Ijmet 10 01_098

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International Journal of Mechanical Engineering and Technology (IJMET)
Volume 10, Issue 01, January 2019, pp. 960–967, Article ID: IJMET_10_01_098
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=01
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication Scopus Indexed
OPTIMIZATION OF DRESSING PARAMETERS FOR
GRINDING TABLET SHAPE PUNCHES BY CBN
WHEEL ON CNC MILLING MACHINE
Le Hong Ky
Vinh Long University of Technology Education, Vinh Long, Vietnam
Tran Thi Hong
Nguyen Tat Thanh University, Ho Chi Minh city, Vietnam
Hoang Tien Dung
Ha Noi University of Industry, Ha Noi, Vietnam
Nguyen Anh Tuan
University of Economic and Technical Industries, Ha Noi, Vietnam
Nguyen Van Tung, Luu Anh Tung and Vu Ngoc Pi*
Thai Nguyen University of Technology, Thai Nguyen, Vietnam
*
Corresponding Author
ABSTRACT
The dressing regime parameters in the process of grinding are the most important
enabling factors that need to be determined. In this study, the influences of the dressing
parameters including the depth of dressing cut, the rate of dressing feed and the speed of
grinding wheel on the surface roughness when grinding tablet shape punches by CBN
wheel on CNC milling machine are investigated. Taguchi technique and analysis of
variance (ANOVA) have been applied to identify the impact of dressing regime
parameters on the surface roughness. The results show that the impact level of the cutting
depth (aed), the wheel speed (RPM), the feed rate (Fe) and errors on surface roughness
(Ra) are 52.63%, 28.45%, 6.59% and 12.33% respectively. By analyzing the experimental
results, optimum dressing parameters with the cutting depth of 0.02 mm, the wheel speed
of 1000 rpm and the infeed rate of 400 mm/min have been determined, that allow to get
the best surface roughness.
Key words: Grinding wheel, Dressing parameters, Taguchi method, CBN abrasives.
Cite this Article: Le Hong Ky, Tran Thi Hong, Hoang Tien Dung, Nguyen Anh Tuan,
Nguyen Van Tung, Luu Anh Tung, and Vu Ngoc Pi, Optimization of Dressing Parameters
for Grinding Tablet Shape Punches by Cbn Wheel on Cnc Milling Machine, International
Journal of Mechanical Engineering and Technology, 10(01), 2019, pp.960–967
http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&Type=01

Le Hong Ky, Tran Thi Hong, Hoang Tien Dung, Nguyen Anh Tuan, Nguyen Van Tung, Luu Anh Tung,
and Vu Ngoc Pi
1. INTRODUCTION
Grinding is one of the most important finish processing methods for hard-to-machine metallic
alloys in order to obtain low surface roughness (Ra 0.1 to 2 µm) and high precision [1-3]. For
grinding process, cubic boron nitride (CBN) wheels provide significant advantages in the field of
modern high-efficiency grinding [4]. An important stage in this process is wheel preparation
whereby the grinding wheel is prepared for cutting by dressing [5, 6]. Therefore, the selection
of suitable dressing tools and parameters plays an important role in the grinding process.
It influences not only the surface quality of grinding parts, but also the machining cost
and efficiency [7].
So far, a number of studies have been conducted on the problem of optimization of dressing
parameters and the selection of suitable dressing methods in order to enhance the quality and
the productivity of grinding processes. The researchers have focused on different targets over
different value sets of variables in specific applications. In 2008, J. M. Derkx et al. [8] developed
an improved form crush profiling system to profile diamond grinding wheels. Furthermore, a
method for synchronization of the form disc that results in optimum synchronization, less
interference with the machine and no operator intervention was obtained. In 2013, P. Puerto [3]
analyzed the surface roughness changes in the grinding process for two different dressing
conditions (soft and aggressive conditions). In 2015, based on statistical and experimental
investigations, A. Fritsche [9] evaluated the influence of grain size changes on the result
of dressing operation. In 2016, an alternative process for dressing electroplated CBN grinding
wheels using an ultrashort pulsed laser was introduced by J. Pfaff [4]. In this work, the process
of laser touch dressing proved to be an efficient method for the preparation of CBN grinding
tools. In 2017, Davide Matarazzo [2] presented an approach using an artificial neural network to
develop an intelligent system for the prediction of dressing in grinding operation. The system
could be employed in real time and support the operator during grinding operation with a view
to optimization and continuous improvement of the performance. In 2018, Jack Palmer [7]
studied the influence of dressing parameters on the topography of grinding wheels in order to
better understand the process and therefore optimize the preparation of grinding wheels for
industrial machining. However, none of previous researches has studied the problem of
optimizing dressing parameters for grinding table shape punches of 9CrSi steel by CBN wheels
on CNC milling machine.
In this paper, based on Taguchi technique in designing experiment and analyzing variance
(ANOVA), the optimum set of dressing parameters for grinding 9CrSi steel by CBN wheels on
CNC milling machine has been determined. In the optimum dressing mode, the surface roughness
of parts (Ra) is the smallest.
2. METHODOLOGY
2.1. Experimental machine and equipment
The experimental setup is shown in Figure 1. The description of the experimental machine and
equipment is presented in Table 1. The worked material was 90CrSi tool steel with hardness of
56-58 HRC. The dressing parameters set up in Table 2 are used for design of all experiments.

