5th International & 26th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12th–14th, 2014,
IIT Guwahati, Assam, India
667-1
Optimization of Cutting Tool Geometry by CAE Approach for
Titanium Alloy
K. Pradeep Kumar Mouli1, Srinivasa Rao Nandam2, P. Vijay Kumar Raju3 ,
G.Appala Raju4 and A.Chandrakanth5
1P.G. Student, SRKR Engineering College, Andhra University, Bhimavaram-534204. [email protected]
2Scientist, Defence Metallurgical Research Laboratory, Hyderabad-500058. [email protected]
3Professor, SRKR Engineering College, Andhra University, Bhimavaram-534204. [email protected]
4Technical Officer, Defence Metallurgical Research Laboratory, Hyderabad-500058. [email protected]
5Asst. Prof., MVSR Engineering College, Osmania University, Hyderabad-500058. [email protected]
Abstract
Titanium alloys are extensively used in aerospace, automobile, chemical and medical applications
due to their classical properties of high strength to weight ratio, specific strength at high temperatures, corrosion
resistance, creep and fatigue strength etc. Manufacture of precision components from titanium alloys is a
challenging task as the alloys comes under difficult to cut material due to the inherent qualities of low thermal
conductivity, low modulus of rigidity, work hardening, high chemical reactivity with tool, built-up edge
formation etc. during machining.
The cutting tools exhibits forces on the work piece and similar forces are experienced by the cutting
tool while cutting the work material. Cutting tool geometries such as cutting angles and nose radius play a vital
role in machining of any work material (titanium alloys). The rake angles have major effects on cutting forces
and chip formation by giving adequate strength to the cutting tool. The tool nose radius has effect on strength of
the cutting edge and surface finish. The manufacturing engineers always quest for optimized cutting tool
geometry, but it is very difficult to carry out the experiments with various tool geometries as it involves
consumption of tools, material and time etc. Hence, to address the above issues, a computer aided engineering
(CAE) approach has been adopted in the recent days. Here, the cutting tool geometry was optimized by design
of experiments (DOE) techniques and a machining simulation and analysis (Deform 3D) software by defining
required material properties of titanium alloy (Ti-6Al-4V), tool geometry, cutting parameters etc. The axial
directional feed force (Fx), radial directional thrust force (Fy) and tangential cutting force (Fz) were calculated
for turning experiments through computer aided machining simulations. These cutting forces were analyses in
statistical (Minitab) software for percentage contribution of cutting tool geometries such as back rake angle, side
rake angle and nose radius and subsequently the optimized values were evaluated. These achieved values are
used to obtain the predicted cutting force values. The predicted cutting forces were validated through
experimental machining of Ti-6Al-4V cylindrical sample on precision lathe machine by positioning optimized
cutting tool geometry on a piezoelectric based precision cutting force dynamometer of Kistler, Singapore. It is
found that the experimental results which are obtained from the experiments are almost near to the computer
aided simulation experimental values. Therefore, the above CAE approach can be employed before machining
of advanced materials to get optimal values.
Keywords: Titanium Alloy, Cutting Force, Tool Geometry, CAE approach, DOE Techniques.
Optimization of Cutting Tool Geometry by CAE Approach for Titanium Alloy
667-2
1 Introduction
Titanium and its alloys are widely used in aerospace
industries due to attractive characteristics of the material,
they offers a combination of high strength, light weight,
formability and corrosion resistance which have made it
a world standard in aerospace applications. However,
these materials have been classified as difficult-to cut
material because of their high temperature strength, low
thermal conductivity, chemical reactivity and relatively
low modulus of elasticity.
In machining process, most of the mechanical
energy used to remove material becomes heat. This heat
generates high temperature in the cutting region. The
higher the cutting speed, the faster the heat generation
and higher temperature resulted. The new challenge in
machining is to use high cutting speed in order to
increase the productivity. This is the main reason for
rapid tool wear. For titanium and its alloy, this problem
is more severe due to their low thermal conductivity.
80% of the heat generated in the cutting region goes to
the cutting tool and cause wear. So it is convenient to use
transient cutting speed for machining the highly reactive
material like titanium alloy. So, it is better to optimize
the variables either cutting parameters or tool geometry
parameters for tool life to increase the productivity at
good surface finish.
