Performance investigation of surface roughness in hard turning of AISI 52100 steel - RSM approach

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ScienceDirect Materials Today: Proceedings 18 (2019) 261–269

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ICAMME-2018

Performance investigation of surface roughness in hard turning of AISI 52100 steel - RSM approach Venkat Pradeep Allua,b,∗, D. Linga Rajuc, S. Ramakrishnad aResearch

Scholar, Department of Mechanical Engineering, JNTUK, Kakinada, AP, India of Mechanical Engineering, Vignan's Institute of Engineering for Women, Visakhapatnam, AP, India cDepartment of Mechanical Engineering, JNTUK, Kakinada, AP, India dDepartment of Mechanical Engineering, Gayatri Vidhya Parishad College of Engineering, Visakhapatnam, AP, India bDepartment

Abstract The influence of type of insert, tool nose radius, cutting speed, feed rate and depth of cut on surface roughness during dry hard turning of AISI 52100 steel was investigated. ANOVA results show type of insert is the most influencing parameter with a contribution of 45.68%, nose radius with 34.11% and feed rate of 17.98%. A quadratic model of high adequacy (R2 = 99.03%) was predicted through Response surface methodology. Response surface optimizer revealed that wiper insert with nose radius of 1.2mm, feed of 0.05mm/rev, cutting speed of 70mm and depth of cut of 0.2mm yield optimum roughness of 1.69µm. © 2019 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Advances in Materials and Manufacturing Engi- neering, ICAMME-2018. Keywords: Hard turning; wiper inserts; nose radius; response surface methodology; surface roughness;

1.

Introduction

In recent days hard turning has been replacement for grinding operation. In general, turning of a material with hardness more than 45 HRC is termed as hard turning. Using hard turning, surface roughness and dimensional tolerances can be achieved as close to grinding operation. As compared to grinding, hard turning has advantages of time, cost and coolant savings. One of the biggest challenge in hard turning is to achieve finest surface integrity. Therefore, optimization of machining parameters is extremely essential [1]. Bartarya et al. [2]performed multiple turning operations on hardened steels using ceramic inserts and confirmed that type of cutting tool, tool geometry and process parameters play a vital role in achieving fine surface integrity and white layer. The authors also validated that the surface finish can be produced better than grinding if the optimum combination of type of insert, nose radius and feed are selected. Wiper geometry is an advanced cutting tool technology, with multi-radii cutting edge, which is shown in Fig. 1. Another characteristic of wiper tool is its enhanced chip-breaking ability. According to Vinayak Neelkanth et al. [3], wiper geometry provides better chip control at small feeds and smooth chip breaking at high feeds. Elbah et al. [4]conducted a comparative study on average surface roughness (Ra) produced by conventional and wiper ceramic insert in turning of AISI 4140 steel ∗

Corresponding author. Tel.: +919866317946. E-mail address: [email protected], [email protected]

2214-7853 © 2019 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Advances in Materials and Manufacturing Engineering, ICAMME-2018.

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(60HRC). Employed response surface methodology (RSM) and ANOVA (Variance analysis) to identify the influential parameters. Results revealed that feed rate has significant effect on Ra. In a study conducted by Aouicia et al. [5], turning operation was performed on AISI H11steel (50HRC). The parameters such as type of insert, cutting speed, feed and depth of cut were examined. RSM and ANOVA were used to find the significant parameters. The results indicate that type of insert and feed rate primarily affected Ra. A series of turning operations on hadfield steel were conducted by Horng et al. [6]. RSM model was developed to find the influence of nose radius, cutting speed, feed and depth of cut on Ra. The nose radius and cutting speed were the influential factors on Ra. Correia et al. [7] measured surface roughness while turning AISI 1045 carbon steel. A comparison was made between wiper and conventional carbide inserts. Results revealed that wiper inserts produced finer surface finish than conventional ones.