Optimization of Dressing Parameters for Grinding Tablet Shape Punches by Cbn Wheel on Cnc Milling
Machine
Table1. Experimental machine and equipment
Machine and Equipment Specifications
Machine for grinding Mitsubishi, Model M-V50C (Japan)
CBN wheel dresser equipment V-TDM-2 (Vertex, Taiwan)
Grinding wheel CBN 325-N75B53-3.0 (Japan)
Workpiece material 90CrSi
Workpiece dimensions 13x13x30 mm3
Roughness measurement
machine
SURFTEST SV-3100 (Japan)
Table 2. Input parameters for dressing experiment
Parameters Unit
Experimental levels
Low level (1)
Base level
(2)
High level (3)
The depth of dressing cut (aed) mm 0.01 0.02 0.03
The speed of wheel (Rpm) rpm 1000 2000 3000
The rate of dressing feed (Fe) mm/min 400 550 700
2.2. Design of experiment
The Taguchi method has been used to design experiments. The application of the Taguchi method
allows the analysis of different parameters without a prohibitively high amount of
experimentation and helps to get a suitable combination of the process parameters with minimal
number of experiments [10, 11]. There were three factors, of which each factor changes 3 levels,
thus, it was essential to select orthogonal experiment matrix L9 (33) [10, 11]. In other words, 09
sets of experimental parameters have been implemented (as shown in Table 2).
The experiment sequence is as follows: First, the CBN grinding wheel is dressed by dresser
equipment (Figure 1.a) according to the dressing parameters shown in Table 2. After that, the
workpieces are ground with schema in Figure 2 and the following grinding regime: the depth of
cut of 0.025 (mm), the feed of wheel of 4000 (rpm) and the grinding feed rate of 2500 (mm/min).
Each experimental run is performed 3 times and then the surface roughness of the ground parts
is measured. The results of the roughness of each run and their average are given in Table 3. After
collecting results, the data are analyzed and processed. In the article, Minitab software is used to
design experiments under Taguchi method. Based on that, the signal-to-noise ratio (S/N) and the
effect of dressing parameters on the surface roughness of part are determined.
a) Dressing setup b) Grinding setup
Figure.1. Experimental setup

and Vu Ngoc Pi
Figure.2. Schema of grinding tablet shape punches
3. RESULTS AND DISCUSSION
The effect of each parameter is determined by using signal-to-noise ratio (S/N), which helps to
find the strength of experiments for the optimization of dressing parameters [10, 11]. With the
goal of the experiment, the smaller the surface roughness is, the better the result is achieved.
Therefore, according to Taguchi method, the signal-to-noise ratios (S/N) for this target is
calculated by formula (1) [10, 11]. The obtained surface roughness results of the conducted
experiments and corresponding signal-to-noise ratios (S/N) are also presented in Table 3.
= −10 ( ∑ ) (1)
Where, n is the number of experiments under the same design parameter conditions; yi is the
value of measured surface roughness for the ith experiment (i = 1, 2, 3).
Table 3. Experiment Plans, the Output Response and S/N ratios
Experiment number
Factors The surface roughness of part (Ra)
S/Naed
(mm)
RPM
(rpm)
Fe
(mm/min)
Ra1
(µm)
Ra2
(µm)
Ra3
(µm)
Ramean
(µm)
1 0.01 1000 400 0.196 0.201 0.219 0.205 13.755
2 0.01 2000 550 0.251 0.267 0.161 0.226 12.725
3 0.01 3000 700 0.223 0.238 0.335 0.265 11.379
4 0.02 1000 550 0.213 0.215 0.183 0.204 13.799
5 0.02 2000 700 0.249 0.230 0.288 0.255 11.815
6 0.02 3000 400 0.213 0.211 0.202 0.209 13.609
7 0.03 1000 700 0.275 0.192 0.280 0.249 11.966
8 0.03 2000 400 0.280 0.387 0.223 0.297 10.335
9 0.03 3000 550 0.345 0.295 0.230 0.290 10.633
Rpm
Fe,gw

