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Experimental study of optimizing turning process parameter of AA6063
D.Balaji1, V.Sivaramakrishnan2, N.Ramanujam3, J. Milton jeysingh4
1, 2, 4 Assistant Professor, EGS Pillay Engineering College, Nagapattinam, TN, India
3 Associate Professor, EGS Pillay Engineering College, Nagapattinam, TN, India
[email protected],
[email protected],
[email protected],
[email protected]
ABSTRACT
The research work aimed to optimize the cutting parameters like spindle speed, feed rate, and depth of cut for minimization of Surface Roughness and maximization of Material Removal Rate (MRR) in center Lathe, turning of Aluminium Alloy AA6063 using alumina coated and uncoated carbide insert tool in dry and wet condition
Experiments were conducted based on the Taguchi design of experiments (DOE) with L8 Orthogonal Array (OA) and excel work is used to analyze the data.
The signal –to –noise (S/N) ratio is employed to study the performance characteristics in center lathe turning operation. Optimum values of process parameters for desired performance characteristics are obtained with identification of most significant factor. The S/N ratio plot has shown that the most significant parameters for surface roughness are feed rate, spindle speed and least significant factor is depth of cut. For MRR depth of cut, spindle speed is the most significant parameters and least significant factor is feed rate.
Keywords
Optimization, Design of Experiments, Planning, Surface Roughness, Turning, Taguchi method
INTRODUCTION
Metal removing industries are continuously suffering from the problem of not running the machine tools at the optimum working conditions [8]. Many industrial practitioners and scientists are dealing with the problem of optimization of process parameters.So, estimating the optimal parametric conditions for a machining operation is mandatory for current industrial situation.
Machining is a process which is used to change the size, shape and surface of a material through removal of materials by straining the material to fracture or by thermal evaporation. In manufacturing industries, turning is a widely used machining operation. Turning is a process in which single point cutting tool removes material from a rotating work piece to form a cylindrical shape.
Surface roughness (Ra) is a measure of the technological quality of a product and it greatly influences manufacturing cost. Surface roughness is used to determine andevaluate the quality of a product, is one of the major qualityattributes of a turning product. It is one of the prime requirementsof customers for machined parts. In order to obtain better surfacefinish, the proper setting of cutting parameters is crucial beforethe process takes place. In addition to surface finish quality, MRRis also an important characteristic in turning operation and highMRR is always desirable.
Aluminium is the least expensive metal that has strong resistance to corrosion. It is easily machinable and has good electrical and thermal conductivities. From the literature reviewed, it is evident that the research gap exists in considering Aluminum alloy AA6063 as the work material. In this work, a cylindrical rod Aluminum alloy AA6063 is turned using uncoated and alumina coated tungsten carbide tool.Aluminium Alloy 6063 is a medium strength alloy commonly referred to as an architectural alloy. It is normally used in manufacturing intricate parts. It has a good surface finish; high corrosion resistance, good weldability and it can be easilyanodized. 6063 is typically used in: architectural applications, extrusions, window frames, doors, shop fittings, irrigation tubing. The experiment is designed using Taguchi's orthogonal array with the three process parameters, spindle speed, depth of cut, and feed rate at three levels. The results obtained are analyzed in order to find the optimal parameter setting for minimum surface roughness and maximum material removal rate.
Taguchi method uses a special design of orthogonal arrays to study the entire parameter space with small number of experiments. The experimental results are transformed into a signal-to-noise(S/N) ratio. The S/N ratio can be used to measure the deviation of the performance characteristics from the desired values. There are three categories of performance characteristics in the analysis of the S/N ratio: the lower-the-better, the higher-the-better and the nominal-the-better. For all the categories of performance characteristic, the largest S/N ratio corresponds to better performance characteristic which means the optimal level of the process parameters is the level with the highest S/N ratio.
