OPTIMIZATION OF PROCESS PARAMETERS OF PLASTIC INJECTION MOLDING FOR POLYPROPYLENE TO ENHANCE PRODUCTIVITY AND REDUCE TIME FOR DEVELOPMENT

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),
ISSN 0976 – 6359(Online), Volume 5, Issue 5, May (2014), pp. 157-169 © IAEME
157
OPTIMIZATION OF PROCESS PARAMETERS OF PLASTIC INJECTION
MOLDING FOR POLYPROPYLENE TO ENHANCE PRODUCTIVITY AND
REDUCE TIME FOR DEVELOPMENT
Mr. A.B. HUMBE(1)
, Dr. M.S. KADAM(2)
(1)
Student, M.E. Manufacturing, Mechanical Engineering Department, Jawaharlal Nehru Engineering
College, Aurangabad, Maharashtra, India
(2)
Professor and Head, Mechanical Engineering Department, Jawaharlal Nehru Engineering College,
Aurangabad, Maharashtra, India
ABSTRACT
The injection molding process itself is a complex mix of time, temperature and pressure
variables with a multitude of manufacturing defects that can occur without the right combination of
processing parameters and design components. In this analysis input processing parameters are melt
temperature (MT), Injection pressure(IP), holding pressure(HP) and cooling time(Cool Time) and
responses considered for investigation of plastic injection molding process are cycle time and tensile
strength. The material used for experimentation is polypropylene. The cycle time and tensile strength
are obtained through series of experiments according to Taguchi’s L-9 Orthogonal Array to develop
the equation. Experimental results are analyzed through Response Surface Method and the method is
adopted to analyze the effect of each parameter on the cycle time and tensile strength to achieve
minimum cycle time and maximum tensile strength. For optimization of input plastic injection
molding processing parameters with responses RSM’s D-Optimal method.
Keywords: Plastic Injection Molding, DOE, RSM’s D-Optimal Method.
1. INTRODUCTION
Increasing the productivity and the quality of the plastic injected parts are the main
challenges of plastic based industries, so there has been increased interest in the monitoring all
aspects of the plastic injection molding parameters. Most production engineers have been using trial-
and-error method to determine initial settings for a number of parameters, including melt
temperature, injection pressure, injection velocity, injection time, packing pressure, packing time,
cooling temperature, and cooling time which depend on the engineers’ experience and intuition to
INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING
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ISSN 0976 – 6340 (Print)
ISSN 0976 – 6359 (Online)
Volume 5, Issue 5, May (2014), pp. 157-169
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158
determine initial process parameter settings. However, the trial-and-error process is costly and time
consuming. Chang et al [1] studied the relationship between input process parameters for injection
molded Dog-bone bar and outputs as weld line width and tensile impact using Taguchi method. He
considered 7 input parameters such as melt and mold temperatures, injection and hold pressures,
cooling and holding times, and back pressure and found that the melt and mold temperature,
injection pressure, and holding time are the most effective, while hold pressure, holding time and
back pressure are least important parameter. Loera et al [2] introduced a concept for deliberately
varying the wall thicknesses of an injection molded part within recommended dimensional tolerance
to reduce part warpage using Taguchi method. From the results, it is seen that varying wall
thicknesses exhibited better warpage characteristics compared to the constant wall thicknesses.
Instead of PIM, rotational molding (RM) is one of the most important polymer processing techniques
for producing hollow plastic part. However, part warpage caused by inappropriate mould design and
processing conditions is the problem that confounds the overall success of this technique. Yanwei
Huyong [3] applied Taguchi method to systematically investigate the effects of processing
parameters on the shrinkage behavior (along and across the flow directions) of three plastic
materials. The results from the research shown that the mould and melt temperature, along with
holding pressure and holding time, are the most significant factors to the shrinkage behavior of the
three materials, although their impact is different for each material. Shuaib et al [4] studied the
factors that contribute to warpage for a thin shallow injection-molded part. The process is performed
by experimental method by Taguchi and ANOVA techniques are employed. The factors that been
taking into considerations includes the mold temperature, melt temperature, filling time, packing
pressure and packing time. The result shows that by S/N response and percentage contribution in
ANOVA, packing time has been identified to be the most significant factors on affecting the warpage
on thin shallow part. Longzhi et al [5] inestigated to avoid the surface sink marks on the automobile
dashboard decorative covers, the combined effects of multi-molding process parameters are analyzed
by the combination of orthogonal experiments and Mold flow simulation tests. By this method, it can
gain the experiment data which can reflect the overall situation using fewer number of simulation
test. Furthermore, the effects degree of different molding process parameters for surface sink marks
are investigated, and the reasonable gate location and optimized parameter combination is obtained.
It can solve the unreasonable appearance of process parameter settings. The mold design can fasten
the mold developing schedule, thus shorten the cycle of product development, and improve the
quality of products and the competitive ability of enterprise. Wen-ChinChen et al [6] in this research
Taguchi method, back-propagation neural networks (BPNN), and Genetic algorithms (GA) are
applied to the problem of process parameter settings for Multiple-Input Single-Output (MISO)
Plastic injection molding. Taguchi method is adopted to arrange the number of experimental runs.
Injection time, velocity pressure switch position, packing pressure, and injection velocity are
engaged as process control parameters, and product weight as the target quality. Then, BPNN and
GA are applied for searching the final optimal parameter settings.
2. EXPERIMENTAL WORK
For plastic injection molding thermoplastics are used. The material used for this
experimentation is polypropylene and L&T Demage plastic injection molding machine 40T to 250T
capacity (shot capacity-50gm to 1000gm) is used while conducting experiments. We have selected 6
different parts which are of polypropylene material and divided them into two categories (large and
small) and three parts in each. The cycle time and tensile strength are obtained through series of
experiment according to Taguchi’s L-9 Orthogonal Array. The process parameters used and their
levels are presented in table 1 and table 2.