The Taguchi method is a well-known technique that
provides a systematic and efficient methodology for
process optimization and this is a powerful tool for the
design of high quality systems. Taguchi approach to
design of experiments (DOE) is easy to adopt and apply
for users with limited knowledge of statistics, hence
gained wide popularity in the engineering and scientific
community. This is an engineering methodology for
obtaining product and process condition, which are
minimally sensitive to the various causes of variation,
and which produce high-quality products with low
development and manufacturing costs. Signal to noise
ratio and orthogonal array are two major tools used in the
design.
The S/N ratio characteristics can be divided into
three categories when the characteristic is continuous
a) Nominal is the best
b) Smaller the better
c) Larger is better characteristics.
For the minimum Cutting Force, the solution is “Smaller
is better” and S/N ratio is determined according to the
Following equation 1
S/N = - log10 {�� ∑ � �} (1)
Where, S/N = Signal to Noise Ratio,
n = No. of Measurements,
y = Measured Value
The influence of each control factor can be more
clearly presented with response graphs. Optimal cutting
conditions of control factors can be very easily
determined from S/N response graphs too. Parameters
design is the key step in Taguchi method to achieve
reliable results without increasing the experimental costs.
1.1 Finite Element Method
In recent years, finite element analysis (FEA) has
become the main tool for simulating metal cutting
process. Simulation of machining test using FEA is
particularly important due to time and cost consuming in
the actual cutting test, hence it is preferred over
experimental work. Moreover, the results obtained from
the finite element analysis stays close to the results
obtained from the experimental work. Finite element
method is proved to be an effective technique for
analyzing chip formation process and in predicting
process variables such as temperatures, forces and
stresses etc.
2 Methodology
2.1 Work Piece
Titanium alloys have found wide applications
owing to its unique characteristics like low density or
high strength to weight ratio (density of titanium is about
60% of that of steel or nickel-based super alloys) and
excellent corrosion resistance (for biomedical, chemical
and other corrosion resistant environments). Titanium is
an expensive metal to extract, melt, fabricate and
machine. Titanium alloys are considered as difficult to
cut material. In the present study Ti-6Al-4V, alloy bars
of 60 mm diameter and length 200 mm were used. They
were annealed and their chemical compositions are given
in the table 1.
Table 1 Composition of Ti-6Al-4V
2.2 Cutting Tool
The Cutting tool used in machining the work
material is an uncoated Carbide Insert No. DNMG 11 04
04 – SF 1105 insert (ISO Specification) of Sandvik
Coromant make. The recommended cutting parameters
from manufacturer catalogue are as follows:
Table 2 Cutting Parameters of Tool Insert
Depth of Cut
(mm)
Feed
(mm/rev)
Cutting Speed
(m/min)
0.3 0.05 38.74
% of Element Actual Values
C 0.027
V 3.89
Fe 0.11
Al 5.81
Ti Balance
5th International & 26th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12th–14th, 2014,
IIT Guwahati, Assam, India
667-3
Table 3 Tool Insert Geometry
Tool Geometry
Insert angle 55°
Approach angle 27.5°
Rake angle -5°
Front Clearance 8.0°
Side Clearance 3.0°
Nose Radius 0.4 mm
2.3 Design of Experiments Taguchi’s technique makes use of a special design of
orthogonal array (OA) to examine the quality
characteristics through a minimum number of
experiments. The experimental results based on the
orthogonal array are then transformed into S/N ratios to
evaluate the performance characteristics. Taguchi’s
Design of experiments is used to design the orthogonal
array for three parameters varied through three levels.
The control parameters and their levels chosen are shown
in table 4.
Table 4 Input parameters and their Levels
Experiments have been carried out using Taguchi’s
L8 Orthogonal Array (OA) experimental design which
consists of eight combinations of cutting side rake angle,
back rake angle and nose radius. . According to the
design catalogue prepared by Taguchi, three process
parameters (without interaction) are to be varied in three
finite levels.
Table 5 L8 Orthogonal array
L8 Orthogonal Array
Test S R A B R A Nose Radius
1 1 1 1
2 1 1 2
3 1 2 1
4 1 2 2
5 2 1 1
6 2 1 2
7 2 2 1
8 2 2 2
The various ISO designated cutting inserts modeled
using solid works 3D CAD software. The detailed
drawing of the cutting insert was shown in Figure 1.
