International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 10, October 2013 ISSN 2319 - 4847
1
2
3
1,2,3 Assistant Professor, Department of Mechanical Engineering,
Madanapalle Institute of Technology and Science, Madanapalle.
Chittor Dst., Andhra Pradesh, India
In this paper Design of Experiments for turning the austenitic AISI 302 material for prediction of Tool life, surface Roughness and cutting force are taken in to consideration as output parameters for these experiments. Process parameters (cutting speed, feed rate, depth of cut and tool nose radius) are used as inputs for indentifying the machinability of the material. Response
Surface Regression method is used for prediction of machinability of the material by using this experimental data.
Key Words: AISI 302, Surface Roughness, Design of Experiments (DOE).
Predicting process of machinability models and determining the optimum values of process parameters in manufacturing systems have been areas of interest for researchers and manufacturing engineers. Machinability database systems are essential for selecting optimum process parameters during the process planning stage which represents an important component in Computer Integrated Manufacturing (CIM) [1]. Most of these researchers have used the generalized empirical equations systems that utilize expanded Taylor tool life equations to determine proper machining conditions based on three criteria: minimum production cost, maximum production rate or maximum profit rate per item. These criteria have been considered in both constrained and unconstrained problems of machining economics. Tool life, surface roughness and cutting forces have an effect on cost, time, design features and quality of manufacturing operations. In addition, these machinability models are very critical constraints for process parameters selection in process planning systems. Therefore, accurate representation of these three important factors in empirical models is very important. These models cannot be predicted without experimental work on specific material using specific conditions.
A considerable amount of studies have investigated the general effects of process parameters (cutting speed, feed rate, depth of cut, etc.) on process functions (tool life, cutting force and surface roughness) [1,3]. Most of these models are based on RA, very few papers used CNN [4–9].
The obtained machinability models are compared against each other using the relative error analysis, descriptive statistics and hypothesis testing. Four machining parameters were considered (cutting speed (v, m/min), feed rate (f, mm/r), depth of cut (d, mm) and tool nose radius (r, mm)). considered four machining parameters (cutting speed (v, m/min), feed rate
(f, mm/r), depth of cut (d, mm) and tool nose radius (r, mm)). The considered outputs are tool life (T, min), cutting force
(Fc, N) and surface finish (Ra, _m). The four process parameters were considered at three levels ( −1, 0 and 1) as shown in Table 1. The experiments are conducted based on Box and Dehnken design. This design is rotatable and consists of blocks in an orthogonal arrangement. This inputs and responses are illustrated in Table 2.
Table - 1: Factors and levels of the experiment for model constructions
Factors
Level level (-1) level (0) level (1)
Cutting Speed (v) m/min
25
60
144
Feed (f) mm/rev
0.1
0.25
0.7
Depth of Cut (d) mm
0.25
0.5
1.6
Nose Radius (r) mm
0.4
0.8
1.6

International Journal of Application or Innovation in Engineering & Management (IJAIEM)
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Volume 2, Issue 10, October 2013
ISSN 2319 - 4847
Table - 2: Design of experiment and Experimental data for model constructions
Exp. no. v f d r T Fc Ra
10
11
12
13
7
8
9
14
15
16
17
4
5
6
1
2
3
1
-1
1
0
0
0
-1
0
0
0
-1
1
0
0
-1
1
-1
0
0
0
-1
0
0
0
1
-1
1
0
1
0
0
-1
-1
1
-1
1
1
0
-1
1
-1
0
0
0
0
0
-1
1
0
0
0
0
0
0
-1
1
1
0
-1
1
1
-1
0
-1
-1
0
0
0
25.67
4.68
14
3
13.5
9.7
15.2
11.2
19
4.1
20.6
3.2
17.83
12.8
13.16
10.6
13
500
400
1520
1450
400
1050
520
1200
700
540
760
650
500
1800
750
2650
400
1.35
1.35
4.5
3.75
2.35
1.8
1.6
1.1
0.8
13.8
14.7
4.5
4
1.35
1.3
15.6
1.8
18
19
20
21
22
23
24
1
-1
1
0
0
0
0
0
0
0
-1
1
-1
1
0
0
0
-1
-1
1
1
-1
1
1
0
0
0
0
6.2
15.4
7.1
27.3
3.63
17.5
3.2
1440
520
1550
450
350
1360
1150
29.6
0.6
11
4.8
3
3.1
2.6
25
26
0
0
0
0
0
0
0
0
10.6
11.7
700
480
3
3.2
27 0 0 0 0 8.1 620 2.7
3.1 Response surface method
Response surface methods are used to examine the relationship between one or more response variables and a set of quantitative experimental variables or factors. These methods are often employed after identifying the controllable factors and the objective is to find the factor settings that optimize the response. Designs of this type are usually chosen when there is suspecting curvature in the response surface. It is clear from the literature that the tool life, cutting force and surface finish equations are not linear and they could be predicted using the response surface method. The initial analysis of the output obtained from RSM includes all parameters and their interactions. The models are reduced by eliminating elements which have no significant effect on the responses. The revised RSM analysis is illustrated in Table
3.1.1. Response Surface Regression: Time versus A, B, C
The analysis was done using coded units.