Machine
3.1. Determination the influence of dressing parameters
Figure. 3. Main effect plots of dressing parameters for means of surface roughness
The ANOVA values of medium surface roughness ( ) are shown in Table 4, Table 5 and
Figure 3. The analysis results show the impact level of dressing parameters on part’s medium
surface roughness (Table 3) as follows: the impact level of the cutting depth (aed) on part’s surface
roughness (Ra) is 52.63%; the impact level of the wheel speed (Rpm) on the ground part surface
roughness is 28.45%; the impact level of the feed rate (Fe) on the surface roughness is 6.59%.
Thus, the influence of the cutting depth on the surface roughness is the greatest, and the effect of
the wheel speed on the surface roughness is the smallest.
According to Figure 3, in the beginning the surface roughness of the ground parts (Ra)
decreases, but when the depth of dressing cut (aed) increases, it rises. It reaches the smallest value
at the cutting depth of 0.02 mm (equivalent to the cutting depth value at the second level). In
contract, the surface roughness initially increases but declines when the speed of grinding wheel
(Rpm) grows. It is minimum at the wheel speed of 1000 rpm (equivalent to the wheel speed value
at the first level). Besides, the surface roughness expands when the rate of dressing feed (Fe) falls.
Its value is minimum at the feed rate of 400 mm/min (equivalent to the feed rate value at the first
level).
Table 4. The ANOVA values of part’s medium surface roughness ( )
Parameters DF SS Adj SS MS F-Value P-Value C %
aed 2 0.005374 0.005374 0.002687 4.27 0.19 52.63
RPM 2 0.002905 0.002905 0.001453 2.31 0.302 28.45
Fe 2 0.000673 0.000673 0.000337 0.53 0.652 6.59
Errors 2 0.001259 0.001259 0.00063 12.33
Total 8 0.01021 100
Table 5. The impact level of dressing parameters on part’s medium surface roughness ( )
Level
Parameters
aed RPM Fe
1 0.2322 0.2192 0.2367
2 0.2226 0.2594 0.2401
3 0.2786 0.2547 0.2565
Delta 0.0560 0.0403 0.0198
Rank 1 2 3
Mean of part’s surface roughness: 0.244

and Vu Ngoc Pi
3.2. Determination of optimum dressing parameters
For determination of optimum dressing parameters, the ANOVA values for signal-to-noise ratios
(S/N) are calculated (as shown in Table 6 and Figure 4).
Table 6 and Figure 3 indicate that the signal-to-noise ratio (S/N) reaches the greatest value at
the cutting depth of 0.02 mm, the wheel speed of 1000 rpm, the feed rate of 400 mm/min (red
points on Fig. 4). These values of the dressing parameters are optimum, which would help to get
the best surface roughness.
Table 6. The ANOVA values for signal-to-noise ratios (S/N)
Parameters DF SS Adj SS MS F-Value P-Value C %
aed 2 7.297 7.297 3.6485 5.2 0.161 51.97
RPM 2 4.150 4.150 2.0750 2.96 0.253 29.55
Fe 2 1.191 1.191 0.5955 0.85 0.541 8.48
Errors 2 1.404 1.404 0.7020 10.00
Total 8 14.042 100
3.3. Determination of optimum surface roughness value
The optimum surface roughness value Raop is determined by the levels of the dressing parameters
that strongly affect the signal-to-noise ratio (S/N) as follows:
= + + − 2 ∗ !" (2)
Where, is the mean surface roughness value corresponding to the cutting depth value at
the second level; is the mean surface roughness value corresponding to the wheel speed
value at the first level; is the mean surface roughness value corresponding to the feed rate
value at the first level; !" is the mean surface roughness value of the total experiment. The values
of these parameters can be determined from Table 5 as follows: a$% = 0.2226 μm; R+,- =
0.2192 μm; F$ = 0.2367 μm.
Thus:
!" =
∑ 2345∑ 234456
789 ∑ 23444
6
789
6
789
:
= 0.244 <=
Therefore:
= 0.2226 + 0.2192 + 0.2367 − 2 ∗ 0.244 = 0.1905 <=
Figure. 4. Main effect plots of dressing parameters for signal-to-noise ratios (S/N)

