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International Journal of Engineering Research and Development
e-ISSN: 2278-067X, p-ISSN: 2278-800X, www.ijerd.com
Volume 7, Issue 2 (May 2013), PP. 12-20
12
Evaluation of chip breaker using flank wear on EN-8 steel and
optimization of machining parameters for maximum tool life and
minimum chip length using RSM methodology
N.Srinivas, 1
T.Vijay kumar, 2
A. Dinesh Kumar, 3
S.Kakati 4
1,2,3
B.E 4th
year, Department of Mechanical Engineering, ANITS College, Visakhapatnam
4
Assistant Professor (M.E) ,Department of Mechanical Engineering, ANITS College, Visakhapatnam
Abstract : Machining is one of the common and essential part of manufacturing of all most all metal products.
In today’s era of automatic machines optimization of machining operations is one of the key requirements
During turning operation, unbroken chips pose a major hindrance, the continuous chip generated during turning
operation deteriorates the work piece precision and causes safety hazards for the operator So, the development
of a effective chip breaker is necessary in order to control the size and shape of the chips thereby increasing the
tool life and for the atomization of turning operations . In this study, the role of different parameters like feed,
speed, depth of cut along with flank wear on chip characteristics have been studied for machining of EN-8 (AISI
1040) steel. The MINITAB software has been used for design and analysis of the experiments conducted.
Optimization of the machining parameters has been done using MINITAB software in order to maximize the
tool life and minimize the chip length by adopting Response Surface Methodology (RSM).
Keywords: EN-8 steel-design of effective chip breaker-RSM-tool life-chip length.
I. INTRODUCTION
Machining (turning), is one of the most important material removal methods. It uses a single point
cutting tool to cut the unwanted portion of material to achieve the desired geometry. Machining process
produces chips due to removal of excess material from the metal surface. The geometrical and metallurgical
characteristics of these chips are very representative of the performances of the process. Indeed, they bear
witness to most of the physical and thermal phenomena occurring during the machining.
Numerous chips are being generated in short time during machining operations which requires
effective control of length, moreover thickness of chips and tool life are some of the most important factors for
work performance. When the chips are out of control , it may lead to system failure which directly affects
productivity and is also very dangerous for the person working on machine.
Failure in chip control has a significant effect on surface roughness of work piece, precision of product and also
on the life of the tool. So, it is important in order to analysis the performance of the chip breaker. Tool life is the
time up to which the tool is functioning satisfactorily.
Tool life:
The Taylor’s equation for tool life is generally given by the formula: TVn
=C, the value of C and n depend upon
the type of tool used. Many researchers have been done in the view of developing some equations for tool life
including parameters like feed, depth of cut, wear, cutting speed etc. So, besides cutting speed, other parameters
also have a significant influence on tool life. So in this project experiments have been done to calculate the tool
life at different flank wear values and the observation values of tool life are tabulated .
I.1 CHIP BREAKER
Design of a chip breaker: In, order to control the long and continuous chips produced during the machining
process, the chip breaker is used. A chip breaker (in-built) type is designed in this experiment in order to
control the long chips produced during machining of EN-8 steel, this chip breaker designed works on the
principle of force breaking. The uncut layer is first elastically deformed followed by plastic deformations and
separation taking place near the cutting edge of the tool.
W = width, H = height
Evaluation Of Chip Breaker Using Flank…
13
Principle of chip breaking
I.2 WORK MATERIAL:
The composition of EN-8 steel is given below:
Carbon Manganese Silicon sulphur phosphorous
0.35% 0.06-1.00% 0.05-0.35% 0.06% 0.06%
II. EXPERIMENTAL PROCEDURE
The different equipments used in the experimentation are given below:
(a) LATHE machine
(b) Grinding machine
(c) Tool maker’s microscope
(d) PDF-Xchange viewer software
(e) MINITAB software
II.1 Design and selection of chip breaker:
Before, starting the experiment we have design an effective chip breaker for the appropriate control of
chips produced. so, for this purpose by adopting trial and error method different chip breakers are made and
tested. So, the following are the different designs proposed and 3rd
design is selected as the best.
TOOL NO. WIDTH(mm) HEIGHT(mm) ɸs
1 3.0 10 0.90
2 3.0 8 1.50
3 2.5 6 0.60
2.2 The experimental setup for chip breaking: 2.3 grinding machine used for tool design
Experimental setup (cutting tool with work piece) Tool grinding machine
II.4 Experimental Conditions:
Condition Units Value
Cutting speed m/min 30,45,50
Depth of cut mm 0.2,0.3,0.4
Feed mm/rev 0.5,0.75,1.0
Work piece material EN-8(AISI 1040) Steel
II.5 experiment input chart:
Response surface methodology: In statistics, response surface methodology (RSM) explores the
relationships between several explanatory variables and one or more response variables. There are two types of
methodologies (designs) in RSM which can be adopted. They are central composite design and box-behnken
design. For our experiment we have concluded that central composite design is the best involving 4 variable
factors and 3 response variables and if experiment is performed under unblocked condition.
The following is the input chart obtained from the MINITAB for the design of the experiments.