159
Table 1: Factors and operating levels for large category components
SR.
NO.
PART NAME
/ FACTORS
MT
(°c)
IP(MPa) HP(MPa) COOL
TIME(Sec)
1 Part Size L=125mm*B=55mm*
H=15mm
Thickness MIN=1mm; MAX=5mm
222 81 55 20
225 84 52 22
228 85 56 23
2 Part Size L=100mm* B=50mm*
H=10mm
217 71 50 19
219 76 48 21
221 79 53 22
H=10mm
Thickness MIN=0.5mm;MAX=2mm
209 68 40 17
212 71 38 18
215 74 43 19
Table 2: Factors and operating levels for small category components
SR.
NO.
PART NAME
/ FACTORS
MT
(°c)
IP(MPa) HP(MPa) COOL
TIME(sec)
H=15mm
202 61 36 14
205 65 32 16
208 67 38 17
H=20mm
198 55 27 15
200 58 26 14
202 62 31 16
H=15mm
192 40 24 12
196 45 26 14
199 48 30 16
Result through experimental work is recorded and experimental data obtained for cycle time
and tensile strength is analyzed.
3 DATA ANALYSIS
3.1 Mathematical equations for size L=125mm*B=55mm* H=15mm
-The regression equation is for cycle time
Cycle Time = - 121 + 0.611 MT + 0.038 IP+ 0.011 HP+ 0.857 Cool Time
S = 0.614239 R-Sq = 95.28% R-Sq(adj) = 90.57%
The parameter R2
describes the amount of variation observed in cycle time is explained by
the input factors. R2
is obtained for above equation is 95.28%, indicate that the model is able to
predict the response with high accuracy. Adjusted R2
is a modified R2
that has been adjusted for the
number of terms in the model. If unnecessary terms are included in the model, R2
can be artificially
high, but adjusted R2
(90.57 % obtained for above equation) may get smaller. The standard deviation
of errors in the modeling, S=0.614239 Comparing the p-value to a commonly used α-level = 0.05, it

