SSRG International Journal of Mechanical Engineering - (ICCREST'17) - Special Issue - March 2017
OPTIMIZATION OF CNC MACHINING PARAMETERS FOR SURFACE ROUGHNESS IN TURNING OF ALUMINIUM 6063 T6 WITH RESPONSE SURFACE METHODOLOGY 1. Mr.B.Radhakrishnan Assistant professor, Mechanical department K.Ramakrishnan College of engineering Trichy, Tamilnadu, India.
[email protected] 2. Mr.S.Tharun Kumar UG scholar Mechanical Engineering K.Ramakrishnan College of engineering Trichy, Tamilnadu, India. Email-
[email protected]
4. Mr.P.Ramakrishnan UG scholar Mechanical Engineering K.Ramakrishnan College of engineering Trichy, Tamilnadu, India.
[email protected] 5. Mr.S.Sarankumar UG scholar Mechanical Engineering K.Ramakrishnan College of engineering Trichy, Tamilnadu, India.
[email protected]
3. Mr.P.Sankarlal UG scholar Mechanical Engineering K.Ramakrishnan College of engineering Trichy, Tamilnadu, India.
[email protected]
Abstract- In this research paper represents optimizing the surface roughness obtained by turning process in CNC machine using various parameters. In this paper the machining is carried out using CCMT tool. The ranges of parameters are selected for the aluminium 6063 before the machining process carried out. Initially, a three-factor central composite design of experiment, considering the cutting speed, feed rate and depth of cut, are conducted in aluminum alloy 6063 T6 and the corresponding surface roughness are obtained. Then a mathematic model is constructed by the response surface methodology (RSM) to fit the relationship between the process parameters and the surface roughness. The prediction accuracy was verified by the one-way ANOVA. Finally, the surface roughness under different combination of process parameters are obtained and used for the optimum surface roughness prediction. The results of the machining experiments were used to optimize the main factors affecting the surface roughness in the aluminium material using the response surface methodology.
Keywords- Aluminium alloy, Surface roughness, Response surface methodology, Design of experiment, RSM.
ISSN: 2348 - 8360
I .INTRODUCTION Aluminium alloys are contains the typical alloy elements such as copper, magnesium, manganese, silicon and zinc and in which aluminium is the predominant metal. Here aluminium 6063 is the work piece material and CCMT is cutting tool. The main properties of aluminium are light weight, strength, recyclability corrosion resistance, durability, formability, ductility and conductivity which make them valuable material. Surface roughness is one of the important requirements in machining process. There are many factors like tool variable work piece variable and cutting conditions which affect the surface roughness. The tool variable include tool material, nose radius, rake angle etc. work piece variables include material, hardness and other mechanical properties. Cutting condition include speed, feed rate and depth of cut. The turning process involves large numbers of parameters very difficult to control the process and select the optimum cutting conditions for achieving the required surface quality. Most of the research articles deals with Average value (Ra), Root mean square (Rz), Peak to valley (Rt). Design of experiments is an influential method to improve manufactured goods design presentation where it can be used to improve a new process. The analysis of variance is a powerful statistical tool for test
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SSRG International Journal of Mechanical Engineering - (ICCREST'17) - Special Issue - March 2017
of significance ANOVA is a separation of variance ascribable to one prove of process of the variance ascribable to other groove. Response surface methodology (RSM) is a collection of mathematical and statistical techniques for empirical model building. By careful design of experiments, the objective is to optimize a response (output variable) which is influenced by several independent variables (input variables). An experiment is a series of tests, called runs, in which changes are made in the input variables in order to identify the reasons for changes in the output response. An important aspect of RSM is the design of experiments usually abbreviated as DOE. These strategies were originally developed for the model fitting of physical experiments, but can also be applied to numerical experiments. The objective of DOE is the selection of the points where the response should be evaluated. Most of the criteria for optimal design of experiments are associated with the mathematical model of the process. The choice of the design of experiments can have a large influence on the