Evaluation Of Chip Breaker Using Flank…
14
Sl no: Std
order
Run
order
Pt type blocks Feed
(mm/rev)
Depth
of cut
(mm)
Velocity
(m/min)
Wear
(mm)
1 1 19 1 1 0.75 0.3 46.23 0.5
2 2 6 1 1 1 0.4 50.26 0.5
3 3 24 1 1 0.5 0.4 50.26 0.5
4 4 23 -1 1 1 0.2 50.26 0.5
5 23 9 1 1 1 0.4 30.66 0.5
6 5 15 1 1 1 0.2 30.66 0.5
7 6 5 1 1 0.5 0.4 30.66 0.5
8 7 26 1 1 0.5 0.2 50.26 0.5
9 8 31 1 1 0.5 0.2 30.66 0.5
10 21 17 -1 1 0.75 0.3 46.23 1.0
11 19 22 -1 1 0.75 0.3 46.23 1.0
12 17 7 -1 1 0.75 0.3 46.23 1.0
13 29 1 0 1 0.75 0.3 46.23 1.0
14 31 8 0 1 0.75 0.3 46.23 1.0
15 25 11 0 1 0.75 0.3 46.23 1.0
16 27 12 0 1 0.75 0.3 46.23 1.0
17 30 14 0 1 1 0.3 46.23 1.0
18 26 21 0 1 0.75 0.4 46.23 1.0
19 28 29 0 1 0.75 0.3 50.26 1.0
20 18 28 -1 1 0.5 0.3 46.23 1.0
21 20 16 -1 1 0.75 0.2 46.23 1.0
22 22 30 -1 1 0.75 0.3 30.66 1.0
23 9 27 1 1 0.75 0.3 46.23 1.5
Evaluation Of Chip Breaker Using Flank…
15
24 10 13 1 1 1 0.4 50.26 1.5
25 11 3 1 1 0.5 0.4 50.26 1.5
26 12 18 1 1 1 0.2 50.26 1.5
27 24 10 -1 1 1 0.4 30.66 1.5
28 13 25 1 1 0.5 0.2 50.26 1.5
29 14 20 1 1 1 0.2 30.66 1.5
30 15 2 1 1 0.5 0.4 30.66 1.5
31 16 4 1 1 0.5 0.2 30.66 1.5
So, the experiments have been conducted by the information obtained from MINTAB, the above chart is
obtained using RSM technique. (central composite design)
II.6 Calculating chip thickness:
The chip thickness is measured by using the tool maker’s microscope just by placing the chip on the
work plane of the tool maker’s microscope. The least count of the microscope is equal to 0.005mm.
II.7 Calculating chip length:
The chip length is measured by using the PDF-Xchange Viewer Software just by importing the
photographs of the chips and by tracing the objects of the chips in the software the lengths can be determined.
The value of the lengths are displaced at the right most bottom.
III. RESULTS AND DISCUSSIONS:
III.1 Observation table for chip length and chip thickness:
The chip which are obtained at different input parameters are taken, and their photographs are imported
to find lengths and the tool maker’s microscope is used for calculating the chip thickness:
Sl no. Feed(mm/re
v)
d.o.c(
mm)
Speed(m/min
)
Wear(m
m)
L1 L2 L3 L(avg)(mm) Chip
thickness(mm)
1 0.3 0.75 46.23 0.5 10.6 9.72 9.2 9.84 0.4324
2 0.4 1 50.26 0.5 12.31 11.98 12.4 12.213 0.943
3 0.4 0.5 50.26 0.5 7.1 7.5 7.43 7.343 0.577
4 0.2 1 50.26 0.5 12.3 10.1 14.3 12.23 0.512
5 0.4 1 30.66 0.5 11.1 14.9 13.6 13.2 0.853
6 0.2 1 30.66 0.5 15.7 10.4 10.9 12.33 0.48
7 0.4 0.5 30.66 0.5 7.9 13.2 10.1 10.4 0.5
8 0.2 0.5 50.26 0.5 7.0 6.8 10.2 8 0.453
9 0.2 0.5 30.66 0.5 10.52 7.5 9.4 9.14 0.395
10 0.3 0.75 46.23 1 8.45 7.8 12.7 9.65 0.943
11 0.3 0.75 46.23 1 8.3 10.3 8.4 9 0.875
12 0.3 0.75 46.23 1 8.2 10.7 6.79 8.566 0.811
13 0.3 0.75 46.23 1 12 7.8 7.7 9.166 0.912
14 0.3 0.75 46.23 1 7.9 10.1 8.4 8.8 0.826
15 0.3 0.75 46.23 1 6.4 9.0 9.8 8.425 0.9546
16 0.3 0.75 46.23 1 8.8 8.4 12.5 9.9 0.911
17 0.3 1 46.23 1 11.5 9.8 10.6 10.6 1.03
18 0.4 0.75 46.23 1 9 8.66 9.2 8.956 1.088
19 0.3 0.75 50.26 1 8.4 10.1 8.92 9.14 1.1053
20 0.3 0.5 46.23 1 7.2 6.6 7.8 7.2 0.8526
21 0.2 0.75 46.23 1 9.8 8.8 11.66 10.089 0.883
22 0.3 0.75 30.66 1 9.1 9.8 8.50 9.135 1.101
Evaluation Of Chip Breaker Using Flank…
16