160
is found that if the p-value is less than or equal to α, it can be concluded that the effect is significant.
This clearly indicates that the MT has greatest influence on cycle time, followed by cool time, HP
and IP. The p-values for MT, IP, HP and Cool time are 0.002, 0.765, 1.000 and 0.006 respectively. It
can be seen that the most influencing parameters to cycle time for polypropylene material is melt
temperature and cooling time followed by injection pressure and holding pressure. The same
conclusion is carried out for following equations
-The regression equation for tensile strength
Tensile Strength = - 137 + 0.611 MT (°c) + 0.247 IP(MPa) + 0.0244 HP(MPa) + 0.133 Cool Time(sec)
S = 0.424088 R-Sq = 96.84% R-Sq(adj) = 93.67%
Graph 1: Residual Plots for cycle time of size L=125mm*B=55mm* H=15mm
Graph 2: Residual Plots for tensile strength of size L=125mm*B=55mm* H=15mm
3.2 Mathematical Equations for Size L=100mm* B=50mm* H=10mm
-The regression equation for cycle time
Cycle Time = - 244 + 1.00 MT + 0.007 IP + 0.439 HP+ 1.88 Cool Time
S = 1.41798 R-Sq = 90.95% R-Sq(adj) = 81.90%
1.00.50.0-0.5-1.0
99
90
50
10
1
Residual
Percent
42.040.539.037.536.0
0.5
0.0
-0.5
-1.0
Fitted Value
Residual
0.500.250.00-0.25-0.50-0.75
3
2
1
0
Residual
Frequency
987654321
0.5
0.0
-0.5
-1.0
Observation Order
Residual
Normal Probability Plot Versus Fits
Histogram Versus Order
Residual Plots for Cycle Time(Sec)
0.80.40.0-0.4-0.8
99
90
50
10
1
Residual
Percent
2726252423
0.50
0.25
0.00
-0.25
-0.50
Fitted Value
Residual
0.60.40.20.0-0.2-0.4
4
3
2
1
0
Residual
Frequency
987654321
0.50
0.25
0.00
-0.25
-0.50
Observation Order
Residual
Residual Plots for Tensile Strength(MPa)

161
Tensile Strength = - 189 + 0.917 MT + 0.133 IP + 0.0193 HP + 0.133 Cool Time
S = 0.380516 R-Sq = 97.45% R-Sq(adj) = 94.91%
Graph 3: Residual Plots for cycle time of size L=100mm* B=50mm* H=10mm
Graph 4: Residual Plots for tensile strength of size L=100mm* B=50mm* H=10mm
3.3 Mathematical Equations of Size L=90mm* B=40mm* H=10mm
Cycle Time = - 135 + 0.556 MT - 0.111 IP + 0.053 HP + 3.00 Cool Time
S = 0.800219 R-Sq = 96.54% R-Sq(adj) = 93.08%
Tensile Strength = - 122 + 0.611 MT + 0.183 IP + 0.0193 HP + 0.183 Cool Time
S = 0.369031 R-Sq = 97.60% R-Sq(adj) = 95.21%
210-1-2
99
90
50
10
1
Residual
Percent
40.037.535.032.530.0
1
0
-1
-2
Fitted Value
Residual1.51.00.50.0-0.5-1.0-1.5
3
2
1
0
Residual
Frequency
987654321
1
0
-1
-2
Observation Order
Residual
0.500.250.00-0.25-0.50
99
90
50
10
1
Residual
Percent
2726252423
0.4
0.2
0.0
-0.2
-0.4
Fitted Value
Residual
0.60.40.20.0-0.2-0.4
4
3
2
1
0
Residual
Frequency
987654321
0.4
0.2
0.0
-0.2
-0.4
Observation Order
Residual

