Optimization of Runner Balance for a Family Product of Injection Molding Process

Abstract

A family mold is used in the injection molding process to produce components of different sizes that would normally be assembled into a single product assembly. The uniform quality of each injection molded part is strongly influenced by the filling balance of the mold cavity. The filling balance can be controlled by optimizing the dimensions of runners that are directed towards each cavity. In this paper, an optimization method is proposed that aims at balancing the filling time that maximizes the quality of a family mold cavity. There are two steps proposed in this research, namely theoretical calculations and simulation of runner balanced design using the software. The results of this study can be used as a practical reference in designing a mold for family products to avoid design errors as early as possible. The result obtained from an industrial case study highlighted the effectiveness of the proposed approach in finding the optimal runner dimension by a restricted number of simulations.

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Optimization of Runner Balance for a Family
Product of Injection Molding Process
To cite this article: C Budiyantoro and H Sosiati 2024
J. Phys.: Conf. Ser.
2739 012034
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ISAIME-2022
Journal of Physics: Conference Series 2739 (2024) 012034
IOP Publishing
doi:10.1088/1742-6596/2739/1/012034
1
Optimization of Runner Balance for a Family Product of
Injection Molding Process
C Budiyantoro* and H Sosiati
Department of Mechanical Engineering, Universitas Muhammadiyah Yogyakarta,
Lingkar Selatan St., Yogyakarta 55183, Indonesia
* cahyo_budi@umy.ac.id
Abstract. A family mold is used in the injection molding process to produce components of
different sizes that would normally be assembled into a single product assembly. The uniform
quality of each injection molded part is strongly influenced by the filling balance of the mold
cavity. The filling balance can be controlled by optimizing the dimensions of runners that are
directed to-wards each cavity. In this paper, an optimization method is proposed that aims at
balancing the filling time that maximizes the quality of a family mold cavity. There are two steps
proposed in this research, namely theoretical calculations and simulation of runner balanced
design using the software. The results of this study can be used as a practical reference in
designing a mold for family products to avoid design errors as early as possible. The result
obtained from an industrial case study highlighted the effectiveness of the proposed approach in
finding the optimal runner dimension by a restrict-ed number of simulations.
Keywords: Family Mold, Runner Balance, Injection Molding, Flow Simulation
1. Introduction
The family mold structure is one type of multi-cavity system in the injection molding process to make
a series of assembled components that can increase efficiency and reduce costs. Family mold involves
making products of different sizes in one mold [1]. Some of the family mold products include
automotive components, smartphone lenses, and children's toys [2]. Along with its development, family
mold products are inseparable from weaknesses because there are complex component features, and the
assembly process is quite sensitive to design and processing during injection molding [2]. One of the
problems in the family mold is that the plastic melt filling time is not uniform for the formation of its
products and there is residual stress in certain areas [2]. Filling imbalance can lead to a pressure spike
at the end of fill that can lead to non-uniform shrinkage of components that can cause alignment issues,
flashing or sink mark development and a narrower process window. All these issues can increase the
cost of the component by increasing the scrap rate or requiring the use of an operator to remove the flash
or visually inspect the parts. This problem can be solved by several approaches that have been used by
previous researchers, including Leo and Cuvelliez [3] modified the dimensions of the gate section and
process parameters, the results showed predictions of overpacking and permanent deformation effects
according to semi-quantitative measurements of mold deformation with simple analytical estimates.
Then, Yen et al. [4] changed the diameter and length of the runner to minimize warpage in the injected
part and optimize the runner system parameters, the research results obtained an abductive network that
can predict the maximum warp according to different control parameters and correlates warp
performance with runner system parameters.