Machine
3.4. Determination of confidence interval
The confidence interval (CI) is calculated by:
?@ = A B(1, D ). E . (FG
+ 2
) (3)
Where, fe is the freedom degrees of error (fe= 2); Ve is the average error (Ve = 0.00063);
∝(1,2) is the Fisher coefficient corresponding to the confidence level (α) of 90% ( ∝(1,2) =
8.5263); R is the number of iterations of an experiment; Ne is the number of effective iterations
which can be computed as follows:
( ) ( )/ 1 27 / 1 2 2 2 3.857e Sum f
N S A= + = + + + =
(4)
In which, Ssum is the total number of experiments and Af is the total freedom of all averaged
parameters.
Substituting ∝, Ve, R and Ne into (3) we have:
?@ = A8.5263 ∗ 0.00063 ∗ JK.LM:
+ K
N = 0.056 (5)
Accordingly, at the confidence level (α) of 90% the surface roughness is predicted with the
optimum level of the input parameters 2 1 1/ /ed PM ea R F as follows:
(0.1905 − 0.056) ≤ PQ ≤ (0.1905 + 0.056) μ= (6)
To evaluate the appropriateness of the optimum parameters, an experiment was conducted.
Table 7 shows the values of the surface roughness which was predicted by using the mathematical
model and the experiment results. The difference between both values is appropriately 6.6 % of
the range. Therefore, this calculation method can be used to accurately predict the surface
roughness of the ground parts.
Table 7. Comparison results between calculation value and experimental value
Output factors
Optimum parameters
Prediction value Experimental value
Error (%)
aed2, RPM1, Fe1 aed2, RPM1, Fe1
The surface roughness of part - Ra (µm) 0.1905 0.203 6.6
Signal-to-noise ratio (S/N) 14.4 13.85
4. CONCLUSION
A study on optimization of dressing parameters for grinding tablet shape punches 90CrSi by CBN
wheel on CNC milling machine was carried out. From the results of the study, several findings
can be presented as follows:
- Among the three dressing parameters, the most influential parameter on part’s surface
roughness is the depth of dressing cut. The second influential parameter on part’s surface
roughness is the speed of wheel. The smallest influential parameter on part’s surface roughness
is the rate of dressing feed.
- By applying Taguchi technique and analysis of variance (ANOVA), with the proposed target
function of the surface roughness (the lower is the better), the optimum dressing parameters for
the minimum surface roughness have been found as follows: aed = 0.02 mm, Rpm = 1000 rpm, Fe
= 400 mm/min.

and Vu Ngoc Pi
- The difference between the values of the surface roughness calculated by the proposed
model and measured from the experimental result is appropriately 6.6 % of the range. This proves
that the model can be used to determine the surface roughness of the ground parts.
ACKNOWLEDGEMENTS
The work described in this paper was supported by Thai Nguyen University of Technology for a
scientific project.
REFERENCES
[1] Jack Palmer, Hassan Ghadbeigi, Donka Novovic, David Curtis 2018, An experimental study
of the effects of dressing parameters on the topography of grinding wheels during roller
dressing, Journal of Manufacturing Processes, Volume 31, January 2018, Pages 348-355.
[2] Doriana M. D’Addona, Davide Matarazzo, Roberto Teti, Paulo R. de Aguiar, Eduardo C.
Bianchi, Arcangelo Fornaro 2017 Prediction of dressing in grinding operation via neural
networks, Procedia CIRP, Volume 62, 2017, Pages 305-310.
[3] P. Puerto, R. Fernández, J. Madariaga, J. Arana, I. Gallego 2013, Evolution of surface
roughness in grinding and its relationship with the dressing parameters and the radial wear,
Procedia Engineering 63 ( 2013 ) 174 – 182.
[4] J. Pfaff, M.Warhanek, S. Huber, T. Komischke, F. Hänni, K.Wegener 2016, Laser Touch
Dressing Of Electroplated CBN Grinding Tool, Procedia CIRP, Volume 46, 2016, Pages 272-
275.
[5] B.Linke 2008, dressing process model for vitrified bonded grinding wheels, CIRP Annals,
Volume 57, Issue 1, 2008, Pages 345-348.
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Conditioning and monitoring of grinding wheels, CIRP Annals, Volume 60, Issue 2, 2011,
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[7] Jack Palmer, Hassan Ghadbeigi, Donka Novovic, David Curtis 2017, An experimental study
of the effects of dressing parameters on the topography of grinding wheels during roller
dressing, Journal of Manufacturing Processes, Volume 31, January 2018, Pages 348-355.
[8] J.M. Derkx, A.M. Hoogstrate, J.J. Saurwalt, B. Karpuschewski 2008, Form crush dressing of
diamond grinding wheels, CIRP Annals, Volume 57, Issue 1, 2008, Pages 349-352.
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Size Distribution Based on Statistical and Experimental Investigations, Procedia CIRP,
Volume 26, 2015, Pages 718-723.
[10] Vuka Karadžića 2016, Use of Orthogonal Arrays and Design of Experiments via Taguchi
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MATHEMATICS (AMATH '13), Budapest, Hungary, December 10-12, 2013.
[11] Hong-Seok Byun, Seok-Hee Lee 2017, Design of a piston forging process using a hybrid
Taguchi method and multiple criteria decision-making, Journal of Mechanical Science and
Technology, April 2017, Volume 31, Issue 4, pp 1869–1876.

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