accuracy of the approximation and the cost of constructing the response surface. II. LITERATURE REVIEW V. Devkumaret al [1] investigated the optimal cutting conditions for attaining the better surface roughness using the mathematical modeling and analysis of machining response and tool wear in the turning of aluminum alloy 6061. There was process parameters such as spindle speed, depth of cut and feed rate used to determine the quality of surface roughness. Experiments were conducted as per central composite face centered design. Analysis of variance is used for the comparison study on tabulated values and experimental values for surface roughness. J.Paulo davim et al [2] studied the effects of turning parameters such as cutting velocity, feed rate on surface roughness of glass fiber reinforced plastics by statistical analysis. They concluded that this cutting parameters is possible to obtain surface with 0.80 to 1.75 µm. arithmetic average roughness (Ra) 4.9 to 9.3 µm of maximum peak to valley height (Rt, R max). They also founded surface roughness increases with feed rate and decreased with cutting velocity. They used two methods HLU and FW. They found HLU is possible to obtain smaller values of surface roughness than FW. C.A.Conceicao Antonio et al [3] investigated the optimal machining parameters cutting speed, feed, and depth of cut for attaining better surface roughness when turning free machining steel 9SMnPb28K with cutting tool made of cemented carbide using experimental data
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and genetic data. They concluded that maximum cutting speed and minimum feed is best solution independently of the criterion used bring analysis except for robustness coefficients. chandra shekar et al [4] optimized the machining parameters speed, feed, depth of cut, nose radius in turning of Al 6063 T6 in CNC machine through design of experiments by taguchi method. They concluded that low surface roughness obtain for the parameter setting of nose radius value 0.8mm ,feed of 10mm/min, speed of 500 rpm, depth of cut 0.8mm. Highest material removal rate for nose radius value 0.4mm, feed 70 mm/min, speed 1500 rpm. P. Jayaraman et al [5] researched on multi response optimization of machining parameters such as cutting speed, feed rate, and depth of cut in turning of AA 6063 T6 using grey relational analysis in Taguchi method. They conclude from this analysis it is revealed that feed rate, depth of cut are the prominent factors which affect the aluminium alloy and feed rate (57.365 %) , depth of cut (25.11%) , speed (17.35%) gives the optimal surface roughness value. R. Rudrapathi et al [6] investigated on the optimization the process parameters using the spindle speed, feed rate, depth of cut, in CNC turning of aluminium alloy using RSM and Teaching Learning Based Optimization. From this analysis they concluded that second order mathematical model is developed for surface roughness by RSM. It is found that hybrid RSM cum TLBO is very useful to optimize surface roughness in CNC turning of aluminium alloy. Doreswamy Deepaket al [7] studied the multi response optimization of process parameters speed, feed, and depth of cut in CNC turning of aluminium 6061 using grey relational analysis in taguchi L9 array method. From this analysis they concluded that high feed rate leads to broken chips and burrs in the surface of the job and high cutting speed gives more MRR and low feed and depth of cut gives good surface finish. Rohit angris et al [8] analyzed surface roughness in CNC turning of aluminium 6063 using various parameters speed, feed, depth of cut by taguchi approach. From this analysis they concluded that feed has more significant effect on surface roughness (50.9%) and MRR is maximum at the parametric condition 2500rpm, 50 mm per rev and 0.1 mm depth of cut. M.S. Ranganath et al [9] inspected on Surface Roughness Prediction Model for CNC Turning of EN-8 Steel Using RSM for various parameters speed, feed, and depth of cut. They form the quadratic equation to conclude that the cutting speed has the strongest effect on
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SSRG International Journal of Mechanical Engineering - (ICCREST'17) - Special Issue - March 2017
the surface roughness and it also inversely proportional to depth of cut. B.Radha Krishnan [10] Review of prediction of surface roughness in machine vision system by using various parameters like speed , feed rate, depth of cut . in this paper as detailed explanation about measuring objective parameters these are surface roughness , peak to valley, root mean square and list out the methodology for the machine vision system.
