INVESTIGATION OF TURNING PROCESS TO IMPROVE PRODUCTIVITY _MRR_ FOR BETTER SURFACE FINISH OF AL.pdf

International Journal of Design and Manufacturing Technology (IJDMT), ISSN 0976 – INTERNATIONAL JOURNAL OF DESIGN AND MANUFACTURING 6995(Print), ISSN 0976 – 7002(Online) Volume 4, Issue 1, January- April (2013), © IAEME

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ISSN 0976 – 6995 (Print) ISSN 0976 – 7002 (Online) Volume 4, Issue 1, January- April (2013), pp. 59-67 © IAEME: IAEME: www.iaeme.com/ijdmt.html Journal Impact Factor (2013): 4.2823 (Calculated by GISI) www.jifactor.com

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INVESTIGATION OF TURNING PROCESS TO IMPROVE PRODUCTIVITY (MRR) FOR BETTER SURFACE FINISH OF AL7075-T6 USING DOE U. D. Gulhane*, S. P. Ayare, V.S.Chandorkar, M .M . Jadhav

Department of Mechanical Engineering, Finolex Academy of Management and Technology, Ratnagiri, Maharashtra 415612, India *Corresponding author- Associate Professor, Dept. of Mechanical Engineering, Finolex Academy of Management and Technology, P-60/61, MIDC, Mirjole Block, RATNAGIRI- (M.S.) 415639, India ABSTRACT

Higher material removal rate with better surface finish is one of the prime requirements of today’s industry. The present paper investigate the effects of cutting parameters like spindle speed, feed and depth of cut on surface finish and material removal rate of Aluminium 7075-T6. Taguchi methodology has been applied to optimize cutting parameters. Feed rate is the most significant factor influencing surface finish whereas material removal rate is significantly affected by cutting speed. For highest MRR with better surface finish. Cutting speed (15.102 m/min) ,feed rate (0.3207 mm/rev.) and depth of cut (0.5 mm) are cutting parameters for higher MRR and optimum surface roughness . roughness, MRR, DOE, DOE, ANOVA, Al-7075 T6 Keywords: Surface roughness, INTRODUCTION

Surface finish is the method of measuring the quality of product and is an important parameter in machining process. It is one of the prime requirements of customers for machined parts. Productivity is also necessary to fulfill the customers demand. For this purpose quality of product and productivity should be high. In addition to surface finish quality, the material removal rate (MRR) is also an important characteristic in turning operation and high MRR is always desirable. Taguchi has proposed off line for quality improvement in place of an attempt to inspect quality in the product on the product line. He observed that no amount of an inspection can put quality back into the product but it merely treats a symptom. Taguchi has 59

International Journal of Design and Manufacturing Technology (IJDMT), ISSN 0976 – 6995(Print), ISSN 0976 – 7002(Online) Volume 4, Issue 1, January- April (2013), © IAEME

recommended three stages to achieve the desirable product quality by design Viz. System design, Parameter design and Tolerate design system which help to identify the working levels of the parameter. The optimal condition is selected so that influence of noise factors causes minimum variation to study performance. The orthogonal arrays, variance and signal to noise analysis are essential tool of parameter design. LITERATURE REVIEW

John et al. (2001) demonstrated a systematic procedure of using Taguchi parameter design to optimize surface roughness performance with particular combination of cutting parameters in end milling operation. Kopac et al. (2002) described the machining parameters influence and levels that provide sufficient robustness of machining process towards the achievement of the desired surface roughness for cold pre-formed steel workpieces in fine turning. Ihsan Korkut et al. (2004) carried turning tests to determine optimum machining parameters for machining of austenitic stainless steel. Ciftci (2006) investigated the machining characteristics of austenitic stainless steel (AISI 304 and AISI 316) using coated carbide tools. Zhang et al. (2007) have used Taguchi method for surface finish optimization in end milling of Aluminum blocks. G. Akhyar et al. (2008) optimized cutting parameters in turning Ti-6% Al-4% V extra low interstitial with coated coated and uncoated cemented carbide tools under dry cutting condition. Anirban Bhattacharya et al. (2009) estimated the effect of cutting parameters on surface finish and power consumption during high speed machining of AISI 1045 steel. Saeed Zare Chayoshi & Mehdi Taidari (2009) developed a surface roughness model in hard turning operation of AISI 4140 using CBN cutting tool.Adeel H.Suhail et al.(2010) conducted conducted experimental study to optimize the cutting parameters parameters using two performance measures,work piece surface temperature and surface roughness.D.Philip Selvaraj and P.Chandramohan P.Chandramohan (2010) concentrated with dry turning turning of AISI AISI 304 Austenitic Stainless Steel Nikolaoset al.(2010) developed a surface roughness model for turning of femoral heads from AISI 316L stainless steel. MATERIALS AND METHODS

The experimental investigation presented here was carried out on Crown lathe machine. The work piece material used for present work was AL7075-T6. The specification used for experimentation was of Al 7075-T6. Table 1 shows Chemical composition of Al 7075-T6 used for study.

