International Journal of Engineering & Technology IJET-IJENS Vol:10 No:06
125
Effect of Differences Core and Cavity
Temperature on Injection Molded Part and
Reducing the Warpage by Taguchi Method
Z. Shayfull*1, M.F. Ghazali1, M. Azaman1, S.M. Nasir1, N.A. Faris2
1
School of Manufacturing Engineering, Universiti Malaysia Perlis, Malaysia
2
Politeknik Sultan Abdul Halim Mu’adzam Shah (POLIMAS), Malaysia
shayfull@unimap.edu.my

Abstract— Warpage is a common issue related with injection
moulding process and frequently be the main target by mould
designers to eliminate. The existence of warpage is considered a
defect and shall be minimized. Therefore many researches and
publications were made on this topic, to study the behavior of
warpage occurred at moulded parts particularly on plastic parts.
In this study, two parts of plastic products was decided as a
model. One is a thin plate and another one is a thin shell.
Polycarbonate/Acrylonitrile Butadiene Styrene (PC/ABS)
thermoplastic is used as a plastic material. Taguchi Method is
applied to determine the optimum value of injection molding
parameters and Moldflow Plastic Insight software is used to
simulate the injection molding process. The temperature
differences on core and cavity plates are considered in simulation
and the experimental shows that the differences mould
temperature helps to minimize the warpage value. This finding is
definitely a good way to prevent stress on a critical point of
warped parts after assembly process.
Index Term— Injection moulding; Plastic Injection Mould;
Warpage; Taguchi Method; ANOVA
I.
INTRODUCTION
THE warpage The warpage issue is one of common effects on
moulded parts after taken out from an injection molding
process. It is important issue to predict the warpage issue
before manufacturing takes place. Many researches and
publications were made on this topic, both on theoretical
simulation and on experimental results to study the behavior of
warpage occurred at moulded parts particularly on plastic
parts. Jacques [1] underwent a simulation on the thermal
warpage resulted from unequal cooling on a plate of
amorphous polymer, and it is understood that the warpage
issue comes from the bending moment due to the asymmetrical
stress distribution over the thickness of plastic parts. The
thinnest spot on moulded parts is normally the most affected
area of warpage due to its relatively small second moment of
area in bending. Warpage is also studied by Matsuoka et al. [2]
using simulation and experimental studies. It can be predicted
from the temperature difference between the surfaces, the heat
distribution, shear stress, shrinkages and mechanical properties
caused by the orientation of materials. Huang and Tai [4]
examined the effects of warpage that is seen in thin shell parts
produced by injection molding using simulation software.
Taguchi method used to determine the optimum value of
injection parameters and this led to a finding that packing
pressure is the most significant factor that affects warpage and
gate locations as well as filling time have only small effects
over warpage. Taguchi method was also applied by Tang [3] in
designing a plastic injection mould to reduce warpage. The
thin plate mold was fabricated and at the same time gate
dimension and mould temperature factors were eliminated
while ANOVA was used to determine the significant factor
affected the warpage mainly. As a result, melting temperature
was found to be the most important factor that contributes to
the existence of warpage. Results acquired by Liao et al. [5]
also agrees that packing pressure is the most influential
parameter in injection moulding process. His study was done
purposely to determine the reactions of a thin walled part
according to shrinkage and warpage issues where mould
temperature, melt temperature, packing pressure and injection
speed were taken as the injection parameters [6]. From the
research, it is found that packing pressure is a big factor
contributes to the occurrence of warpage. Basic design
principles for assembly suggest that removing the fastener on
the product for to reduce costs. For that reason, a snap fit
concept is introduced to replace fasteners where it can reduce
the assembly time by eliminating the screwing process [7].
