Comparison of performance of coated carbide inserts with uncoated carbide

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME
392
COMPARISON OF PERFORMANCE OF COATED CARBIDE INSERTS
WITH UNCOATED CARBIDE INSERTS IN TURNING GRAY CAST
IRON
Yuvaraj P. Ballal1
, Manjit M. Khade2
, Ajit R. Mane3
1
(Department of Mechanical Engineering, ADCET, Ashta,India)
2
(Department of Mechanical Engineering, ADCET, Ashta,India )
3
(Department of Mechanical Engineering, ADCET, Ashta,India)
ABSTRACT
In this study, machining performance of a series of commercially available coated
tungsten carbide inserts were investigated during turning of gray cast iron brake drum. The
inserts tested had a coating of TiCN and TiAlN respectively. For comparison, uncoated
cemented tungsten carbide insert of K10 grade was also tested under the same cutting
conditions. Taguchi analysis using ANOVA for 3 parameter, 3 level experimentation - full
factorial (L27 array) were done with output response variables like surface roughness, material
removal rate, tool wear. Main effects of factors and their interactions were studied.
Keywords: ANOVA, Cemented tungsten carbide insert, Coating, Gray cast iron, Machining
performance, Taguchi analysis.
1. INTRODUCTION
The challenge of modern machining industries is mainly focused on the achievement of
high quality, in terms of work piece surface finish, high production rate, less wear on the
cutting tools, economy of machining in terms of cost saving and increase the performance of
the product[1]. Effective machining of work material depends upon the selection of
appropriate cutting tool. A wide range of cutting tool materials is available with variety of
properties, performance capabilities, and cost. These include high speed steels, cemented
carbides, ceramics, cermets, cubic boron nitride, and diamond. The cutting tool material,
cutting parameters and tool geometry directly influence the productivity of machining operation
[2].
INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING
AND TECHNOLOGY (IJMET)
ISSN 0976 – 6340 (Print)
ISSN 0976 – 6359 (Online)
Volume 4, Issue 2, March - April (2013), pp. 392-400
© IAEME: www.iaeme.com/ijmet.asp
Journal Impact Factor (2013): 5.7731 (Calculated by GISI)
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IJMET
© I A E M E

393
2. EXPERIMENTAL DETAILS
2.1. Selection of work and tool material
Gray iron is one of the oldest cast ferrous products. In spite of competition from newer
materials and their energetic promotion, gray iron is still used for those applications where its
properties have proved it to be the most suitable material available. Gray iron castings are readily
available in nearly all industrial areas and can be produced in foundries representing
comparatively less investments. Chemical composition of FG260 gray cast iron is shown in
following table 1. The recently developed tool materials like coated carbides have improved the
productivity levels of difficult-to-machine materials. The coated carbide tool was selected for
turning of cast iron. Cemented carbide is chosen as uncoated cutting tool material. The ISO grade
selected is K10. Other details are:
Designation : CNMA 120408
Nose radius : 0.8 mm
Tool Holder : PCLNR 2525 M 12.
Table 1: Chemical composition of gray cast iron
Elements Composition %
Carbon 2.5 -3.7
Silicon 0.10-0.30
Manganese 0.5-1.0
Sulphur 0.07-0.1
Phosphorous 0.1-0.9
Iron remainder
2.2. Selection of work and tool material
In Taguchi method-based design of experiments, to select an appropriate orthogonal array
for experimentation, the total degrees of freedom (DOF) needs to be computed. The DOF is
defined as the number of comparisons between machining parameters that need to be made to
determine, which level is better and specifically how much better it is. For example, a three-level
machining parameter has two DOF. The DOF associated with interaction between two machining
parameters are given by the product of the DOF for the two machining parameters. In the present
study, interactions between the three machining parameters will be considered. Therefore, there
are 18 DOF owing to three three-level independent parameters, refer table 2 [3].
Table 1: Machining parameters and their levels
Process Parameters
Parameter
Designation
DOF
Levels
I II III
Cutting speed (mm/min) A 2 350 400 450
Feed (mm/rev) B 2 0.2 0.25 0.3
Tool type C 2
Uncoated
K10 carbide
insert
TiCN coated
K10 carbide
insert
TiAlN coated
K10 carbide
insert
Interactions (AB,AC, BC) -
[(3-1) X (3-
1)] X
3=12
- - -
Total DOF - 18 - - -
The machine used for turning is SIMPLE TURN 5075 CNC LATHE (Fanuc Series).

