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Research Article
Volume 6 Issue No. 11
Optimization of Process Parameters to Achieve Reduced Tool Wear
and Increased Tool Life Using Alumina Based Ceramic Cutting
Tools in Milling
U. Rajesh 1 , B. Sreenivasulu 2 , Dr. R. Ramachandra 3
PG student1 , Associate Professor2 , Principal3
Sri Krishnadevaraya Engineering College, Gooty, Anantapur, Andhrapradesh, India
Abstract:
A cutting tool or cutter is any tool that is used to remove material fro m the workp iece by means of shear deformation. Cutt in g
may be acco mplished by single-point or mult ipoint tools. Singlepoint tools are used in turning, shaping, plaining and similar
operations, and remove material by means of one cutting edge. M illing and drilling tools are often mu lt ipoint tools. Cutting too ls
must be made of a material harder than the material wh ich is to be cut, and the tool must be able to withstand the heat gener ated in
the metal-cutting process. In this thesis different experiments are conducted to optimize the process parameters to imp rove the
tool life and reduce the tool wear of an alu mina based ceramic cutting tool while mach ining turbine blade of Titaniu m alloy. A
series of experiments are done by varying the milling parameters spindle speed, feed rate and depth of cut considering L9
orthogonal array by Taguchi Method. The optimizat ion is done using Regression analysis for less tool wear and more tool life.
The experiment has been done with process parameters feed rate 2000mm/ min, 250mm/ min, 3000 mm/ min, spindle speeds are
1000rp m, 1500rp m, 2000rp m, and depth of cut 0.3mm, 0.4 and 0.5mm. The milling process is conducted on a CNC Vertical
milling machine.
I. INTRODUCTION
Metal cutting is one of the most important and widely used
manufacturing p rocesses in engineering industries and in
today’s manufacturing scenario, optimizat ion of metal cutting
process is essential for a manufacturing unit to respond
effectively to severe competit iveness and increasing demand
of quality wh ich has to be achieved at minimal cost. As
flexib ility and adaptability needs increased in the
manufacturing industries, computer numerical control systems
was introduced in metal cutting processes that provided
automation of processes with very high accuracies and
repeatability. Based on the literature rev iew it was evident that
the factors that highly influence the process efficiency and
output characteristics in a CNC mach ine tool are tool
geometry, cutting velocity, feed rate, depth of cut and cutting
environment. Experimental works have been carried out on
the above mentioned parameters. A significant improvement
in process efficiency may be obtained by process parameter
optimization that identifies and determines the regions of
critical process control factors leading to desired outputs or
responses with acceptable variation ensuring a lower cost of
manufacturing. Of the many goals focused in a manufacturing
industry, energy consumption plays a vital and dual role. One,
it cuts down the cost per product and secondly the
environmental impact by reducing the amount of carbon
emissions that are created in using the electrical energy. Many
have worked in optimizing the parameters of co mputer
numerically controlled mach ine tools for minimu m power
requirement but in high tare machine tools, time dominates
over power when optimizing for reduced energy. The current
work considers the most common ly selected process
parameters viz. cutting velocity, feed rate and depth of cut
optimized for minimu m energy consumption.
International Journal of Engineering Science and Computing, November 2016
Figure.1. Cutti ng Tool Translator and Rotati onal Motion
Figure – Cutting Tool Translator and Rotational Motion
Milling is a cutting process that uses a milling cutter to
remove material fro m the surface of a wo rk- p iece. The
milling cutter is a rotary-cutting tool, often with mu ltip le
cutting points. As opposed to drilling, where the tool is
advanced along its rotation axis, the cutter in milling is usually
moved perpendicular to its axis so that cutting occurs on the
circu mference of the cutter. As the milling cutter enters the
work-piece, the cutting edges (flutes or teeth) of the tool
repeatedly cut into and exit fro m the material, shaving off
chips (swarf) fro m the work-piece with each pass. The cutting
action is shear deformat ion; material is pushed off the workpiece in t iny clu mps that hang together to a greater or lesser
extent (depending on the material) to form ch ips. This makes
metal cutting so mewhat d ifferent (in its mechanics) fro m
slicing softer materials with a blade. The milling process
removes material by performing many separate, small cuts.