Aluminium 6063T6 contains Si, Fe, Cu, Mn, Mg, Zn, Ti, Cr, Al in following percentage represented in table 2. Table 3 CHEMICAL COMPOSITION OF ALUMINIUM 6063T6 Element
(%)
Si
0.2 to 0.6
Fe
0.0 to 0.35
Cu
0.0 to 0.1
Mn
0.0 to 0.1
Mg
0.45 to 0.9
Zn
0.0 to 0.1
Ti
0.0 to 0.1
Cr
0.1 max
Al
Balance
III.MATERIALS AND EQUIPMENTS A) MATERIALS PROPERTIES 1) Work piece material: In this experiment work Al6063 is used as a material. It has generally good mechanical properties and is heat treatable and weld able. It is similar to the British aluminium alloy HE9. The table 1 represents the physical properties of Al6063T6
Table 1 MECHANICAL PROPERTIES OF ALUMINIUM 6063T6 Tensile strength
220 Mpa
Elongation
5%
Proof stress
190Mpa
Aluminium 6063T6 has following mechanical properties tensile strength, elongation, and proof stress in following units represented in table 3. 2) Working tool material:
Table 2 PHYSICAL PROPERTIES OF ALUMINIUM 6063T6 PROPERTY
VALUE
Melting Point
655 °C
Density
2.70 g/cm³
Thermal Expansion
23.5 x10^-6 /K
Modulus of Elasticity
69.5 GPa
Thermal Conductivity
201 W/m.K
Electrical Resistivity
0.033 x10^-6 Ω .m
The CCMT is cemented carbide tool used for machining the work piece as per the given input values. The carbide inserts are replaceable and usually indexable bits of cemented carbide used in machining steels, cast iron, aluminium, high temperature alloys. They allow faster machining and make better surfaces on the material parts. The CCMT is stands the following meanings. C= tool shape- 60 degree diamond shape C= tool clearance angle- 7 degree tool rake M= tolerance = +/- 0.002 T= cutting style- (40 to 60 degree) one countersink B) Equipment 1) Stylus probe: Stylus probe was traditional surface roughness measuring method in industries. In contact method stylus probe was very sensitive, and the diamond stylus probe could scratch the surface particularly when the materials are
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SSRG International Journal of Mechanical Engineering - (ICCREST'17) - Special Issue - March 2017
soft. A common drawback of this approach is the small area using which the roughness is evaluated at any one time and also the transducer is very sensitive and the stylus tip is fragile. Therefore, the instrument must be handled carefully in a fairly, clean environment. Another problem with the stylus measurement technique is the size of the stylus radius and the crevices of the surface. If the crevices are narrow such that the stylus cannot penetrate all the way to the bottom, the measurement will not be accurate.
Fig.1.Stylus probe
Processor
With Siemens 802D SI control with standard equipments
Power supply
3 phase 415 volt AC 12 KVA
Axis travel
Length –x –axis-190 mm Z – axis- 210 mm
Total cost
Rs 913943
Table 4 CNC lathe specifications
Fig.2.Machining on CNC lathe during operation
2) CNC lathe
The cutting variables and their levels are given in table.