23 0.3 0.75 46.23 1.5 7.67 7.9 11.2 8.924 0.923
24 0.4 1 50.26 1.5 10.1 7.53 8.8 8.81 1.2643
25 0.4 0.5 50.26 1.5 6.5 9.4 6.7 7.53 0.9923
26 0.2 1 50.26 1.5 8.4 6.5 10.6 8.5 0.99
27 0.4 1 30.66 1.5 12.5 7.9 8.0 9.466 1.253
28 0.2 0.5 50.26 1.5 8.9 8.8 8.4 8.71 0.933
29 0.2 1 30.66 1.5 7.8 7.7 7.2 7.56 0.88
30 0.4 0.5 30.66 1.5 9.29 11.9 7.4 9.53 0.975
31 0.2 0.5 30.66 1.5 8.9 11.2 6.93 9.01 0.873
III.2 Estimated Regression Coefficients for chip length
Response Surface Regression: chip length versus feed, doc, velocity, wear
term Coef SE Coef T P
Constant 9.43105 0.2612 36.113 0.001
feed 0.97690 0.1194 8.813 0.002
doc 0.13152 0.1194 1.102 0.287
velocity -0.40528 0.1191 -3.403 0.004
wear -0.94037 0.1194 -7.877 0.000
feed*feed -0.20057 0.3137 -0.639 0.532
doc*doc 0.42193 0.3137 1.345 0.197
velocity*velocity -0.30870 0.4920 -0.627 0.539
wear*wear 0.28143 0.3197 0.897 0.383
feed*doc 0.19538 0.1263 1.546 0.142
feed*velocity 0.39210 0.1240 3.163 0.006
feed*wear -0.97088 0.1263 -7.685 0.002
R-Sq = 93.71% R-Sq(pred) = 76.89% R-Sq(adj) = 88.20%
So, here when the experiment is conducted at 95% confidence levels, 93% has been obtained which
gives us the validity of the experiment.
Analysis of Variance for chip length before modification:
So, the
parameters whose p-value is less than 0.05 are taken and again the co-efficient table is modified.
The predicted equation for chip length after modification is obtained from MINITAB software:
Chip length=3.75128+ (5.19933*f)+(18.4344*doc)-(0.0381443*v)+(3.97458*w)+(0.160042*f*v)-
(7.767*f*w)-(0.423116*d*v)
The graphs for chip length are given below:
Source DF Seq SS Adj SS Adj MS F P
Regression 14 60.853 60.853 4.3466 4.02 0.000
Linear 4 37.719 36.209 9.0523 5.45 0.000
Square 4 1.152 1.152 0.2881
Interaction 6 21.982 21.982 3.6637 4.35 0.000
Residual Error 16 4.086 4.086 0.2554
Lack-of-Fit 10 2.304 2.304 0.2304 0.78 0.656
Pure Error 6 1.782 1.782 0.2971
Total 30 64.939
Evaluation Of Chip Breaker Using Flank…
17
1.000.750.50
10.5
10.0
9.5
9.0
8.5
0.40.30.2
50.2646.2330.66
10.5
10.0
9.5
9.0
8.5
1.51.00.5
feed
Mean
doc
velocity wear
Main Effects Plot for chip length
Data Means
3210-1-2-3
99
95
90
80
70
60
50
40
30
20
10
5
1
Standardized Residual
Percent
Normal Probability Plot
(response is chip length)
3.3 Estimated co-efficient for chip thickness:
Response Surface Regression for chip thickness versus feed, wear, depth of cut, speed.
Term Coef SE Coef T P
Constant 0.818266 0.02648 30.906 0.006
feed 0.091804 0.01210 7.585 0.002
doc 0.114005 0.01210 9.419 0.004
velocity 0.025550 0.01208 2.116 0.050
wear 0.218896 0.01210 18.085 0.05
feed*feed -0.013395 0.03180 -0.421 0.679
doc*doc 0.030805 0.03180 0.969 0.347
velocity*velocity 0.250249 0.04988 5.017 0.03
wear*wear -0.276995 0.03180 -8.710 0.007
feed*doc 0.066312 0.01281 5.177 0.0052
feed*velocity 0.001638 0.01257 0.130 0.898
feed*wear -0.015563 0.01281 -1.215 0.242
doc*velocity -0.004669 0.01257 -0.372 0.715
doc*wear -0.014025 0.01281 -1.095 0.290
velocity*wear -0.001636 0.01257 -0.130 0.898
R-Sq = 97.45% R-Sq(pred) = 92.80% R-Sq(adj) = 95.22%
So, here when the experiment is conducted at 95% confidence levels, 93% has been obtained which gives us the
validity of the experiment.
