Local-Temperature-and-Flatness-Error-in-the-Process-of-Injection-Moulding

Local Temperature and Flatness Error in
the Process of Injection Moulding
41738 Experimental Plastics Technology – June 2016
Authors:
Georgios Pitsilis, s152087
Ida Bertelsen, s114040
Supervisors:
Nikolaos Giannekas
Guido Tosello

Local Temperature and Flatness Error in the Process of Injection Moulding
41738 Experimental Plastics Technology June, 2016
Ida Bertelsen, s114040 Page 1
Contents
1 Introduction ....................................................................................................................................................... 2
1.1 Project tasks............................................................................................................................................... 2
1.2 Study case.................................................................................................................................................. 3
1.2.1 Time schedule and planned activities...................................................................................................... 4
1.2.2 Challenges during the project................................................................................................................. 4
2 Theory................................................................................................................................................................ 5
2.1 Shrinkage ................................................................................................................................................... 5
2.2 Warpage and flatness error ........................................................................................................................ 6
2.3 Design of experiment (DOE)........................................................................................................................ 7
3 Materials............................................................................................................................................................ 8
3.1 Cyclic Olefin Copolymer (COC) .................................................................................................................... 8
3.2 Acrylonitrile butadiene styrene (ABS).......................................................................................................... 8
3.3 Polypropylene (PP) ..................................................................................................................................... 9
3.4 PVT-curves and viscosity............................................................................................................................. 9
4 Processing parameters.......................................................................................................................................11
4.1 Fixed processing parameters .....................................................................................................................12
5 Influence of processing parameters according to literature ................................................................................13
6 Software............................................................................................................................................................14
6.1 Autodesk Moldflow Insight – Simulation setup...........................................................................................14
6.2 Metrological software, GOM Inspect .........................................................................................................15
6.3 Statistical analysis, Minitab........................................................................................................................15
7 Results...............................................................................................................................................................16
7.1 Results for Model 1 ...................................................................................................................................16
7.2 Model 2 – Simulation data.........................................................................................................................20
7.2.1 Minitab plots – ABS – Simulations..........................................................................................................20
7.2.2 Minitab plots – PP – Simulations............................................................................................................21
7.3 Model 2 - experimental data......................................................................................................................22
7.3.1 Minitab plots – ABS – Experimental data ...............................................................................................22
7.3.2 Minitab plots – PP – Experimental data..................................................................................................23
7.3.3 Discussion of Minitab results - Model 2..................................................................................................24
7.4 Comparison of simulation data and experimental data ..............................................................................25
8 Conclusion.........................................................................................................................................................26
9 References.........................................................................................................................................................27

1 Introduction
Injection moulding of thermoplastic products is a well-known and suitable processing technique for many applications
and products. The reason for this is its suitability for mass-production, high repeatability, and low costs production. In
this case, two thin-walled plate-shaped models with respectively nano- or micro structures on the surfaces were
investigated by simulating the injection moulding process. Both models are a so-called “lab on a chip” used for nano-
or micro fluidics and are shown in Figure 1.
For manufacturing of such fine structures, a high precision level and low tolerances in the injection moulding process
is required. Shrinkage and warpage are two common defects in the injection moulding process of thin-walled
structures. In order to find the optimal processing conditions of the two models, simulations of the injection moulding
process are conducted in Autodesk Moldflow Insight (AMI).
Process simulations of nano- or microscale injection moulding were carried out for the same reasons as in other areas
of engineering. The main advantages of using simulation tools before physical processing are (Marhöfer, Tosello,
Islam, & Hansen, 2015; Tosello, Costa, & Hansen, 2011):
 to optimize the processing conditions before the physical processing
 to avoid the risk of costly re-engineering
 to reduce the amount of physical proto-typing
The purpose of this report is to find the optimal processing settings for the two injection-moulded models shown in
Figure 1 through simulations and to compare the results with empirical datasets. The flatness-error of the plates was
evaluated as major quality criterion of the process simulations. For this project, one simulation program, one
geometrical and one statistical software were used in order to perform simulations of the shrinkage, polymeric filling,
packing- and cooling process and viscoelastic analysis of the polymer. Moreover, thermal stress distribution and
pressure–volume–temperature (PVT) connection were examined.
Figure 1 Model 1 (left) and Model 2 (right).
1.1 Project tasks
An outline of the project tasks is shown in the bullet points below:

