Artificial Immune System (AIS) Algorithm For Optimizing Shear Zone Temperature In CNC Turning Operation.
iDECON 2012 – International Conference on Design and Concurrent Engineering
Universiti Teknikal Malaysia Melaka (UTeM)
1516 October 2012
Artificial Immune System (AIS) Algorithm For Optimizing Shear Zone
Temperature In CNC Turning Operation.
Adnan, J.A.1, Mohamad, B.M.2 and Md.Nizam, A.R.3
1,2,3
Faculty of Manufacturing Engineering,
Universiti Teknikal Malaysia Melaka, Durian Tunggal, 76100 Melaka, Malaysia
Phone: +606-3316002, Fax: +606-3316411, Email: abc@utem.edu.my
1
Email: mahahop1975@yahoo.com
2
Email: mohdm@utem.edu.my
3
Email: mdnizam@utem.edu.my
Abstract - The temperature in metal cutting operations is a
result of heat that influences the cutting tool properties such
as tool durability. It is necessary to investigate this parameter
and reduce it to the lowest possible value. This study deal with
the shear zone temperature (Ts) and its effect on the
mechanical properties of the carbide insert cutting tool in
(CNC) turning operation using dry machining. An Artificial
Immune System (AIS) algorithm is proposed as the
optimization algorithm to find the optimum cutting parameters
that minimize the shear zone temperature. Therefore, the tool
durability can be enhanced. The optimum parameters are used
directly in (CNC) turning machine as inputs to find the
corresponding cutting tool temperatures. Many sensors and
devices were used, thermocouple for temperature measuring,
dynamometer for cutting forces measuring, signal processing
interface and intelligent decision making strategies. Finally,
the ideal network can be used directly (on-line) on CNC
turning machine to become a suitable guide of cutting tool
temperature.
Keywords- Artificial Immune System (AIS) algorithm; shear
zone temperature (Ts); carbide insert cutting tool;
computerized numerical control (CNC) turning machine;
Temperature and force sensors.
I. INTRODUCTION
The cutting tool condition is affected by temperature
which relies on many parameters. Generated high
temperature affects on the work piece and cutting tool
properties and then increases the cutting tool wear and
reduce the tool life, therefore, it is necessary to reduce the
amount of this heat as low as possible. The total work
done by cutting tool to remove metal is converted into
heat which is converted into the chip, tool and work piece
material. The major amount of the heat is extracted by the
chip but it does not matter because chips are thrown out,
so the attempts should be made such that the chips take
away more and more amount of heat leaving small
amount of heat to damage the tool and work piece [1].
Three areas of heat generation can be distinguished in
turning operation, a shear zone, chip-tool interface zone
and tool-work piece interface zone. It can be noticed that
the shear zone temperature affects the mechanical
properties of the work piece-chip material [2]. AIS
algorithm is used to find the optimum cutting parameters
that help in decreasing the shear zone temperature based
on the clonal selection and affinity maturation principles
of the immune system.
There are many previous studies showed a good result
about temperature effects on cutting operation. In [3] it is
showed that the tool wear generally considered to be a
result of mechanical (thermo dynamic wear like abrasion)
and chemical (thermo chemical wear like diffusion)
interactions between the cutting tool and work piece and
the appearance of the chemical wear become clearer
especially when the speeds move into the high-speedcutting range.
Paper [4] studied the tool-chip interface temperature
during turning of EN-31 steel alloy with tungsten inserts
at different parameters using a tool-work thermocouple
technique. The results showed that the increasing in
cutting speed, feed rate and depth of cut led to increase
the cutting temperature, while increasing nose radius
reduced it. The author concluded also that the total wear
rate and crater wear on the rake face are strongly
influenced by the temperature at chip-tool interface. The
significant effect parameters on chip-tool temperature
interface in turning of steel with cemented carbide inserts
using a tool-work thermocouple technique were studied
[5] and it can be concluded that these parameters were
cutting speed, feed, and also showed that the cutting
temperature directly affects the cutting tool wear, work
piece surface safety and machining accuracy. Paper [6]
showed that the depth of cut has the greatest influence on
temperature when turning hardened steel using multilayer
coated carbide tools at high cutting speeds. Finally, The
cutting tool temperature if not controlled properly cutting
tool will undergo for severe flank wear , notch wear ,
loose sharpness of the cutting edge , become blunt by
welded built –up edge and weaken the product quality [1].