Evaluation Of Chip Breaker Using Flank…
18
Analysis of Variance for chip thickness before modification:
Source DF Seq SS Adj SS Adj MS F P
Regression 14 1.60428 1.60428 0.114591 3.66 0.045
Linear 4 1.26677 1.25412 0.313531 1.45 0.04
Square 4 0.25968 0.25968 0.064921 4.73 0.028
Interaction 6 0.07783 0.07783 0.012972 4.94 0.07
Residual Error 16 0.04200 0.04200 0.002625
Lack-of-Fit 10 0.02353 0.02353 0.002353 0.76 0.663
Pure Error 6 0.01847 0.01847 0.003078
Total 30 1.64628
So, the parameters whose p-value is less than 0.05 are taken and again the co-efficient table is modified.
The predicted equation for chip thickness after modification is obtained from MINITAB software as:
Chip thickness=3.63227-(0.48106*f)-(0.852375*doc)-(0.217756*v)+(2.59453*w)+(0.00272322*v*v)-
(1.07848*w*w)+ (2.6250*f*doc)
The graphs for chip thickness is given below:
210-1-2-3
99
95
90
80
70
60
50
40
30
20
10
5
1
Standardized Residual
Percent
Normal Probability Plot
(response is ct)
1.000.750.50
1.0
0.9
0.8
0.7
0.6
0.40.30.2
50.2646.2330.66
1.0
0.9
0.8
0.7
0.6
1.51.00.5
feed
Mean
doc
velocity wear
Main Effects Plot for ct
Data Means
Evaluation Of Chip Breaker Using Flank…
19
3.4 OBSERVATION TABLE FOR TOOL LIFE:
Sl no: Feed (mm/rev) Doc(mm) Speed(m/min) Wear(mm) tool life(min)
1 0.75 0.3 46.23 0.5 4.63
2 1 0.4 50.26 0.5 2.7923
3 0.5 0.4 50.26 0.5 4.778
4 1 0.2 50.26 0.5 3.0982
5 1 0.4 30.66 0.5 11.5632
6 1 0.2 30.66 0.5 12.83
7 0.5 0.4 30.66 0.5 19.787
8 0.5 0.2 50.26 0.5 5.6301
9 0.5 0.2 30.66 0.5 21.955
10 0.75 0.3 46.23 1 26.2058
11 0.75 0.3 46.23 1 26.2058
12 0.75 0.3 46.23 1 26.2058
13 0.75 0.3 46.23 1 26.2058
14 0.75 0.3 46.23 1 26.2058
15 0.75 0.3 46.23 1 26.2058
16 0.75 0.3 46.23 1 26.2058
17 1 0.3 46.23 1 20.9686
18 0.75 0.4 46.23 1 25.099
19 0.75 0.3 50.26 1 20.611
20 0.5 0.3 46.23 1 35.881
21 0.75 0.2 46.23 1 27.849
22 0.75 0.3 30.66 1 85.357
23 0.75 0.3 46.23 1.5 72.2147
24 1 0.4 50.26 1.5 43.5277
25 0.5 0.4 50.26 1.5 74.4842
26 1 0.2 50.26 1.5 82.6454
27 1 0.4 30.66 1.5 180.2607
28 0.5 0.2 50.26 1.5 82.6454
29 1 0.2 30.66 1.5 200.0118
30 0.5 0.4 30.66 1.5 308.4604
31 0.5 0.2 30.66 1.5 342.2583
3.5 OPTIMIZATION FOR CHIP LENGTH, THICKNESS AND TOOL LIFE
Evaluation Of Chip Breaker Using Flank…
20
The values in (red color) the above plot show the optimum conditions for machining. So, from the optimization
plot the optimum machining parameters for maximum chip length and maximum tool life are obtained as
chip length=8.8243mm, tool life =299.6741min, chip thickness =0.8687 mm at
Depth of cut =0.2182 mm, feed =0.50mm/rev, velocity=31.3472m/min, wear=1.4779mm
4.CONCLUSIONS:
(a) In this experimental study , the effect of different parameters like feed, depth of cut, cutting speed and flank
wear on the chip length and chip thickness is studied. Main aim of the study was to analyze the effect of
flank wear on response parameters.
(b) By analyzing the result , we can conclude that besides different parameters like feed , depth of cut, cutting
speed , the flank wear contributes a lot in order to control the chip lengths and there by increasing the tool
life.
(c) Besides the cutting speed , flank wear is also an important factor which contributes for the increase in tool
life .
By considering the optimal values of parameters from optimal graph, we can a perfect machining conditions for
maximum tool life and minimum chip length for an optimal value of chip thickness.
REFERENCES
Journal Papers:
[1] Kim J.D., Kweun O.B. A chip-breaking system for mild steel in turning, International Journal of machine
tools manufacture, 37 (1997), pp. 607-617.
[2] 2) Life Prediction of Cutting Tool by the Workpiece Cutting Condition -by Noemia Gomes de Mattos de
Mesquita. Advanced Materials Research Vol. 223 (2011) pp 554-563.
[3] Evaluation of chip breaking using flank wear by SumitRroyal –NIT Rourkela.
