Analysis of surface roughness and chip cross-sectional area while
machining with self-propelled round inserts milling cutter
Uday A. Dabade1, S.S. Joshi*, N. Ramakrishnan
Mechanical Engineering Department, Indian Institute of Technology, Mumbai 400076, India
Abstract
An interesting development in the ®eld of machining is to improve the cutting tool performance by providing continuous indexing to the
cutting edge actuated either by cutting velocity vector or by an external power. This paper presents an analysis of cutting process performed
using a speci®cally designed and fabricated self-propelled rotary inserts face milling cutter. Statistically designed experiments were
performed using Taguchi method with surface roughness and chip cross-sectional area as response variables. Analysis of experimental results
using analysis of means and analysis of variance is discussed in detail. It is observed that inclination angle is the most signi®cant factor
in¯uencing both surface roughness and chip cross-sectional area and can give better results in the range of 30±458.
Keywords: Face milling cutter; Rotary tools; Surface roughness; Chip cross-sectional area; Taguchi methods
1. Introduction
Material removal is one of the oldest and major shaping
processes for economic production of components. Continuous research and development activities in the ®eld of
metal cutting have led to the evolution of a number of
new manufacturing processes. One such interesting development is use of rotary tools in machining. These tools use
continuous indexing of cutting edge actuated by either
cutting velocity vector or an external power.
James Napier used this concept initially in 1865 in a
turning operation [1]. In early 1960s, pioneering work on
metal cutting using rotary tools was done by Ramaswamy
and Koenigsberger [1]. They found that increase in the life of
rotary tools could be 20 times as that of the stationary tools.
Venuvinod and Barrow [2] concluded that chip length ratio
as high as two could be achieved using rotary tools. Venkatesh et al. [3] used rotary as well as stationary inserts in a
face milling operation to compare tool wear, surface ®nish
and to observe color of chips. Based on the color of chips
they concluded that cutting process using rotary tools takes
place at comparatively lower temperatures than that of the
stationary tools. However, in the later part of the century,
active research on this topic was not pursued due to the
apprehensions about the quality of surface generated. Nevertheless, in recent years there has been renewed interest in the
technology of rotary tools primarily to meet the machining
demands of new materials many of which are considered as
`dif®cult-to-machine' materials. Armarego et al. [4,5] analyzed rotary tool machining process in detail to formulate
models for tool velocity, chip ¯ow angle and cutting forces.
Chen and Hoshi [6] used rotary tools in turning operation for
the precision machining of composite materials. Recently,
Joshi et al. [7,8] found the use of rotary carbide tools as a
feasible alternative to PCD, CBN or ®xed carbide tools in the
intermittent machining of Al/SiCp composite materials.
The mechanism of cutting using rotary tools is a complex
process as it is in¯uenced by a number of cutting tool related
parameters such as tool velocity, insert diameter, inclination
angle in addition to the usual process dependent parameters.
Moreover, much of the earlier work is mainly related to the
generation of cylindrical surfaces [1,2,4±7], except for the
work by Venkatesh et al. [3] that involves use of multi-point
rotary cutter for generation of plain surfaces. Therefore, it is
felt that an exercise to develop self-propelled round insert
milling cutter and to analyze the cutting process using these
tools could be very valuable.
This paper discusses analysis of the cutting process performed using a face milling cutter mounted with self-propelled
306
Table 1
Cutter specifications
Sr. no.
Cutter parameter
Cutter body
1
2
3
Cutter diameter
Cutter material
Number of insert
mounting slots
4
Cutter body bore
diameter
Insert holding arrangement
5
Diameter of insert
mounting shaft
6
Insert diameter
7
Insert material
8
Rotary tool
inclination angle
Cutter holding arrangement
9
Milling adapter
Specification
110 mm
Carbon steel C20
5
f 27 mm
8 mm
20 mm
Carbide (grade: THM
equivalent to K-10±K-20)
208, 308, 458
21 mm height f 27 mm
diameter, with morse taper shank
round inserts designed and fabricated in this work. Statistically designed experiments using Taguchi methods were
performed with surface roughness and chip cross-sectional
area as response variables. It was evident that surface roughness is mainly in¯uenced by inclination angle; whereas the
chip cross-section is in¯uenced by inclination angle, feed
rate and depth of cut. The proposed statistical models for
surface roughness and chip cross-sectional area agreed
experimental results with fairly good accuracy.