The experimental was carried out on the CNC center turning MTab using CCMT cutting tool. The following diagram clearly shows the experiment setup of CNC turning center The CNC machine is checked and prepared for performing of designed experimental machining operation suitably. The machining of aluminium 6063 is accomplished by varying the cutting parameters such as speed, feed rate, depth of cut. After the machining operation the surface finish of the specimen is carried out by using the stylus probe apparatus. The specification of the machine is given in table. Machine
CNC LATHE
Machine make
M – Tab
Year
2011
Model
Flux turn
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Parameters
Units
Level 1
Level 2
Spindle speed
RPM
1200
2000
Feed
mm / rev
0.1
0.2
Depth of cut
Mm
0.5
1.5
Table 5 various parameters and levels of material IV. RESPONSE SURFACE METHODOLOGY In Response Surface Methodology the factors that are considered as most important are used to build a polynomial model in which the independent variable is the experiment’s response. RSM is the collection of mathematical and statistical techniques that are useful for the modeling and analysis of problems in which a response of interest is influenced by several variables and the objective is to optimize the response. In many engineering fields, there is a relationship between output variables ‘y’ of interest and a set of controllable input variables. In some system, the nature of the relationship
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SSRG International Journal of Mechanical Engineering - (ICCREST'17) - Special Issue - March 2017
between and values may be known. Then, a model can be written in the form, V. RESULTS AND DISCUSSION
Where € represents error observed in the response .
If we denote the expected response as then the surface represented by,
Fig.3.Aluminium 6063T6 Before machining
The main objective of the experiment is to optimize the machining parameters to achieve the high surface finish. The RSM is the method is not only used to judge and determine effect of individual parameters on entire process. Contribution of individual parameters of the process can be determined using ANOVA. Minitab software of ANOVA module was used to proceed the machining parameters speed, feed, depth of cut. The experimental data for the surface finish values are tabulated as follows. Work Speed Feed Depth Rz Ra piece (rpm) (mm/min) of cut (µm) (µm) (mm) 1
1200
0.1
0.5
4.84
2
1300
0.2
0.6
6.9
3
1400
0.1
0.7
4.14
4
1500
0.2
0.8
6.65
5
1600
0.1
0.9
4.47
6
1700
0.2
1
7.79
7
1800
0.1
1.1
7.24
8
1900
0.2
1.2
7.45
9
2000
0.1
1.3
5.38
10
1200
0.2
1.4
5.96
11
1300
0.1
1.5
4.8
12
1400
0.2
1.5
6.56
13
1500
0.1
1.4
5.486
14
1600
0.2
1.3
6.32
15
1700
0.1
1.2
5.96
16
1800
0.2
1.1
7.33
Fig.4.Aluminium 6063T6 after machining
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1.15 1.78 0.85 1.62 0.87 1.79 1.93 1.41 1.18 1.44 0.96 1.67 1.103 1.24 1.21 1.84
SSRG International Journal of Mechanical Engineering - (ICCREST'17) - Special Issue - March 2017
ANOVA for Response Surface Linear ModelAnalysis of variance table [Partial sum of squares] on Ra : Table 7 ANOVA table for the data of Ra Source
Sum of squares
Df
Mean square
F – value
Model
0.93
3
0.31
3.71
0.12
1
0.12
1.49
B – Feed
0.83
1
0.83
9.91
C - Depth of cut
0.030
1
0.030
0.36
Residual
1.00
12
0.084
Cor Total
1.93
15
Final Equation in Terms of Coded Factors: Ra =+1.41+0.15 * A+0.23* B-0.072* C Final Equation in Terms of Actual Factors:
Color points by value of Ra: 1.93
The Model F-value of 3.71 implies the model is significant. There is only a 4.25% chance that 0.85 a "Model F-Value" this large could occur due to noise. Values of "Prob > F" less than 0.0500 indicate model terms are Sgnificant. In this case B are significant model terms. Values greater than 0.1000 indicate the model terms are not significant. Response surfaces were developed for the empirical relationship, taking two parameters in the ‘X’ and ‘Y’ axis and response in ‘Z’ axis. The response surfaces clearly indicate the optimal response point. The different colored surfaces show that the value of surface roughness obtained for the corresponding values of input parameters. The relationship between independent and dependent variables was graphically represented by three dimensional response surface graphs and two
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0.2454 0.0084 0.5613
Design-Expert® Software Ra
Normal Plot of Residuals P r o b a b ility
Ra=+0.26631+3.77608E-004*S+4.56384*F0.14438*D
0.0425
dimensional contour plots. The analysis values on the data of Ra as plotted as per the given values and shown in the following diagrams. The residual graph indicates that the decreases of the feed and depth of cut with the increase of speed will get the high surface finish.