However, there is an issue identified on the snap fit concept
which is the gap around the product is uneven because of this
concept cannot close the assembled gap tightly as compared to
screwing method. The situation become worse when the snap
fit concept is to bind between warped parts. Although this
situation can be improved by adding more snap fits, but the
more snap fits in a design, the higher number of side pull and
lifter is needed and this therefore increases the cost of
fabricating mould. As far as this issue is concerned, the
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126
challenge for the manufacturing engineers nowadays is to
produce moulded parts at minimum warpage.
II.
FEED SYSTEM DESIGN
A mold designer must determine the type of mold, mold
dimension, materials for cavity insert, core insert and mold
base in designing a mould [8]. For a thin plate parts (120 x 50
x 1) mm, the type of gate to be used is an edge gate (two-plate
mold). Pin point gate (three-plate mold) is used for thin shell
part (120 x 50 x 8)mm with thickness 1mm. Fig. 1 and 2 shows
the feed system used for both of parts respectively.
Fig. 4. Pin point gate design for thin shell plate
Figs. 5 and 6 show the size in millimeters of the cavity and
core insert and cooling channel design. The size of cooling
channel is Ø6mm.
Fig. 1. Feed system design for thin plate (two-plate mold)
Fig. 5. Cooling channel design for thin plate
Fig. 2. Feed system design for thin shell plate (three-plate mold)
The detail dimensions in millimeters of feed system are
shown in Fig. 3 and 4.
Fig. 6. Cooling channel design for thin shell plate
III. EXPERIMENT
There are many factors affecting the injection molding
process, which may include types of plastic material used,
types of mold base material, types of cavity insert material,
types of machine, the shape of the product, the selection of
coolant runners as well as selection of the coolant liquid.
However, to make the experimentations and simulations
achievable according to the scope of research, only some
major factors are considered. Thus, the analysis is carried out
under the following assumptions:
i. Gate dimension factor is eliminated because the gating
systems design for every part is different.
ii. The temperature of the environment is assumed constant.
iii. The coolant is pure water.
iv. Only the effects of the filling, packing, and cooling
processes are discussed.
v. The layout of the cooling channels is assumed to maintain
a constant temperature everywhere in the mold. The
Fig. 3. Edge gate design for thin plate
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effects due to the shape and size of the mold and product
are neglected due to varieties of product’s shapes.
vi. The plastic material used in all of the simulations is the
amorphous thermoplastic PC/ABS blend, Cycoloy
C2950HF from GE. Its viscosity is between 102 and 104
poise where the shear rate is in the 102-103 s-1 range. The
range of melt temperature is between 220 and 400oC
approximately.
The basic physical and mechanical properties of PC/ABS are
shown in Table I.
TABLE I
THE PHYSICAL PROPERTIES OF PC/ABS
Taguchi method is used in the design of the experiments in
this research. There are six factors identified to control the
injection process; cavity temperature (A), core temperature
(B), melt temperature (C), filling time (D), packing pressure
(E), and packing time (F). Each factor consists of five levels
where an orthogonal array L25 56 is chosen and all parameters
have been identified. These three factor-level, orthogonal array
variance and parameters control factors are shown in Table II,
III and IV respectively.
TABLE II
THE FIVE LEVEL OF EFFECTIVE FACTOR FOR
EXPERIMENT VARIANCE
Cavity temperature, A (°C)
1
45
2
55
Level
3
65
75
5
85
74
Core temperature, B (°C)
45
55
65
75
85
2.63 x 103
Specific heat, Cp (J/kgoC)
1871
Glass transition temperature, Tg (oC)
112
Thermal expansion coefficient, α (mm/moC)
Elastic modulus, E (MPa)
127
Factor
4
Melt temperature, C (°C)
230
245
260
0.23
Filling time, D (s)
0.1
0.2
0.3
275
0.4
290
Poisson's ratio, υ
Thermal conductivity, K (w/moC)
0.27
Packing pressure, E (MPa)
50%
60%
70%
80%
90%
Packing time, F (s)
0.6
0.7
0.8
0.9
1.0
The parts to be simulated are inclusive of both cavities
divided into 12000 pieces of triangular elements for the thin
plate and 18192 pieces of triangular elements for thin the shell
plate. The meshes of the parts are shown Fig. 7 and 8.