[4] Application of Taguchi Method in Optimization of Tool Flank Wear Width in Turning Operation of AISI
1045 Steel, a journal on ISSN 2224-6096 (Paper) ISSN 2225-0581 (online) Vol 2, No.8, 2012
[5] study of chip breaking on stainless steel by Sarath Nayak-NIT Rorkela, journal published in 2011
[6] Books:
[7] Production Technology by R.K. Jain

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International Journal of Engineering Research and Development (IJERD)

  • 1. International Journal of Engineering Research and Development e-ISSN: 2278-067X, p-ISSN: 2278-800X, www.ijerd.com Volume 7, Issue 2 (May 2013), PP. 12-20 12 Evaluation of chip breaker using flank wear on EN-8 steel and optimization of machining parameters for maximum tool life and minimum chip length using RSM methodology N.Srinivas, 1 T.Vijay kumar, 2 A. Dinesh Kumar, 3 S.Kakati 4 1,2,3 B.E 4th year, Department of Mechanical Engineering, ANITS College, Visakhapatnam 4 Assistant Professor (M.E) ,Department of Mechanical Engineering, ANITS College, Visakhapatnam Abstract : Machining is one of the common and essential part of manufacturing of all most all metal products. In today’s era of automatic machines optimization of machining operations is one of the key requirements During turning operation, unbroken chips pose a major hindrance, the continuous chip generated during turning operation deteriorates the work piece precision and causes safety hazards for the operator So, the development of a effective chip breaker is necessary in order to control the size and shape of the chips thereby increasing the tool life and for the atomization of turning operations . In this study, the role of different parameters like feed, speed, depth of cut along with flank wear on chip characteristics have been studied for machining of EN-8 (AISI 1040) steel. The MINITAB software has been used for design and analysis of the experiments conducted. Optimization of the machining parameters has been done using MINITAB software in order to maximize the tool life and minimize the chip length by adopting Response Surface Methodology (RSM). Keywords: EN-8 steel-design of effective chip breaker-RSM-tool life-chip length. I. INTRODUCTION Machining (turning), is one of the most important material removal methods. It uses a single point cutting tool to cut the unwanted portion of material to achieve the desired geometry. Machining process produces chips due to removal of excess material from the metal surface. The geometrical and metallurgical characteristics of these chips are very representative of the performances of the process. Indeed, they bear witness to most of the physical and thermal phenomena occurring during the machining. Numerous chips are being generated in short time during machining operations which requires effective control of length, moreover thickness of chips and tool life are some of the most important factors for work performance. When the chips are out of control , it may lead to system failure which directly affects productivity and is also very dangerous for the person working on machine. Failure in chip control has a significant effect on surface roughness of work piece, precision of product and also on the life of the tool. So, it is important in order to analysis the performance of the chip breaker. Tool life is the time up to which the tool is functioning satisfactorily. Tool life: The Taylor’s equation for tool life is generally given by the formula: TVn =C, the value of C and n depend upon the type of tool used. Many researchers have been done in the view of developing some equations for tool life including parameters like feed, depth of cut, wear, cutting speed etc. So, besides cutting speed, other parameters also have a significant influence on tool life. So in this project experiments have been done to calculate the tool life at different flank wear values and the observation values of tool life are tabulated . I.1 CHIP BREAKER Design of a chip breaker: In, order to control the long and continuous chips produced during the machining process, the chip breaker is used. A chip breaker (in-built) type is designed in this experiment in order to control the long chips produced during machining of EN-8 steel, this chip breaker designed works on the principle of force breaking. The uncut layer is first elastically deformed followed by plastic deformations and separation taking place near the cutting edge of the tool. W = width, H = height
  • 2. Evaluation Of Chip Breaker Using Flank… 13 Principle of chip breaking I.2 WORK MATERIAL: The composition of EN-8 steel is given below: Carbon Manganese Silicon sulphur phosphorous 0.35% 0.06-1.00% 0.05-0.35% 0.06% 0.06% II. EXPERIMENTAL PROCEDURE The different equipments used in the experimentation are given below: (a) LATHE machine (b) Grinding machine (c) Tool maker’s microscope (d) PDF-Xchange viewer software (e) MINITAB software II.1 Design and selection of chip breaker: Before, starting the experiment we have design an effective chip breaker for the appropriate control of chips produced. so, for this purpose by adopting trial and error method different chip breakers are made and tested. So, the following are the different designs proposed and 3rd design is selected as the best. TOOL NO. WIDTH(mm) HEIGHT(mm) ɸs 1 3.0 10 0.90 2 3.0 8 1.50 3 2.5 6 0.60 2.2 The experimental setup for chip breaking: 2.3 grinding machine used for tool design Experimental setup (cutting tool with work piece) Tool grinding machine II.4 Experimental Conditions: Condition Units Value Cutting speed m/min 30,45,50 Depth of cut mm 0.2,0.3,0.4 Feed mm/rev 0.5,0.75,1.0 Work piece material EN-8(AISI 1040) Steel II.5 experiment input chart: Response surface methodology: In statistics, response surface methodology (RSM) explores the relationships between several explanatory variables and one or more response variables. There are two types of methodologies (designs) in RSM which can be adopted. They are central composite design and box-behnken design. For our experiment we have concluded that central composite design is the best involving 4 variable factors and 3 response variables and if experiment is performed under unblocked condition. The following is the input chart obtained from the MINITAB for the design of the experiments.