Figure 1 Detailed drawing of Cutting Tool
Insert
2.4 Computer Aided Engineering
DEFORM-3D is a computer aided engineering
(CAE), machining simulation and analysis software. It
uses Usui’s tool wear model to compute cutting inserts
flank wear, which is used only with non-isothermal run,
as it requires interface temperature calculations. The
initial temperature for the work piece and tool is set as
300 C (room temperature). Simulations are carried out to
reach the transient condition and then transformed to
steady-state condition. Tool stress analysis is performed
to obtain maximum principal stresses acting on the
cutting inserts. The cutting insert is a rigid object, which
turns a plastic work piece. During simulation the length
of work piece to be machined is fixed. During meshing
of cutting inserts, the number of tetrahedron elements is
fixed as 30,000, while the number of elements in work
piece is kept at 25% of feed rate. During meshing, a finer
mesh can be seen at tool tip and in the work piece, where
the tool comes in contact with the work piece. The
tetrahedral mesh of uncoated carbide cutting insert and
work piece are shown in Figure 2.
Figure 2 Tetrahedral Mesh of Cutting Tool and
Work Piece
In Usui’s tool wear model, the wear rate is a
function of constant pressure, relative velocity and
absolute temperature at the contact surface as given in
Equation 2
��� = �� ������
�� (2)
Process parameters Levels
Side Rake Angle -5° -7°
Back Rake Angle -5° -10°
Nose Radius 0.4 mm 0.8 mm
Optimization of Cutting Tool Geometry by CAE Approach for Titanium Alloy
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Here, A, B are experimentally calibrated
coefficients, the constants of the wear rate model are
taken as A=0.0000000078 and B=5302.
σ = Interface pressure in N/mm2 , V = Sliding
velocity in m/min, T = Initial temperature in degree and
dt = Time increment in seconds.
3 CAE Results and Analysis
Based on the L8 orthogonal array, simulations are
conducted to examine the effects of Side rake angle, back
rake angle and nose radius. After performing the
simulations, the output character resultant cutting forces,
which are determined, are tabulated in Table 6.
Table 6 Control Factors and Quality
Characteristics of Simulation Analysis
Based on the combined S/N ratio the response table
for the 3 levels of input parameters such as side rake
angle, back angle and nose radius are determined under
smaller is better criteria.
Table 7 Response Table of S/N ratio
Level S R A B R A N R
1 -40.58 -39.29 -34.46
2 -39.46 -40.75 -45.58
Delta 1.12 1.45 11.12
Rank 3 2 1
Response Table for Signal to Noise Ratios and
ranking for side rake angle, back rake angle, nose radius
are shown in table 7.
Analysis of variance (ANOVA) is an important
technique for analyzing the effect of categorical factor on
a response. An ANOVA decomposes the variability in
the response variable among the different factors.
Depending upon the type of analysis, it may be important
to determine. Which factors have significant effect on the
response, and how much of the variability in the response
variable is attributable to each factor. The main effect
plots of cutting forces for the defined input variables
were shown in figure 3. Which illustrates that higher the
nose radius lower cutting force due to distribution of
cutting forces over more contact area during machining.
Figure 3 Main Effects Plot of S/N Ratio for
SRA, BRA, NS
3.1 Optimized Value for Cutting Forces
The side rake angle, back rake angle and nose
radius of cutting tool values ware values optimized
for lower cutting force values by using Minitab tab
software were shown in table 8.
Table 8 :Optimized Values for Minimum
Cutting Process
Process Parameters Optimized Value
Side Rake Angle -5°
Back Rake Angle -10°
Nose Radius 0.4 mm
4 CAE Simulations for Cutting Forces With the identified optimum tool geometry, a new
tool is modeled using solid works and a validation
simulation analysis is conducted and the quality
characteristics were obtained.
Table 9: Output Quality Characteristics of
Optimal Tool Geometry
Run Cutting
Force Fx
(N)
Cutting
Force Fy
(N)
Cutting
Force Fz
(N)
1 6.98 46.5 8.14
Cutting forces obtained while turning of Ti-6Al-4V
with optimum tool geometry. Figure 4 show the
distribution of cutting forces acting on the cutting insert.