Signal-to-noise (S/N) ratio is calculated according to Table
4. The deflection of the thin plates and thin shell plates
obtained from the experiment is used to calculate the signal-tonoise (S/N) ratio to acquire the best setting of parameters
arrangement. From this method, the percentage of contribution
has been calculated to determine which of the factor will affect
the warpage significantly.
Fig. 7. Cooling channel design for thin plate
0.5
TABLE III
L25 ORTHOGONAL ARRAY VARIANCE
Trial
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
A
1
1
1
1
1
2
2
2
2
2
3
3
3
3
3
4
4
4
4
4
5
5
5
5
5
Control Factor
B
C
D E
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
1
2
3
4
2
3
4
5
3
4
5
1
4
5
1
2
5
1
2
3
1
3
5
2
2
4
1
3
3
5
2
4
4
1
3
5
5
2
4
1
1
4
2
5
2
5
3
1
3
1
4
2
4
2
5
3
5
3
1
4
1
5
4
3
2
1
5
4
3
2
1
5
4
3
2
1
5
4
3
2
F
1
2
3
4
5
5
1
2
3
4
4
5
1
2
3
3
4
5
1
2
2
3
4
5
1
Fig. 8. Cooling channel design for thin shell plate
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TABLE IV
THE COMBINATION PARAMETERS FOR THE CONTROL
FACTORS
Control Factor
Trial
No.
A
B
C
D
E
F
45
45
230
0.1
50%
0.6
1
2
3
4
5
6
7
8
9
10
11
45
45
45
45
55
55
55
55
55
65
55
65
75
85
45
55
65
75
85
45
245
260
275
290
245
260
275
290
230
260
0.2
0.3
0.4
0.5
0.3
0.4
0.5
0.1
0.2
0.5
60%
70%
80%
90%
80%
90%
50%
60%
70%
60%
0.7
0.8
0.9
1.0
1.0
0.6
0.7
0.8
0.9
0.9
12
13
14
15
16
17
18
19
20
21
22
23
24
25
65
65
65
65
75
75
75
75
75
85
85
85
85
85
55
65
75
85
45
55
65
75
85
45
55
65
75
85
275
290
230
245
260
275
230
245
260
290
230
245
260
275
0.1
0.2
0.3
0.4
0.2
0.3
0.4
0.5
0.1
0.4
0.5
0.1
0.2
0.3
70%
80%
90%
50%
90%
50%
60%
70%
80%
70%
80%
90%
50%
60%
1.0
0.6
0.7
0.8
0.8
0.9
1.0
0.6
0.7
0.7
0.8
0.9
1.0
0.6
The warpage data obtained from the simulation process are
also analyzed using Analysis of Variance (ANOVA) and the
level of confidence is set at 0.05. The results are used by
comparing it with the results obtained from the SN ratio
method. In addition, the interaction effect of factors is
identified and the contribution of each factor to the total effect
is to be calculated. From this method, the percentage of
contribution has been calculated to determine which of the
factor will affect the warpage most significantly.
IV. RESULT AND DISCUSSION
In determining the S/N ratio, the smaller the better quality
characteristic has been targeted.
MSD is the mean square deviation,
represents the
observation and is the number of tests in one trial. Table 5
and 6 show the S/N for the thin plate and thin shell plate
obtained from the experiment.