Motivation of the study
There are three motivations for this study; the first one
is the handbook recommendations or human experience
which is used to select convenient machine parameters in
manufacturing industry. These may not guarantee the
optimum performance and minimization of costs. Another
motivation is that the high temperature generated leads to
many problems like chip and built-up-edge formation,
reducing the shear strength, dimensional inaccuracy of the
work piece, surface damage by oxidation, rapid corrosion
and burning, rapid tool wear which reduce tool life,
plastic deformation of the cutting edge, thermal flanking
and fracturing of the cutting edges due to the thermal
shocks. The third motivation is because of most
optimization algorithms are concentrated on optimization
surface roughness followed by machining/production
costs and material removal rate and there are very few
studies dealing with the cutting temperature [7] as shown
in Figure 1.
The algorithms typically exploit the immune system's
characteristics of learning to solve a problem. This
algorithm includes three basic mechanisms, proliferation
of cells to make antibodies against the antigen, generation
of diverse antibodies to achieve affinity maturation
through somatic hyper mutation process, and antigenic
receptors handling which has low affinity. Artificial
immune system algorithm methodology is shown in
Figure 2.
Start
Initialize antibodies
randomly
Affinity evaluation of
antibodies
Select n antibodies from
population
Clone antibodies to produce
cloned antibodies
Mutate cloned antibodies
Affinity evaluation of
mutated antibodies
Select n antibodies from
mutated antibodies
Combine selected
antibodies and new
antibodies
Stopping
Criteria reached?
No
Fig.1. The percentage of papers which study the cutting temperature.
The temperature effects shear zone on the mechanical
properties of the work piece-chip material [2], this study
aims to minimize operation of this temperature using AIS
algorithm.
II. METHODOLOGY AND APPROACH
A. Artificial Immune System
Yes
Generate new (d)
antibodies
randomly
mutated
antibodies
Endart
Fig.2. Artificial immune system (AIS) algorithm
B. Objective function
The objective function of this study is used for
minimizing the shear zone temperature (Ts) where the
major part of the energy is converted into heat as shown
in (1) [8]:
Ts =
B . C . Vc .(a 2 . Fz−F . f .sin ф)/a 2
J .θ . f . Vc . a
+ Ta
Start
(1)
Whereas:Ts:- Shear zone temperature ( ℃ ).
B:- Shear energy that is converted into heat = (0.95 ~ 1)
in turning operation.
C :- Heat that goes from the shear zone to the chip = (0.7
~ 0.9) in turning operation.
Vc :- Main cutting velocity (m / min).
a2:- Chip thickness (m).
Fz:- Main cutting force (N).
F: - Friction force at the rake surface (N).
f :- Feed rate (m / rev).
ф :−cutting tool edge angle (Degree).
J:- Mechanical equivalent of heat of the chip / work
material (dependent on workpiece material).
θ :- Volume specific heat
( dependent on workpiece material )
a :- Depth of cut (m) .
Ta:- Ambient temperature = 25 ℃.
C .The constraints
Depth of cut (a), feed rate (f) , chip thickness (a2), main
cutting force (Fz) and friction force at the rake surface (F)
mainly affect on the operation temperature, therefore it
should be taken as constraints as in (2), (3),(4),(5),(6)
[9]:a min ≤ a ≤ a max
(2)
f min ≤ f ≤ f max
(3)
a2 min ≤ a2 ≤ a2 max
(4)
Fz min ≤ Fz ≤ Fz max
(5)
F min ≤ F ≤ Fmax
(6)
The objective function and the constraints should be
used in AIS algorithm to find the optimum cutting
parameters that help in shear zone temperature decreasing
in turning operation. The flow chart to minimize the shear
zone temperature using AIS algorithm is shown in Figure
3.