 To run Autodesk Moldflow Insight simulations of the two models. The geometry and design of the gate,
runners and sprue as well as the meshing of both models are given.
 To investigate Model 1 with one material (COC), and Model 2 with two materials (ABS, PP).
 To investigate the influence of four different processing parameters for Model 1 (mould temperature, melt
temperature, injection speed and packing pressure), and two different processing parameters for Model 2
(packing pressure and packing time).
 To determine the temperature distribution, shrinkage, warpage and pressure results of the moulded plate
parts by simulations in AMI.
 To measure the flatness-error based on the simulated warpage of the moulded parts using metrological
software, GOM Inspect.
 To analyse the data and present comparisons based on process parameters and materials. The results are
presented by using the statistical software, Minitab.
 To compare the results for the flatness-error with empirical data of Model 2
1.2 Study case
The geometric design, design of fan gate, runners, sprue and cooling channels, polymeric materials and AutoCAD
models were given beforehand for the two models. These were ready to import to Autodesk Moldflow Insight (AMI).
The design parameters are shown in
Table 1. This study does not include the nano- or microstructure design on the plate surfaces, but only the plane
plates. The geometry is shown in Figure 2.
Figure 2 Geometry of Model 1 (left) and Model 2 (right). From warpage analysis in AMI
Table 1. Given properties/parameters for Model 1 and Model 2
Model 1 Model 2
“Lab on a chip” for Nano fluidic system Micro fluidic system
Materials COC ABS, PP
Colour Transparent Black
Plate thickness t = 1 mm t = 3 mm
Plate length L = 80 mm L = 80 mm
Plate width B = 30 mm B = 30 mm
Mesh type in Autodesk Moldflow Insight (AMI) 3D, tetrahedral elements 3D, tetrahedral elements
Total number of mesh elements in AMI 1.782.562 elements 1.140.376 elements

1.2.1 Time schedule and planned activities
The time schedule for the three weeks project is shown below.
Table 2 Rough time schedule for the 3 weeks
Week 1 Week 2 Week 3
Software tutorials X
Literature survey X X
Simulations in Autodesk Moldflow Insight X X (X)
Measurements of flatness-error in GOM X (X)
Result analysis in Minitab (X) X
Comparison of results with empirical data X
Comparison with similar studies X
Reporting, writing (X) X X
1.2.2 Challenges during the project
Before and during the project period, some challenges occurred, which caused some changes in the project. These are
described in the bullet points below.
 Initially, the purpose of the project included a simulation part as well as an experimental part. Due to
technical difficulties with the mould, the project scope was necessary to convert to purely simulating the
parts by computational software.
 One of the challenges in the project was related to the computers’ breakdown when simulating Model 1. The
reason was the extensive number of meshing elements that AMI had to simulate. In order to resolve this
issue, we had to diminish the total number of elements of Model 1, which were reduced from 3.300.000 to
1.700.000. Even after this change, the model was still running extremely slow, why the simulations were
running until Wednesday in the third week.
 Despite of the reduction of meshing elements, we did not complete all the simulations of Model 1, but only
16 out of 24. Due to these large gaps in the results, it was not possible to make a valid statistical analysis in
Minitab.
 The outputs of GOM consisted by the deviations of the flatness measurement on the plates. The problem
emanated from the small number of decimals that it was imported initially. Due to small precision (only two
decimals), almost the 60 % of the Minitab matrix was identical, so no analysis could be done. In order to solve
this, six decimals were chosen as a more proper approach.
 Minitab requires a free variable in order to perform statistical analysis, thus one of the existing process
combinations had to be deleted in order to find at least one degree of freedom for Model 2. (In order to find
the F value that shows if the factor is critical or not. F value calculates the mean square values of the variants
divided by the false mean square. If there is no free variant the false cannot be calculated (equal to zero) so it
blocks.)