128
TABLE V
SUMMARY OF THE RESULTS FOR
THIN PLATE
Control Factor
Trial
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
A
45
45
45
45
45
55
55
55
55
55
65
65
65
65
65
75
75
75
75
75
85
85
85
85
85
B
45
55
65
75
85
45
55
65
75
85
45
55
65
75
85
45
55
65
75
85
45
55
65
75
85
C
230
245
260
275
290
245
260
275
290
230
260
275
290
230
245
275
290
230
245
260
290
230
245
260
275
D
0.1
0.2
0.3
0.4
0.5
0.3
0.4
0.5
0.1
0.2
0.5
0.1
0.2
0.3
0.4
0.2
0.3
0.4
0.5
0.1
0.4
0.5
0.1
0.2
0.3
E
50%
60%
70%
80%
90%
80%
90%
50%
60%
70%
60%
70%
80%
90%
50%
90%
50%
60%
70%
80%
70%
80%
90%
50%
60%
F
0.6
0.7
0.8
0.9
1.0
1.0
0.6
0.7
0.8
0.9
0.9
1.0
0.6
0.7
0.8
0.8
0.9
1.0
0.6
0.7
0.7
0.8
0.9
1.0
0.6
Thin Plate
Max,
z
S/N
0.0073
42.7327
0.0076
42.3807
0.0066
43.6051
0.0060
44.4370
0.0057
44.8812
0.0053
45.5129
0.0061
44.2946
0.0081
41.8310
0.0065
43.7366
0.0059
44.5842
0.0070
43.0980
0.0059
44.5842
0.0066
43.6051
0.0061
44.2946
0.0078
42.1610
0.0089
41.0122
0.0073
42.7335
0.0075
42.4949
0.0079
42.0482
0.0069
43.2239
0.0064
43.8722
0.0068
43.3536
0.0091
40.8197
0.0067
43.4775
0.0067
43.4775
TABLE VI
SUMMARY OF THE RESULTS FOR THIN SHELL PLATE
Control Factor
Trial
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
A
45
45
45
45
45
55
55
55
55
55
65
65
65
65
65
75
75
75
75
75
85
85
85
85
85
B
45
55
65
75
85
45
55
65
75
85
45
55
65
75
85
45
55
65
75
85
45
55
65
75
85
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C
230
245
260
275
290
245
260
275
290
230
260
275
290
230
245
275
290
230
245
260
290
230
245
260
275
D
0.1
0.2
0.3
0.4
0.5
0.3
0.4
0.5
0.1
0.2
0.5
0.1
0.2
0.3
0.4
0.2
0.3
0.4
0.5
0.1
0.4
0.5
0.1
0.2
0.3
E
50%
60%
70%
80%
90%
80%
90%
50%
60%
70%
60%
70%
80%
90%
50%
90%
50%
60%
70%
80%
70%
80%
90%
50%
60%
F
0.6
0.7
0.8
0.9
1.0
1.0
0.6
0.7
0.8
0.9
0.9
1.0
0.6
0.7
0.8
0.8
0.9
1.0
0.6
0.7
0.7
0.8
0.9
1.0
0.6
Thin Shell Plate
Max,
z
S/N
0.0073
42.7327
0.0076
42.3807
0.0066
43.6051
0.0060
44.4370
0.0057
44.8812
0.0053
45.5129
0.0061
44.2946
0.0081
41.8310
0.0065
43.7366
0.0059
44.5842
0.0070
43.0980
0.0059
44.5842
0.0066
43.6051
0.0061
44.2946
0.0078
42.1610
0.0089
41.0122
0.0073
42.7335
0.0075
42.4949
0.0079
42.0482
0.0069
43.2239
0.0064
43.8722
0.0068
43.3536
0.0091
40.8197
0.0067
43.4775
0.0067
43.4775
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The data in Table V and VI are also analyzed using
Analysis of Variance (ANOVA) where the relative percentage
contribution of all factors is determined by comparing the
relative variance. The ANOVA will then compute the degrees
of freedom, variance, F-ratio, sums of squares, pure sum of
square and percentage contribution. The examples of
calculations are shown below and the results of S/N ratio for
both Thin Plate and Thin Shell Plate are listed in Table 7 and
8.