Fig. 1. Wiper geometry against conventional insert

Also roughness increased when feed rate is augmented. Singh and Rao [8] conducted experimental trails on AISI 52100 steel. An RSM model was developed to find the affecting parameters on surface roughness. It was concluded that feed and nose radius are primarily influencing Ra. In an experiment by Cakir et al. [9], coated carbide inserts were used for turning of AISI P20 steel. A linear and quadratic models were developed to predict Ra. Consequently, it was found that the second order regression model is more adequate. Grzesik and Wanat [10] performed a turning experiment on low chromium steel using ceramic tools, comparing conventional and wiper inserts. It was concluded that the wiper inserts have twice the surface finish of conventional inserts for same feed rate. Also, it was found that the wiper inserts exhibited improved bearing properties for definite cutting parameters. In an experimental work conducted by Gaitonde et al. [11], AISI D2 high chromium tool steel was machined using wiper and conventional ceramic inserts. From the quadratic equations developed, it was concluded that wiper inserts provide better surface finish and tool wear in comparison to conventional ones. Paulo Davim and Figueira [12] carried out turning experiment on hardened AISI D2 steel using wiper and conventional ceramic inserts. The influence of inserts on surface roughness and tool wear was inspected using ANOVA. Consequently, high dimensional tolerance and surface finish were observed in case of wiper inserts. In a study performed by Guddat et al. [13], the influence of wiper inserts on surface integrity and cutting forces was investigated. The hardened AISI 52100 steel was turned using PCBN wiper inserts. The surface and sub-surface examination revealed that the surface finish and compressive residual stresses were superior in wiper inserts. A work executed by Fnides and Yallese [14] aimed at evaluating cutting forces and surface roughness during turning of AISI H11 hardened steel (50 HRC) using mixed ceramic tool. The parameters inspected were cutting speed, feed and depth of cut. The mathematical models suggest that the thrust force is the major force component and the surface roughness was achieved very close to grinding. Meddour et al. [15] conducted 30 experimental runs to study the performance over roughness and tool forces in turning of AISI 52100 steel (59 HRC) using ceramic tool. The turning operation was held at various cutting speed, feed, depth of cut and nose radii. A mathematical model was established using RSM and was validated using ANOVA. It was confirmed that the un-deformed chip thickness got reduced, which helped in improving the surface

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finish. In a work done by Denni Kurniawan et al. [16], martensitic stainless steel was machined using TiAlN coated carbide insert with wiper geometry. RSM was employed to identify the influential parameters on tool life and surface roughness. Eventually, it was deduced that the wiper inserts used with coating were capable of machining hard materials. From the literature, it is evident that most of the hard turning operations are being done by ceramic and CBN tools, which are high in cost. Also many previous studies indicate that surface roughness of machined component is of high importance. An attempt is made to identify the optimum parameters which produce superior surface finish at low cost. The objective of present work is to examine the surface roughness in hard turning of AISI 52100 steel using conventional and wiper geometry. A regression model was developed using RSM and ANOVA was performed to identify the influential parameters. Response optimizer was used to optimize the parameters which produce minimum surface roughness. 2.

