EXPERIMENTAL INVESTIGATIONS ON MATERIAL REMOVAL RATE AND SURFACE ROUGHNESS IN TURNING OF EN 8 STEEL

International Journal of Mechanical and Production Engineering Research and Development (IJMPERD) ISSN (P): 2249–6890; ISSN (E): 2249–8001 Vol. 10, Issue 3, Jun 2020, 1769-1776 © TJPRC Pvt. Ltd.

EXPERIMENTAL INVESTIGATIONS ON MATERIAL REMOVAL RATE AND SURFACE ROUGHNESS IN TURNING OF EN 8 STEEL CH. LAKSHMI SRINIVAS1, P. UMAMAHESWARRAO2, T. SRINAG3 & M. CHANDRA SEKHAR4 1 2 3

Professor, Department of Mechanical Engineering, Bapatla Engineering college, Andhra Pradesh, India

Assistant Professor, Department of Mechanical Engineering, Bapatla Engineering college, Andhra Pradesh, India

Assistant Professor, Department of Mechanical Engineering, Prasad V. Potluri Siddhartha Institute of Technology, Andhra Pradesh, India

4

Professor, Department of Mechanical Engineering, Chalapathi Institute of Engineering & Technology, Andhra Pradesh, India ABSTRACT The influence of cutting parameters on Material Removal Rate (MRR) while turning of EN 8 steel is presented in this article. Tests were planned and executed based on Central Composite Rotatable Design (CCRD) of Response Surface Method (RSM). Analysis of variance was performed to investigate the influence of machining parameters on MRR. Results reported that MRR was significantly affected by Depth of Cut (DOC) followed by spindle speed and fee rate.

KEYWORDS: Turning, Material Removal Rate, EN 8 Steel, Speed & Feed

Received: May 25, 2020; Accepted: Jun 15, 2020; Published: Jun 30, 2020; Paper Id.: IJMPERDJUN2020162

INTRODUCTION

Original Article

Surface roughness was notably affected by speed followed by depth of cut and feed

The given machining process is said to be a good process when it can improve MRR and produce a good surface finish (Ra), which is mandate to accomplish the customers need [1-2]. Hassan et al. [3] studied the influence of machining parameters on MRR while turning C34000. They concluded that machining speed and feed will mainly influence the MRR. Kaladhar et al. [4] also evaluated the influence of machining parameters on surface finish and MRR while turning AISI 304 steel. Results revealed that DOC and feed rate are the noteworthy elements influencing MRR. Dinesh et al. [5] examined the influence of cutting parameters on MRR while turning EN 24 alloy steel using cemented carbide tool. Results revealed that speed and DOC have substantial effect on MRR. Baskaran et al.

[6] discussed the influence of machining factors on MRR while turning EN 8 steel on CNC machine using coated carbide tool.

Taguchi method was used by Sujit Kumar Jha [7] for optimizing machining parameters while turning Mild steel bar with TIN-coated carbide tools. It was observed that speed, DOC and feed will mainly affect the MRR. Venkateshwar Reddy et al. [8] examined the effect of machining parameters on MRR while machining EN8 steel using tungsten carbide tool on CNC Lathe. Venkata Ramana and Goutham kumar [9] studied the effect of input parameter on MRR while Turning AISI 321 Stainless Steel by using Taguchi Methodology. Results revealed that DOC has a significant influence on MRR. Sreejith Nair et al optimized [10] machining parameters while turning of AISI 1040 steel. Sayak Mukherjee et al. [11] optimized process parameters by using Taguchi technique while www.tjprc.org

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turning SAE 1020 steel.

EXPERIMENTAL DETAILS Machining was carried out on a CNC lathe having spindle speed 50-3500 rpm (model: super jobber CNC). Workpiece of length 60 mm and Ø32 mm was used for experimentation. The length of machining is 40 mm in the present study.

Before and after machining the workpiece was cleaned, dried and weighed to determine the MRR. In this study a total of 20 experiments were performed by varying feed rate, spindle speed and DOC for dry machining conditions.