129
Fig. 9. S/N response for cavity temperature
Fig. 10. S/N response for core temperature
TABLE VII
THE RESPONSE TABLE OF S/N RATIO FOR THIN PLATE
Level
A
B
C
D
E
F
1
43.607
43.246
43.492
43.019
42.587
43.232
2
43.992
43.469
42.585
43.012
43.038
43.120
3
43.549
42.471
43.540
43.925
43.739
42.774
4
42.303
43.599
43.068
43.452
44.027
43.134
5
43.000
43.666
43.766
43.042
43.060
44.190
1.689
1.195
1.181
0.913
1.440
1.416
Fig. 11. S/N response for melt temperature
Fig. 12. S/N response for filling time
TABLE VIII
THE RESPONSE TABLE OF S/N RATIO FOR THIN SHELL PLATE
Level
A
B
C
D
E
F
1
12.954
14.953
15.760
13.334
14.737
9.669
2
13.748
13.951
16.481
12.994
13.169
10.408
3
14.953
12.090
12.262
14.195
11.692
14.197
4
10.782
11.589
10.485
11.348
15.603
15.289
5
13.352
13.206
10.801
13.919
10.588
16.225
4.171
3.364
5.996
2.847
5.015
6.556
Fig. 13. S/N response for packing pressure
Fig. 9 – 14 show S/N response diagram constructed for thin
plate based on data from Table 7.
Fig. 14. S/N response for packing time
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Fig. 15 – 20 show S/N response diagram constructed for thin
shell plate based on data from Table 8.
130
From the S/N ratio response in Table 7 and Table 8, it is
identified that the best combination of parameters can be
identified by selecting the highest value of each factor. Table 9
shows the summary of best parameters setting for thin plate
and thin shell plate. The result can also be observed from S/N
response diagram in Figs. 9–14 for thin plate and Figs. 15-20
for thin shell plate.
TABLE IX
BEST SETTING OF COMBINATION PARAMETERS
Fig. 15. S/N response for cavity temperature
Factor
Cavity temperature, (°C)
Core temperature, (°C)
Melt temperature, (°C)
Filling time, (s)
Packing pressure, (MPa)
Packing time, (s)
Fig. 16. S/N response for core temperature
Fig. 17. S/N response for melt temperature
Thin
plate
55
85
290
0.3
80%
1.0
Thin shell
plate
65
45
245
0.3
80%
1.0
Besides, the difference between levels in Table 8 and 9 also
shows which factor is more significant that give effects on
warpage for thin plate as well as thin shell plate. The most
significant factor that has an effect on warpage for thin plate
are cavity temperature (A) followed by packing pressure (E),
packing time (F), core temperature (B), melting temperature
(C) and filling time (D). On the other hand, for thin shell plate,
the most significant factors are packing time (F) followed by
melt temperature (C), packing pressure (E), cavity temperature
(A), core temperature (B) and filling time (D).
The data in Table 5 and 6 is also analyzed using Analysis of
Variance (ANOVA). The ANOVA computes the quantities
such as sums of squares, degrees of freedom, variance and
percentage contribution. The examples of calculations for
these quantities are shown below and the results for thin plate
and thin shell plate are summarized in Table 10 and Table 11
respectively.
Fig. 18. S/N response for filling time
Total sum of squares,
Fig. 19. S/N response for packing pressure
Fig. 20. S/N response for packing time
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131
TABLE XI
ANOVA TABLE FOR THIN SHELL PLATE
Source
Cavity
temperature, (°C)
Core
temperature, (°C)
Melt
temperature, (°C)
For error,
S
V
F
P(%)
4
0.03177
0.0079425
-
9.63
4
0.00468
0.00117
-
1.42
4
0.08632
0.02158
-
26.16
Filling time, (s)
Packing
pressure, (MPa)
4
0.02566
0.006415
-
7.78
4
0.05375
0.0134375
-
16.29
Packing time, (s)
4
0.12780
0.03195
-
38.73
Pooled error
0
0
Total
24
0.32998
100
The last column in Table X and XI show the percentage of
contribution for each factor. For thin plate, cavity temperature
contributes the most which is 24.59% followed by packing
pressure 19.54%, melt temperature 16.06%, packing time
15.4%, core temperature 14.78% and filling time 9.63%. It
proves that packing pressure, melt temperature, cavity
temperature, packing time and core temperature are the
significant factor where filling time does not have much effect
on warpage issue. For thin shell plate, packing time contributes
the most which is 38.73% followed by melt temperature
26.16%, packing pressure 16.29%, cavity temperature 9.63%,
filling time 7.78% and core temperature 1.42%. This explains
that all factors except filling time and core temperature give
significant effects on warpage defects.