394
2.3. Experimental results
The experimental results obtained after performing experiments are shown in the table 3[3,4].
Table 3: Experimental results
St.
order
Cutting
speed
m/min
Feed
mm/rev
Tool
type
Depth of
cut (mm)
(const.)
Surface
finish(Ra)
( µm)
Tool wear
rate
(gms/min)
Flank
wear
(mm)
MRR
1 350 0.2 UC 2 2.08 0.00125 0.513 242.214
2 350 0.2 TiCN 2 3.52 0.00021 0.096 300.775
3 350 0.2 TiAlN 2 1.03 0.00006 0.024 372.235
4 350 0.25 UC 2 2.86 0.00182 0.972 288.315
5 350 0.25 TiCN 2 4.23 0.00017 0.062 340.153
6 350 0.25 TiAlN 2 1.82 0.00012 0.034 420.857
7 350 0.3 UC 2 3.26 0.00224 0.920 316.123
8 350 0.3 TiCN 2 5.38 0.00034 0.098 418.192
9 350 0.3 TiAlN 2 2.38 0.00008 0.028 436.324
10 400 0.2 UC 2 1.80 0.00240 1.087 304.278
11 400 0.2 TiCN 2 3.28 0.00020 0.092 315.441
12 400 0.2 TiAlN 2 2.20 0.00003 0.017 406.451
13 400 0.25 UC 2 1.38 0.00260 0.966 308.970
14 400 0.25 TiCN 2 2.62 0.00024 0.094 413.397
15 400 0.25 TiAlN 2 3.93 0.00003 0.014 543.157
16 400 0.3 UC 2 3.08 0.00326 1.130 324.671
17 400 0.3 TiCN 2 5.48 0.00030 0.126 469.148
18 400 0.3 TiAlN 2 2.46 0.00038 0.143 568.420
19 450 0.2 UC 2 1.89 0.00302 1.184 343.210
20 450 0.2 TiCN 2 2.58 0.00004 0.023 497.435
21 450 0.2 TiAlN 2 4.38 0.00008 0.032 582.690
22 450 0.25 UC 2 2.42 0.00602 1.820 401.238
23 450 0.25 TiCN 2 5.28 0.00006 0.038 714.000
24 450 0.25 TiAlN 2 3.35 0.00019 0.064 879.545
25 450 0.3 UC 2 3.58 0.00436 1.540 447.058
26 450 0.3 TiCN 2 4.83 0.00138 0.532 865.116
27 450 0.3 TiAlN 2 4.68 0.00014 0.056 998.120
3. STATISTICAL ANALYSIS OF VARIANCE
The statistical analysis of variance for all responses is as follows [1, 2, 3, 4, 5, and 6],
3.1. Statistical Analysis of Surface Roughness (Ra)
Statistical ANOVA shown in Table 4, indicate that tool type is the most significant factor
for surface roughness which has P-value of 0.002.

395
Table 4: ANOVA for Surface finish (Ra)
S=0.892127 R-Sq= 61.09 % R-Sq(adj) = 49.42 % SS= sum of squares
The main effect plot (Fig.1) shows that cutting speed has almost no effect on the surface
roughness at higher levels. Feed rate has linear relationship with the surface roughness, it
increases as feed rate is increased due to the fact that more forces of the tool on the workpiece
due to higher feed rates tends to lose the surface finish, so for good surface quality, a low feed
rate is essential. Uncoated tools exhibit lower surface roughness than coated tools, this is due
to loss of tool edge at continuous machining by uncoated tools.
450400350
4.0
3.5
3.0
2.5
0.300.250.20
TiAlNTiCNUC
4.0
3.5
3.0
2.5
cutting speed
Mean
feed
tool type
Data Means
Fig 1. Main effect plot for Surface finish (Ra)
The interaction plot as shown in Fig 2, indicates that at higher speeds and higher feeds, the
surface roughness increases and this is same for both coated and uncoated tools. Surface
roughness values decreases as speeds increases from 350 to 450 m/min in comparison to both
coated and uncoated tools.
Source DF SS MS F P
Cutting
speed
2 3.2278 1.6139 2.03 0.158
Feed rate 2 8.5834 4.2917 5.39 0.013
Tool type 2 13.1821 6.5911 8.28 0.002
Error 20 15.9178 0.7959 - -
Total 26 40.9112 - - -

396
Fig 2. Interaction plot for surface finish
3.2. Statistical Analysis of Tool Flank Wear
It is found that the tool wear progresses rapidly during machining of cast iron. Statistical
ANOVA shows that the tool type is the most significant factor for the wear and cutting speed is
the next influencing factor(refer table 5).
Table 5: ANOVA for tool flank wear
S = 0.203854 R-Sq = 89.39% R-Sq(adj) = 86.20%
The main effects plots (Fig 3) shows that an increase in cutting speed causes rapid increase in the
tool wear.Feed rate shows very linear effect on the wear. As feed is increased, there is more
increment in the wear since feed rate increases with increase in force on cutting edge of tool.
Uncoated tools have higher wear as compared to coated tools, the least wear is observed in
TiAlN coating as shown in Fig 3.
Source DF SS MS F P
Cutting
speed
2 0.3680 0.1840 4.43 0.026
Feed rate 2 0.1302 0.0651 1.57 0.233
Tool type 2 6.5007 3.2503 78.21 0.000
Error 20 0.8311 0.0416 - -
Total 26 7.8300 - - -

