean of Rz

4 3 2 0.1

0.2

0.3

0.4

0.04

0.08

0.12

0.16

v

Ra = 3.921 – 7.7006 d – 25.517 f – 0.0306 v – 1.625 d2 + 85.9375 f2 + 0.0001 v2 + 39.6591 df + 0.0374 dv + 0.0577 fv R2 = 97.8 %, R-sq (adj) = 94.4 % ------------------ (1)

6

Rz = 6.833 – 37.412 d + 1.982 f – 0.024 v + 19.375 d2 + 127.344 f2 + 0.00 v2 + 94.205 df + 0.12 dv – 0.074fv R2 = 95.5 %, R-sq (adj) = 88.9 % ---------------- (2)

5 4 3 2 70

110

150

190

Figure 2 Main effect plot of Rz.

Table 2 ANOVA for Ra. Source

DF

SS

MS

F

P

d

3

0.0627

0.0209

0.25

0.86

f

3

1.3741

0.458

5.45

0.038

0.89

0.498

v

3

0.2246

0.0748

Error

6

0.5047

0.0841

Total

15

2.1661

Table 3 ANOVA for Rz. Source

DF

SS

MS

F

P

d

3

1.2278

0.4092

0.51

0.692

f

3

30.7002

10.2334

12.69

0.005

v

3

2.4242

0.8081

1

0.454

Error

6

4.8401

0.8067

Total

15

39.1923

4.2. Response surface model Considering the response variable values as output, and process parameters as inputs; it is possible to attain a second order (quadratic) model expressing the relationship between the output and inputs for inserts in the machining with 95% confidence level. These model equations were used to develop cutting temperature and surface roughness contours for different cutting conditions. Response surface methodology (RSM) is a collection of mathematical and statistical techniques that are useful for the modeling and analysis of problems (Montgomery, 1997). These relations were then modeled by using least square error fitting of the response surface. These models would be of great use during the optimization of the process variables. RSM methodology was practical, economical and relatively easy for use. The relationship between the

The important coefficient, R2, called determination coefficients, defined as the ratio of the explained variation to the total variation and is a measure of the degree of fit. When R2 approached to unity, the better the response model fits the actual data. The RSM model presented high determination coefficient (R2 = 0.97 and 0.95 close to unity) explaining 97% and 95% of the variability in the Ra and Rz which indicates the goodness of fit for the model and high significance of the model. When R2 is closer to the 1, the better the estimation of regression equation fits the sample data. In our model the adjusted R2 value was very close to the predicted R2. So the predicted R2 was in reasonable agreement with the adjusted R2. The adjusted R2 value was particularly useful when comparing models with different number of terms. It is understood that unnecessary terms are not added in the model. The statistical significances of the fitted quadratic model for the surface roughness parameters were evaluated by the F-test and P-value (probability of significance) of ANOVA. When the P-value of the term of models was less than 0.05 (at 95% confidence), this indicated that the obtained models were considered to be statistically significant. In the ANOVA table, the degrees of freedom were used to calculate the mean square (MS). In general, the degrees of freedom measured how much ‘independent’ information was available to calculate each sum of squares (SS). The ANOVA of quadratic regression model (Table 4 and 5) demonstrated that the model was highly significant, as the F-value was higher than critical Fvalue and P value was less than 0.05 at 95 % confidence level to represent the relationship between the machining response and the considered machining parameters of the hard turning process. Table 4 ANOVA for Ra model. Source

DF

SS

MS

F

P

Regression

9

2.1177

0.2353

29.16

0.000

Residual error Total

6

0.0484

0.008

15

2.1661

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Table 5 ANOVA for Rz model.

4.3 Optimization

Source

DF

SS

MS

F

P

Regression Residual error Total

9 6

37.4475 1.7448

4.1608 0.2908

14.31

0.002

15

39.1923

Furthermore, the actual (experimental) and predicted values from response surface equations were shown in Fig. 3 and 4. They were very close to each other for all responses (i.e. cutting temperature and surface roughness parameters). It is clearly understood that the developed models were quite adequate and predicts the responses well.

Finally optimal results are verified by conducting some confirmation runs shown in Table 6. The improvements of S/N ratio from initial parameter level to optimal parameter level are found to be 1.5145dB and 1.8551 dB respectively for Ra and Rz respectively.

Experimental Predicted

1.4 1.2 1.0 0.8 0.6

Main Effects Plot (data means) for SN ratios (Ra) d

0.4

f

7.5 5.0

0.2

2.5

Mean of SN ratios

Surface roughness (Ra)

The major focus of research is to find cutting conditions for which desired surface roughness can be achieved. The optimization of process parameter has been done using Taguchi approach and taking signal-to-noise ratio based on lower-the-better characteristics (Nalbant et al., 2007). The main effect plot for S/N ratio has been plotted (Fig. 5 and 6) and optimal combination has been taken considering the higher S/N ratio. From main effect plot of S/N of Ra and Rz, the optimal parametric combination for Ra has been found to be d4 (0.4 mm)-f1 (0.04 mm/rev)-v2 (110 m/min) and for Rz, it has been found to be d3 (0.3 mm)-f1 (0.04 mm/rev)-v2 (110 m/min).

