Investigation of turning process to improve productivity mrr for better surface finish of al

International Journal of Design and Manufacturing Technology (IJDMT), ISSN 0976 –
6995(Print), ISSN 0976 – 7002(Online) Volume 4, Issue 1, January- April (2013), © IAEME
59
INVESTIGATION OF TURNING PROCESS TO IMPROVE
PRODUCTIVITY (MRR) FOR BETTER SURFACE FINISH OF AL-
7075-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 .
Keywords: Surface roughness, MRR, DOE, ANOVA, Al-7075 T6
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
INTERNATIONAL JOURNAL OF DESIGN AND MANUFACTURING
TECHNOLOGY (IJDMT)
ISSN 0976 – 6995 (Print)
ISSN 0976 – 7002 (Online)
Volume 4, Issue 1, January- April (2013), pp. 59-67
© IAEME: www.iaeme.com/ijdmt.html
Journal Impact Factor (2013): 4.2823 (Calculated by GISI)
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IJDMT
© I A E M E

60
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 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 experimental study to optimize the cutting parameters using
two performance measures,work piece surface temperature and surface roughness.D.Philip
Selvaraj and P.Chandramohan (2010) concentrated with dry turning of 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.
Table1 Chemical composition of AL7075-T6
Chemical composition
Limits
Weight % Al Si Fe Cu Mn Mg Cr Zn Ti Each
Tota
l
Minimum - - 102 - 2.1 0.18 5.1 - - -
Maximum Rem
0.
4
0.
5 2 0.3 2.9 0.28 6.1 0.2 0.05 0.15

61
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 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)
For MRR machining time for each sample has been calculated accordingly. After
machining, weight of each machined parts have been again measured precisely with the help
of digital balance meter.
RESULTS AND DISCUSSION
Table 3 shows experimental design matrix and 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)
Machining Parameters Level 1 Level 2 Level 3
Cutting speed (m/min) 9.354 15.102 23.004
Depth of cut (mm) 0.1146 0.3207 0.3345
Feed rate (mm/rev) 0.5 1 1.5
Table 2: Machining parameters and level

62
ܵ
ܰൗ ൌ െ10 log ቀ
ଵ
௡
∑ ܻ݅ଶ௡
௜ୀଵ ቁ -------- (1)
ܵ
ܰൗ ൌ െ10 log ቀ
ଵ
௡
∑
ଵ
௒௜మ
௡
௜ୀଵ ቁ --------- (2)
Table 3-Experimental design matrix and response variable
Expt.
Turning
Parameters Surface Roughness Ra S/N MRR S/N
No.
Depth
of cut
Feed
rate
Cutting
speed Ra1 Ra2 Ra avg ratio Wt base ratio
1 0.5 0.1146 9.354 0.67 0.64 0.655 3.67517 287.29 49.1664
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 57.4139
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 58.6444
7 1.5 0.1146 23.004 1.18 0.96 1.07 5.19275 2318.14 67.3028
8 1.5 0.3207 9.354 2.21 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
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 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 shown in Figure2 explains that there is less variation for
change in feed where as there is significant variation for change in cutting speed.

63
Table 4- Response Table for Signal to
Noise Ratios Smaller is better
Level
Depth
of cut Feed Speed
1 0.0475 4.96284 -0.4119
2 -0.5562 -2.6703 2.06905
3 -0.786 -3.5872 -2.9517
Delta 0.83348 8.55005 5.02079
Rank 3 1 2
Table 5- Response Table for Signal to
Noise ratios larger is better
Level
Depth
of cut Feed Speed
1 59.18 57.96 57.47
2 61.88 65.51 63.30
3 67.34 64.92 67.63
Delta 8.16 7.55 10.16
Rank 2 3 1
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
1.51.00.5
4
2
0
-2
-4
0.33450.32070.1146
23.00415.1029.354
4
2
0
-2
-4
Depth
MeanofSNratios
feed
speed
Main Effects Plot for SN ratios
Data Means
Signal-to-noise: Smaller is better
Fig. 2 Effect of Depth of cut, Feed rate and speed on surface finish

64
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 P
Depth of
cut 2 103.716 103.716 51.8581 828.31 0.001
Feed 2 105.868 105.868 52.9338 845.49 0.001
Speed 2 155.954 155.954 77.9769 1245.49 0.001
Residual
error 2 0.125 0.125 0.0626
Total 8 365.663
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.
1.51.00.5
69
66
63
60
57
0.33450.32070.1146
23.00415.1029.354
69
66
63
60
57
Depth
MeanofSNratios
Feed
Speed
Main Effects Plot for SN ratios
Data Means
Signal-to-noise: Larger is better
Fig. 3 Effect of Depth of cut, Feed rate and speed on MRR

65
Table 6 shows Analysis of variance for S/N ratio. F value (3.03) for 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 No.4 (cutting speed 15.102 m/min; depth of
cut 1 mm; feed 0. 1146 mm/rev.) .
Fig. 5 Specimen with higher MRR and optimum surface 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 that maximum MRR is obtained at the cutting speed
(15.102m/min), feed rate (0.3345mm/rev) and depth of cut (1.5mm)
Fig 4 Graph of surface roughness and MRR vs Expt. No.
0
1000
2000
3000
4000
1 2 3 4 5 6 7 8 9
MRR
MRR
Power (MRR)
0
0.5
1
1.5
2
2.5
1 2 3 4 5 6 7 8 9
S/F ROUGHNESS
S/F ROUGHNESS
Power (S/F
ROUGHNESS)
Power (S/F
ROUGHNESS)

66
The power regression type is used to calculate the 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
mm3
/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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