LITERATURE REVIEW
Due to the importance of the subject, problems of optimizing parameters in machining, a few number of works published in this field are reviewed as follows:
1. RanganathM S, Vipin, R S Mishra. Investigated the effect of the cutting speed, feed rate and depth of cut on surface roughness and material removal rate (MRR), in conventional turning of Aluminium (6061) in dry condition. The feed and speed are identified as the most influential process parameters on surface roughness
2. N.B.Doddapattar(research scholar, department of mechanical engineering, uvce, bangalore university,b'lore), N Lakshmana swamy(professor, department of mechanical engineering, uvce, bangalore university, b'lore) has carried out research on the topic of machining characteristics analysis of 6061-t6 aluminium alloy with diamond coated and uncoated tungsten carbide tool. Based on the analysis feed is seen to be the most important single factor affecting the surface roughness. 3. E. Daniel kirby (industrial technology program, department of agricultural and bio systems engineering iowa state university) has carried out research on the topic of an optimization of machinability of aluminium alloy 7075 and cutting tool parameters by using Taguchi technique. The use of a modified l8 orthogonal array, with three control parameters and one noise factor, required only sixteen work pieces to conduct the experimental portion, half the number required for a full factorial design.
4. Alagarsamy S.V., Rajakumar N. have studied the use of Taguchi's technique for minimizing required surface roughness and maximizing the material removal rate in machining Aluminium Alloy 7075 using TNMG 115 100 tungsten carbide tool. The experimental results revealed that the feed is the most significant parameter for surface roughness followed by speed and depth of cut. 5. Gulhane U. D., Ayare S. P. investigated 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.
EXPERIMENTATION
A. Work material
We have used AA6063 in this study, in the form of cylindrical bar of diameter 20 mm.
Fig 1: Work material-AA 6063
Table 1: Chemical composition
Chemical Element
(Mn)
(Fe)
(Mg)
(Si)
(Zn)
% Present
0.0 - 0.10
0.0 - 0.35
0.45 - 0.90
0.20 - 0.60
0.0 - 0.10
(Ti)
(Cr)
(Cu)
Other (Each)
Others (Total)
Aluminium (Al)
0.0 - 0.10
0.0 - 0.10
0.0 - 0.10
0.0 - 0.05
0.0 - 0.15
Balance
Table 2: Physical Properties
Density
Melting
Modulus
of
Poisson's
Point
Elasticity
Ratio
2.7 g/cm3
655 °C Approx
69.5 GPa
0.33
Table 3: Mechanical properties
Ultimate
0.2% Proof
Brinell
Elongation
Tensile
Stress
Hardness
50mm
dia
Strength
(MPa)
(500kg
load,
(%)
260-310
240-276
73
12
B. Cutting tool material
The tool used for turning is tungsten carbide of side 12.68mm and thickness 4.89mm.
Fig 2: Alumina coated tungsten carbide tool
Fig 3: Uncoated tungsten carbide tool
C. Machine tool
Specification of Lathe machine used for turning
Type
:
170 G 2
Size
:
1960
RPM
:
65-1000
Cross slide
:
260 mm
Fig 4: Lathe machine used
D. Constraints
This experimental study considers three parameters each with two levels of values.
Table 4: Machining Parameters and their values
Parameter
Level 1
Level 2
Spindle speed(RPM)
290
465
Feed rate(mm/rev)
0.193
0.244
Depth of cut(mm)
0.5
1
E. Experimental design using L8 Orthogonal array
Table 5: L8 OA Taguchi design for the experiment
S.no
Speed
Feed
Depth of
(rpm)
(mm/rev)
Cut(mm)
1
290
0.193
0.5
2
290
0.193
1
3
290
0.244
0.5
4
290
0.244
1
5
465
0.193
0.5
6
465
0.193
1
7
465
0.244
0.5
8
465
0.244
1
F. Calculation of Surface Roughness
Surface Roughness is measured using Perthometer. The average of the observed values of Surface Roughness (Ra) are tabulated below for both dry and wet condition using two different tools..