  • 3. Evaluation Of Chip Breaker Using Flank… 14 Sl no: Std order Run order Pt type blocks Feed (mm/rev) Depth of cut (mm) Velocity (m/min) Wear (mm) 1 1 19 1 1 0.75 0.3 46.23 0.5 2 2 6 1 1 1 0.4 50.26 0.5 3 3 24 1 1 0.5 0.4 50.26 0.5 4 4 23 -1 1 1 0.2 50.26 0.5 5 23 9 1 1 1 0.4 30.66 0.5 6 5 15 1 1 1 0.2 30.66 0.5 7 6 5 1 1 0.5 0.4 30.66 0.5 8 7 26 1 1 0.5 0.2 50.26 0.5 9 8 31 1 1 0.5 0.2 30.66 0.5 10 21 17 -1 1 0.75 0.3 46.23 1.0 11 19 22 -1 1 0.75 0.3 46.23 1.0 12 17 7 -1 1 0.75 0.3 46.23 1.0 13 29 1 0 1 0.75 0.3 46.23 1.0 14 31 8 0 1 0.75 0.3 46.23 1.0 15 25 11 0 1 0.75 0.3 46.23 1.0 16 27 12 0 1 0.75 0.3 46.23 1.0 17 30 14 0 1 1 0.3 46.23 1.0 18 26 21 0 1 0.75 0.4 46.23 1.0 19 28 29 0 1 0.75 0.3 50.26 1.0 20 18 28 -1 1 0.5 0.3 46.23 1.0 21 20 16 -1 1 0.75 0.2 46.23 1.0 22 22 30 -1 1 0.75 0.3 30.66 1.0 23 9 27 1 1 0.75 0.3 46.23 1.5
  • 4. Evaluation Of Chip Breaker Using Flank… 15 24 10 13 1 1 1 0.4 50.26 1.5 25 11 3 1 1 0.5 0.4 50.26 1.5 26 12 18 1 1 1 0.2 50.26 1.5 27 24 10 -1 1 1 0.4 30.66 1.5 28 13 25 1 1 0.5 0.2 50.26 1.5 29 14 20 1 1 1 0.2 30.66 1.5 30 15 2 1 1 0.5 0.4 30.66 1.5 31 16 4 1 1 0.5 0.2 30.66 1.5 So, the experiments have been conducted by the information obtained from MINTAB, the above chart is obtained using RSM technique. (central composite design) II.6 Calculating chip thickness: The chip thickness is measured by using the tool maker’s microscope just by placing the chip on the work plane of the tool maker’s microscope. The least count of the microscope is equal to 0.005mm. II.7 Calculating chip length: The chip length is measured by using the PDF-Xchange Viewer Software just by importing the photographs of the chips and by tracing the objects of the chips in the software the lengths can be determined. The value of the lengths are displaced at the right most bottom. III. RESULTS AND DISCUSSIONS: III.1 Observation table for chip length and chip thickness: The chip which are obtained at different input parameters are taken, and their photographs are imported to find lengths and the tool maker’s microscope is used for calculating the chip thickness: Sl no. Feed(mm/re v) d.o.c( mm) Speed(m/min ) Wear(m m) L1 L2 L3 L(avg)(mm) Chip thickness(mm) 1 0.3 0.75 46.23 0.5 10.6 9.72 9.2 9.84 0.4324 2 0.4 1 50.26 0.5 12.31 11.98 12.4 12.213 0.943 3 0.4 0.5 50.26 0.5 7.1 7.5 7.43 7.343 0.577 4 0.2 1 50.26 0.5 12.3 10.1 14.3 12.23 0.512 5 0.4 1 30.66 0.5 11.1 14.9 13.6 13.2 0.853 6 0.2 1 30.66 0.5 15.7 10.4 10.9 12.33 0.48 7 0.4 0.5 30.66 0.5 7.9 13.2 10.1 10.4 0.5 8 0.2 0.5 50.26 0.5 7.0 6.8 10.2 8 0.453 9 0.2 0.5 30.66 0.5 10.52 7.5 9.4 9.14 0.395 10 0.3 0.75 46.23 1 8.45 7.8 12.7 9.65 0.943 11 0.3 0.75 46.23 1 8.3 10.3 8.4 9 0.875 12 0.3 0.75 46.23 1 8.2 10.7 6.79 8.566 0.811 13 0.3 0.75 46.23 1 12 7.8 7.7 9.166 0.912 14 0.3 0.75 46.23 1 7.9 10.1 8.4 8.8 0.826 15 0.3 0.75 46.23 1 6.4 9.0 9.8 8.425 0.9546 16 0.3 0.75 46.23 1 8.8 8.4 12.5 9.9 0.911 17 0.3 1 46.23 1 11.5 9.8 10.6 10.6 1.03 18 0.4 0.75 46.23 1 9 8.66 9.2 8.956 1.088 19 0.3 0.75 50.26 1 8.4 10.1 8.92 9.14 1.1053 20 0.3 0.5 46.23 1 7.2 6.6 7.8 7.2 0.8526 21 0.2 0.75 46.23 1 9.8 8.8 11.66 10.089 0.883 22 0.3 0.75 30.66 1 9.1 9.8 8.50 9.135 1.101