ISAIME-2022
Journal of Physics: Conference Series 2739 (2024) 012034
IOP Publishing
doi:10.1088/1742-6596/2739/1/012034
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M Zhai et al., [5] analyzed a balanced flow through modification of size, runner volume, and part
quality in terms of warpage, the results show runner volume and injection pressure can reduce
production costs of plastic products. Wang and Sun [6] analyzed the runner imbalance in the top cover
cavity and brought the product box with the Moldflow Insight 5.0 analysis, the results showed that
through optimization, the imbalance of plastic fluid flow in the cavity decreased from 28.6% to 0.7%,
the pressure imbalance rate injection can be decreased from 42.0% to 4.2%, and the pressure distribution
in the cavity is more uniform throughout the injection- process. Raz et al., [7] modified the runner angle
and balanced the injection of plastic material in the three products, the results showed that the 90º angle
configuration for the runner was proven to be able to uniform the filling time of the plastic material to
the three products. Azaman et al., [8] examined the process of filling plastic material in the cavity for
analysis of residual stress and warpage in both types of thin parts of the cavity with Moldflow software.
The results of the research showed that the shallow part of the cavity, having a stiffer structure, had
lower residual stresses (surface center 15–23 MPa) and warpage (0.02–0.42 mm). Azaman et al., [9]
performed a residual stress analysis using numerical methods for thin-walled parts produced after the
filling stage using Moldflow software. The results showed that the residual stress in the entire thickness
experienced high tensile stress on the surface, then changed to a peak value of low tensile stress in the
surface area and experienced a parabolic tensile stress peak. The optimum parameter ranges are mold
temperature of 40–45 C, cooling time of 20-30 seconds, packing pressure of 0.85 of injection pressure,
and packing time of 15-20 seconds. Research by Tsai et al [10] focuses on minimizing the imbalance of
plastic liquid filling into multi-cavity for PVC injection molding with Moldflow software and the
Taguchi method. The result is that the optimization of the injection rate at filling time has been verified
to reduce the imbalance and the fitting temperature in filling and packing is lower than the PVC
degradation temperature.
Another study that discusses warpage analysis with Moldflow software [11] designs and
manufactures transmission parts for micro-winged air vehicles (FW-MAV), which are made by injection
molding, and analyzes the warpage phenomenon with Moldflow, the results show that the cause of
warpage in the transmission factor of the mold temperature, injection pressure, packing time, and
injection temperature. According to Wilczyński et al. [12], several experiments showed that the injection
rate, molding, and melting temperature substantially affect the filling imbalance. Experimental and
theoretical studies were carried out to analyze the filling imbalance in a geometrically balanced injection
molding. The result proved that the charging imbalance problem is still not solved. There is no universal
solution that can be successfully applied to mold design and will be accepted by all engineers and
researchers. The proper design of the runner system is dependent on the material characteristics and
process parameters. If the design parameters do not match, then imbalance still occurs,
simulation/optimization is one way to deal with this phenomenon effectively. Wang et al., [13] analyzed
the filling imbalance on the top and bottom lids of a plastic soap box produced by injection molding at
one time, the filling imbalance appeared due to the different dimensions of the two products. The results
showed that the optimized runner cross section can reduce the filling balance ratio from 3.38% to 0.73%,
and the filling time can meet operational requirements.
From the literature above, it is known that to balance the filling time and minimize stress
concentration, several methods of modification approaches to runner, gate, gate angle, and process
parameters can be used. This study uses the theoretical calculation method and runner balance
simulation with Autodesk Moldflow Plastic Insight software which aims to balance the filling time and
maximize the quality of the family mold product.
2. Research Methodology
2.1. Material and Product Dimension
As mentioned in the introduction, there have been many approaches to balancing filling time in
plastic products. This study uses two problem-solving methods, namely theoretical calculations and