Experimental Procedure

In the present study, AISI 52100 steel of 50HRC was used as workpiece material. The experiment was carried out on the specimens of diameter 40mm and a cutting length of 250mm. The carbide inserts with different geometry, conventional and wiper (multi-radii) were used in the experiment. The inserts were a make of Sandvik Coromant with the following ISO designation : DNMG 11 04 08 PM 4325 (conventional, 0.8mm radius), DNMG 11 04 08 WF 4325 (wiper, 0.8mm radius), DNMG 11 04 12 PM 4325 (conventional, 1.2mm radius) and DNMG 11 04 12 WF 4325 (wiper, 1.2mm radius). The inserts were mounted onto a right hand tool holder with the ISO designation as PDJNR 25 25 M 11. The whole system yielded the following cutting geometry: 930 major cutting edge angle, 50 clearance angle, 800 including angle, 950 approach angle and -60 back rake angle. A 7.5 kW lathe machine with a spindle speed of 45-2000 rpm is used to perform the experiment. As indicated by the tool manufacture's catalogue and the previous study, the machining parameters were chosen. As per the Taguchi partial factorial design matrix (L 36 orthogonal array), 36 experimental runs were carried out using new cutting edge for each run. Type of insert and tool nose radius were selected at two levels. Whereas, cutting speed, feed rate and depth of cut were selected at three levels each. Table.1 shows the machining parameters and their levels. Throughout the experiment, the conventional inserts are coded as C and wiper as W. The schematic diagram illustrating the experimental procedure is presented in Fig. 2. Surface roughness of the machined component was measured using portable roughness meter (Mitutoyo Surftest SJ-210). Roughness was measured at three separate locations for each workpiece, and the average of each value was computed. The tester has the following specifications: 5µm- stylus tip radius, 0.5mm/s- traverse speed, 17.5mmmeasuring range and 4mN- measuring force.

Fig. 2. Schematic illustration of experimental procedure

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Table 1. Cutting parameters and levels Cutting Parameters

Unit

Type of insert

3.

Symbol

Levels 1

2

T

C

W

0.8

1.2

3

Tool nose radius

mm

r

Cutting speed

m/min

v

70

110

150

Feed rate

mm/rev

f

0.05

0.1

0.15

Depth of cut

mm

d

0.1

0.2

0.3

Results and discussions

3.1. Analysis of Variance (ANOVA) Variance analysis was performed on the results obtained through Taguchi experimental design matrix. The design of experiments with output responses are presented in Table 2. Minitab version 17 software was used to execute the analysis. ANOVA is used to identify the influential parameters affecting the response, i.e. surface

Fig. 3. Main effects plot for Ra

roughness. The significance of a parameter is determined from its corresponding P-value. As the confidence level is selected 95%, a parameter is said to be significant if the P-value is less than 0.05. Table 3 shows the ANOVA results performed for surface roughness. It is quite apparent that the type of insert, nose radius and the feed rate were the significant factors affecting roughness. Of these, type of insert exhibited maximum influence on the response, followed by nose radius and feed. The contributions were 45.68%, 34.11% and 17.98% respectively. It is also found that cutting speed has very less influence, whereas depth of cut has negligible effects over roughness. Main effect graphs were plotted to identify the individual influence of parameter over the roughness, which are presented in Fig. 3. From the figure, wiper insert exhibited enhanced surface finish compared to conventional ones. The average value of roughness by conventional insert is 5.2µm, while by wiper insert is 2.9µm. The surface finish got improved by 67.9% when wiper inserts are used against conventional inserts. This enhancement may be due to the modified geometry (multi-radii) in wiper insert which wipes off the additional peaks of the surface roughness. While turning, as the main edge of insert cuts the surface, the additional radii wipe off the surface irregularities providing improved surface finish. Fig. 4. compares the roughness profile of the workpiece machined using conventional and wiper insert at 1.2mm nose radius, 70m/min cutting speed, feed rate of

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0.15mm/rev and 0.3mm depth of cut. It is quite evident that the roughness profile is more uniform in case of wiper insert than conventional insert through the complete sampling length. The conventional insert exhibited higher crests and troughs with irregular profile. Table 2. L36 design matrix with output response Type of insert

Nose radius (mm)

Cutting speed (m/min)

Feed (mm/rev)

DOC (mm)

Ra (µm)