N o rm a l %
A – Speed
P – value > F
99
95 90 80 70 50 30 20 10 5
1
-2.00
-1.00
0.00
1.00
2.00
Internally Studentized Residuals
Fig.5.Nominal plot residuals graph for Ra
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3.00
0.85
Design-Expert® Software Factor Coding: Actual Ra Design Points 1.93
Residuals vs. Run 3.00
Ra
0.20
0.85
1.6 2.00
X1 = A: Speed X2 = B: Feed Rate Actual Factor C: Depth of Cut = 1.00
1.00
0.18
B : F e e d R a te
Color points by value of Ra: 1.93
0.00
0.15
-1.00
1.4
0.13
1.2 -2.00 0.10
-3.00
1200
1
4
7
10
13
1400
1600
16
1800
2000
A: Speed
Fig.7.Feed rate vs. speed for Ra data
Run Number
Fig.6.Residual vs. Run graph for Ra Design-Expert® Software Factor Coding: Actual Ra Design points above predicted value 1.93
Surface interaction dimensional views and direct effect views of feed rate and speed over surface roughness of Ra: The figure explains that the feed rate and speed on the surface roughness of turning process has a significant effect on Ra. It has been concluded that higher feed rate and speed was increase the surface roughness whereas lower feed rate and speed decrease the surface roughness. 0.85
2.5
X1 = A: Speed X2 = B: Feed Rate Actual Factor C: Depth of Cut = 1.00
2
1.5
Ra
Design-Expert® Software Ra
In te r n a lly S tu d e n tiz e d R e s id u a ls
SSRG International Journal of Mechanical Engineering - (ICCREST'17) - Special Issue - March 2017
1
0.5
0.20
2000 0.18
1800 0.15
1600 0.13
B: Feed Rate
1400 0.10
1200
A: Speed
Fig.8.Feed rate vs. speed for Ra data on 3d view
ANOVA for Response Surface Linear ModelAnalysis of variance table [Partial sum of squares] on Rz : Mean square
F – value
P – value > F
4.49
9.16
0.0020
1
3.43
6.99
10.29
1
10.29
20.98
C - Depth of cut
0.020
1
0.020
0.041
Residual
5.88
12
0.49
Cor Total
19.35
15
Source
Sum of squares
Model
13.47
A – Speed
3.43
B – Feed
Df 3
0.0214 0.0006 0.8429
Table 8 ANOVA table for the data of Rz Final Equation in Terms of Coded Factors:
Rz =+6.19+0.79* A+0.80 * B -0.059 * C
Final Equation in Terms of Actual Factors: Rz =+0.73029+1.97947E-003 * S+16.07938 * F -0.11851 * D
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SSRG International Journal of Mechanical Engineering - (ICCREST'17) - Special Issue - March 2017
Design-Expert® Software Factor Coding: Actual Rz Design Points 7.79
Rz
0.20
The Model F-value of 9.16 implies the model is significant.There is only a 0.20% chance that a Model F-Value" this large could occur due to noise. Values of "Prob > F" less than 0.0500 indicate model terms are significantIn this case A, B are significant model terms. Values greater than 0.1000 indicate the model terms are not significant. 4.15
Actual Factor C: Depth of Cut = 1.00
7
0.18
B : F e e d R a te
X1 = A: Speed X2 = B: Feed Rate
0.15
6
0.13
5
0.10 1200
The analysis values on the data of Rz as plotted as per the given values and shown in the following diagrams. The residual graph indicates that the decreases of the feed and depth of cut with the increase of speed will get the high surface finish on the material. Design-Expert® Software Rz
1800
2000
Fig.10.Feed rate vs. speed for Rz data in graphical model
4.15
8
X1 = A: Speed X2 = B: Feed Rate Actual Factor C: Depth of Cut = 1.00
7
99 6
Rz
P r o b a b ility
4.15
1600
A: Speed
Design-Expert® Software Factor Coding: Actual Rz Design points above predicted value 7.79
Normal Plot of Residuals
N o rm a l %
Color points by value of Rz: 7.79
1400
95 90
5
4
80 70
0.20
2000 0.18
50
1800 0.15
B: Feed Rate
30
1600 0.13
1400 0.10
1200
A: Speed
20 10 5
Fig.11.Feed rate vs. speed for Rz data on 3d view
1
VI. CONCLUSION -2.00
-1.00
0.00
1.00
2.00
3.00
Internally Studentized Residuals
Fig.9.Nominal plot residuals graph for Rz Surface interaction dimensional views and direct effect views of feed rate and speed over surface roughness of Rz: The figure explains that the feed rate and speed on the surface roughness of turning process has a significant effect. It has been concluded that increases of feed rate and speed was increase the surface roughness whereas decreases of feed rate and speed was decrease the surface roughness.