  • 5. Evaluation Of Chip Breaker Using Flank… 16 23 0.3 0.75 46.23 1.5 7.67 7.9 11.2 8.924 0.923 24 0.4 1 50.26 1.5 10.1 7.53 8.8 8.81 1.2643 25 0.4 0.5 50.26 1.5 6.5 9.4 6.7 7.53 0.9923 26 0.2 1 50.26 1.5 8.4 6.5 10.6 8.5 0.99 27 0.4 1 30.66 1.5 12.5 7.9 8.0 9.466 1.253 28 0.2 0.5 50.26 1.5 8.9 8.8 8.4 8.71 0.933 29 0.2 1 30.66 1.5 7.8 7.7 7.2 7.56 0.88 30 0.4 0.5 30.66 1.5 9.29 11.9 7.4 9.53 0.975 31 0.2 0.5 30.66 1.5 8.9 11.2 6.93 9.01 0.873 III.2 Estimated Regression Coefficients for chip length Response Surface Regression: chip length versus feed, doc, velocity, wear term Coef SE Coef T P Constant 9.43105 0.2612 36.113 0.001 feed 0.97690 0.1194 8.813 0.002 doc 0.13152 0.1194 1.102 0.287 velocity -0.40528 0.1191 -3.403 0.004 wear -0.94037 0.1194 -7.877 0.000 feed*feed -0.20057 0.3137 -0.639 0.532 doc*doc 0.42193 0.3137 1.345 0.197 velocity*velocity -0.30870 0.4920 -0.627 0.539 wear*wear 0.28143 0.3197 0.897 0.383 feed*doc 0.19538 0.1263 1.546 0.142 feed*velocity 0.39210 0.1240 3.163 0.006 feed*wear -0.97088 0.1263 -7.685 0.002 R-Sq = 93.71% R-Sq(pred) = 76.89% R-Sq(adj) = 88.20% So, here when the experiment is conducted at 95% confidence levels, 93% has been obtained which gives us the validity of the experiment. Analysis of Variance for chip length before modification: So, the parameters whose p-value is less than 0.05 are taken and again the co-efficient table is modified. The predicted equation for chip length after modification is obtained from MINITAB software: Chip length=3.75128+ (5.19933*f)+(18.4344*doc)-(0.0381443*v)+(3.97458*w)+(0.160042*f*v)- (7.767*f*w)-(0.423116*d*v) The graphs for chip length are given below: Source DF Seq SS Adj SS Adj MS F P Regression 14 60.853 60.853 4.3466 4.02 0.000 Linear 4 37.719 36.209 9.0523 5.45 0.000 Square 4 1.152 1.152 0.2881 Interaction 6 21.982 21.982 3.6637 4.35 0.000 Residual Error 16 4.086 4.086 0.2554 Lack-of-Fit 10 2.304 2.304 0.2304 0.78 0.656 Pure Error 6 1.782 1.782 0.2971 Total 30 64.939
  • 6. Evaluation Of Chip Breaker Using Flank… 17 1.000.750.50 10.5 10.0 9.5 9.0 8.5 0.40.30.2 50.2646.2330.66 10.5 10.0 9.5 9.0 8.5 1.51.00.5 feed Mean doc velocity wear Main Effects Plot for chip length Data Means 3210-1-2-3 99 95 90 80 70 60 50 40 30 20 10 5 1 Standardized Residual Percent Normal Probability Plot (response is chip length) 3.3 Estimated co-efficient for chip thickness: Response Surface Regression for chip thickness versus feed, wear, depth of cut, speed. Term Coef SE Coef T P Constant 0.818266 0.02648 30.906 0.006 feed 0.091804 0.01210 7.585 0.002 doc 0.114005 0.01210 9.419 0.004 velocity 0.025550 0.01208 2.116 0.050 wear 0.218896 0.01210 18.085 0.05 feed*feed -0.013395 0.03180 -0.421 0.679 doc*doc 0.030805 0.03180 0.969 0.347 velocity*velocity 0.250249 0.04988 5.017 0.03 wear*wear -0.276995 0.03180 -8.710 0.007 feed*doc 0.066312 0.01281 5.177 0.0052 feed*velocity 0.001638 0.01257 0.130 0.898 feed*wear -0.015563 0.01281 -1.215 0.242 doc*velocity -0.004669 0.01257 -0.372 0.715 doc*wear -0.014025 0.01281 -1.095 0.290 velocity*wear -0.001636 0.01257 -0.130 0.898 R-Sq = 97.45% R-Sq(pred) = 92.80% R-Sq(adj) = 95.22% So, here when the experiment is conducted at 95% confidence levels, 93% has been obtained which gives us the validity of the experiment.