Estimated Regression Coefficients for Time
Term Coef SE Coef T P
Constant
A
B
C
A*A
B*B
C*C
A*B
11.3637
-5.8392
-5.6858
-1.4442
-1.4597
0.7178
1.2278
4.8275
A*C
B*C
-0.625
2.3425
S = 3.573 R-Sq = 81.7% R-Sq(adj) = 72.1%
1.537
1.031
1.031
1.031
1.458
1.458
1.458
1.786
1.786
1.786
7.392
-5.662
-5.513
-1.4
-1.001
0.492
0.842
2.703
-0.35
1.311
0
0
0
0.179
0.331
0.629
0.412
0.015
0.731
0.207

International Journal of Application or Innovation in Engineering & Management (IJAIEM)
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Volume 2, Issue 10, October 2013
Analysis of Variance for Time
ISSN 2319 - 4847
Source DF Seq SS Adj SS Adj MS F P
Regression
Linear
Square
Interaction
Residual Error
Lack-of-Fit
9
3
3
3
17
9
971.77
822.12
32.91
116.73
216.97
190.98
971.77
822.12
32.91
116.73
Pure Error 8 25.99
Total 26 1188.73
3.1.2. Response Surface Regression: Fc versus A, B, C
The analysis was done using coded units.
Estimated Regression Coefficients for Fc
216.97
190.98
25.99
Term
Constant
A
B
C
A  A
B  B
C  C
A  B
Coef
957.41
229.17
320
267.5
-165.69
140.56
-188.19
287.5
A  C 12.5
B  C -27.5
S = 501.1 R-Sq = 46.1% R-Sq(adj) = 17.6%
Analysis of Variance for Fc
SE Coef
215.6
144.7
144.7
144.7
204.6
204.6
204.6
250.5
250.5
250.5
Source
Regression
Linear
Square
Interaction
Residual Error
9
3
3
3
17
DF
3652298
2717683
600340
334275
4268420
Seq SS
3652298
2717683
600340
334275
4268420
Lack-of-Fit
Pure Error
9
8
3819420
449000
Total 26 7920719
3.1.3. Response Surface Regression: Ra versus A, B, C
The analysis was done using coded units.