Input Ts =
B . C . Vc .(a 2 . Fz−F . f .sin ф)/ a 2
J .θ . f . Vc . a
Input constraints
a min ≤ a ≤ a max
f min ≤ f ≤ f max
a2 min ≤ a2 ≤ a2 max
Fz min ≤ Fz ≤ Fz max
F min ≤ F ≤ Fmax
Find the optimum parameters
Find minimum value of (Ts)
No
Stopping Criteria
reached?
Yes
Enter the optimum parameters to
CNC turning machine
Find the actual cutting temperature
Comparison between the theoretical
and actual cutting temperature
Find the optimum (AIS) algorithm
+Ta
End
Fig.3. Flow chart to minimize shear zone temperature
using AIS algorithm.
When the operation of finding the optimum cutting
parameters by AIS algorithm finishes using stopping
criteria which achieve minimum shear zone temperature,
it can be used as inputs to the CNC turning machine to
find the shear zone temperature. The results will collect
and the ideal algorithm which achieved optimum cutting
parameters and minimum shear zone temperature will be
selected as intelligent guide for CNC turning machine
using on-line pattern.
D. Experimental setup (Hardware)
The experiment devices and tools used to find the
actual results on CNC turning machine in this work are:(i) CNC turning machine.
(ii) Carbide inserts as turning cutting tool.
(iii) Work piece material.
(iv) Thermocouple for temperature measurement.
(v) Dynamometer for cutting forces measurement.
The experimental setup is shown in Figure 4:Work piece
In this study, a mild steel work piece and NRS carbide
insert cutting tool were taken to achieve the experiment.
The magnitudes of the variables are being assumed with
constraints while the constants were taken from standard
machining handbooks. The main cutting velocity (Vc) is
set as 125.6 m/min, volume specific heat θ = 825
cal/m3/Co for mild steel material , cutting tool edge
angle ф is set as 20º , mechanical equivalent of heat of
the chip J= 4.2 J/Cal for mild steel, ambient temperature
Ta = 25ºC. The constraints of variables taken as below:0.001 m
0.00010 m/rev
0.0006 m
1000 N
400 N
≤
≤
≤
≤
≤
a
f
a2
Fz
F
≤
≤
≤
≤
≤
0.003 m
0.00012 m/rev
0.0008 m
1200 N
500 N
After using the above parameters in AIS algorithm by
the objective function the following results have been
obtained.
III Results
The maximum number of generations (500) and the
population size (30) are selected, then the ideal cutting
parameters and corresponding shear zone temperature for
the optimum iteration will be obtained as shown in Table
1.
Table 1. Ideal cutting parameters and corresponding shear zone
temperature.
SecA-A
Cutting tool
Depth
of cut
a (m)
Feed
rate f
(m/rev
)
Chip
thickn
es-s
a2 (m)
Main
cutting
force
Fz
(N)
0.0011
1
0.0010
3
0.0010
3
0.0010
1
0.0010
2
0.0010
0
0.0001
1
0.0001
0
0.0001
1
0.0001
0
0.0001
0
0.0001
0
0.0006
6
0.0007
1
0.0006
5
0.0007
8
0.0007
1
0.0007
0
1161.7
8
1115.1
4
1036.8
9
1058.6
6
1033.2
9
1021.7
0
Dynamometer
carbide insert
mv
Section A- A
(Thermocouple)
Charge
Amplified
Dynamic signal
of cutting
forces Fx, Fy,
Fz.
Fig. 4. Experimental setup
E. Case study
Frictio
n force
at the
rake
surfac
e
F (N)
482.29
3
498.81
7
431.07
8
490.04
2
439.73
6
434.82
2
Shear
zone
temper
at-ure
Ts (
℃
).
306.56
9
266.07
256.60
0
240.45
8
232.45
9
228.02
8
Numb
er of
Epoch
s
1-5
6-25
26-34
35-51
52-249
250500
The best objective value (minimum shear zone
temperature Ts) is represented by performance and equal
to 228.028 ºC. This value occurs at the iteration 250-500
as shown in the diagram represented by Figure 5.
(CLONALG) terminology such as antibody, antigen and
clonal that is directly derived from the immunology term.