  • 7. Evaluation Of Chip Breaker Using Flank… 18 Analysis of Variance for chip thickness before modification: Source DF Seq SS Adj SS Adj MS F P Regression 14 1.60428 1.60428 0.114591 3.66 0.045 Linear 4 1.26677 1.25412 0.313531 1.45 0.04 Square 4 0.25968 0.25968 0.064921 4.73 0.028 Interaction 6 0.07783 0.07783 0.012972 4.94 0.07 Residual Error 16 0.04200 0.04200 0.002625 Lack-of-Fit 10 0.02353 0.02353 0.002353 0.76 0.663 Pure Error 6 0.01847 0.01847 0.003078 Total 30 1.64628 So, the parameters whose p-value is less than 0.05 are taken and again the co-efficient table is modified. The predicted equation for chip thickness after modification is obtained from MINITAB software as: Chip thickness=3.63227-(0.48106*f)-(0.852375*doc)-(0.217756*v)+(2.59453*w)+(0.00272322*v*v)- (1.07848*w*w)+ (2.6250*f*doc) The graphs for chip thickness is given below: 210-1-2-3 99 95 90 80 70 60 50 40 30 20 10 5 1 Standardized Residual Percent Normal Probability Plot (response is ct) 1.000.750.50 1.0 0.9 0.8 0.7 0.6 0.40.30.2 50.2646.2330.66 1.0 0.9 0.8 0.7 0.6 1.51.00.5 feed Mean doc velocity wear Main Effects Plot for ct Data Means
  • 8. Evaluation Of Chip Breaker Using Flank… 19 3.4 OBSERVATION TABLE FOR TOOL LIFE: Sl no: Feed (mm/rev) Doc(mm) Speed(m/min) Wear(mm) tool life(min) 1 0.75 0.3 46.23 0.5 4.63 2 1 0.4 50.26 0.5 2.7923 3 0.5 0.4 50.26 0.5 4.778 4 1 0.2 50.26 0.5 3.0982 5 1 0.4 30.66 0.5 11.5632 6 1 0.2 30.66 0.5 12.83 7 0.5 0.4 30.66 0.5 19.787 8 0.5 0.2 50.26 0.5 5.6301 9 0.5 0.2 30.66 0.5 21.955 10 0.75 0.3 46.23 1 26.2058 11 0.75 0.3 46.23 1 26.2058 12 0.75 0.3 46.23 1 26.2058 13 0.75 0.3 46.23 1 26.2058 14 0.75 0.3 46.23 1 26.2058 15 0.75 0.3 46.23 1 26.2058 16 0.75 0.3 46.23 1 26.2058 17 1 0.3 46.23 1 20.9686 18 0.75 0.4 46.23 1 25.099 19 0.75 0.3 50.26 1 20.611 20 0.5 0.3 46.23 1 35.881 21 0.75 0.2 46.23 1 27.849 22 0.75 0.3 30.66 1 85.357 23 0.75 0.3 46.23 1.5 72.2147 24 1 0.4 50.26 1.5 43.5277 25 0.5 0.4 50.26 1.5 74.4842 26 1 0.2 50.26 1.5 82.6454 27 1 0.4 30.66 1.5 180.2607 28 0.5 0.2 50.26 1.5 82.6454 29 1 0.2 30.66 1.5 200.0118 30 0.5 0.4 30.66 1.5 308.4604 31 0.5 0.2 30.66 1.5 342.2583 3.5 OPTIMIZATION FOR CHIP LENGTH, THICKNESS AND TOOL LIFE
  • 9. Evaluation Of Chip Breaker Using Flank… 20 The values in (red color) the above plot show the optimum conditions for machining. So, from the optimization plot the optimum machining parameters for maximum chip length and maximum tool life are obtained as chip length=8.8243mm, tool life =299.6741min, chip thickness =0.8687 mm at Depth of cut =0.2182 mm, feed =0.50mm/rev, velocity=31.3472m/min, wear=1.4779mm 4.CONCLUSIONS: (a) In this experimental study , the effect of different parameters like feed, depth of cut, cutting speed and flank wear on the chip length and chip thickness is studied. Main aim of the study was to analyze the effect of flank wear on response parameters. (b) By analyzing the result , we can conclude that besides different parameters like feed , depth of cut, cutting speed , the flank wear contributes a lot in order to control the chip lengths and there by increasing the tool life. (c) Besides the cutting speed , flank wear is also an important factor which contributes for the increase in tool life . By considering the optimal values of parameters from optimal graph, we can a perfect machining conditions for maximum tool life and minimum chip length for an optimal value of chip thickness. REFERENCES Journal Papers: [1] Kim J.D., Kweun O.B. A chip-breaking system for mild steel in turning, International Journal of machine tools manufacture, 37 (1997), pp. 607-617. [2] 2) Life Prediction of Cutting Tool by the Workpiece Cutting Condition -by Noemia Gomes de Mattos de Mesquita. Advanced Materials Research Vol. 223 (2011) pp 554-563. [3] Evaluation of chip breaking using flank wear by SumitRroyal –NIT Rourkela. [4] Application of Taguchi Method in Optimization of Tool Flank Wear Width in Turning Operation of AISI 1045 Steel, a journal on ISSN 2224-6096 (Paper) ISSN 2225-0581 (online) Vol 2, No.8, 2012 [5] study of chip breaking on stainless steel by Sarath Nayak-NIT Rorkela, journal published in 2011 [6] Books: [7] Production Technology by R.K. Jain