397
450400350
1.00
0.75
0.50
0.25
0.00
0.300.250.20
TiAlNTiCNUC
1.00
0.75
0.50
0.25
0.00
cutting speed
Mean
feed
tool type
Main Effects Plot for flank wear (mm)
Data Means
Fig 3. Main effect plot for tool flank wear (mm)
The wear behavior at the lower to higher speeds is almost the same at different feed rates, as
seen in Fig 4. At lower feed of 0.2 mm/rev, the wear is least. Uncoated tools exhibiting higher
flank wear than coated tools. It is also seen that as speed is increasing, the flank wear is also
increasing, this is due to the loss of hot hardness at high cutting speed. All the tools exhibit
higher flank wear at higher feeds.
Fig 4. Interaction plot for tool flank wear

398
3.3. Statistical Analysis of Material Removal Rate
Statistical ANOVA shown in Table 6, indicate that in case of MRR, all process
parameters namely cutting speed, feed and tool type are significant factors.
Table 6: ANOVA for MRR
S = 90.1397 R-Sq = 83.53% R-Sq(adj) = 78.59
Fig 5. Main effect plot for MRR (gm/min)
The main effects plots (Fig 5) and interaction plots (Fig 6) shows that MRR increases
with increase in cutting speed, feed and tool type and most significant at 450 m/min, 0.3
mm/rev and TiAlN coated cutting tool.
Source DF SS MS F P
Cutting
speed
2 418421 209211 25.75 0.000
Feed rate 2 124567 62283 7.67 0.003
Tool type 2 281027 140514 17.29 0.000
Error 20 162503 8125 - -
Total 26 986518 - - -

399
Fig 6. Interaction plot for MRR
4. CONCLUSION
The experiments were conducted as per Taguchi L 27 orthogonal array. After fixing
process parameters (cutting speed, feed, depth of cut and cutting tool material) observations
for response variables (SF, Tool wear, MRR) were taken. The following are the conclusions
made after experimentation and statistical analysis of variance.
1. Tool type is the most significant factor for surface roughness. Coated cutting tools
shows higher roughness values than uncoated cutting tools at lower feed rates and
roughness value increased as feed rate increased.
2. The roughness value for TiAlN coated cutting tools increases with increase in
cutting speed and roughness value for TiCN is high and roughness value for
uncoated cutting tools is low for selected cutting speed range.
3. Tool wear rate is much higher for uncoated cutting tool than coated cutting tools
for selected process parameters. For TiAlN coated cutting tool have least tool
wear.
4. MRR increases with increase in cutting speed , feed and most significant at 450
m/min, 0.3 mm/rev and TiAlN coated cutting tool.
REFERENCES
[1]Thamizhmanii S., Saparudin S. and Hasan S. “Analyses of surface roughness by turning
process using Taguchi method”, Journal of achievements in material and manufacturing
engineering, Pg. 503-506, Vol. 20, Issue 1-2, Jan- Feb, 2007
[2]Khandey Umesh, “Optimization of surface roughness, material removal rate and cutting
tool flank wear in turning using extended Taguchi approach”, masters dissertation report,
NIT Rourkela, August 2009.

400
[3]Ramji B.R, H.N. Narasimha Murthy and M. Krishna, “Analysis of roughness and flank
wear in turning gray cast iron using cryogenically treated cutting tools”, Research Journal
of Applied Sciences, Engineering and Technology 2(5), Pg. 414-417, 2010, ISSN: 2040-
7467
[4]Gopalsamy Bala Murugan, Mondal Biswanath and Ghosh Sukamal, “Taguchi method and
ANOVA: An approach for process parameters optimization of hard machining while
machining hardened steel”, Journal of Scientific and Industrial Research, Vol. 8, August
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[5]Dolinšek S. and Kopač J., “Mechanism and types of tool wear particularities in advanced
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[7]K.Dharma Reddy and Dr.P.Venkataramaiah, “Experimental Investigation on Responses
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[8]R. R. Deshmukh and V. R. Kagade, “Optimization of Surface Roughness in Turning
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[9]Vipin Kumar Sharma, Qasim Murtaza and S.K. Garg, “Response Surface Methodology &
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