The results were reasonable and can be developed using
other modern heuristics algorithms like bat algorithm,
monkey search and spiral optimization. Finally, the ideal
network can be applied on the CNC turning machine
directly using on-line pattern.
ACKNOWLEDGMENT
Best objective value:228.0284
310
300
290
Performance
280
270
260
250
Special thanks go to the manufacturing and conference
teams.
240
230
220
REFERENCES
0
50
100
150
200
250
Epochs
300
350
400
450
500
Fig.5. minimum shear zone temperature (Ts)
The ideal cutting parameters that give the best
(minimum) shear zone temperature) is shown in Table 2.
Table 2. The ideal cutting parameters
Depth
of cut a
(m)
0.0010
0
Feed
rate f
(rev/s
)
0.00
010
Chip
thickne
ss a2
(m)
0.0007
0
Main
cuttin
g
force
Fz
(N)
1021
.70
Fricti
on
force
at the
rake
surfac
e
F (N)
Shear
zone
temp
eratur
e Ts (
434.8
2
228.
02
Number
of
Epochs
[1]
Md. Abdulla Al Masud ,Syed Mithun and Nikhil Ranjan Dhar
“Modeling of chip Tool Interface Temperature in Machining
Steel – An Artificial Intelligence (AI)Approach,” Department of
Industrial and Production Engineering – Bangladesh International
Conference , pp. 22-24, (2011).
D. O Sullivan and M.Cotterell ,“Temperature measurement in
single point turning,”Cork Institute Technology, vol.118, pp. 301308, 2001.
[2]
[3] S. Dolinšek and J. Kopac “Mechanism and types of tool wear;
particularities in advanced cutting materials,” Journal of
Achievements in Materials and Manufacturing Engineering,
vol.19, pp. 11-18, 2006.
[4] L.B. Abhang and M. Hameedullah “Chip-tool interface
temperature prediction model for turning process,” International
journal of English science and technology , vol.2 , pp. 382-393 ,
(2010).
℃
).
250500
The results show that the shear zone temperature
decreases with reducing the value of the depth of cut (a)
and main cutting force (F), while the feed rate (f) has not
significant effect on the shear temperature. Also it is
shown that the depth of cut has the greatest influence on
temperature.
[5] Milenko Sekulić, Marin Gostimirović, Pavel Kovač, Borislav
Savković and Zoran Jurković, “Optimization of cutting
parameters based on tool-chip interface temperature in turning
process using Tagugi’s method,” 15th International Research/
expert conference, pp. 69-72, (2011).
[6] Federico M. Aneiro, Reginaldo T. Coelho, ndLincoln
C.Brandão“Turning Hardened Steel Using Coated Carbide at
High Cutting Speeds,” , vol.30, (2008).
[7]
Norfadzlan Yusup,Azlan Mohd Zain and Siti Zaiton Mohd
Hashim, ,“Evolutionary techniques in optimizing machining
parameters: Review and recent application (2007-2011),” Expert
systems with applications, Malaysia, vol.39 , pp. 9909-9927,
(2012).
[8]
Kharagpur, e-Book ,“Cutting temperature causes, effects,
assessment and control,” Lesson 11, Module2 of mechanics
machining, Indian Institute of technology , V2 ME IIT,2009.
[9]
Ramon Quiza,Marcelino Rivas Santana and Eleno Alfonso
Brindis ,“Genetic algorithm-based multi-objective optimization of
cutting parameters in turning processes,” Engineering Application
of Artificial Intelligence, vol.19, pp. 127-133, (2006).
IV Conclusion
In many real applications the makers face the problem
of simultaneous optimization of several conflicting and
incomparable objectives. The cutting temperature if not
controlled properly, cutting tool will undergo for severe
flank wear , notch wear , loose sharpness of the cutting
edge , become blunt by welded built –up edge and
become blunt by welded built –up edge and weaken the
product quality. The most optimization algorithms
considered by the researchers at the last time are
concentrated on optimization another parameters and
there are very few papers to study the cutting temperature.