ISAIME-2022
Journal of Physics: Conference Series 2739 (2024) 012034
IOP Publishing
doi:10.1088/1742-6596/2739/1/012034
3
runner balance analysis with Autodesk Moldflow Plastic Insight. Both methods can be a practical
reference in designing family molds to avoid design errors.
The plastic products are shown in Figure 1, and the dimensions and weight of the products are in
Table 1. The material used for plastic products is Polypropylene (PP) under the name BP Amaco 1046
produced by the BP Chemicals industry. In the theoretical calculation method, the researcher uses
equation 1 which aims to get the pressure drop value. The pressure drop for each runner is obtained by
calculating the runner radius using equation 2. Microsoft Excel was used to obtain theoretical
calculations, which were then the results entered the Moldflow Simulation.
The case of the family mold product is shown in Figure 1, there are 3 products of different sizes and
weights and will be made in one mold. The three products are made of the same material and will be
assembled after molding. To achieve an equal service life of the three components, it is necessary to
achieve uniform quality by ensuring uniform filling times in the injection molding process. The
dimensions and weight of the three components are shown in Table 1.
Figure 1. Design product.
Table 1. Dimensions and weight of plastic products.
Dimension Part 1 Part 2 Part 3
Maximum length (mm) 240 40 128
Maximum width (mm) 150 40 28
Average thickness (mm) 2 2 1
Weight (gr) 122 6 6
Volume (cm
3
) 124.86 6.12 5.98
2.2. Variable factors of runner balancing
For molds that utilize a cold runner the control of the flow to the different cavities is often achieved by
either varying the size of the runner channel or by varying the flow length the polymer needs to take to
reach the cavity. Since injection molding is a pressure-driven process, it is possible either to reduce the
size of the runner or increase the length of the runner to the smaller cavities so the material will
preferentially fill the larger cavities first. Thus, equilibrium flow through the runner system can be
affected by runner layout, runner, and gate sizes. A simple approach based on the calculation of the
pressure drop can help to determine the proportional dimensions of the runner system so that the filling
time in the three cavities can be balanced. The pressure drop can be calculated using Eq. 1 [14]. As
Part 1
Part 2
Part 3

ISAIME-2022
Journal of Physics: Conference Series 2739 (2024) 012034
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doi:10.1088/1742-6596/2739/1/012034
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Equation 1 highlights, a change in runner radius (diameter), R, has a much more significant effect on
our flow resistance as compared to a change in the runner length, L. Because of this sensitivity, it is
preferred to balance the filling pattern of the runners rather than the gates. The use of the gate size alone
to address any fill imbalance will minimize the amount of time to influence the molten material and
increase the sensitivity to any dimensional inconsistencies in the gate. Therefore, it is more likely to
have a narrow process window and a less robust solution over time.
∆=
  






 



(1)
Where:
∆P = pressure drop (MPa)
R = channel radius (mm)
L = channel length (mm)
k = polymer melt viscosity (Pa s)
n = power law index at a melt temperature
 = volumetric flow rate (cc/s)
Then, the power law model can be used to calculate the radius of the runner:
=
  
∆










/
(2)
Figure 2. Design Layout.
Based on the mold design layout in Figure 2, the runner balance calculation is carried out with Eq.
(1) and Eq. (2). The calculation with equation (1) produces a pressure drop on the runner that flows the
plastic to part 1. Based on the equilibrium pressure drop for all parts, these results are used to calculate
the diameters of runner 2 and runner 3 by equation (2). The length and diameter of the runner were
applied in the Moldflow Plastic Insight simulation with Sequence Flow analysis. Nine variations of
runner length and diameter were simulated to get the most balanced value of filling time, minimum in-
cavity residual stress, and minimum volumetric shrinkage.
The first pressure drop calculation is carried out on the sprue as the entrance of the plastic melt
into the mold. The analysis assumes that the product made of Polypropylene (PP) is molded with a

ISAIME-2022
Journal of Physics: Conference Series 2739 (2024) 012034
IOP Publishing
doi:10.1088/1742-6596/2739/1/012034
5
volumetric flow rate at the inlet of 125 cm
3
/s. To avoid calculating the shear rate in each portion of the
runner, the power law model is used with k equal to 2006.4 Pa s and n equal to 0.35. The bore of the
sprue bushing is 90 mm in length and has an average radius of 3 mm, then the pressure drop through the
sprue is calculated using equation 3.
∆

=
× .× .
.