Figure 1 shows experimental setup and workpieces after machining are shown in Figure 2. CNMG 09 03 08-PF 4325 type tool was deployed for experimentation made by Sandwik Coromant. Specification of tools is provided in Table 1. Parameters and levels were depicted in Table 2. Experimental matrix with response is depicted in Table 3.

Table 1: Tool Specifications Parameter Value Length of cutting edge 9.671 mm Thickness the insert 3.175 mm Corner radius 0.8 mm Inscribed circle diameter 9.525 mm Grade 4325 Tool style code CNMG-PF Insert shape code C

Table 2: Levels of Process Parameters Factors Notation Levels 1 2 3 4 Spindle speed (rpm) ν 330 500 750 1000 Feed rate (mm/rev) f 0.09 0.125 0.175 0.225 DOC (mm) d 0.061 0.3 0.65 1.0

5 1170 0.259 1.238

Figure 1: Experimental setup

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Figure 2: Workpieces After Machining

S. No

Table 3: Experimental Matrix along with response Spindle speed Feed rate DOC MRR Ra (rpm) (mm/rev) (mm) (mm3/min) (µm)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

500 500 1000 1000 500 500 1000 1000 750 750 330 1170 750 750 750 750 750 750 750 750

0.125 0.225 0.125 0.225 0.125 0.225 0.125 0.225 0.09 0.259 0.175 0.175 0.175 0.175 0.175 0.175 0.175 0.175 0.175 0.175

0.3 0.3 0.3 0.3 1.0 1.0 1.0 1.0 0.65 0.65 0.65 0.65 0.061 1.238 0.65 0.65 0.65 0.65 0.65 0.65

50.9 44.6 47.7 77.7 86.3 144.6 158.3 270.1 60.0 143.6 50.2 153.8 96.3 203.1 111.8 102.5 111.8 111.8 111.8 111.8

2.53 2.90 2.04 3.41 4.08 2.92 1.03 1.38 3.40 2.13 2.59 0.98 2.56 1.33 2.63 2.58 2.6 2.18 2.64 2.47

RESULTS AND DISCUSSIONS From the main effects plot (Figure 3) it is clear that MRR is proportional to spindle speed, feed and DOC. High MRR is observed at higher cutting speeds because work-piece has more strain energy which leads to easy and continuous material removal. A higher feed rate promotes the higher MRR owing to fast axial movement of the tool wrt the workpiece. The results are in good agreement with the findings of Rajendra and Deepak [12].

Residuals for MRR indicate that they lie fairly close to the straight line as shown in Figure 4 & Figure 7. From the ANOVA (shown in Table 4) it is understood that DOC (59.2%) has highest influence on MRR followed by spindle speed (22.1%) and feed (15.5%). Mean response table (presented in Table 5) also indicated that DOC effect is high followed by cutting speed and feed. Estimated regression coefficients for MRR are presented in Table 6. Partial interaction is observed among speed & depth of cut, feed & depth of cut whereas no interaction is found between speed & feed (Depicted in Figure 5)

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Factors ν (rpm) f (mm/rev) d (mm) Error Total

Degrees of Freedom 4 4 4 7 19

Parameters ν (rpm) f (mm/rev) d (mm)

Table 4: ANOVA for MRR Sum of Mean FProbability Squares Squares ratio 12256 3064 0.96 0.460 8626 2157 0.63 0.652 32857 8214 4.48 0.014 1718 458 55457

Table 5: Mean Response Table for MRR Levels 1 2 3 4 5 50.2 81.6 116.45 138.45 153.8 60 85.8 116.49 134.25 143.6 96.3 55.225 106.91 164.82 203.1

% Contribution 22.1 15.5 59.2 3.09

Delta

Rank

103.6 83.6 147.8

2 3 1

Main Effects Plot for MRR Spindle Speed (rpm)

Feed rate (mm/rev)

200 150 Mean of MRR

100 50 330

500 750 1000 Depth of cut (mm)

1170

0.090

0.125

0.175

0.225

0.259

200 150 100 50 0.061

0.300

0.650

1.000

1.238

Figure 3: Main Effects Plot for MRR

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99

95 90 80

Percent

70 60 50 40 30 20 10 5

1

-50

-25

0 Residual

25

50

75

Figure 4: Normal probability plot of MRR

Interaction Plot (data means) for MRR 09 0.