Therefore, this study discussed about the optimization
process of the shear zone temperature in CNC turning
machines using (AIS) intelligent algorithm which used
Universiti Teknikal Malaysia Melaka (UTeM)
1516 October 2012
Artificial Immune System (AIS) Algorithm For Optimizing Shear Zone
Temperature In CNC Turning Operation.
Adnan, J.A.1, Mohamad, B.M.2 and Md.Nizam, A.R.3
1,2,3
Faculty of Manufacturing Engineering,
Universiti Teknikal Malaysia Melaka, Durian Tunggal, 76100 Melaka, Malaysia
Phone: +606-3316002, Fax: +606-3316411, Email: abc@utem.edu.my
1
Email: mahahop1975@yahoo.com
2
Email: mohdm@utem.edu.my
3
Email: mdnizam@utem.edu.my
Abstract - The temperature in metal cutting operations is a
result of heat that influences the cutting tool properties such
as tool durability. It is necessary to investigate this parameter
and reduce it to the lowest possible value. This study deal with
the shear zone temperature (Ts) and its effect on the
mechanical properties of the carbide insert cutting tool in
(CNC) turning operation using dry machining. An Artificial
Immune System (AIS) algorithm is proposed as the
optimization algorithm to find the optimum cutting parameters
that minimize the shear zone temperature. Therefore, the tool
durability can be enhanced. The optimum parameters are used
directly in (CNC) turning machine as inputs to find the
corresponding cutting tool temperatures. Many sensors and
devices were used, thermocouple for temperature measuring,
dynamometer for cutting forces measuring, signal processing
interface and intelligent decision making strategies. Finally,
the ideal network can be used directly (on-line) on CNC
turning machine to become a suitable guide of cutting tool
temperature.
Keywords- Artificial Immune System (AIS) algorithm; shear
zone temperature (Ts); carbide insert cutting tool;
computerized numerical control (CNC) turning machine;
Temperature and force sensors.
I. INTRODUCTION
The cutting tool condition is affected by temperature
which relies on many parameters. Generated high
temperature affects on the work piece and cutting tool
properties and then increases the cutting tool wear and
reduce the tool life, therefore, it is necessary to reduce the
amount of this heat as low as possible. The total work
done by cutting tool to remove metal is converted into
heat which is converted into the chip, tool and work piece
material. The major amount of the heat is extracted by the
chip but it does not matter because chips are thrown out,
so the attempts should be made such that the chips take
away more and more amount of heat leaving small
amount of heat to damage the tool and work piece [1].
Three areas of heat generation can be distinguished in
turning operation, a shear zone, chip-tool interface zone
and tool-work piece interface zone. It can be noticed that
the shear zone temperature affects the mechanical
properties of the work piece-chip material [2]. AIS
algorithm is used to find the optimum cutting parameters
that help in decreasing the shear zone temperature based
on the clonal selection and affinity maturation principles
of the immune system.
There are many previous studies showed a good result
about temperature effects on cutting operation. In [3] it is
showed that the tool wear generally considered to be a
result of mechanical (thermo dynamic wear like abrasion)
and chemical (thermo chemical wear like diffusion)
interactions between the cutting tool and work piece and
the appearance of the chemical wear become clearer
especially when the speeds move into the high-speedcutting range.
Paper [4] studied the tool-chip interface temperature
during turning of EN-31 steel alloy with tungsten inserts
at different parameters using a tool-work thermocouple
technique. The results showed that the increasing in
cutting speed, feed rate and depth of cut led to increase
the cutting temperature, while increasing nose radius
reduced it. The author concluded also that the total wear
rate and crater wear on the rake face are strongly
influenced by the temperature at chip-tool interface. The
significant effect parameters on chip-tool temperature
interface in turning of steel with cemented carbide inserts
using a tool-work thermocouple technique were studied
[5] and it can be concluded that these parameters were
cutting speed, feed, and also showed that the cutting
temperature directly affects the cutting tool wear, work
piece surface safety and machining accuracy. Paper [6]
showed that the depth of cut has the greatest influence on
temperature when turning hardened steel using multilayer
coated carbide tools at high cutting speeds. Finally, The
cutting tool temperature if not controlled properly cutting
tool will undergo for severe flank wear , notch wear ,
loose sharpness of the cutting edge , become blunt by
welded built –up edge and weaken the product quality [1].