2 Theory
In this section, the physical phenomena’s “shrinkage”, “warpage” and “flatness-error” are described. As already
mentioned, the flatness-error of the plates was evaluated as major quality criterion of the process simulations.
The principles behind design of experiment (DEO) are briefly gone through and it is explained how the theory can help
us to understand the influence of the different processing parameters, which were investigated in simulations of the
injection moulding.
2.1 Shrinkage
Moulding shrinkage is the phenomenon where the volume of a molten plastic item filled inside the cavity of a mould is
shrinking at the time it becomes solid due to cooling. The in-mould shrinkage is highly dependent on the type of
processing pressure and -temperature, and on the polymeric microstructure. The design and geometry of the
injection-moulded part, the gate, runners and machine nozzle can also be of importance for the shrinkage.
Both semi-crystalline and amorphous polymers have generally high thermal expansion because of the increased
molecular vibrations at higher temperatures are only weakly resisted by the Van der Waals forces between the
polymer chains (Mills, 2005). The process of heating the molecular mass of polymers increases the molecular motion,
which causes the polymer chains to occupy more local volume, is increasing the specific volume of the plastic (Fischer,
2013).
In order to understand the behaviour of thermal expansion of respectively amorphous and semi-crystalline polymers,
it is important to know how the two different molecular structures react when heated. Amorphous polymers do not
have a sharp melting point (Tm) as the semi-crystalline polymers do, but instead a glass transition point (Tg), which is
the temperature where the material goes from being in the “glassy” state to the softer, rubbery state. This change
results in a change in slope at of the thermal expansion rate, which increases with increasing temperatures.
For semi-crystalline materials, the main molecular transition occurs when the crystalline regions break down, which
happens at Tm (Fischer, 2013). When the temperature reaches Tm, the temperature of the polymer will be steady for a
while, even though the polymer is kept being heated. This phase change in molecular structure for the semi-crystalline
polymers is the reason for the abrupt increase in specific volume. Semi-crystalline polymers develops crystalline
structures within the amorphous matrix when the temperature goes below the melting point, which results in a more
compact and ordered structure with a lower volume than in the melted state (Fischer, 2013). See Figure 3.
Figure 3 PVT-curves for amorphous and semi-crystalline polymers. ("Santa Clara University, n.d.)

The direction of the thermal expansion is dependent on the orientation of the chains. Amorphous polymers have an
unstructured order of polymer chains, which causes an isotropic shrinkage, whereas semi-crystalline polymers have
regions with structured parallel oriented crystals, which causes an anisotropic shrinkage (Mills, 2005). For semi-
crystalline materials, the shrinkage is usually highest in the flow direction.
The shrinkage described as the decrease in specific volume, Δv, is shown in Figure 3. The decrease in specific volume is
the change between the specific volume under processing condition (processing (packing) pressure and processing
(melt) temperature) and the specific volume for the final, natural condition (atmospheric pressure and room
temperature). An increase in melt temperature and decrease in packing pressure would cause higher shrinkage
according to the PVT-curves.
The optimum conditions for minimising shrinkage of injection-moulded parts will occur when a maximum amount of
material is pressed into the mould cavity and the pressure is maintained until the plastic is sufficiently hardened and
ready to be extracted and to use a melt temperature, which is not too high for the specific polymer (Fischer, 2013).
According to (Fischer, 2013), the following processing settings and design specifications are commonly causing an
increase/decrease of the in-mould shrinkage.
Table 3 Increase in different parameters causing an increase or decrease of the in-mould shrinkage (Fischer, 2013)
An increase in: Effect on in-mould shrinkage:
Mould temperature Increases or decreases
Melt temperature May increase or decrease
Packing pressure Decreases
Packing time Decreases until gate freezes
Injection speed May increase or decrease (minor effect)
Melt flow rate Decreases
Filler content Decreases
Uniformity of wall thicknesses Decreases
Wall thickness of moulded item Increases
As seen from Table 3, the shrinkage can e.g. be reduced by decreasing the mould and melt temperature, or increasing
the packing pressure or packing time.
Furthermore, the design of gate, runners and sprue effects the shrinkage together with uncontrollable environmental
effects such as increase in humidity or room temperature may as well lead to an increasing shrinkage (Fischer, 2013).
Post-mould shrinkage is another aspect. After being ejected from the mould, the part still continues to shrink.
Commonly, the post-mould shrinkage is measured after at least 24 h after ejection. This effect is not considered in this
project.
2.2 Warpage and flatness error
Warpage of an injection-moulded item is highly related to the shrinkage - especially to the variations in shrinkage for
different parts of the model. In other words, it is caused by anisotropic shrinkage along the surface of the model,
which often occurs during the cooling phase, and it is defined as the distortion of the moulded parts, which do not
follow the original (or intended) shape (Mills, 2005).
Compared to the flatness-error, the warpage is the distortion of the entire model including gate, runners and sprue,
whereas the flatness-error is the deviation only of the model (the plate in this project). The two phenomena are often
related, but in some cases, the entire structure can be extremely warped, whereas the flatness of the model is small
(the model is in this case the plate). This is especially important to mention this difference for this specific project,
where we investigate the flatness-error (and not the warpage) of the two thin-walled plate-shaped models.