0

5 5 5 9 1 0 0 0 8 12 .17 .22 .25 .06 .30 .65 .00 . 23 0.

0 0 0 0 0 0 1 1 180 120

Speed (rpm)

60 180 120

Feed (mm/rev)

60

Speed (rpm) 330 500 750 1000 1170 Feed (mm/rev) 0.090 0.125 0.175 0.225 0.259

Depth of cut (mm)

Figure 5: Interaction plot for MRR Table 6: Estimated Regression Coefficients for MRR Term Coef SE Coef T P Constant 110.19 5.923 18.604 0.000 Significant ν 49.19 6.605 7.447 0.000 Significant f 41.33 6.627 6.237 0.000 Significant d 75.76 6.608 11.464 0.000 Significant ν*ν -13.28 10.816 -1.228 0.248 Insignificant f *f -13.18 10.840 -1.216 0.252 Insignificant d*d 34.53 10.820 3.192 0.010 Significant ν*f 31.87 14.576 2.186 0.054 Significant ν*d 59.18 14.503 4.081 0.002 Significant f *d 52.00 14.589 3.564 0.005 Significant S = 14.52 R-Sq = 96.5% R-Sq(adj) = 93.4% www.tjprc.org

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Main Effects Plot (data means) for Surface roughness Speed (rpm)

Feed (mm/rev)

Mean of Surface roughness

3 2 1 330

500 750 1000 Depth of cut (mm)

1170

0.090

0.125

0.175

0.225

0.259

3 2 1 0.061

0.300

0.650

1.000

1.238

Figure 6: Main Effects Plot for Surface Roughness

Source ν (rpm) f (mm/rev) d (mm) Error Total

Table 7: ANOVA for Surface Roughness DF SS MS F P % Contribution 4 4.831 1.208 2.54 0.083 57.6 4 1.530 0.382 0.55 0.702 18.2 4 1.586 0.396 0.57 0.686 19.3 15 0.44 0.1187 4.9 8.387

Normal Probability Plot of the Residuals (response is Surface roughness)

99

95 90

Percent

80 70 60 50 40 30 20 10 5

1

-1.5

-1.0

-0.5

0.0 Residual

0.5

1.0

1.5

Figure 7: Normal Probability plot of Surface Roughness

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Interaction Plot (data means) for Surface roughness 0

.0

9

0 0

.1

2

5 0

.1

7

5 0

.2

2

5 0

.2

5

9 0

.0

1 6 0

.3

0

0 0

.6

5

0 1

.0

0

0 1

.2

3

8

3

2

Speed (rpm)

1 3

2

Feed (mm/rev)

1

Speed (rpm) 330 500 750 1000 1170 Feed (mm/rev) 0.090 0.125 0.175 0.225 0.259

Depth of cut (mm)

Figure 8: Interaction Plot for Surface Roughness Partial interaction is observed among all the parameters for surface roughness presented in Figure 8. From the ANOVA (shown in Table 7) it is understood that Speed (57.6%) has highest influence on surface roughness followed DOC (19.3%) and feed (18.2%). Mean response table is shown in Table 8 & estimated regression coefficients for Ra are presented in Table 9. Ra decreases with an increase in speed & depth of cut (shown in Figure 6) Table 8: Mean Response Table for Ra Parameters levels Delta 1 2 3 4 5 ν (rpm) 2.59 3.10 2.45 1.96 0.98 2.12 f (mm/rev) 3.4 2.42 2.25 2.65 2.13 1.27 d (mm) 2.56 2.72 2.42 2.35 1.33 1.39

Rank 1 3 2

Table 9: Estimated Regression Coefficients for Ra Term Coef SE Coef T P Constant 2.4201 0.1036 23.357 0.000 Significant ν -0.8996 0.2107 -4.269 0.001 Significant f -0.1500 0.2115 -0.709 0.491 Insignificant d -0.4302 0.2109 -2.040 0.062 Insignificant ν*f 0.8908 0.4651 1.915 0.078 Insignificant ν*d -1.6278 0.4627 -3.518 0.004 Significant f*d -0.9058 0.4655 -1.946 0.074 Insignificant S = 0.4633 R-Sq = 76.6% R-Sq(adj) = 65.9%

CONCLUSIONS The study investigated the effect of cutting parameters on MRR while turning EN 8 Steel on CNC Lathe. From the results following conclusions are drawn 

MRR was mainly influenced by DOC followed by spindle speed and feed rate.