2. Design and fabrication of milling cutter
A face milling cutter with self-propelled round inserts has
three main partsÐcutter body, insert holding arrangement
and cutter holding arrangement. Detailed speci®cations of
the cutter are given in Table 1.
The cutter thus designed and fabricated was capable of
machining up to a depth of cut of 2 mm. A needle roller
bearing, designation: INA HK0810B without inner race was
selected for mounting the insert holding arrangement on the
body of the cutter. The milling cutter was mounted on a
Table 2
Control parameters, their levels and interactions
Parameter/interactions
DOF
(A) Inclination angle (8)
(B) Cutting speed (m/min)
(C) Feed rate (mm/rev)
(D) Depth of cut (mm)
Interactions AB, AC, BC
2
2
2
2
‰…3 1† …3
3 ˆ 12
20
Total DOF
Level
1†Š
1
2
3
20
70.68
0.08
0.25
30
141.37
0.16
0.50
45
282.74
0.32
1.00
Fig. 1. Experimental set-up.
vertical milling machine using standard adaptor, see Fig. 1
for the photograph of the experimental set-up.
3. Design of experiments and procedure
3.1. Design of experiment
It involves selection of response variables, independent
variables, their interactions and an orthogonal array. Roughness of the surface generated and the cross-sectional area of
chips produced during face milling operation were taken as
response variables. Since the surface roughness depends
upon the direction of measurement, two variations in it:
(1) surface roughness along feed direction; (2) across the
feed direction were considered. Various control parameters,
their levels, interactions and degrees of freedom (DOF)
chosen for this experimentation are given in Table 2.
It is known that four independent factors can have six twofactor interactions. Further, there could be strong as well as
weak interactions among these factors causing stronger or
weaker effects on the response variable. It was thought that
of the four factors, inclination angle, cutting speed and feed
rate interact with each other by in¯uencingÐthe speed of
rotation of round insert and the area of chip cross-section,
see Fig. 2 for logic for the selection of interactions. Hence,
these were chosen for this study.
Since the total DOF for this experiment is 20 (see Table 2)
a L-27 orthogonal array was selected [9]. Assignment of
various factors and interactions to this orthogonal array was
done as per the linear graph as shown in Fig. 3.
3.2. Experimental procedure
Experiments were performed as per the design details
mentioned above using the self-propelled round insert face
307
Fig. 2. Logic for selection of interactions (criterion: surface roughness).
milling cutter. A vertical milling machine was used for this
purpose. Machining experiments were carried out on rolled
aluminum plates of 150 mm 75 mm 12 mm dimension.
In all, 54 experiments (including one replication) were
performed. Measurement of surface roughness was carried
out using a Taylor Hobson SURTRONIC-3 surface roughness measurement instrument. It is understood from the
geometry of face milling operation using rotary tools that
the radius of round inserts (10 mm) is the tool nose radius.
Since it is considerably large as compared to the usual tool
nose radius (0.8 mm), the cut-off length for surface roughness measurement was chosen to be 2.5 mm. At least 4±5
measurements of surface roughness were taken per experimental run and the average value of measurements is used as
response variable.
For the second response variable i.e. chip cross-sectional
area, height and thickness of chip was measured using Tool
Makers Microscope (Nikon make). Assuming the chip
cross-section to be approximately triangular in shape, area
of chip cross-section was calculated.
4. Results and discussion
Statistical analysis of experimental result was done by
preparing means tables and mean effect plots based on
analysis of means (AOM), and analysis of variance
(ANOVA) using STATGRAPHICS-PLUS software [9].
4.1. Statistical results and discussions
(surface roughness)
Mean tables are used to estimate variation in the response
variable as the independent variables change from levels 1 to
3. The same results can also be presented in the form of
means plots. In the present analysis, response tables for
surface roughness and chip cross-sectional area are presented elsewhere [10], and the means plots are shown in
Figs. 4 and 5.
ANOVA helps in formally testing the signi®cance of all
main factors and their interactions by comparing the mean
square against an estimate of the experimental errors at
speci®c con®dence levels. In the analysis, F-ratio is a ratio of
mean square error to residual, and is traditionally used to
determine signi®cance of a factor. However, F-ratio does not
indicate the extent of deviation in the results therefore Pvalue called as level of signi®cance, given in the last column
of the ANOVA table is estimated [11]. If P-value for a factor
is less than 0.05, then the factor is considered as statistically
signi®cant at 95% con®dence level. Accordingly, it is evident from Table 3 that only inclination angle signi®cantly
in¯uences the surface roughness along feed direction at 95%
Fig. 3. Linear graph.