Motivation of the study
There are three motivations for this study; the first one
is the handbook recommendations or human experience
which is used to select convenient machine parameters in
manufacturing industry. These may not guarantee the
optimum performance and minimization of costs. Another
motivation is that the high temperature generated leads to
many problems like chip and built-up-edge formation,
reducing the shear strength, dimensional inaccuracy of the
work piece, surface damage by oxidation, rapid corrosion
and burning, rapid tool wear which reduce tool life,
plastic deformation of the cutting edge, thermal flanking
and fracturing of the cutting edges due to the thermal
shocks. The third motivation is because of most
optimization algorithms are concentrated on optimization
surface roughness followed by machining/production
costs and material removal rate and there are very few
studies dealing with the cutting temperature [7] as shown
in Figure 1.
The algorithms typically exploit the immune system's
characteristics of learning to solve a problem. This
algorithm includes three basic mechanisms, proliferation
of cells to make antibodies against the antigen, generation
of diverse antibodies to achieve affinity maturation
through somatic hyper mutation process, and antigenic
receptors handling which has low affinity. Artificial
immune system algorithm methodology is shown in
Figure 2.
Start
Initialize antibodies
randomly
Affinity evaluation of
antibodies
Select n antibodies from
population
Clone antibodies to produce
cloned antibodies
Mutate cloned antibodies
Affinity evaluation of
mutated antibodies
Select n antibodies from
mutated antibodies
Combine selected
antibodies and new
antibodies
Stopping
Criteria reached?
No
Fig.1. The percentage of papers which study the cutting temperature.
The temperature effects shear zone on the mechanical
properties of the work piece-chip material [2], this study
aims to minimize operation of this temperature using AIS
algorithm.
II. METHODOLOGY AND APPROACH
A. Artificial Immune System
Yes
Generate new (d)
antibodies
randomly
mutated
antibodies
Endart
Fig.2. Artificial immune system (AIS) algorithm
B. Objective function
The objective function of this study is used for
minimizing the shear zone temperature (Ts) where the
major part of the energy is converted into heat as shown
in (1) [8]:
Ts =
B . C . Vc .(a 2 . Fz−F . f .sin ф)/a 2
J .θ . f . Vc . a
+ Ta
Start
(1)
Whereas:Ts:- Shear zone temperature ( ℃ ).
B:- Shear energy that is converted into heat = (0.95 ~ 1)
in turning operation.
C :- Heat that goes from the shear zone to the chip = (0.7
~ 0.9) in turning operation.
Vc :- Main cutting velocity (m / min).
a2:- Chip thickness (m).
Fz:- Main cutting force (N).
F: - Friction force at the rake surface (N).
f :- Feed rate (m / rev).
ф :−cutting tool edge angle (Degree).
J:- Mechanical equivalent of heat of the chip / work
material (dependent on workpiece material).
θ :- Volume specific heat
( dependent on workpiece material )
a :- Depth of cut (m) .
Ta:- Ambient temperature = 25 ℃.
C .The constraints
Depth of cut (a), feed rate (f) , chip thickness (a2), main
cutting force (Fz) and friction force at the rake surface (F)
mainly affect on the operation temperature, therefore it
should be taken as constraints as in (2), (3),(4),(5),(6)
[9]:a min ≤ a ≤ a max
(2)
f min ≤ f ≤ f max
(3)
a2 min ≤ a2 ≤ a2 max
(4)
Fz min ≤ Fz ≤ Fz max
(5)
F min ≤ F ≤ Fmax
(6)
The objective function and the constraints should be
used in AIS algorithm to find the optimum cutting
parameters that help in shear zone temperature decreasing
in turning operation. The flow chart to minimize the shear
zone temperature using AIS algorithm is shown in Figure
3.
