Optimization of Process Parameter for CNC Turning using Response Surface Methodology
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Transcript of Optimization of Process Parameter for CNC Turning using Response Surface Methodology
INTERNATIONAL JOURNAL OF R&D IN ENGINEERING, SCIENCE AND MANAGEMENT
Vol.3, Issue 1, Oct 2015, p.p. 26-38, ISSN 2393-865X
Available at :www.rndpublications.com/journal Page 26 © R&D Publications
Optimization of Process Parameter for CNC Turning using
Response Surface Methodology (RSM)
Nitish Kumar1*
, Ashwani Kumar2, Pankaj Kumar
3
1 M.Tech Scholar, Department of Mechanical Engineering, University Institute of Engineering and
Technology, Maharishi Dayanand University Rohtak-124001, Haryana ,INDIA 2
Associate Professor, Department of Mechanical Engineering, University Institute of Engineering and
Technology, Maharishi Dayanand University Rohtak-124001, Haryana ,INDIA 3Senior Manager in R&D Department, Lakshmi Precision Screws Limited, Rohtak-124001, Haryana,
INDIA ABSTRACT
Material removal by turning is the basic and the most important process in metal cutting. Product quality is mainly depends on
the process used and the parameters which affect them. The important parameter which affect the quality of product in turning
are cutting speed, feed rate, depth of cut, cutting tool used, cutting fluid and the material of the workpiece. The present study
involves the identification of the optimized process parameters in CNC turning of AISI 15B25 alloy steel. Three important
parameters like cutting speed, feed and depth of cut have been considered as a machining parameter and the output parameter
which are to be optimized are material removal rate (MRR) and surface roughness (Ra). Response surface methodology of the
Design Expert has been considered for the optimization of process parameters. The optimal values of the Surface finish and
MRR found to be 2.29µa and 1327.93mm3/min respectively.
Keywords – Optimization, CNC turning, Response Surface Methodology(RSM), surface roughness and material removal rate
(MRR).
____________________________________________________________________________
1. INTRODUCTION
Machining is the most important part of a manufacturing process. In metal cutting process
turning is the most commonly employed machining operation. In turning, work material is held
in between the chunk and the tailstock and rotated. The tool post in which tool is mounted
moved at a constant rate along the cutting axis removing a layer of material form the workpiece.
Surface roughness and MRR are the most important response parameters in the manufacturing
process and there optimization depends on different input parameters. During turning operation
the factors which affect the MRR and the surface roughness are spindle speed, feed rate, depth of
Optimization of Process Parameter for CNC Turning using Response Surface Methodology
Page 27
cut, tool nose radius, etc. The change in the factors will produce a significant effect on the
output. According to Prasad et al. (1997) reported the development of an optimization module
for determining process parameters for turning operations as part of a pc-based generative CAPP
system.. The objective is taken as to minimize the production time. The constraints considered in
this study include power, surface finish, tolerance, workpiece rigidity, range of cutting speed,
maximum and minimum depth of cut and total depth of cut. The conveyed models are solved by
the combination of geometric and linear programming techniques. J. Paulodavim and
Francisco Mata (2004) presented an optimization study of surface roughness in turning fiber
reinforced plastic (FRP) using polycrystalline diamond cutting tool. The tool geometry used is as
follow rake angle 5ᵒ clearance angle 5
ᵒ edge major tool cutting 55
ᵒ, cutting edge inclination angle
-7ᵒ, and corner radius 0.8mm. surface roughness Ra increase with the feed rate and decrease with
the cutting velocity. Saparudin et al. (2006) focused on the analysis of optimum cutting
conditions to get lowest surface roughness in turning SCM 440 alloy steel by Taguchi method.
The results were analyzed using analysis of variance (ANOVA) method. Taguchi method had
shown that the depth of cut has significant role to play in producing lower surface roughness
followed by feed. E. Daniel Kirby (2006) discussed an investigation into the use of Taguchi
Parameter Design for optimizing surface roughness generated by a CNC turning operation. The
study produced a verified combination of controlled factors and a predictive equation for
determining surface roughness with a given set of parameters. Dilbag Singh and P.