C

0.8

70

0.1

0.1

5.42

C

0.8

110

0.15

0.2

6.89

C

0.8

150

0.05

0.3

4.56

C

0.8

70

0.1

0.2

5.46

C

0.8

110

0.15

0.3

6.91

C

0.8

150

0.05

0.1

4.32

C

0.8

70

0.15

0.2

6.23

C

0.8

110

0.05

0.3

4.15

C

0.8

150

0.1

0.1

5.62

C

1.2

70

0.15

0.2

5.42

C

1.2

110

0.05

0.3

4.15

C

1.2

150

0.1

0.1

5.12

C

1.2

70

0.15

0.3

5.92

C

1.2

110

0.05

0.1

4.23

C

1.2

150

0.1

0.2

5.03

C

1.2

70

0.15

0.1

5.74

C

1.2

110

0.05

0.2

3.86

C

1.2

150

0.1

0.3

5.14

W

0.8

70

0.05

0.2

2.96

W

0.8

110

0.1

0.2

3.16

W

0.8

150

0.15

0.3

3.92

W

0.8

70

0.05

0.1

2.9

W

0.8

110

0.1

0.2

3.24

W

0.8

150

0.15

0.3

4.01

W

0.8

70

0.05

0.2

2.95

W

0.8

110

0.1

0.3

3.38

W

0.8

150

0.15

0.1

3.87

W

1.2

70

0.05

0.3

1.56

W

1.2

110

0.1

0.1

2.45

W

1.2

150

0.15

0.2

3.84

W

1.2

70

0.1

0.3

2.25

W

1.2

110

0.15

0.1

3.41

W

1.2

150

0.05

0.2

1.72

W

1.2

70

0.1

0.1

2.23

W

1.2

110

0.15

0.2

3.37

W

1.2

150

0.05

0.3

1.79

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Table 3. ANOVA for Ra Source Linear

DF

Sum of squares

Contribution

F-Value

P-Value

5

68.6804

96.10%

274.67

0.000

Type of insert

1

47.0596

45.68%

688.93

0.000

Speed (m/min)

1

0.0004

3.27%

3.71

0.071

Feed (mm/rev)

1

17.2735

17.98%

67.75

0.000

DoC (mm)

1

0.0325

1.03%

0.42

0.957

Radius (mm)

1

4.4944

34.11%

215.21

0.000

Error

17

0.696

0.97%

Total

35

71.6531

100.00%

Significant

Significant

Significant

Fig. 4. Roughness profile of (a) conventional and (b) wiper insert.

From Fig. 3, roughness increases with the increase in feed rate. This is in agreement to the basic principle of metal cutting [1]. Ra=f2/32r

(1)

where f is feed rate in mm/rev and r is tool nose radius in mm. The feed rate of 0.15mm/rev resulted in highest average surface roughness of 4.96µm whereas 0.05mm/rev produced an average of 3.26µm. This trend may be due to increased friction with the increase in feed, which elevates the tool-workpiece temperature enormously, causing severe plastic deformation. Also form Fig.3, surface roughness improves with larger tool nose radius. This is in

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agreement with equation 1, which shows the inverse relation between surface roughness and nose radius. Nose radius of 0.8mm produced an average roughness of 4.44µm while 1.2mm yielded 3.73µm. This may be due to the improved material side flow with larger nose radius, resulting in reduced temperature and shear deformation. Change in cutting speed and depth of cut have negligible effects on roughness, which can be observed from Fig.3. Fig. 5 shows the 3D surface plots drawn for the combination of influential parameters on roughness. From Fig. 5a, roughness is high when conventional insert with 0.8mm nose radius was used. The least value of roughness is obtained with wiper insert having 1.2mm nose radius. Fig. 5b illustrates that the roughness is maximum when cutting is performed with conventional insert at 0.15mm/rev feed. Though wiper insert produced better surface finish, higher feed rate deteriorated the finish. Roughness was least when wiper insert is used at the feed rate of 0.05mm/rev. Fig. 5c reveals that lower roughness was obtained when higher nose radius (1.2mm) with low feed rate (0.05mm/rev) is selected. Roughness scaled up to highest value when low nose radius (0.8mm) with high feed (0.15mm/rev) was chosen. Surface Plot of Ra (µm) vs r (mm), Type of in

Surface Plot of Ra (µm) vs f (mm/rev), Type of

6

R a (µm)

Surface Plot of Ra (µm) vs f (mm/rev), r (m

6

R a (µm)

4

6

R a (µm)

4

1 .2 2

1 .0 1.0 1.5

Type of insert

2 0.1 0

r ( mm) 1.0

2 .0

(a)

4

0.15

1.5

0.8

Type of insert

2.0

0.15 2

f (mm

0.05

(b)

0.1 0 0.8 1.0

r (mm)

1.2

f (mm

0.05

(c)

Fig. 5. 3D surface plots for Ra vs. (a) Type of insert and radius, (b) Type of insert and feed and (c) radius and feed.