0.0 0

2

4

6

8

10

12

14

16

18

Observations

0.0 0.1

0.2

0.3

0.4

150

190

0.04

0.08

0.12

0.16

v 7.5 5.0 2.5 0.0

Figure 3 Experimental vs. predicted values of surface roughness (Ra).

70

110

Signal-to-noise: Smaller is better

7

Main Effects Plot (data means) for SN ratios (Rz) d

6

f

-8 -10

5

-12

Mean of SN ratios

Maximum Peak-to-valley height (Rz)

Figure 5 Main effect plot for S/N ratios (Ra) Experimental Predicted

4 3

-14 -16 0.1

0.2

0.3

0.4

150

190

0.04

0.08

0.12

0.16

v -8 -10 -12

2

-14 -16

1

70

0

2

4

6

8

10

12

14

16

18

110

Signal-to-noise: Smaller is better

Observations

Figure 6 Main effect plot for S/N ratios (Rz) Figure 4 Experimental vs. predicted values of maximum peak-to-valley height (Rz).


Table 6 Results of confirmation experiment

Level

Initial Process Parameters d2-f2-v2

Optimal Process Parameters d4-f1-v2 (Ra)/d3-f1v2(Rz)

Ra 0.5 0.42 Rz 2.6 2.1 S/N Ratio (Ra) 6.0205 7.535 S/N Ratio (Rz) -8.2994 -6.4443 Improvement of S/N Ratio (Ra) = 1.5145 Improvement of S/N Ratio (Rz) = 1.8551

5. Conclusions Based on the above discussions, following conclusions can be drawn. 1.

2.

3.

4.

5.

6.

7.

Better surface quality generated using coated carbide insert even 0.2 microns at run 9 justify its application during hard machining. The improved surface roughness obtained from coated carbide may be attributed to the retained cutting edge geometry with better hardness, wear resistance and low friction properties. An increase of feed deteriorates the surface finish with feed noted as a detrimental factor and highly significant noticed from ANOVA study. The RSM model presented high determination coefficient (R2 = 0.97 and 0.95 close to unity) explaining 97% and 95% of the variability in the Ra and Rz which indicates the goodness of fit for the model and high significance of the model. The experimental and predicted values are very close to each other. From main effect plot of S/N of Ra and Rz, the optimal parametric combination for Ra has been found to be d4 (0.4 mm)-f1 (0.04 mm/rev)-v2 (110 m/min) and for Rz, it has been found to be d3 (0.3 mm)-f1 (0.04 mm/rev)-v2 (110 m/min). The improvements of S/N ratio from initial parameter level to optimal parameter level are found to be 1.5145dB and 1.8551 dB respectively for Ra and Rz respectively. From the experimental investigation and observations on surface roughness in hard turning with multilayer coated carbide tools, it has been concluded that the coated carbide tool out performs over the range of parameters chosen.

References Grezesik, W. (2008), Influence of tool wear on surface roughness in HT using differently shaped ceramic tools, Wear, Vol. 265, pp. 327-335. Lahiff, C., Gordon, S., and Phelan, P. (2007), PCBN tool wear modes and mechanism in finish hard turning, RCIM, Vol. 23, pp. 638-644. Lalwani, D.I., Mehta, N.K., and Jain, P.K. (2008), Experimental investigations of cutting parameters influence on cutting forces and surface roughness in finish hard turning of MDN250 steel, Journal of materials processing technology, Vol. 206, pp. 167179. Montgomery, D.C. (1997), Design and Analysis of Experiments, fourth ed., Wiley, New York. More, A.S., Jiang, W., Brown, W.D. and Malshe, A.P. (2006), Tool wear and machining performance of CBN–TiN coated carbide inserts and PCBN compact inserts in turning AISI 4340 hardened steel, Journal of Materials Processing Technology, Vol. 180, pp. 253-262. Nalbant,M., Go¨kkaya, H., and Sur, G. (2007), Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning, Materials and Design, Vol. 28, pp. 1379-1385. Ozel, T., and Karpat, Y. (2005), Predictive modelling of surface roughness and tool wear in hard turning using regression and neural networks, International Journal of Machine Tools and Manufacture, Vol. 45, pp. 467-479. Pavel, R., Marinescu, I., Deis, M., and Pillar, J. (2005), Effect of tool wear on surface finish for a case of continuous and interrupted hard turning, Journal of Materials Processing Technology, Vol. 170, pp. 341-349. Suresh, R., Basavarajappa, S., and Samuel, G.L. (2012), Some studies on hard turning of AISI 4340 steel using multilayer coated carbide tool, Measurement, Vol. 45, pp. 1872-1884. Zhou, J.M., Andersson, M., and St˚ ahl, J.E. (2004), Identification of cutting errors in precision hard turning process, Journal of Materials Processing Technology, Vol. 153-154, pp. 746-750.

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