Venkateswara Rao(2007) investigated the effect of cutting condition and tool geometry on the
surface roughness in the finished hard turning of bearing steel (AISI 52100). Mixed ceramic
inserts made up of aluminum oxide and titanium carbonitride having different nose radius and
different rake angles were used as cutting tool. The study shows that feed is the dominant factor
followed by nose radius and cutting velocity. M.Kaladhar et al (2010) dealt with optimization
of AISI 202 unaustentic stainless Steel using CVD coated cemented carbide tools. During the
experiment the process parameter such as speed, feed, depth of cut and nose radius to explode
their effect on the surface roughness (Ra) of the workpiece. The experiments have been
conducted using full factorial design in design of experiment (DOE) on CNC lathe. From the
analysis it is observed that feed is the feed is the most significant factor that influences the
surface roughness. S.Ranganathan and T. Senthilvelan(2011) envisaged the multi-response
optimization of machining parameter in hot turning of stainless steel type 316 based on taguchi
Optimization of Process Parameter for CNC Turning using Response Surface Methodology
Page 28
method. The workpiece is heated with liquid petroleum gas flame burned with oxygen and
machined under different parameter i.e cutting speed, feed, depth of cut and workpiece
temperature and effect on surface roughness, tool life and material removal rate (MRR) have
been optimized by conducting response analysis. Experimental results reveal that feed rate and
cutting speed are the dominant variables on multiple performance analysis and further can be
improved by hot turning. Poornima et al (2012) involved in identifying the optimized parameter
in CNC turning of martensitic stainless steel. The optimization technique used in this study was
response surface methodology and Genetic algorithm. The input parameter are basically speed,
feed and depth of cut and their effect is studies on the surface roughness of material. The best
range is 119.93m/min, feed 0.15 and depth of cut 0.5mm. Vipindas M.P. and Dr. Govindan P.
(2013) optimized the quality of machined surface in turning operation through taguchi method.
The comprehensive experimentation and analysis was performed on Al 6061 material .the
commonly used parameter speed, feed, and depth of cut were used for assessment. The
roughness value vary from between 0.3 and 4.4. it is observed that feed has strongest influence
on the quality of machined surface in CNC turning. Harish Kumar et al.( 2013) In metal cutting
turning is the one of the most fundamental cutting process used . Feed rate, speed and the depth
of cut were taken as input parameter and dimensional tolerance as the output parameter. L9 array
has been used in design of experiment for optimization of input parameter. The work material
used was MS 1010 with the tool material of High Speed Steel (HSS). The most affecting
parameter having the impact on dimension tolerance is speed as 59.9%. Sayak Mukherjee et al
(2014) studied the optimization of the process parameter viz. cutting speed, feed and depth of
cut with respect to material removal rate (MRR) in cnc lathe. The material selected was SAE
1020 with carbide cutting tool. The experiment was performed on EMCO Concept turn 105
CNC lathe. Taguchi method L 25 array was used to conduct the experiment. The analysis
showed that depth of cut had the most significant effect on the MRR followed by feed.
From the literature review, it shows the number of studies related to optimization of turning
process using different materials but not much work is reported for optimizing the process
parameters for 15B25 steel using carbide insert. In current study, an optimization model has been
proposed using response surface methodology (RSM) to study the effect of process parameters
on surface roughness (SR) and material removal rate (MRR). The input parameter selected for
Optimization of Process Parameter for CNC Turning using Response Surface Methodology
Page 29
optimizing are rotational speed, feed rate and depth of cut which are the dominant factor in
turning operation.
2. MATERIAL AND METHOD
Low carbon alloy steel AISI 15B25 has been used for manufacturing the 8.8 grade Bolts, Rivets,
Screws and other fasteners or alloy chains. The material composition and its property are shown
in table 1 and table 2 respectively.
Table 1: Material composition of AISI 15B25
Material Percentage Composition (%)
C 0.22-0.30
Si 0.15-0.30
Mn 0.75-1.25
P 0.04
S 0.04
B 0.0005
Table 2: Properties of AISI 15B25
Density(kg/m3) 7.7-8.03x10
3)
Possion ratio 0.27-0.30
Elastic Modulus(GPa) 190-210
Brinell Hardness(HB) 163
Yield Strength(psi) 69000
Tensile strength 82000
Elongation(%) 12
Reduction in area (%) 35
Optimization of Process Parameter for CNC Turning using Response Surface Methodology
Page 30
2.1 Response Surface Methodology
Response Surface Methodology (RSM) is a collection of mathematical and statistical techniques
useful for the modeling and analysis of problems in which a response of interest or output is
influenced by several variables and the objective is to optimize this response. It has many
application in the design and improvement of product and processes.
y=f(x1,x2)+e (1)
x1,x2 are the independent variable where y is the response ,depends on them. The dependent
variable is a function of x1,x2 and the experimental error term denoted by e. a first and second
order polynomial can be fitted to develop a model.
The purpose of considering a model is as:
1. To establish a relationship, between y and x1, x2, . . . , xk that can be used to predict response
values for given settings of the control variables.