Warpage can often not be avoided, but it is possible to be limited through the packing conditions and of the polymer’s
geometry and type. Using simulation software, this effect can be predicted and prevented.
Possible causes of warpage are especially related the non-uniformities of the processing parameters or model
geometry, such as wall thicknesses, deviations in molecular orientation, non-uniform packing pressures in the mould
cavities causing different stress-levels, and non-uniform moulding temperatures or cooling rates (Hakimian & Sulong,
2012).
Fillers also significantly affect a material’s warpage and shrinkage behaviour. Hakimian & Sulong (2012) investigated
the warpage and found that the percentage of glass filler had the greatest influence on the warpage and shrinkage
behaviour (Hakimian & Sulong, 2012).
2.3 Design of experiment (DOE)
The basic principles of DOE were used to obtain knowledge about the influence on the output of the different
processing parameters used in the simulations of Model 1 and 2. In this case the output is the flatness-error of the
plates.
Briefly explained, DOE is an approach, where it is possible to vary different factors (parameters) at the same time and
still be able to obtain knowledge the influence of the single parameter and of the interactions between the
parameters (Vechio, 1997).

3 Materials
In this section, the material selection of the two different injection-moulded models is explained. The material used
for Model 1 is Cyclic Olefin Copolymer (COC), and the materials used for Model 2 are both Polypropylene (PP) and
Acrylonitrile Butadiene Styrene (ABS). All materials are thermoplastics. COC and ABS both have an amorphous
structure, and PP has a semi-crystalline one. No fillings were used in any of the materials.
The thermoplastic are polymers that soften when their temperature is increased and harden when cooled. Those
processes are revisable and repeatable. They are linear polymers and have relatively flexible chains. The most widely
used method for their production is by injection moulding by application of heat and high pressure (Callister &
Rethwisch, 2009).
Selected properties for the three different polymers were obtained from Autodesk Moldflow, CES EduPack 2015 or
from the manufacturer’s datasheets. The properties are shown in
Table 4 in order to get an overview of the differences between the three materials
.
Table 4 Properties of the three polymers: Novodur P2H-AT, SABIC PP579S and TOPAS® 5013L-10
Unit ABS PP COC
Manufacturer STYROLUTION, INEOS SABIC TOPAS
Adv. Polymers
Trade name Novodur P2H-AT PP 579S 5013L-10
Polymer structure - Amorphous Semi-crystalline Amorphous
Density kg/m
3
1050 905 1020
Melt density (from AMI) kg/m3
945 720 904
Moulding shrinkage, normal % 0.4-0.6 - 0.1-0.3
Viscosity (from AMI) Pa s VI(260)0104 VI(240)0039 VI(260)0064
Melt volume rate (MVR) cm
3
/ 10 min 37.0 - 48.0
Melt flow rate (MFR) g / 10 min - 47.0 44.0
Test condition - 220 ℃, 10kg 230 ℃, 2.16kg 260 ℃, 2.16kg
Tensile modulus (1 mm/min) MPa 2500 1750 3200
Tensile strength at yield (50mm/min) MPa 44 35 46
Tensile strain at yield (50mm/min) % >15 8-11 1.7
Processing conditions recommended by Autodesk Moldflow (temperature range in parenthesis):
Recommended mould temperature ℃ 80 (60-85) 45 (30-60) 102 (95-110)
Recommended melt temperature ℃ 260 (220-280) 240 (210-270) 270 (240-300)
3.1 Cyclic Olefin Copolymer (COC)
Cyclic Olefin Copolymer (COC) is the polymer used for Model 1. The manufacturer is TOPAS Advanced Polymers and
the tradename is 5013L-10. COC is an amorphous thermoplastic. The COC is a copolymer. The basic copolymer
structure is composed of two basic units and its characteristics depend on the polymerization process and the relative
fractions.
According to TOPAS Advanced Polymers (TOPAS Advanced Polymers, n.d.), it is used for injection moulding of high-
performance components for the optical, diagnostic, and microfluidic markets. The polymer has a high flow and is
characterized by the highest level of flowability among transparent resins.
3.2 Acrylonitrile butadiene styrene (ABS)
Acrylonitrile butadiene styrene (ABS) is one of the two polymers used for Model 2. The manufacturer is in this case
either INEOS or Styrolution and the tradename is Novodur P2H-AT. ABS is an amorphous thermoplastic, which is made
by co-polymerizing 3 monomers, the acrylonitrile, butadiene and styrene.

