Optimization of Cutting Tool Geometry by CAE Approach for

5th International & 26th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12th–14th, 2014,

IIT Guwahati, Assam, India

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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

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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



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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


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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



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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


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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

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Optimization of Cutting Tool Geometry by CAE Approach for