Variance for factor A,
Variance for error,
F-ratio for factor A,
Percentage contribution (P) for factor A,
TABLE X
ANOVA TABLE FOR THIN PLATE
Source
Cavity
temperature,(°C)
Core
temperature, (°C)
Melt
temperature, (°C)
f
f
S
V
F
P(%)
4
5.418 x 10-6
1.354 x 10-6
-
24.59
4
3.258 x 10-6
8.145 x 10-6
-
14.78
4
3.538 x 10-6
0.8845 x 10-6
-
16.06
Filling time, (s)
Packing
pressure, (MPa)
4
2.122 x 10-6
0.5305 x 10-6
-
9.63
4
4.306 x 10-6
1.077 x 10-6
-
19.54
Packing time, (s)
4
3.394 x 10-6
0.8485 x 10-6
-
15.4
Pooled error
0
0
Total
24
22.038x10-6
V.
CONCLUSION
There are many plastic products produced by injection
molding and some factors must be determined in order to
design a mould such as feed system, cooling channel position,
gate size. These factors have effects on the quality of product
produced and by the help of simulation technology, it reduces
time taken to test moulds by simulating it in software as
compared to traditional trial and error concept which also
requires higher costs. In addition, Taguchi method helps to
simplify the experiment in identifying the best setting
parameters to produce parts with minimum defects.
Previous studies used fixed temperature value for mold
temperature (cavity temperature and core temperature). For
instance, Tang [3] and Huang and Tai [4] maintained same
temperature for cavity and core temperature in simulation and
experimental of warpage on thin plate and thin shell plate. In
contrast, this research focuses the effect of difference value of
temperature on core and cavity on thin plate and thin shell
plate.
The conclusions of the research are as follows.
100
109006-8686 IJET-IJENS © December 2010 IJENS
IJENS
International Journal of Engineering & Technology IJET-IJENS Vol:10 No:06
132
1. Cavity temperature is the most significant which is 24.59%
followed respectively by packing pressure 19.54%, melt
temperature 16.06%, packing time 15.4%, core temperature
14.78% and filling time 9.63%. The mold temperature
(cavity and core temperature) has shown a large contribution
rate of 39.37%, which must not be neglected.
2. For thin shell part, the most effective factor contributes to
warpage is packing time 38.73% followed by melt
temperature 26.16%, packing pressure 16.29%, cavity
temperature 9.63%, filling time 7.78% and core temperature
1.42%. The mold temperature (cavity and core temperature)
has also shown a contribution rate of 11.05%, which is
significant.
3. Taguchi orthogonal array can effectively reduce the number
of trials in mold testing. The effective factors can be
determined using ANOVA.
i. For thin plate, results show that cavity temperature,
packing pressure, melt temperature, packing time and
core temperature are the significant factors while filling
time is insignificant.
ii. Result for thin shell plate shows that packing time, melt
temperature, packing pressure and cavity temperature
are the significant factors while filling time while core
temperature is trivial.
4. The influence of all factors that contributes to warpage has
been characterized believed to be helpful in determining
more precise process conditions in determining injection
molding parameters.
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[2]
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109006-8686 IJET-IJENS © December 2010 IJENS
IJENS