It has a high stiffest compared to the PP material and presents excellent resistant to many solvents . Another
interesting fact is that is easily moulded because of its high flowability which makes it an excellent choice for the
project (INEOS Sturolution, n.d.).
3.3 Polypropylene (PP)
Polypropylene (PP) is the other type of polymers used for Model 2. The manufacturer is SABIC Europe B.V. and the
trade name is SABIC PP 579S. PP is a semi-crystalline thermoplastic. It has the lowest stiffness of the three materials
and tends to shrink the most, which is due to the semi-crystalline structure. The difference in shrinkage can be
observed from the PVT-curves in Figure 3.
According to the datasheet, the polymer is suitable in the production of thin walled injection-moulded articles and it
gives low warpage tendency, high lot to lot consistency, and good flow properties for excellent part filling that allows
for short cycle times, good contact transparency and high gloss (“CES EduPack,” 2015).
3.4 PVT-curves and viscosity
In Figure 4, the PVT-curves and viscosity vs. shear-rate are plotted for all three materials. The plots are taken from
Moldflow’s material database. It is seen that the shape of the PVT-plots have a different shape when comparing the
semi-crystalline PP to the amorphous ABS and COC.
The relative change in specific volume (cm
3
/g) is calculated as
%100/)(  MeltFinalMelt vvvv
Where VMelt is the specific volume of the material inside the mould with the respective packing pressure and melt
temperature, and VFinal is the specific volume of the part and room temperature and atmospheric pressure. The
specific volume at different processing conditions and the relative volumetric shrinkage for Model 1 and 2 is shown in
Table 5. The values were extracted from AMI and exact values were calculated by interpolation.
It is seen that increasing melt temperature and decreasing packing pressure result in a larger volumetric shrinkage.
Furthermore, the semi-crystalline polymer was much more exposed to volumetric shrinkage than the amorphous
ones.
Table 5 Specific volume at different processing conditions for Model 1 and 2.
Model 1 - COC Final condition Processing condition
Melt temperature 25 °C 255 °C 255 °C 265 °C 265 °C
Packing pressure 0 MPa 50 MPa 60 MPa 50 MPa 60 MPa
Specific volume (cm3
/g) 0.987884 1.05598 1.050302 1.0615 1.055642
Change in specific volume (shrinkage) 6.4% 5.9% 6.9% 6.4%
Model 2 - ABS Final condition Processing condition
Temperature 25 °C 210 °C 210 °C
Pressure 0 MPa 28 MPa 56 MPa
/g) 0.959401 1.007271 0.987185
Change in specific volume (shrinkage) 4.8 % 2.8 %
Model 2 - PP Final condition Processing condition
Temperature 25 °C 240 °C 240 °C
Pressure 0 MPa 28 MPa 56 MPa
/g) 1.11048 1.340696 1.296144
Change in specific volume (shrinkage) 17.2 % 14.3 %

Specific volume vs. temperature Viscosity vs. shear rate
StyrolutionABS(Amorphous)SABICPP(Semi-crystalline)TOPASCOC(Amorphous)
Figure 4 Plots from Autodesk Moldflow of (left) Specific volume vs. Temperature and (right) Viscosity vs. Shear rate