.
××

 ×.


.
=2.9  (3)
As can be seen in Figure 3, the initial simulation of part 1 resulted in a filling time of 2 seconds, this
value is used as the initial assumption of filling time.
Figure 3.
Filling time Part 1
For a filling time of two seconds, the flow length from the gate of Part 1 to the opposite side of the
cavity is approximately 190 mm. The average linear melt velocity will be 95 mm/s (equal to the flow
length divided by the filling time) corresponding to a volumetric flow rate of 62.4 cm
3
/s (equal to the
volume of the mold cavity divided by the filling time). The flow length from the gate of Part 2 to the
opposite side of the cavity is approximately 45 mm, the average linear melt velocity will be 22.5 mm/s
and the volumetric flow rate of 3 cm
3
/s. The defined volumetric values are used as the basis for
calculations with equations 1 and 2, while the variable factors are runner length and or runner diameter.
Table 2 is the process parameters for injection molding simulation, these parameters follow the
recommendations from Moldflow which generally refers to the type of plastic material being processed.
Table 2. Processing parameter.
Parameter Value
Melt temperature 230 C
Mold temperature 50 C
Cooling time 20 s
3. Result and Discussion
From Run 1 to Run 4, the runner diameter for Part 1 (D
1
) is varied to calculate D
2
and D
3
while the
length of all runners is constant. In Run 5 to Run 7 the runner length is extended by 15 mm. Moldflow
simulation can help to optimize the runner system automatically by using a Runner Balance Analysis.
This type of analysis allows the computer to resize the runners through an automatic algorithm that will
yield a more balanced filling pattern for the mold. From the Runner Balance Analysis, the software will
guide the designer to appropriate sizes to help optimize the filling balance between the cavities and
attempt to stay below a target pressure for the mold. From this analysis, the computer can automatically
update the runner sizes for each part and get an initial mold filling study performed so it can be confirmed

ISAIME-2022
Journal of Physics: Conference Series 2739 (2024) 012034
IOP Publishing
doi:10.1088/1742-6596/2739/1/012034
6
that all parts can be filled and packed uniformly. Therefore, in Run 8 and Run 9, all runner diameters
were generated automatically by setting the runner balance sequence in the Moldflow simulation. Table
3 shows the calculation and simulation results.
Automatic runner diameter balancing by Moldflow simulation in Run 8 and Run 9 can result in
uniform filling times for all three parts. The uniformity of filling time is proven to minimize the
difference in residual stress and shrinkage of the three parts to ensure balanced product quality
[4][6][15]. The simulation results of Run 8 are shown in Figure 4.
The simulation in Run 6 produces the largest gap for filling time, residual stress, and volumetric
shrinkage, as shown in Figure 5. The large volumetric shrinkage gap will increase the difficulty of
assembling parts from the mold family product, [16] while the large residual stress difference will
influence the difference in product resistance when loading so that the lifetime of each family mold
product will also be different [17][18]. The existence of in-cavity residual stresses will directly affect
the mechanical properties of the part, and in severe cases will cause warping and cracking. Therefore, it
is necessary to minimize residual stresses in the injection molding process for high-quality molding.
(a)
(b)
Max. Clamping tonnage: 267.4 tons
(c)
(d)
Figure 4. Simulation results of Run 8: (a) Filling time; (b) Residual stress; (c) Volumetric Shrinkage;
(d) Clamping tonnage