162
Graph 5: Residual Plots for cycle time of Size L=90mm* B=40mm* H=10mm
Graph 6: Residual Plots for tensile strength of Size L=90mm* B=40mm* H=10mm
Cycle Time = - 5.52 + 0.0556 MT + 0.152 IP + 0.0609 HP+ 0.411 Cool Time
S = 0.262398 R-Sq = 92.25% R-Sq(adj) = 84.51%
-The regression equation is
Tensile Strength = - 94.3 + 0.533 MT+ 0.123 IP+ 0.0280 HP+ 0.011 Cool Time
S = 0.492526 R-Sq = 94.38% R-Sq(adj) = 88.76%
1.00.50.0-0.5-1.0
99
90
50
10
1
Residual
Percent
35.032.530.027.525.0
0.5
0.0
-0.5
-1.0
Fitted Value
Residual
0.60.40.20.0-0.2-0.4-0.6-0.8
4
3
2
1
0
Residual
Frequency
987654321
0.5
0.0
-0.5
-1.0
Observation Order
Residual
0.500.250.00-0.25-0.50
99
90
50
10
1
Residual
Percent
262422
0.6
0.4
0.2
0.0
-0.2
Fitted Value
Residual
0.40.20.0-0.2
3
2
1
0
Residual
Frequency
987654321
0.6
0.4
0.2
0.0
-0.2
Observation Order
Residual

163
Cycle Time = - 67.2 + 0.333 MT + 0.0979 IP - 0.145 HP+ 1.31 Cool Time
S = 0.432661 R-Sq = 95.19% R-Sq(adj) = 90.37%
Tensile Strength = - 93.7 + 0.567 MT + 0.0797 IP+ 0.0007 HP- 0.0499 Cool Time
S = 0.178819 R-Sq = 98.46% R-Sq(adj) = 96.93%
0.500.250.00-0.25-0.50
99
90
50
10
1
Residual
Percent
25.024.524.023.523.0
0.4
0.2
0.0
-0.2
Fitted Value
Residual
0.40.30.20.10.0-0.1-0.2
3
2
1
0
Residual
Frequency
987654321
0.4
0.2
0.0
-0.2
Observation Order
Residual
1.00.50.0-0.5-1.0
99
90
50
10
1
Residual
Percent
2625242322
0.5
0.0
-0.5
Fitted Value
Residual
0.750.500.250.00-0.25
4.8
3.6
2.4
1.2
0.0
Residual
Frequency
987654321
0.5
0.0
-0.5
Observation Order
Residual

164
Cycle Time = - 23.8 + 0.189 MT + 0.0010 IP - 0.107 HP+ 0.667 Cool Time
S = 0.102259 R-Sq = 99.70% R-Sq(adj) = 99.40%
Tensile Strength = - 15.0 + 0.185 MT + 0.0565 IP+ 0.0048 HP + 0.0011 Cool Time
S = 0.0882733 R-Sq = 98.91% R-Sq(adj) = 97.83%
0.80.40.0-0.4-0.8
99
90
50
10
1
Residual
Percent
2322212019
0.5
0.0
-0.5
Fitted Value
Residual
0.750.500.250.00-0.25
6.0
4.5
3.0
1.5
0.0
Residual
Frequency
987654321
0.5
0.0
-0.5
Observation Order
Residual
0.300.150.00-0.15-0.30
99
90
50
10
1
Residual
Percent
25242322
0.2
0.1
0.0
-0.1
-0.2
Fitted Value
Residual
0.20.10.0-0.1-0.2
4.8
3.6
2.4
1.2
0.0
Residual
Frequency
987654321
0.2
0.1
0.0
-0.1
-0.2
Observation Order
Residual