Trial
No.
Inner Array Outer Array
SRA
(Deg
)
BRA
(Deg)
Nose
Radius
(mm)
Cutting Forces
Fx
(N)
Fy
(N)
Fz
(N)
1 -5 -5 0.4 6.62 47.4 7.88
2 -5 -5 0.8 20.6 120 15.5
3 -5 -10 0.4 6.83 46.5 7.95
4 -5 -10 0.8 19.5 398 14.3
5 -7 -5 0.4 7.07 47.2 7.96
6 -7 -5 0.8 20.3 537 14.8
7 -7 -10 0.4 6.62 47.4 7.88
8 -7 -10 0.8 18.7 115 13.9
5th International & 26th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12th–14th, 2014,
IIT Guwahati, Assam, India
667-5
Figure 4 Chip Formation in Machining
Simulation
It is observed that maximum cutting forces are
acting at edge during initial flight, where the cutting
insert gets into contact with work piece. The detailed
cutting forces graph in axial feed direction (Fx), radial
direction along the depth of cut (Fy) and tangential
direction (Fz) were shown as graph 1, 2 and 3
respectively.
Graph 1 Maximum Cutting Forces in Fx
Graph 2 Maximum Cutting Forces in Fy
Graph 3 Maximum Cutting Forces in Fz
5 Validation of Cutting Tool Geometry
Experimental verification is carried out on precision
conventional lathe machine of HMT make (NH-26) to
determine the cutting forces generated during machine.
After performing the preliminary machining operation
for skin removal components the actual cutting forces
were measured using Kistler, Singapore make a
piezoelectric based precision multi component cutting
force dynamometer (9265B) installed to the above lathe
machine. The measured cutting force values were
reported in the table 10 and cutting force graphs were
shown as graph 4,5 and 6 respectively.
Table 10 Experimental validation output value
of cutting forces
Ru
n
Spin
dle
Spee
d
rpm
Feed
Rate
mm/rev
Depth
of
Cut
mm
Cutting Forces N
1
930
0.05
0.3
Fx
Fy
Fz
9.94
13.98
10.13
Graph 4 Measured cutting force Fx
Graph 5 Measured cutting force Fy
Optimization of Cutting Tool Geometry by CAE Approach for Titanium Alloy
667-6
Graph 6 Measured cutting force Fz
6 Results and Discussions
The simulated and experimental cutting force
values for optimized cutting tools geometry are shown in
the table 11. It is found that the experimental results are
much near to the results, which are obtained from the
computer-aided simulation. The variations of the results
are due to the influence of other cutting angles and
coating material on the insert, which were not considered
in the simulation process.
It is proved that the computer-aided simulation of
machining process can be employed effectively before
actual machining of advanced and costly materials. The
Simulation gives preliminary data on appropriate cutting
tools, machining parameters and thereafter optimization
of process.
Table 11 Comparison of Deform Result and
Experimentation Result
7 Conclusion
The influences of tool geometries such back
rake angle, side rake angle and nose radius while cutting
titanium alloy (Ti-6Al-4V) has been studies thoroughly
through design of experimental (DOE) techniques,
computer aided engineering (CAE). The tool radius
found to be has higher influence in generating cutting
force among other cutting tool rake angles while
machining titanium alloy. The optimized tool geometry
has been evaluated and the predicted cutting force values
were calculated through computer aided simulation and
analysis. The CAE results were validated through
experimental machining of titanium alloy by a precision
cutting force Dynamometer.
The Computer Aided Engineering Techniques
play a vital role in machining of advanced materials,
evaluation of appropriate cutting tool geometry and
machining parameters etc to get optimal result while
reducing the consumption of tools, material and time etc.
8 Future Work
� Estimate of Tool Life.
� Evaluation of Surface Integrity of Machined
Component.
� Analysis of Tool Wear Mechanism.
� Evaluation of Heat Transfer and Cutting
Temperature during machining.
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S.No Cutting
Forces
(N)
CAE
Result
(N)
Experimentation
(N)
1 Fx 6.98 9.94
2 Fy 46.5 13.98
3 Fz 8.14 10.13