ISAIME-2022
Journal of Physics: Conference Series 2739 (2024) 012034
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doi:10.1088/1742-6596/2739/1/012034
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Table 3. Experimental design and results
Run
Defined Size (mm) Runner Balance
Calculation Max. Filling Time (s) Max. Residual Stress (MPa) Max. Volumetric Shrinkage
(%)
D1 L2 L3
Max
p
(Mpa)
D2
(mm)
D3
(mm)
Part
1
Part
2
Part
3 t Part
1
Part
2
Part
3  Part
1
Part
2
Part
3 s
1
5
125
87
4.1
3.14
2.63
2.27
1.95
2.20
0.17
39.97
30.26
37.43
5.04
6.77
4.78
6.27
1.04
2
6
125
87
3.7
3.29
2.76
2.29
1.90
2.11
0.19
39.89
30.10
35.36
4.90
6.78
4.52
5.74
1.13
3
7
125
87
3.5
3.4
2.84
2.28
1.82
2.09
0.23
39.95
28.49
34.33
5.73
6.70
4.40
5.79
1.16
4
4
125
87
4.8
2.91
2.44
2.12
2.10
2.40
0.17
39.74
33.51
38.84
3.37
6.84
5.64
6.60
0.63
5
5
140
102
4.1
3.32
2.63
2.10
2.05
2.95
0.51
39.91
31.17
37.03
4.45
6.63
4.66
6.69
1.16
6
7
140
102
3.5
3.58
2.84
2.15
1.79
2.74
0.48
39.84
28.55
35.24
5.68
6.72
4.18
6.18
1.34
7
4
140
102
4.8
3.08
2.44
2.08
2.09
3.31
0.71
39.87
32.29
38.27
4.00
6.49
4.75
6.64
1.05
8*
3.56
125
87
5.3
2.72
2.32
2.25
2.25
2.25
0.00
38.97
34.73
38.38
2.30
5.32
4.54
5.41
0.48
9*
3.59
140
102
5.3
3
2.67
2.24
2.24
2.24
0.00
39.65
32.76
37.13
3.49
5.60
4.10
5.25
0.78
*Auto balancing
(a)
(b)
Max. Clamping tonnage: 106.5 tons
(c)
(d)
Figure 5. Simulation results of Run 6: (a) Filling time; (b) Residual stress; (c) Volumetric Shrinkage;
(d) Clamping tonnage
Although Run 8 and Run 9 were able to produce a balanced product quality, the diameter of the runner
suggested by the Moldflow simulation was too small, resulting in a high-pressure drop (5.3 MPa) and
high clamping tonnage (267 tons). Clamping tonnage is the basis of the selection of injection machines,

ISAIME-2022
Journal of Physics: Conference Series 2739 (2024) 012034
IOP Publishing
doi:10.1088/1742-6596/2739/1/012034
8
the greater the clamping tonnage the greater the capacity of the machine energy required. In economic
considerations for mass production, this is not profitable [19]. As an alternative recommendation, Run
4 can provide a solution to this problem. Figure 6 shows the simulation results from Run 4. Gap filling
time, residual stress, and volumetric shrinkage are considerably low and more importantly, the low
clamping tonnage (110 tons) gives the possibility of selecting injection machines with lower capacities
and more economical. In real production cases, the clamping force always is an important consideration,
a smaller clamping force can increase the possibility of selecting the machine which may significantly
reduce the production cost [20].
(a)
(b)
Max. Clamping tonnage: 110 tons
(c)
(d)
Figure 6. Simulation results of Run 4: (a) Filling time; (b) Residual stress; (c) Volumetric Shrinkage;
(d) Clamping tonnage
4. Conclusions
Runner balance calculation and simulation is an effective method to ensure the quality balance of the
mold family product. The balance of product quality is influenced by several factors including residual

ISAIME-2022
Journal of Physics: Conference Series 2739 (2024) 012034
IOP Publishing
doi:10.1088/1742-6596/2739/1/012034
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stress and volumetric shrinkage. Both factors proved to be strongly influenced by the uniformity of
filling time. The results of automatic runner balancing cannot be used in real production conditions,
other considerations are needed, such as the clamping capacity of the injection machine.
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