ISSN: 2348 - 8360
The operational design was carried out using response surface methodology to reduce the number of experiments and to achieve high accuracy results. This process has been conducted to optimize the cutting parameters for turning of aluminium alloy Al6063T6 on a CNC machine. In RSM following parameters like surface roughness, peak to valley was determined by the optimum machining parameters setting in the statistical analysis we can conclude that The lowest surface roughness value is optained for a parameter setting of speed 1400rpm, feed rate 0.1 mm/min and depth of cut 0.7 mm (Ra=0.85) The worst surface roughness value is optained for a parameter setting of speed 1800 rpm, feed rate 0.2 mm/min, depth of cut 1.1 mm (Ra=1.84)
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SSRG International Journal of Mechanical Engineering - (ICCREST'17) - Special Issue - March 2017
The lowest Rz value optained for a parameter setting of speed 1400rpm, feed rate0.1mm/min, depth of cut 0.7 mm(Rz=4.14) The highest Rz value is optained for a parameter setting of speed 1400 rpm, feed rate 0.2 mm/min, depth of cut 0.1 mm (Rz=7.79) VII.
7.
Doreswamy deepak, rajendra beedu,, multi response optimization of process parameters using grey relational analysis for turning of al6061
8.
Rohit angiras, onkar singh bhatia,, analysis of surface roughness during turning operation of aluminium-6063 using taguchi approach, international journal of advance research in science and engineering, ijarse, vol. No.4, issue 06, june 2015
9.
Ranganath m. S, vipin, r. S. Mishra, prateek, nikhil, optimization of surface roughness in CNC turning of aluminium 6061 using taguchi techniques, ijmer / issn: 2249–6645 /vol. 5 /iss. 5 / May 2015 / 42.
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10. B.Radha Krishnan, Dr.G.Senthilkumar, Review of prediction of surface roughness in machine vision system by using various parameter.
3. C.a. Conceicao antonio, andj.p. Davim, optimal machining parameters based on surface roughness experimental data and genetic search, industrial lubrication and tribology 57/6 (2005) 249–254 4. Chandra shekar, n b d pattar, y vijaya kumar, optimization of machining parameters in turning of al6063t6 through design of experiments , international journal of mechanical engineering and technology (IJMET) volume 7, issue 6, November–December 2016, pp.96–104, article id: IJMET_07_06_010 5. P. Jayaraman, l. Mahesh kumar, multi-response optimization of machining parameters of turning aa6063 t6 aluminium alloy using grey relational analysis in taguchi method, procedia engineering 97 ( 2014 ) 197 –204 6. R rudrapati1 , p sahoo2 and a bandyopadhyay3, optimization of process parameters in CNC turning of aluminium alloy using hybrid RSM cum tlbo approach, op conf. Series: materials science and engineering 149 (2016) 012039
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