Estimated Regression Coefficients for Ra
3819420
449000
T
4.44
1.584
2.212
1.849
-0.81
0.687
-0.92
1.148
0.05
-0.11
107.974
274.041
10.971
38.91
12.763
21.22
3.248
Adj SS
405811
905894
200113
111425
251084
424380
56125
8.46
21.47
0.86
3.05
6.53
Adj MS
1.62
3.61
0.8
0.44
P
0
0.132
0.041
0.082
0.429
0.501
0.37
0.267
0.961
0.914
7.56
0
0
0.481
0.057
0.007
F
0.188
0.035
0.512
0.725
0.005
Term
Constant
A
B
Coef
5.52963
4.20833
2.1125
SE Coef
2.619
1.757
1.757
T
2.111
2.395
1.202
P
0.05
0.028
0.246

International Journal of Application or Innovation in Engineering & Management (IJAIEM)
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Volume 2, Issue 10, October 2013 ISSN 2319 - 4847
C -0.5583
A  A
B  B
C  C
1.76597
-0.9903
-2.6965
A  B
A  C
3.55
0.05
B  C 0.325
S = 6.086 R-Sq = 39.2% R-Sq(adj) = 7.1%
Analysis of Variance for Ra
1.757
2.485
2.485
2.485
3.043
3.043
3.043
-0.318
0.711
-0.399
-1.085
1.167
0.016
0.107
0.755
0.487
0.695
0.293
0.259
0.987
0.916
Source
Regression
Linear
Square
Interaction
Residual Error
Lack-of-Fit
DF
9
3
3
3
17
9
Seq SS
406.7
269.81
86.05
50.84
629.65
345.77
Adj SS
406.7
269.81
86.05
50.84
629.65
345.77
Adj MS
45.19
89.94
28.68
16.95
37.04
38.42
F
1.22
2.43
0.77
0.46
P
0.345
0.101
0.524
0.715
1.08 0.461
Pure Error 8 283.88 283.88 35.48
Total 26 1036.35
From the above table the empirical models of the tool life, cutting force and surface finish are as follows
Time (t) = 11.36337-5.83917xA – 5.68583 x B – 1.44417 x C – 1.45972 x A2 + 0.717778 X B2+ 1.22778 x C2 +
4.82750 x A x B – 0.625000 x A x C + 2.34250 x B x C
Cutting Force (Fc) = 957.407 229.167 A+320.000 B+267.500 C-165.694 A2+140.556 B2-188.194 C2 +287.500
A B+12.5000 A C-27.5000 B C
Surface Roughness (Ra) = 5.52963+4.20833 A+2.11250 B -0.558333 C+1.76597A2 -0.990278 B2-2.69653
C2+3.55000 A B+ 0.0500000 A C+0.325000 B C
In this paper, empirical data for prediction of machinability model (tool life, cutting force and surface roughness) have been developed based on response surface methodology. The developed machinability model can be utilized to formulate an optimization model for the machining economic problem to determine the optimal values of process parameters for the selected material.
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[4] U. Zuperl, F. Cus, B. Mursec, T. Ploj, A hybrid analytical-neural network approach to the determination of optimal cutting conditions, J. Mater. Process. Technol. 175 (2004) 82–90.
[5] S. Wong, A. Hamouda, Machinability data representation with artificial neural network, J. Mater. Process. Technol.
138 (2003) 538–544.
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[7] C. Feng, X.Wang, Digitizing uncertainty modeling for reverse engineering applications: regression vs. neural networks, J. Intell. Manufact. 13 (2002) 189–199.
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[9] W. Chien, C. Chou, The predictive model for machinability of 304 stainless steel, J. Mater. Process. Technol. 118
(2001) 441–447.

International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 10, October 2013 ISSN 2319 - 4847
Manu Ravuri received the B.Tech in Mechanical Engineering from Dr. Paul Raj Engineering Collage,
Bhadarachalam in year 2008, and received Masters Degree in Manufacturing Engineering form National
Institute of Technology Karanataka, Surathkal in the year 2012, he is staying in Madanapalle and working as
Assistant Professor in Department of Mechanical Engineering at Madanapalle Institute of Technology &
Science, Madanapalle. Have many International and National Journals and attended many conferences and workshops, editorial board Reviewer for International Journal of Technology Enhancements and Emerging Engineering Research
(IJTEEE).
Sharmas vali.S received the B.Tech in Mechanical Engineering from MITS, Madanapalle in year 2007, and received Masters Degree in R&AC form JNTUA, Anatapur in the year 2011, he staying in Madanapalle and working as Assistant Professor in Department of Mechanical Engineering at Madanapalle Institute of
Technology & Science, Madanapalle. Have many International and National Journals and attended many conferences and workshops.
Akbar Basha.S received the B.Tech in Mechanical Engineering from JNTUH, Hyderarabad in year 2008, and received Masters Degree in Product Design form JNTUA, Anatapur in the year 2012, he is working as Assistant
Professor in Department of Mechanical Engineering at Madanapalle Institute of Technology & Science,
Madanapalle. Have many International and National Journals and attended many conferences and workshops.