308
Fig. 4. Mean effect plot for surface roughness Ra (mm) along feed direction.
Fig. 5. Mean effect plot for surface roughness Ra (mm) across feed direction.
con®dence level. Similar conclusions were arrived from the
ANOVA of surface roughness across feed direction, the
results of which is present elsewhere [10]. In the following
sections, these effects are discussed in detail.
4.1.1. Effect of inclination angle
As the inclination angle changes from level 1 (208) to
level 2 (308), there is a signi®cant improvement in surface
roughness measured along the feed direction. Whereas, this
improvement is not observed when the inclination angle
changes from level 2 (308) to level 3 (458), see Fig. 4.
Similarly, variation in surface roughness measured across
feed direction also shows similar trend however, there is a
small increment in the surface roughness as the inclination
angle changes from 308 to 458, see Fig. 5.
As seen from the conceptual model of effect of inclination
angle (see Fig. 6), with an increase in the inclination angle,
the length of contact between the round inserts and work-
Table 3
ANOVA for surface roughness along feed direction
Source
(A) Inclination angle
(B) Cutting speed
(C) Feed rate
(D) Depth of cut
AB
AC
BC
Residual
Total (corrected)
Sum of squares
320.028
15.7373
8.59557
14.7869
31.2961
28.8255
140.576
766.890
1326.74
DOF
Mean square
F-ratio
P-value (significance level)
2
2
2
2
4
4
4
33
160.014
7.86863
4.29779
7.39346
7.82403
7.20638
35.1439
23.2391
6.89
0.34
0.18
0.32
0.34
0.31
0.51
±
0.0032
0.7152
0.8320
0.7297
0.8513
0.8691
0.2212
±
±
±
53
±
309
Fig. 6. Model for length of contact at various inclination angles.
piece increases for the equal magnitude of feed. Thus, Fig. 6
shows variation of contact lengths `oa', `ob' and `oc' for
three inclination angles 208, 308 and 458, respectively.
Referring to Fig. 6, in triangle `aod'
cos…i† ˆ
feed
length of contact
(1)
where, i is the inclination angle. However, with an increase
in the length of contact, the effective nose radius of the round
insert also increases. We know that surface roughness is
inversely proportional to the tool nose radius and in a
conventional milling operation it is given by [12]
surface toughness …Ra † ˆ
f2
32re
(2)
where, f is the feed rate, re the nose radius. Therefore, an
increase in the inclination angle increases the effective nose
radius and hence improves the surface roughness in selfpropelled round inserts milling operation.
4.1.2. Effect of cutting speed, feed rate and depth of cut
As can be observed from the mean effect plots in Figs. 4
and 5, and ANOVA analysis presented in Table 3, the cutting
speed, feed rate and depth of cut does not in¯uence surface
roughness signi®cantly when measured in both the directions. As far as the effect of cutting speed is concerned, it
con®rms with the results mentioned in the relevant literature
[6].
The in¯uence of feed rate on the surface roughness
although deviates from the traditional relationship between
feed rate and surface roughness, it could be due to the
relatively closer levels of feed rate employed during the
present experiment.
The in¯uence of depth of cut can be explained with the
help of a model for surface irregularities, see Fig. 7. As
evident from the model, a change in depth of cut does not
contribute directly to the change in height of surface irregularities and hence the surface roughness. It was earlier
thought that an increase in the depth of cut might lead to
vibrations during machining consequently in¯uencing the
surface roughness. Therefore, it may be concluded here that
the milling cutter designed for this experimentation can be
safely used in the chosen range of depth of cut.
4.2. Statistical results and discussions (chip crosssectional area)
Results of AOM and ANOVA on the chip cross-sectional
area are presented in the form of mean effect plots in Fig. 8
and Table 4, respectively. It is evident that the inclination
angle, feed rate and depth of cut in¯uence the chip crosssectional area signi®cantly at 95% con®dence level.
Fig. 7. Model for effect of depth of cut on surface irregularities.
310
Fig. 8. Mean effect plot for chip cross-sectional area.