Table 6: Time, Dia, and Chip Weight Tabulation of Uncoated tool with Dry condition
No. Of exp
Cutting
Feed
Depth of cut
Tot. Time
Final dia
Chip wt
Speed
(mm/
(mm)
(min)
(mm)
(gms)
(rpm)
Rev)
1
290
0.193
0.5
14.42
16.3
14.58
2
290
0.193
1
7.46
17.48
13.26
3
290
0.244
0.5
7.51
20.06
10.94
4
290
0.244
1
2.37
21.1
9.08
5
465
0.193
0.5
1.08
24.8
1.35
6
465
0.193
1
4.2
17.95
11.61
7
465
0.244
0.5
3.39
21.01
8.8
8
465
0.244
1
1.31
22.16
6.47
Table 7: Surface Roughness Tabulation of Uncoated tool with Dry condition
No. Of exp
Speed
Feed
Depth Of cut
Ra
(RPM)
(mm/rev)
(mm)
(average)
(µm)
1
290
0.193
0.5
1.85
2
290
0.193
1
1.58
3
290
0.244
0.5
2.69
4
290
0.244
1
2.59
5
465
0.193
0.5
1.12
6
465
0.193
1
2.5
7
465
0.244
0.5
2.18
8
465
0.244
1
2.66
Table 8: Time, Dia, and Chip Weight Tabulation of Alumina coated tool with Dry condition
No. Of Exp
Cutting
Feed
Depth Of Cut
Tot. Time
Final Dia
Chip Wt
Speed
(Mm/
(Mm)
(Min)
(Mm)
(Gms)
(Rpm)
Rev)
1
290
0.193
0.5
11.28
16.82
9.49
2
290
0.193
1
4.98
18.28
9.51
3
290
0.244
0.5
4.58
21.22
9.53
4
290
0.244
1
1.53
22.46
9.52
5
465
0.193
0.5
5.96
17.08
9.52
6
465
0.193
1
2.18
18.07
9.51
7
465
0.244
0.5
2.96
21.38
9.52
8
465
0.244
1
1.76
22.39
9.52
Table 9: Surface Roughness Tabulation of Alumina coated tool with Dry condition
No. Of exp
Speed
(RPM)
Feed
(mm/Rev)
Depth
Of Cut
(mm)
Ra
(Average)
(µm)
1
290
0.193
0.5
1.81
2
290
0.193
1
3.28
3
290
0.244
0.5
2.68
4
290
0.244
1
2.32
5
465
0.193
0.5
3.15
6
465
0.193
1
2.19
7
465
0.244
0.5
2.35
8
465
0.244
1
1.26
Table 10: Time, Dia, and Chip Weight Tabulation of Uncoated tool in Wet condition
No. Of exp
Cutting
Feed
Depth of cut
Tot. Time
Final dia
Chip wt
Speed
(mm/
(mm)
(min)
(mm)
(gms)
(rpm)
Rev)
1
290
0.193
0.5
13.32
16.4
14.48
2
290
0.193
1
7.38
17.38
13.24
3
290
0.244
0.5
7.42
20.16
10.86
4
290
0.244
1
2.26
20.8
9.09
5
465
0.193
0.5
1.02
24.4
1.24
6
465
0.193
1
3.8
17.85
11.61
7
465
0.244
0.5
3.22
21.1
8.6
8
465
0.244
1
1.30
22.06
6.37
Table 11: Surface Roughness Tabulation of Uncoated tool in Wet condition
No. Of exp
Speed
Feed
Depth Of cut
Ra
(RPM)
(mm/rev)
(mm)
(average)
(µm)
1
290
0.193
0.5
1.55
2
290
0.193
1
1.48
3
290
0.244
0.5
2.48
4
290
0.244
1
2.38
5
465
0.193
0.5
1.01
6
465
0.193
1
2.01
7
465
0.244
0.5
1.36
8
465
0.244
1
2.46
Table 12: Time, Dia, and Chip Weight Tabulation of Alumina coated tool with Dry condition
No. Of Exp
Cutting
Feed
Depth Of Cut
Tot. Time
Final Dia
Chip Wt
Speed
(Mm/
(Mm)
(Min)
(Mm)
(Gms)
(Rpm)
Rev)
1
290
0.193
0.5
11.18
16.42
9.34
2
290
0.193
1
4.86
18.18
9.38
3
290
0.244
0.5
4.44
21.18
9.48
4
290
0.244
1
1.46
22.25
9.46
5
465
0.193
0.5
5.68
17.00
9.46
6
465
0.193
1
2.04
18.02
9.47
7
465
0.244
0.5
2.75
21.16
9.48
8
465
0.244
1
1.56
22.28
9.48
Table 13: Surface Roughness Tabulation of Alumna coated tool in Wet condition
No. Of exp
Speed
(RPM)
Feed
(mm/Rev)
Depth
Of Cut
(mm)
Ra
(Average)
(µm)
1
290
0.193
0.5
1.51
2
290
0.193
1
1.58
3
290
0.244
0.5
2.26
4
290
0.244
1
2.32
5
465
0.193
0.5
1.00
6
465
0.193
1
2.10
7
465
0.244
0.5
1.28
8
465
0.244
1
2.38
H. Analysis of the S/N Ratio
S/N ratio is the tool used for analysis purpose which measures the performance of individual process parameters towards the surface roughness and material removal rate. The S/N Ratio for surface roughness was calculated using smaller the better characteristics.And the S/N Ratio was calculated using larger the better characteristics for MRR
Signal-to-noise ratio is defined as the power ratio between a signal and noise. It can be derived from the formula
SNR = Psignal/Pnoise = μ/σ
where
μ is the signal mean or expected value
σis the standard deviation of the noise
RESULTS AND ANALYSIS
The input and output parameters of the study are tabulated below.