Input Ts =
B . C . Vc .(a 2 . Fz−F . f .sin ф)/ a 2
J .θ . f . Vc . a
Input constraints
a min ≤ a ≤ a max
f min ≤ f ≤ f max
a2 min ≤ a2 ≤ a2 max
Fz min ≤ Fz ≤ Fz max
F min ≤ F ≤ Fmax
Find the optimum parameters
Find minimum value of (Ts)
No
Stopping Criteria
reached?
Yes
Enter the optimum parameters to
CNC turning machine
Find the actual cutting temperature
Comparison between the theoretical
and actual cutting temperature
Find the optimum (AIS) algorithm
+Ta
End
Fig.3. Flow chart to minimize shear zone temperature
using AIS algorithm.
When the operation of finding the optimum cutting
parameters by AIS algorithm finishes using stopping
criteria which achieve minimum shear zone temperature,
it can be used as inputs to the CNC turning machine to
find the shear zone temperature. The results will collect
and the ideal algorithm which achieved optimum cutting
parameters and minimum shear zone temperature will be
selected as intelligent guide for CNC turning machine
using on-line pattern.
D. Experimental setup (Hardware)
The experiment devices and tools used to find the
actual results on CNC turning machine in this work are:(i) CNC turning machine.
(ii) Carbide inserts as turning cutting tool.
(iii) Work piece material.
(iv) Thermocouple for temperature measurement.
(v) Dynamometer for cutting forces measurement.
The experimental setup is shown in Figure 4:Work piece
In this study, a mild steel work piece and NRS carbide
insert cutting tool were taken to achieve the experiment.
The magnitudes of the variables are being assumed with
constraints while the constants were taken from standard
machining handbooks. The main cutting velocity (Vc) is
set as 125.6 m/min, volume specific heat θ = 825
cal/m3/Co for mild steel material , cutting tool edge
angle ф is set as 20º , mechanical equivalent of heat of
the chip J= 4.2 J/Cal for mild steel, ambient temperature
Ta = 25ºC. The constraints of variables taken as below:0.001 m
0.00010 m/rev
0.0006 m
1000 N
400 N
≤
≤
≤
≤
≤
a
f
a2
Fz
F
≤
≤
≤
≤
≤
0.003 m
0.00012 m/rev
0.0008 m
1200 N
500 N
After using the above parameters in AIS algorithm by
the objective function the following results have been
obtained.
III Results
The maximum number of generations (500) and the
population size (30) are selected, then the ideal cutting
parameters and corresponding shear zone temperature for
the optimum iteration will be obtained as shown in Table
1.
Table 1. Ideal cutting parameters and corresponding shear zone
temperature.
SecA-A
Cutting tool
Depth
of cut
a (m)
Feed
rate f
(m/rev
)
Chip
thickn
es-s
a2 (m)
Main
cutting
force
Fz
(N)
0.0011
1
0.0010
3
0.0010
3
0.0010
1
0.0010
2
0.0010
0
0.0001
1
0.0001
0
0.0001
1
0.0001
0
0.0001
0
0.0001
0
0.0006
6
0.0007
1
0.0006
5
0.0007
8
0.0007
1
0.0007
0
1161.7
8
1115.1
4
1036.8
9
1058.6
6
1033.2
9
1021.7
0
Dynamometer
carbide insert
mv
Section A- A
(Thermocouple)
Charge
Amplified
Dynamic signal
of cutting
forces Fx, Fy,
Fz.
Fig. 4. Experimental setup
E. Case study
Frictio
n force
at the
rake
surfac
e
F (N)
482.29
3
498.81
7
431.07
8
490.04
2
439.73
6
434.82
2
Shear
zone
temper
at-ure
Ts (
℃
).
306.56
9
266.07
256.60
0
240.45
8
232.45
9
228.02
8
Numb
er of
Epoch
s
1-5
6-25
26-34
35-51
52-249
250500
The best objective value (minimum shear zone
temperature Ts) is represented by performance and equal
to 228.028 ºC. This value occurs at the iteration 250-500
as shown in the diagram represented by Figure 5.
(CLONALG) terminology such as antibody, antigen and
clonal that is directly derived from the immunology term.