165
Graph 4.11: Residual Plots for cycle time of Size L=60mm* B=25mm* H=15mm
4. RESULT AND DISCUSSIONS
To optimize processing parameters with result in the plastic injection process of
polypropylene RSM’s D-Optimal method is used for optimum result. By graph no 13 following
observations are made.
Melt Temperature (MT):- By increasing melt temperature responses like cycle time and tensile
strength increases. Therefore optimal setting is in the middle of the range (222.1818) because goal is
to minimize cycle time and maximize tensile strength. Vertical faint line in the second column of
graph represents optimal setting of melt temperature.
Injection Pressure (IP):- Increasing IP also increases both the responses like cycle time and tensile
strength also, but effect on cycle time is minimum as compare to tensile strength. Therefore
composite desirability decreases by increasing pulse on time. Hence optimal setting is in the lower of
the range (81.9293) because goal is to maximize tensile strength and minimize cycle time. Vertical
faint line in the third column of graph represents optimal setting of IP.
Holding Pressure (HP):- Increasing holding pressure tensile strength and cycle time decreases, but
goal is to increase tensile strength and decrease cycle time .Therefore composite desirability
increases by increasing holding pressure. Hence optimal setting is in the higher of the range (56)
0.20.10.0-0.1-0.2
99
90
50
10
1
Residual
Percent
2221201918
0.10
0.05
0.00
-0.05
-0.10
Fitted Value
Residual
0.100.050.00-0.05-0.10-0.15
3
2
1
0
Residual
Frequency
987654321
0.10
0.05
0.00
-0.05
-0.10
Observation Order
Residual
Residual Plots for Cycle Time (sec)
0.10.0-0.1
99
90
50
10
1
Residual
Percent
24.524.023.523.0
0.10
0.05
0.00
-0.05
-0.10
Fitted Value
Residual
0.100.050.00-0.05-0.10
2.0
1.5
1.0
0.5
0.0
Residual
Frequency
987654321
0.10
0.05
0.00
-0.05
-0.10
Observation Order
Residual

166
because goal is to minimize cycle time and maximize tensile strength. Vertical faint line in the fourth
column of graph represents optimal setting of holding pressure.
Cooling Time (Cool Time):- Increasing cooling time cycle time and tensile strength increases, but
goal is to decreases cycle time. Therefore composite desirability decreases slightly by decreasing.
Hence optimal setting is in the lower of the range (20) because goal is to maximize tensile strength
and minimize cycle time. Vertical faint line in the fifth column of graph represents optimal setting of
cool time.
By the similar way observations are made for graph no. 14 to graph no.18 of different size.
Graph 13: Optimization Plot for cycle time and tensile strength of size L=125mm*B=55mm*
H=15mm
Graph 14: Optimization Plot for cycle time and tensile strength of size L=100mm* B=50mm*
H=10mm
Graph 15: Optimization Plot for cycle time and tensile strength Size of component L=90mm*
B=40mm* H=10mm
Cur
High
Low0.98923
D
Optimal
d = 0.97858
Minimum
Cycle Ti
y = 35.1285
d = 1.0000
Maximum
Tensile
y = 23.0043
0.98923
Desirability
Composite
20.0
23.0
52.0
56.0
81.0
85.0
222.0
228.0
IP(MPa) HP(MPa) Cool TimMT (°c)
[222.1818] [81.9293] [56.0] [20.0]
Cur
High
Low0.90220
D
Optimal
d = 0.81435
Minimum
Cycle Ti
y = 33.8565
d = 0.99953
Maximum
Tensile
y = 26.9982
0.90220
Desirability
Composite
19.0
22.0
48.0
53.0
71.0
79.0
217.0
221.0
[220.8788] [79.0] [48.0] [19.0]
Cur
High
Low0.90102
D
Optimal
d = 0.81387
Minimum
Cycle Ti
y = 28.6752
d = 0.99751
Maximum
Tensile
y = 26.7883
0.90102
Desirability
Composite
17.0
19.0
38.0
43.0
68.0
74.0
209.0
215.0
[214.6364] [74.0] [38.0] [17.0]