Chemical composition Limits Weight % Minimum Maximum

Al

Si Fe Cu Mn Mg Cr Zn Ti 102 2.1 0.18 5.1 0. 0. Rem 4 5 2 0.3 2.9 0.28 6.1 0.2 Table1 Chemical composition composition of AL7075-T6

60

Each -

Tota l -

0.05

0.15


It was subjected to turning operation, which was carried out on Lathe machine (Crown lathe machine). As Al 7075-T6 is soft material HSS tool was selected. HSS leaves a better finish on the part and allows faster machining. HSS tool can withstand moderate temperature. Cylindrical specimen of 15mm diameter was safely turned in three jaw chuck by supporting the free end of work. As the work piece was quite long it was needed to face and centre drill the free end supported by the tail stock. Without such support, the force of the tool on the work piece would cause it to bend away from the tool, producing a strangely shaped result In this experiment,in order to investigate the surface finish of the machined machined workpiece and material removal rate,during cutting of the AL 7075-T6,HSS tool was used.A view of the cutting zone arrangement is shown in Fig.1 The surface roughness of the finished work surface was measured with the help of a surface roughness tester. The material,characteristics of tool and detail of experimental design set-up are listed in Table 2 and conditions are given in Tables 3

Fig. 1 View of cutting zone (Actual arrangement and schematic arrangement)

Table 2: Machining parameters and level

Machining Parameters

Level 1

Level 2

Level 3

Cutting speed (m/min) Depth of cut (mm)

9.354 0.1146

15.102 0.3207

23.004 0.3345

Feed rate (mm/rev)

0.5

1

1.5

For MRR machining time for each sample sample has been calculated accordingly. accordingly. After machining, weight of each machined parts have been a gain measured precisely with the help of digital balance meter. RESULTS AND DISCUSSION

Table 3 shows experimental design matrix and surface surface roughness value (Ra) and MRR for Al 7075-T6. S/N ratio for surface roughness is calculated using lower the better characteristics and S/N ratio for MRR is calculated using larger the better characteristic shown in Table 3.The S/N ratio is calculated using equation (1) and equation (2)

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௡ ଶቁ -------- (1) ൗ ൌ െ10 lolog ௡ଵቀ∑௜ୀଵ ௡ ଵ ቁ --------- (2) ൗ ൌ െ10 lolog ௡ଵቀ∑௜ୀଵ ௒௜మ Table 3-Experimental design matrix and response variable

Expt.

Turning Parameters

Surface Roughness Ra

S/N

MRR

S/N

ratio

Wt base

ratio

Depth of cut

Feed rate

Cutting speed

Ra1

Ra2

Ra avg

1

0.5

0.1146

9.354

0.67

0.64

0.655 0. 655

3.67517 287.29

2

0.5

0.3207

15.102

0.86

0.49

0.675

3.41392 1313.61 62.3693

3

0.5

0.3345

23.004

2.21

2.24

2.225

-6.9466

1996.18 66.0040

4

1

0.1146

15.102

0.46

0.54

0.5

6.0206

742.5

5

1

0.3207

23.004

2.26

2.27

2.265

-7.1014

3011.51 69.5757

6

1

0.3345

9.354

0.58

0.52

0.55

-0.5877

855.5

7

1.5

0.1146

23.004

1.18

0.96 0.96

1.07

5.19275 2318.14 67.3028

8

1.5

0.3207

9.354

2.21

1.08 1.08

1.645

-4.3233

1697.01 64.5940

9

1.5

0.3345

15.102

1.35

1.55

1.45

-3.2274

3208.09 70.1249

No.

49.1664

57.4139

58.6444

Responses for Signal to Noise Ratios of Smaller is better characteristics for surface roughness is shown in Table 4. and Responses for Signal to Noise Ratios of larger is better characteristics for MRR is shown in Table 5. Significance of machining parameters (difference between max. and min. values) indicates that feed is significantly contributing towards the machining performance as difference gives higher values. Plot for S/N ratio shown in Figure Figure 1 explains that there is less variation for change in depth of cut where as there is significant variation for change in feed rate. Significance of machining parameters (difference between max. and min. values) indicates that cutting speed is significantly contributing towards the MRR as difference gives higher values. Plot for S/N ratio ratio shown in Figure2 explains that there is less variation for change in feed where as there is significant variation for change in cutting speed.