The results were reasonable and can be developed using
other modern heuristics algorithms like bat algorithm,
monkey search and spiral optimization. Finally, the ideal
network can be applied on the CNC turning machine
directly using on-line pattern.
ACKNOWLEDGMENT
Best objective value:228.0284
310
300
290
Performance
280
270
260
250
Special thanks go to the manufacturing and conference
teams.
240
230
220
REFERENCES
0
50
100
150
200
250
Epochs
300
350
400
450
500
Fig.5. minimum shear zone temperature (Ts)
The ideal cutting parameters that give the best
(minimum) shear zone temperature) is shown in Table 2.
Table 2. The ideal cutting parameters
Depth
of cut a
(m)
0.0010
0
Feed
rate f
(rev/s
)
0.00
010
Chip
thickne
ss a2
(m)
0.0007
0
Main
cuttin
g
force
Fz
(N)
1021
.70
Fricti
on
force
at the
rake
surfac
e
F (N)
Shear
zone
temp
eratur
e Ts (
434.8
2
228.
02
Number
of
Epochs
[1]
Md. Abdulla Al Masud ,Syed Mithun and Nikhil Ranjan Dhar
“Modeling of chip Tool Interface Temperature in Machining
Steel – An Artificial Intelligence (AI)Approach,” Department of
Industrial and Production Engineering – Bangladesh International
Conference , pp. 22-24, (2011).
D. O Sullivan and M.Cotterell ,“Temperature measurement in
single point turning,”Cork Institute Technology, vol.118, pp. 301308, 2001.
[2]
[3] S. Dolinšek and J. Kopac “Mechanism and types of tool wear;
particularities in advanced cutting materials,” Journal of
Achievements in Materials and Manufacturing Engineering,
vol.19, pp. 11-18, 2006.
[4] L.B. Abhang and M. Hameedullah “Chip-tool interface
temperature prediction model for turning process,” International
journal of English science and technology , vol.2 , pp. 382-393 ,
(2010).
℃
).
250500
The results show that the shear zone temperature
decreases with reducing the value of the depth of cut (a)
and main cutting force (F), while the feed rate (f) has not
significant effect on the shear temperature. Also it is
shown that the depth of cut has the greatest influence on
temperature.
[5] Milenko Sekulić, Marin Gostimirović, Pavel Kovač, Borislav
Savković and Zoran Jurković, “Optimization of cutting
parameters based on tool-chip interface temperature in turning
process using Tagugi’s method,” 15th International Research/
expert conference, pp. 69-72, (2011).
[6] Federico M. Aneiro, Reginaldo T. Coelho, ndLincoln
C.Brandão“Turning Hardened Steel Using Coated Carbide at
High Cutting Speeds,” , vol.30, (2008).
[7]
Norfadzlan Yusup,Azlan Mohd Zain and Siti Zaiton Mohd
Hashim, ,“Evolutionary techniques in optimizing machining
parameters: Review and recent application (2007-2011),” Expert
systems with applications, Malaysia, vol.39 , pp. 9909-9927,
(2012).
[8]
Kharagpur, e-Book ,“Cutting temperature causes, effects,
assessment and control,” Lesson 11, Module2 of mechanics
machining, Indian Institute of technology , V2 ME IIT,2009.
[9]
Ramon Quiza,Marcelino Rivas Santana and Eleno Alfonso
Brindis ,“Genetic algorithm-based multi-objective optimization of
cutting parameters in turning processes,” Engineering Application
of Artificial Intelligence, vol.19, pp. 127-133, (2006).
IV Conclusion
In many real applications the makers face the problem
of simultaneous optimization of several conflicting and
incomparable objectives. The cutting temperature if not
controlled properly, cutting tool will undergo for severe
flank wear , notch wear , loose sharpness of the cutting
edge , become blunt by welded built –up edge and
become blunt by welded built –up edge and weaken the
product quality. The most optimization algorithms
considered by the researchers at the last time are
concentrated on optimization another parameters and
there are very few papers to study the cutting temperature.
Therefore, this study discussed about the optimization
process of the shear zone temperature in CNC turning
machines using (AIS) intelligent algorithm which used