From the ANOVA, depth of cut (59.3%) has highest contribution on MRR followed by speed (22.1%) and feed

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(15.5%). 

Ra was notably affected by speed followed by depth of and feed.



Speed (57.6%) has highest influence on surface roughness after that DOC (19.3%) and feed (18.2%).

REFERENCES 1.

Padmini, R., Krishna, P.V., & Rao, G.K.M. (2016), Effectiveness of vegetable oil based nanofluids as potential cutting fluids in turning AISI 1040 steel, Tribology International, Vol. 94, pp.490–501.

2.

Sahoo, A.K., Baral, A.N., Rout, A.K., & Routra, B.C. (2012), Multi-objective optimization and predictive modeling of surface roughness and material removal rate in turning using grey relational and regression analysis, Procedia Engineering, Vol.38, pp.1606–27.

3.

Kamal Hassan, Anish Kumar, Garg, M.P. (2012), Experimental investigation of Material removal rate in CNC turning using Taguchi method, International Journal of Engineering Research and Applications, Vol. 2, pp.1581-1590.

4.

Kaladhar, M., Venkata Subbaiah, K., Srinivasa Rao, Ch. (2012), Determination of Optimum Process Parameters during turning of AISI Austenitic Stainless Steels using Taguchi method and ANOVA, International Journal of Lean Thinking, Vol.3, pp.1-19.

5.

Dinesh, S., Rajaguru, K., Vijayan, V., Godwin Antony, A. (2016) Investigation and Prediction of Material Removal Rate and Surface Roughness in CNC Turning of EN24 Alloy Steel, Mechanics and Mechanical Engineering, Vol. 20, No. 4, pp.451–466.

6.

Baskaran, V., Prakash, S., & Lilly Mercy, J., (2017) Experimental Investigation of Machining Parameters In CNC Turning EN 8 Steel By Taguchi Design of Experiments, ARPN Journal of Engineering and Applied Sciences, Vol. 12, No. 20, pp.57515757.

7.

Sujit Kumar Jha, (2014), Optimization of Process Parameters For optimal MRR during Turning Steel Barusing Taguchi Method and ANOVA, International Journal of Mechanical Engineering and Robotics Research, Vol. 3, No. 3,

8.

Venkateshwar Reddy, P., Veerabhadra Reddy, B., Srinivasa Rao, P., & Mohana Krishnudu, D. (2019), Multi Response Optimization of Turning Process by Considering its Cutting Parameters Implementing Grey Relational Analysis, International Journal of Integrated Engineering, Vol. 11, No. 8, pp.109-117.

9.

Venkata Ramana, M.., & Goutham kumar. (2018), Optimization of Material Removal Rate in Turning of AISI 321 Stainless Steel Using Taguchi Methodology, Materials Today: Proceedings, Vol.5, pp.4965–4970.

10. Sreejith S Nair, Ramkumar, T., Selva kumar, M., & Fabian Netto. (2019), Experimental investigation of dry turning of AISI 1040 steel with TiN coated insert, Engineering Research Express, 1, 025038, pp.1-13.

11. Sayak Mukherjee, Anurag Kamal, & Kaushik Kumar. (2014), Optimization of Material Removal Rate During Turning of SAE 1020 Material in CNC Lathe using Taguchi Technique, Procedia Engineering, Vol.97, pp.29–35.

12. Rajendra, B., & Deepak, D. (2016), Optimization of Process Parameters for Increasing Material Removal Rate for Turning Al6061 Using S/N ratio, Procedia Technology, Vol.24 , pp.399–405.

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