3.2. Prediction model Regression technique was employed to establish a prediction model for surface roughness. The factors investigated were type of insert, nose radius, cutting speed, feed and depth of cut. A quadratic regression equation was developed using response surface methodology. The determination coefficient (R2) for the generated model was inspected. The model term and the regression equation were shown below. Ra (µm) = 8.45 - 0.748 T - 3.91 r + 0.0040 v - 6.11 d+ 0.47 f - 0.000109 v *v + 5.95 d *d + 62.1 f *f 0.407 T*r - 0.00401 T*v + 0.287 T*d - 8.59 T*f+ 0.01873 r *v+ 1.24 r*d + 6.32 r *f + 0.0067 v *d + 0.0875 v*f + 13.0 d*f R2=99.03%, R2 (adj)= 98.00% From the obtained model, it was found that the determination coefficient (R2) is very close to unity, which denotes high adequacy of the model. Furthermore as the R2 and adj R2 values are very close to each other, the model is considered to be highly accurate. This signifies the existence of excellent correlation between the input factors and the output response. Fig. 6 shows the probability plot drawn for the residuals of the surface roughness model. From the figure, the residuals fall quite near to the mean line without diverging from the upper and lower deviation curves, being very close to the mean line. This reveals that there is no much variation between the actual and predicted values. Henceforth, this analysis validates the established model being accurate for predicting surface roughness.

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V. Pradeep Allu et al. / Materials Today: Proceedings 18 (2019) 261–269 Probability Plot of Ra (µm) Normal - 95% CI

99

Mean StDev N AD P-Value

95 90

4.088 1.431 36 0.245 0.743

80

Percent

70 60 50 40 30 20 10 5 1

0

1

2

3

4

5

6

7

8

9

Ra (µm)

Fig. 6. Normal probability plot for roughness

3.3. Response optimizer Response surface optimization is one of the best practice to identify the best possible cutting parameters in machining. In the present work, the goal is to minimize the surface roughness using response optimization through RSM approach. The optimized factors with minimum roughness was shown in Fig. 7. From the figure, the optimum parameters of 70m/min cutting speed, feed of 0.05mm/rev and depth of cut of 0.2mm with wiper insert having 1.2mm nose radius generate minimum roughness of 1.69µm.

Fig. 7. Response surface optimization for surface roughness

4.

Conclusions

Analysis of surface roughness, regression model and parametric optimization in hard turning using conventional and wiper inserts is performed. The significance of cutting parameters over roughness is investigated using ANOVA and surface plots. A regression model is developed using RSM to predict the roughness. Response optimizer was employed to optimize the parameters. From the above analysis, the following conclusions are drawn. 

Wiper geometry insert provided improved surface finish of 67.9% as compared to conventional insert.



Surface roughness is mainly influenced by type of insert followed by tool nose radius and feed rate. The cutting speed and depth of cut are found to be insignificant.

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

On the surface roughness, type of insert, nose radius and feed rate has a contribution of 45.68%, 34.11% and 17.98% respectively.



The developed regression model to predict roughness has high adequacy with correlation coefficient, R2=99.03%. This was validated using 3D surface and normal probability plots.



The optimized parameters to obtain minimum surface roughness (1.69µm) are wiper insert with 1.2mm nose radius, cutting speed of 70m/min, feed rate of 0.05mm/rev and depth of cut of 0.2mm.

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