2. To determine, through hypothesis testing, significance of the factors whose levels are
represented by x1, x2, . . . , xk.
3. To determine the optimum settings of x1, x2, . . . , xk that result in the maximum (or minimum)
response over a certain region of ii1interest
A central composite design(CCD) approach is used to analysis the design. The no. of design
point in CCD are 2k
+ 2.k+ no
2.2 Cutting Tool Used
Tool material- tungsten carbide tool
Tool insert used:
TCMT090208-HM
Optimization of Process Parameter for CNC Turning using Response Surface Methodology
Page 31
Table 3 Cutting Tool Dimension
Basic dimension of the tool insert used
L Φ I.C S Φd R (radius)
9.6 5.56 2.38 2.5 0.8
Machine used: The maximum turning diameter is 200mm and the length which can be admitted
is 262mm. The capacity of the spindle motor is 15KW.
Figure 1: CNC Turning Machine
2.3 Process Parameter and Range
From the study of literature survey, three important process parameter i.e speed, feed rate and
depth of cut has been selected. These are the factor which effect or contribute towards the
machining quality of the finished product.
The ranges of the parameter are shown in table 4.
Optimization of Process Parameter for CNC Turning using Response Surface Methodology
Page 32
Table 4 Process Parameter Range
Parameter Range
Speed(rpm) 2500-2900
Feed(mm/rev) 0.15-0.22
Depth of cut(mm) 0.4-0.5
2.4 Data Collection
Experiments are planned using full factorial central composite design of RSM using three
cutting parameters: rotational speed, feed rate and depth of cut with 20 experimental run. The
data corresponding to these run is collected and shown in table 5.
Table 5 Data Collection
Run rotational
speed
(rpm)
Feed
rate(mm)
Depth of
cut(mm)
Material
removal
rate(MRR)
(mm3/min)
Surface
roughness Ra
(µm)
1 2700 0.19 0.45 795 2.56
2 2700 0.24 0.45 1300 3.78
3 2700 0.19 0.53 1980 2.66
4 3036 0.19 0.45 697 2.70
5 2700 0.19 0.37 788 2.70
6 2700 0.19 0.45 890 2.58
7 2700 0.19 0.45 1050 2.50
8 2900 0.22 0.40 1250 3.54
9 2364 0.19 0.45 1400 2.56
10 2900 0.15 0.50 1320 3.80
11 2700 0.19 0.45 1110 2.54
12 2500 0.22 0.50 1410 3.24
13 2900 0.22 0.50 1680 3.40
14 2500 0.22 0.40 1580 3.28
15 2500 0.15 0.40 865 1.98
16 2700 0.13 0.45 1000 1.46
17 2700 0.19 0.45 670 2.60
18 2500 0.15 0.50 1200 2.40
19 2900 0.15 0.40 940 2.02
20 2700 0.19 0.45 835 2.60
Optimization of Process Parameter for CNC Turning using Response Surface Methodology
Page 33
The surface roughness is measured using stylus type profilometer talysurf (Taylor Hobson,
Surtronic 3+, UK). MRR is calculated using the formula given below.
MRR=
(2)
Where Wi and Wf are weight of workpiece measured before and after turning operation. is the
density of material and t is the time for machining.
3. RESULTS AND DISCUSSIONS
3.1 ANOVA
There are a large number of variables controlling the process, so some mathematical models are
required to represent the process. However, using only the significant parameters which
influencing the process are taken into consideration rather than including all the parameters. In
order to achieve this, statistical analysis of the experimental results will have to be processed
using the analysis of variance. ANOVA is a computational technique that estimates the relative
contributions of each of the control factors to the overall measured response. In the present work,
only the important parameters will be used to develop mathematical models using response
surface methodology (RSM). These models would be of great use during the optimization of the
process variables. The value of “p” for a model should be less than 0.05 which indicates that the
terms the terms included in the model are significant, which is desirable as it indicates that the
term in the model have significant effect on the response.
From the table 6 it is clear that the value of p for model is less than 0.05 which indicates that
model is significant. And the value of p for lack of Fit is greater than 0.05. from the table 6 it can
be clearly seen that the factor A, B and C are the significant factor as their value for p is very
small hence they are most concluding factor during machining of AISI 15B25 which gives better
surface finish.