This is acco mplished by using a cutter with many teeth,
spinning the cutter at high speed, or advancing the material
through the cutter slowly; most often it is some co mbination
of these three approaches. The speeds and feeds used are
varied to suit a comb ination of variables. The speed at which
the piece advances through the cutter is called feed rate,
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II. EXPERIMENTAL WORK
The main aim of the project is to determine the influence of
Alumina Based Ceramic tool in metal working. The
investigation is based on tool life and tool wear during milling
of Titaniu m with ceramic tool. The cutting parameters
considered are feed rate, spindle speed and depth of cut. This
experiment employed a CNC vertical milling mach ine.
Ceramic cutting tool is used. The experiment has been done
under conditions of feed rate 2000mm/ min, 2500mm/ min,
3000 mm/ min, spindle speeds are 1000rp m, 1500rp m,
2000rp m, and depth of cut 0.3mm, 0.4 and 0.5mm.
MACHINE SPECIFICATIONS
Machine Model – Feeler
Control – Siemens 840d
Travel Size X – 1000mm, Y – 500mm, Z – 500mm
SELECTION OF PROCESS
TAGUCHI TECHNIQUE
Table.1. Process Parameters as
PROCESS
LEVEL1
PARAMETERS
CUTTING
1000
SPEED(rp m)
FEED
RATE 2000
(mm/ min)
DEPTH
OF 0.3
CUT(mm)
PARAMETERS AS PER
per Taguchi Techni que
LEVEL2
LEVEL3
1500
2000
2500
3000
0.4
0.5
IV.THEORETICAL
TOOL
LIFE
CALCULATIONS
The Taylor tool life equation can be written as: vTn = C,
where
V is the cutting speed, m/ min
T is the tool life, in minutes
C is the cutting speed for a tool life of 1 minute = 3000m/ min
n is the Taylor exponent = 0.7 (Ceramic Tool)
1. Cutting S peed – 2000rp m = 628.3m/ min
VTn = C
628.3 T0.7 = 3000
T0.7 = 4.774
T = 9.32min
2. Cutting S peed – 2500rp m = 785.38m/ min
785.38 T0.7 = 3000
T0.7 = 3.819
T = 6.776min
1. Cutting S peed – 3000rp m = 942.46m/ min
942.46 T0.7 = 3000
T0.7 = 3.183
T = 5.224min
REGRESSION
SOFTWARE
ANALYS IS
US ING
MINITAB
Design of Orthogonal Array
Table.2. L9 Orthog onal Array
JOB SPINDLE
FEED RATE
NO.
SPEED (rpm) (mm/mi n)
01
1000
2000
02
1000
2500
03
1000
3000
04
1500
2000
05
1500
2500
06
1500
3000
07
2000
2000
08
2000
2500
09
2000
3000
DEPTH OF
CUT (mm)
0.3
0.4
0.5
0.4
0.5
0.3
0.5
0.3
0.4
III. OBS ERVATION
While machining, tool wear is measured and tool life is
measured for every experiment at given spindle speed, feed
rate and depth of cut. Tool wear is measured for the removal
of material fro m the edge of the tool. Too l Life is the time
taken for the cutting tool to wear out in min’s.
First Taguchi Orthogonal Array is designed in Minitab17to
calculate S/N rat io and Means which steps is given below:
Figure.2. Measured Tool Li fe and Tool Wear val ues
Table.3. Measured Tool Life and Tool Wear Values
JO
SPINDL
FEED
DEPT
TOO
TOOL
B
E SPEED RATE
H OF L
WEA
NO. (rpm)
(mm/mi n CUT
LIFE
R
)
(mm)
(min)
(mm)
01
1000
2000
0.3
29
0.16
02
1000
2500
0.4
25
0.19
03
1000
3000
0.5
22
0.25
04
05
1500
1500
2000
2500
0.4
0.5
19
16
0.18
0.21
06
1500
3000
0.3
15
0.29
07
08
09
2000
2000
2000
2000
2500
3000
0.5
0.3
0.4
13
11
9
0.2
0.26
0.38
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Regression Equation
TOOL W EAR (mm) = -0.2478 + 0.000080 SPINDLE SPEED
(rp m) + 0.000127 FEED RATE (mm/ min)- 0.083 DEPTH OF
CUT (mm)
Fits and Diagnostics for Unusual Observations
TOOL W EAR (mm) Std
Obs Fit Resid Resid
9 0.3800 0.3389 0.0411 2.13 R
R Large residual
Figure.3. Graph – Residual Vs Spindle S peed (Response –
Tool Life)
Figure.5. Graph – Residual Vs Spindle S peed (Response –
Tool Wear)
Figure.4. Graph – Surface Plot of Tool Life vs Depth of
Cut, Feed Rate
-By observing above graph, to maximize tool life, the Feed
Rate should be set at 2000mm/ min and Depth of Cut at
0.3mm.