167
Graph 16: Optimization Plot for cycle time and tensile strength Size of component: L=70mm*
B=30mm* H=15mm
B=20mm* H=20mm
B=25mm* H=15mm
To overcome the problem of conflicting responses of single response optimization, multi
response optimization was carried out using desirability function in conjunction with response
surface methodology. Various multi-characteristic models have been developed. Goals and limits
were established for each response in order to accurately determine their impact on overall
desirability. A maximum or minimum level is provided for all response characteristics which are to
be optimized. The ranges and goals of input parameters viz. melt temperature, injection pressure,
holding pressure and cooling time and the response characteristics cycle time and tensile strength are
given.
The goal of optimization is to find a good set of conditions that will meet all the goals. It is
not necessary that the desirability value is 1.0 as the value is completely dependent on how closely
Cur
High
Low0.93538
D
Optimal
d = 0.99814
Minimum
Cycle Ti
y = 23.0037
d = 0.87657
Maximum
Tensile
y = 25.2309
0.93538
Desirability
Composite
14.0
17.0
32.0
38.0
61.0
67.0
202.0
208.0
[208.0] [61.0606] [32.0] [14.0]
Cur
High
Low0.86638
D
Optimal
d = 0.78870
Minimum
Cycle Ti
y = 19.8452
d = 0.95172
Maximum
Tensile
y = 24.8696
0.86638
Desirability
Composite
14.0
16.0
26.0
31.0
55.0
62.0
198.0
202.0
[202.0] [59.8081] [31.0] [14.0]
Cur
High
Low0.90227
D
Optimal
d = 0.84829
Minimum
Cycle Ti
y = 18.6068
d = 0.95967
Maximum
Tensile
y = 24.6274
0.90227
Desirability
Composite
12.0
16.0
24.0
30.0
40.0
48.0
192.0
199.0
[199.0] [48.0] [30.0] [12.0]

168
the lower and upper limits are set relative to the actual optimum. A set of 9 optimal solutions is
derived along with one global solution for the specified design space constraints.
5. CONCLUSION
In present work, experimental investigation has been reported on plastic injection molding
process of Polypropylene material part. Response surface methodology (RSM) has been utilized to
investigate the influence of four important parameters of PIM – melt temperature, injection pressure,
holding pressure and cooling time on two responses namely cycle time and tensile strength. Taguchi
design was employed to conduct the experiments and to develop a correlation between the PIM
parameters and each response. The analysis of experimental work is performed using MINITAB 16
statistical software. The important conclusions found that most significant factors for cycle time are
melt temperature and cooling time least significant factors are injection pressure and holding
pressure. For tensile strength most significant factors are melt temperature and injection pressure and
least significant factors are holding pressure and cooling time. The influence of all factors has been
identified and believed can be a key factor in helping mould designers in determining optimum
process conditions injection moulding parameters to enhance productivity and reduce time for new
product development.
Table 3: Optimal Setting with responses for large type component using RSM’s D-Optimal
Method
SR.
NO.
PART NAME/ OPTIMIZE SETING
PARAMETERS AND RESPONSE
MT
(°c)
IP
(MPa)
HP
(MPa)
COOL
TIME(Sec)
CYCLE
TIME(Sec)
TENSILE
STRENGTH
(MPa)
1 Part Size
L=125mm*B=55mm*H=15mm
222.1 81.9 56 20 35.1285 23.0043
2 Part Size
L=100mm* B=50mm* H=10mm
220.8 79 48 19 33.8565 26.9982
3 Part Size
L=90mm* B=40mm* H=10mm
Thickness MIN=0.5mm;MAX=2mm
214.6 74 38 17 28.6752 26.7883
Table 4: Optimal Setting with responses for small type component using RSM’s D-Optimal
Method
SR.
NO.
PART NAME/ OPTIMIZE
SETING PARAMETERS AND
RESPONSE
MT
(°c)
IP (MPa) HP
(MPa)
COOL
TIME(Sec)
CYCLE
TIME(Sec)
TENSILE
STRENGTH
(MPa)
4 Part Size
Thickness MIN=2mm;MAX=4mm
208 61.06 32 14 23.0037 25.2309
5 Part Size
202 59.808 31 14 19.8452 24.8696
6 Part Size
199 48 30 12 18.6068 24.6274

169
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OPTIMIZATION OF PROCESS PARAMETERS OF PLASTIC INJECTION MOLDING FOR POLYPROPYLENE TO ENHANCE PRODUCTIVITY AND REDUCE TIME FOR DEVELOPMENT