Optimization of Process Parameter for CNC Turning using Response Surface Methodology
Page 34
Table 6 ANOVA for Surface Roughness
Source Sum of
Squares
DF Mean
Square
F
Value
Prob > F
Model 5.34 9 0.59 56.21 < 0.0001 Significant
A 0.36 1 0.36 33.87 0.0002
B 4.08 1 4.08 387.16 < 0.0001
C 0.37 1 0.37 35.42 0.0001
A2 5.840E-003 1 5.840E-003 0.55 0.4740
B2 0.056 1 0.056 5.35 0.0434
C2 0.36 1 0.36 34.11 0.0002
AB 0.024 1 0.024 2.29 0.1609
AC 0.034 1 0.034 3.20 0.1038
BC 0.072 1 0.072 6.84 0.0258
Residual 0.11 10 0.011
Lack of Fit 0.087 5 0.017 4.57 0.0604 not
significant
Pure Error 0.019 5 3.787E-003
Cor. Total 5.44 19
3.2 ANOVA for MRR
Table 7 ANOVA for Material Removal Rate
Source Sum of
Squares
DF Mean
Square
F
Value
Prob > F
Model 1.813E+006 9 2.015E+005 3.42 0.0342 Significant
A 86566.21 1 86566.21 1.47 0.2530
B 3.352E+005 1 3.352E+005 5.70 0.0382
C 6.328E+005 1 6.328E+005 10.76 0.0083
A2 72395.08 1 72395.08 1.23 0.2933
B2 1.643E+005 1 1.643E+005 2.79 0.1257
C2 5.175E+005 1 5.175E+005 8.80 0.0141
AB 10878.13 1 10878.13 0.18 0.6763
AC 58653.13 1 58653.13 1.00 0.3416
BC 30628.13 1 30628.13 0.52 0.4871
Residual 5.883E+005 10 58831.51
Lack of Fit 4.539E+005 5 90776.35 3.38 0.1039 not
significant
Pure Error 1.344E+005 5 26886.67 F
Cor. Total 2.402E+006 19 Mean Value Prob > F
Optimization of Process Parameter for CNC Turning using Response Surface Methodology
Page 35
From the table 7 shown above it may be concluded that depth of cut is the most significant factor
which affect the material removal rate (MRR).
Regression equation obtained as follows:
(a)Regression equation for surface roughness:
(b) Regression equation for material removal rate (MRR):
Figure 2 Surface Plot of MRR Vs Feed Rate and Depth Of Cut
As from the figure 2 it is clear that depth of cut has a significant effect on the MRR. As the depth
of cut increases the MRR also increases. Rotational speed and feed rate also effect the material
removal rate but less as compared to depth of cut.
DESIGN-EXPERT Plot
MRRX = B: feed rateY = C: depth of cut
Actual FactorA: rotational speed = 2700.00
737.979
927.385
1116.79
1306.2
1495.61
MRR
0.15
0.17
0.19
0.20
0.22
0.40
0.43
0.45
0.47
0.50
B: feed rate
C: depth of cut
Optimization of Process Parameter for CNC Turning using Response Surface Methodology
Page 36
Figure 3: Surface Plot of Surface Roughness Vs Feed Rate and Rotational Speed
From the graph it can be clearly defined that as the feed rate and rotational speed increases the
surface roughness also increases. i.e they are the significant factor for surface roughness. Feed
rate is more dominant factor as compared to rotational speed.
SOLUTION
Number Rotational Speed Feed Rate Depth of Cut Surface Finish MRR Desirability
1 2512.00 0.15 0.50 2.29679 1327.93 0.623
4. CONCLUSIONS
Response surface methodology coupled with ANOVA has been employed to estimate the
optimum combination of spindle speed, feed rate and depth of cut for simultaneous
minimization of surface roughness and maximization of MRR. The following are conclusions:
Developed an analytical model for surface roughness and material removal rate (MRR) for
machining based on rotational speed, feed, and depth of cut.
The effect of each parameter on each response and the interactions between the parameters are
studied. It is found that the surface roughness and material removal rate could be controlled in
the design stage which is the most effective and inexpensive way.
DESIGN-EXPERT Plot
surface finishX = A: rotational speedY = B: feed rate
Actual FactorC: depth of cut = 0.45
1.92766
2.28199
2.63632
2.99066
3.34499
surf
ace f
inish
2500.00
2600.00
2700.00
2800.00
2900.00
0.15
0.17
0.19
0.20
0.22
A: rotational speed
B: feed rate
Optimization of Process Parameter for CNC Turning using Response Surface Methodology
Page 37
feed rate, depth of cut and rotational speed are the significant factors which effect the surface
roughness. Feed rate is the dominant factor and it is directly proportional to the response.
Depth of cut has significant effect on the material removal rate(MRR) followed by rotational
speed. As the depth of cut and rotational speed increases MRR also increases.
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