Regression Analysis: TOOL WEAR(mm versus SPINDLE
SPEE, FEED RATE (m, DEPTH OF CUT
The residual plots indicate that the determin istic portion
(predictor variables) of the model Spindle Speed, Feed Rate
and Depth of Cut are not capturing some explanatory
informat ion that is ―leaking‖ into the residuals. The graph
could represent several ways in wh ich the model is not
explaining all that is possible. Possibilit ies include:
Amissing variable A missing higher-order term of a variab le
in the model to exp lain the curvature
A missing interaction between terms already in the model
Figure.6. Graph – Surface Pl ot of Tool Wear vs Feed Rate,
Spindle S peed
Figure.4. Regression analysis: tools Wear (mm versus
spindle s pee, feed rate (m, depth of cut
International Journal of Engineering Science and Computing, November 2016
By observing above graph, to minimize tool wear, the Spind le
Speed should be set at 2000rp mand Feed Rate at
2000mm/ min.
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V. CO NCLUSIONS
Different experiments are conducted to optimize the process
parameters in milling to achieve reduced tool wear and
increased tool life using alu mina based ceramic cutting tool
while machining turbine blade of Titaniu m alloy. A series of
experiments are done by varying the milling parameters
spindle speed, feed rate and depth of cut considering L9
orthogonal array by Taguchi Method. The optimization is
done using Regression analysis for less tool wear and mo re
tool life. The experiment has been done with process
parameters feed rate 2000mm/ min, 2500mm/ min, 3000
mm/ min , spindle speeds are 1000rp m, 1500rp m, 2000rp m,
and depth of cut 0.3mm, 0.4 and 0.5mm. The milling process
is conducted on a CNC Vertical milling machine. By
observing the experimental results and fro m Regression
analysis, the following conclusions can be made: The analysis
for tool life is about 98.4% accurate and fo r tool wear is about
91.08% accurate. The residual plots indicate that the
deterministic portion (predictor variables) of the model
Spindle Speed, Feed Rate and Depth of Cut are not capturing
some exp lanatory informat ion that is ―leaking‖ into the
residuals. Fro m the surface p lots of tool life and tool wear, for
more tool life and reduced tool wear, the optimized
parameters are Sp indle Speed – 2000rp m, Feed Rate –
2000mm/ min and Depth of Cut – 0.3mm.
[7]. Ersan Aslan, Necip Camuscu, Burak Birgoren
(1993), ―Design Optimization of Cutting Parameters When
Turning Hardened AISI40 Steel (63 HRC) with Al2O3 +
TiCN Ceramic Tool‖ , Materials & Design, 28, 1618-1622.
[8]. Penevala M. L., A rizmend i M., Diaz F., Fernandez J.
(2007), ―Effect of Tool Wear on Roughness in Hard
Turning‖, Annals of CIRP, 51, 57-60.
[9]. Cora Lahiff, Seamus Go rdon; Pat Phelan (2007), ―PCBN
Tool Wear Modes and Mechanisms in Finish Hard Turning‖,
Robotics and Co mputer-Integrated Manufacturing, 23, 638644.
[10] J. P. Costes, Y. Gu illet, G. Poulachon, M. Dessoly
(2007), ―Tool Life and Wear Mechanisms of CBN Tools in
Machining of Inconel 718‖, International Journal of Machine
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