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Table 4- Response Table for Signal to Noise Ratios Smaller is better

Level 1 2

Depth of cut 0.0475 -0.5562

Feed Speed 4.96284 -0.4119 -2.6703 2.06905

3

-0.786

-3.5872

Delta Rank

Table 5- Response Table for Signal to Noise ratios larger is better

-2.9517

0.83348 8.55005 5.02079 3

1

2

Level 1 2

Depth of cut 59.18 61.88

Feed 57.96 65.51

Speed 57.47 63.30

3

67.34

64.92

67.63

Delta

8.16

7.55

10.16

Rank

2

3

1

Main Effects Plot for SN r atios

Data Means Depth

feed

4 2 s 0 o i t -2 a r N -4 S f o n a e 4 M

0. 5

1. 0 speed

1. 5

9.354

15.102

23.004

0.1146

0.3207

0.3345

2 0

-2 -4

Signal-to-noise: Sm aller is better better

Fig. 2 Effect of Depth of cut, Feed rate and speed on surface s urface finish

Table 6-Analysis of Variance for S/N ratios for surface roughness Source DF Seq SS Adj SS Adj MS F P Depth of cut 2 1.112 1.112 0.5559 0.03 0.975 Feed 2 132.208 132.208 66.1042 3.03 0.248 Speed 2 37.814 37.814 18.9071 0.87 0.536 Residual error 2 43.7 43.7 21.8502 Total 8 214.835

63


Main Effects Plot for SN r atios

Data Data Mea ns Depth

69

Feed

66 63

s o i t 6 0 a r N 5 7 S f o n 6 9 a e M6 6

0. 5

1 .0

1. 5

0.1146

0.3207

0.3345

Speed

63 60 57 9.354

15.102

23.004

Signal-to-noise: Larger is be tter tter

Fig. 3 Effect of Depth of cut, Feed rate and speed on MRR

Taguchi method cannot judge and determine effect of individual parameters on entire process while percentage contribution of individual parameters can be well determined using ANOVA. MINITAB software of ANOVA module was employed to investigate effect of process parameters (Depth of cut, Feed rate and speed) Table 7-Analysis of Variance for S/N ratios for MRR Source DF Seq SS Adj SS Adj MS F Depth of cut 2 103.716 103.716 51.8581 828.31 Feed 2 105.868 105.868 52.9338 845.49 Speed 2 155.954 155.954 77.9769 1245.49 Residual error 2 0.125 0.125 0.0626 Total 8 365.663

P 0.001 0.001 0.001

Theory suggests that surface roughness is function of feed rate. In practice it is more like directly related to feed rate. This can be due to flattening of ridges due to side flow or tool work relative vibrations when feed rate is lower the roughness becomes independent of feed rate.

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Table 6 shows shows Analysis of variance for for S/N ratio. F value (3.03) (3.03) for S/N S/N ratio parameter indicates that feed rate is significantly contributing towards machining performance. F value (0.03) for S/N ratio of parameter indicates that depth of cut is contributing less towards surface finish. It can be observed rough surface from surface texture for the specimen No.5 (cutting speed 23.004 m/min; depth of cut 1 mm; feed 0.3207 mm/rev.) and smooth surface for the specimen specimen No.4 (cutting speed 15.102 15.102 m/min; depth of cut 1 mm; feed 0. 1146 mm/rev.) .

MRR 4000 3000 2000

MRR

1000

Power (MRR)

0 1

2

3

4

5

6

7

8

9

S/F ROUGHNESS 2.5

S/F ROUGHNESS

2 1.5

Power (S/F ROUGHNESS)

1 0.5 0 1

2

3

4

5

6

7

8

9

Power (S/F ROUGHNESS)

Fig 4 Graph of surface roughness and MRR vs Expt. No.

Fig. 5 Specimen with higher MRR and optimum surf ace roughness

Table 7 shows analysis of variance S/N ratio for MRR. F value (1245.49) for S/N ratio parameter indicates that cutting speed is significantly contributing towards MRR. F value (828.31) for S/N ratio of parameter indicates that depth of cut is contributing less towards MRR. It was observed observed that maximum MRR is obtained obtained at the cutting speed (15.102m/min), feed rate (0.3345mm/rev) and depth of cut (1.5mm)

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The power regression type is used to calculate the trend trend of each graph. By observing the graph of surface roughness vs experiment found that the optimum values of surface roughness lies in between 0.6 to 1.2 µm and for MRR lies in between 1000 to 2000 3 mm /min. CONCLUSION

Following are the conclusions drawn based on the test conducted on Al 7075-T6 alloy during Turning operation with HSS 1. From response Table for Signal to Noise ratios for surface roughness, based on the ranking it can be concluded that Feed has a greater influence on the Surface Roughness followed by Speed. Depth of Cut had least influence on Surface Roughness. 2. From response Table for Signal to Noise ratios for MRR, based on the ranking it can be concluded that cutting speed has a greater influence on the Surface MRR followed by feed rate. Depth of Cut had least influence on MRR 3. Cutting speed (15.102 m/min), feed rate (0.3207 mm/rev.) and depth of cut (0.5 mm) are cutting parameters for higher MRR with better surface finish. ACKNOWLEDGEMENT

Quality control department of Adler Mediequip PVT.LTD, Ratnagiri are gratefully acknowledged. REFERENCES

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2

3 4

5

6

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INVESTIGATION OF TURNING PROCESS TO IMPROVE PRODUCTIVITY _MRR_ FOR BETTER SURFACE FINISH OF AL.pdf

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