Table 4
ANOVA for chip cross-sectional area
Source
Sum of squares
DOF
Mean square
F-ratio
P-value (significance level)
(A) Inclination angle
(B) Cutting speed
(C) Feed rate
(D) Depth of cut
AB
AC
BC
Residual
20.8119
0.14711
1.62270
3.01347
1.34709
0.92983
0.29291
5.44278
2
2
2
2
4
4
4
33
10.4059
0.07355
0.81134
1.50674
0.33677
0.23246
0.073247
0.16493
63.09
0.450
4.920
9.140
2.040
1.410
0.440
±
0.0000
0.6440
0.0135
0.0007
0.1112
0.2525
0.7758
±
Total (corrected)
33.6079
53
4.2.1. Effect of inclination angle
As the inclination angle changes from levels 1 to 2 i.e.,
from 208 to 308, there is a signi®cant decrease in chip crosssectional area. Similar decrement is not observed as the
inclination angle changes from level 2 (308) to level 3
(458). If the chip cross-sectional area is considered to be
triangular in shape [2], then with an increase in the inclination angle, the height of triangular chip cross-section
decreases as shown in Fig. 9. At the same time the length
of the chip increases so as to maintain the constancy of
volume as can be seen from the photograph of actual chips
in Fig. 10.
Fig. 9. Mean effect plot for chip height.
±
±
±
4.2.2. Effect of feed rate, depth of cut and cutting speed
Feed rate in¯uences the chip cross-sectional area signi®cantly since it contributes directly to a change in the height of
chip cross-section. Therefore, chip cross-sectional area
increases with an increase in the feed rate. Similarly, depth
of cut also contributes to the evaluation of chip cross-sectional
area and is also found to be statistically signi®cant, see Fig. 8.
The fourth factorÐcutting speed does not in¯uence the chip
Fig. 10. Photographs of chips produced during self-propelled round insert
milling operation.
311
Using multiple regression analysis, statistical models
were developed for the surface roughness (SR) (measured
along the feed direction) and the chip cross-sectional area
(Ac). The models giving relationships between response
variables and corresponding independent variables are as
given below
It can be seen from Fig. 11 that the multiple regression
model for the surface roughness agrees fairly well the
experiment data. The R-squared statistics indicates that the
model as ®tted has 18.65% of the variability in surface
roughness. Similarly, the model for chip cross-sectional
area agrees with the experimental data with 57.62% of
variability. A relatively large error in the prediction of chip
cross-sectional area could be attributed to the assumption
of triangular shape of chip cross-section and to some
extent to the errors in the manual measurement of chip
height and thickness while estimating chip cross-sectional
area.
SR …Ra ; mm† ˆ 19:5943 0:20142i 0:004693v
3:4025f 0:3589d
6. Conclusions
cross-sectional area signi®cantly since a change in cutting
speed changes the rate of material removal.
5. Multiple regression models
2
Ac …mm † ˆ 1:93167
0:0507862i
‡ 1:72419f ‡ 0:747969d
(3)
0:000553v
(4)
where, i is the inclination angle, v the cutting speed, f the
feed rate, and d the depth of cut.
A comparison of experimental results with the predicted
once using regression models for surface roughness and chip
cross-sectional area is shown in Figs. 11 and 12, respectively.
Fig. 11. Comparison of experimental vs. predicted surface roughness (in
mm Ra) along feed direction.
1. A face milling cutter with five self-propelled round
inserts was designed and fabricated which is capable of
machining up to a maximum depth of cut 2 mm.
2. Statistically designed experiments based on Taguchi
methods were performed using L-27 orthogonal array to
analyze surface roughness and chip cross-sectional area
as response variables.
3. Statistical results indicate that surface roughness is
significantly influenced (at 95% confidence level) by
inclination angle. An increase in the inclination angle
reduces the surface roughness. This could be due to an
increase in the contact length between the workpiece
and cutting edge on round inserts.
4. Similarly, statistical results for chip cross-sectional area
indicate that it is significantly influenced (at 95%
confidence level) by inclination angle, feed rate and
depth of cut. The chip cross-sectional area increases
with the decrease in the inclination angle. It could be
due to an increase in the height of chip cross-section at
lower inclination angles. Whereas, an increase in feed
rate and depth of cut, increases the height and width of
chip cross-section hence these two-factors also significantly influence the chip cross-sectional area.
5. Thus, a face milling cutter with rotary inserts at an
inclination angle between 308 and 458 could give better
surface finish and form a feasible alternative for face
milling at lower depths of cut.
Acknowledgements
The authors would like to thank Mr. D. Sarathy (Deputy
General Manager, R&D, WIDIA Ltd., Bangalore) for providing carbide rotary inserts for our experiments.
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Fig. 12. Comparison of experimental vs. predicted chip cross-sectional
area (in mm2).
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