The Surface roughness is the quality characteristic with the concept of "smaller-the-better".
Fig 5: S/N ratio vs turning parameter for alumina coated tool in dry condition.
Fig 6: S/N ratio vs turning parameter for alumina coated tool in wet condition.
Fig.7: Interaction between speed, feed and depth of cut
Interaction Plot for Ra
Data Means
0.193
0.244
2.5
speed
290
465
2.0
speed
1.5
2.5
feed
0.193
0.244
feed
2.0
1.5
2.5
depth
of cut
CONCLUSION
From the effect of turning parameters on Surface Roughness for S/N ratio (Fig.4&5), it is evident that the optimal values of speed, feed and depth of cut for the good surface finish of the turned product are 290 RPM, 0.193 mm/rev and 0.5 mm with Wet condition.
The Rank values obtained from the response table for S/N ratio values show that the most significant factor is feed and the next significant factor is depth of cut.
The interaction plot (Fig.7) reveals that the effect of speed on surface roughness is depending on the values of feed and depth of cut, effect of feed is independent and effect of depth of cut is slightly depending on speed value.
REFERENCES
[1] Ranganath M S , Vipin, R S Mishra,"Optimization Of Surface Roughness And Material Removal Rate On Conventional Dry Turning Of Aluminium (6061)", International Journal Of Advance Research And Innovation, Vol. 1, March 2014,
Pp. 62-71.
[2] Narayana B. Doddapattar & chetana S. Batakurki ,optimization of cutting parameters for turning aluminium alloys using Taguchi method. International journal of engineering research & technology (ijert) vol. 2 issue 7, july – 2013 issn: 2278-0181
[3] Issac thamban, biju cherian abraham, sabu kurian, machining characteristics analysis of 6061-t6 aluminium alloy with diamond coated and uncoated tungsten carbide tool. International journal of latestresearch in science and technologyissn:2278-5299 (2013)
[4] E. Daniel kirby (industrial technology program, an optimization of machinability of aluminium alloy 7075 and cutting tool parameters by using Taguchi technique. The technology interface/fall 2006.
[5] Alagarsamy S.V., Rajakumar N.,"Analysis of Influence of Turning Process Parameters on MRR & Surface Roughness of AA7075 Using Taguchi's Method and Rsm", International Journal of Applied Research and Studies (IJARS), Vol. 3,
Issue 4,2014, pp. 1-8.
[6] Gulhane U. D., Ayare S. P., Chandorkar V.S., Jadhav M .M.,"Investigation Of Turning Process To Improve Productivity (Mrr) For Better Surface Finish Of Al-7075-T6 Using Doe", International Journal Of Design And Manufacturing
Technology (Ijdmt), Vol. 4, Issue 1, January-April, 2013, Pp. 59-67.
[7] J. Kouam, V. Songmene, M. Balazinski and P. Hendrick have carried out research on the topic ofdry, semi-dry and wet machining of 6061-t6 aluminium alloy. Http://dx.doi.org/10.5772/51351
[8] Ali riza motorcu .the optimization of machining parameters using the Taguchi method for surface roughness of aisi 8660 hardened alloy steel. Strojniški vestnik - journal of mechanical engineering 56(2010)6, 391-401 udc 669.14:621.7.015: 621.9.02
[9] K. Kadirgama , M. M. Noor , M. M. Rahman , M. R. M. Rejab,"surface roughness prediction of 6061-t6 aluminium alloy machining using statistical method" european journal of scientific research issn 1450-216x vol.25 no.2 (2009), pp.250-256.
[10] M. Nalbant, H. Go¨kkaya& G. Sur, "Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning",
Elsevier Journal, Materials and Design 28, pp. 1379–1385, 2007
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