4 Processing parameters
In order to optimise the quality and precision of the injection-moulded parts, design of experiment (DOE) principles
were used to investigate the influence of different processing parameters. The parameters used for the analysis of the
flatness-error were the (A) Mould temperature (Model 1), (B) Melt temperature (Model 1), (C) Injection speed (Model
1), (D) Packing pressure (Model 1 and 2), and (E) Packing time (Model 2).
The parameters are briefly described below and their values are shown in Table 6 and Table 7.
(A) Mould temperature. Generally, a high mould temperature will allow the polymeric material to stay in the
molten phase for longer period than a colder mould would do, which will ensure a better packing of the
chains. If the mould is too cold the molecules will solidify before they are packed and will shrink at differing,
uncontrolled rates. This, together with temperature differences in the mould is the prime cause of warpage
(Huang & Tai, 2001; plastictroubleshooter, n.d.).
(B) Melt temperature. A high temperature of the molten material will often result in large shrinkages because of
the large temperature difference. Therefore, the lowest possible melt temperature that still gives a good
melt flow and a satisfactory moulded product should be chosen (Fischer, 2013). A too low melt temperature
will cause higher internal stresses in the part (Improve-your-injection-molding.com, n.d.). The melt
temperature also affects the flow rate and the curing time of the melt. The solidification time is related to the
warpage, and an increased solidification time will often result in larger warpage (Huang & Tai, 2001).
(C) Injection speed. If the injection speed is too low it causes a lower viscosity in the material which will require
higher injection pressure to push the material into the mould cavity. On the other hand, a high injection
speed can result in a high shear rate of the material at the gate. If the shear rate is too high it will add to the
internal stress in the part causing it to warp. (Improve-your-injection-molding.com, n.d.)
(D) Packing pressure. A high packing pressure in the cavity when the gate freezes may result in a greater mass of
plastic material inside the cavity, which generally leads to reduced shrinkages. For thin-walled structures with
a large area there is a possibility for pressure differential over the length/area of the part, which as well can
result in shrinkage and warpage (Fischer, 2013).
(E) Packing time. During the packing time, the cavity must be filled with plastic corresponding to the theoretical
weight of the moulded part. Any less plastic will result in an under-packed model. Therefore, the packing
time together with the packing pressure must be kept long enough for the cavity to be filled. If this is the
case, the packing time has a small influence on the shrinkage and warpage. Commonly, 99 % of the mould
cavity is filled and the remaining 1 % compensates for the majority of the shrinkage.
Table 6 Parameters for Model 1
Factor Description Unit Model 1 - COC
Levels
1 2 3
A Mould temperature °C 115 120 125
B Melt temperature °C 255 265 -
C Injection speed mm/s 150 200 -
D Packing pressure bar 500 600 -

Table 7 Parameters for Model 2
Factor Description Unit Model 2 – ABS & PP
Levels
1 2
D Packing pressure bar 280 560
E Packing time s 0.5 4.0
An L24 orthogonal array was selected for the experimental design for each of the four factors for Model 1. The two to
three levels for the four parameters were identified during the 24 simulations.
An L4 orthogonal array was selected for the experimental design for each of the two factors for Model 2. The two
levels for the four parameters were identified during the 4 simulations for each of the two materials.
4.1 Fixed processing parameters
Some of the processing parameters were fixed in the simulations. These are shown in Table 8.
Table 8 Fixed processing parameters
Processing parameter Unit ABS PP COC
Mould temperature °C 40 40 Varying
Melt temperature °C 210 240 Varying
Filling time s 1 1 Varying
Cooling time s 20 20 20
Velocity/pressure switch-over % 99 99 99

5 Influence of processing parameters according to literature
The answer to which processing parameters that have the greatest influence is ambiguous. A small literature study
was carried out in order to find whether there generally are correlations between the warpage and the different
processing conditions. According to (Hakimian & Sulong, 2012), processing conditions such as packing time, cooling
temperature, mould- and melt temperature, packing- and injection pressure and content of (glass) fibres are the most
important factors influencing warpage and shrinkage.
The majority of the examined studies (Altan, 2010; Huang & Tai, 2001; Oktem, Erzurumlu, & Uzman, 2007; Ozcelik &
Sonat, 2009) stated that the packing pressure has the greatest influence on the warpage and shrinkage.
However (Sha, Dimov, Griffiths, & Packianather, 2007) stated that the temperature of the barrel and mould, and the
injection speed had the greatest influence.
(Erzurumlu & Ozcelik, 2006) found that the significance of processing parameters on the warpage was dependent on
the polymeric material/polymeric blend. As already mentioned in Section 3.4, the semi-crystalline polymers tend to
have higher shrinkage than the amorphous polymers. This was as well stated by (Altan, 2010; Erzurumlu & Ozcelik,
2006; Kwon, Isayev, & Kim, 2005).
Reference Polymer Model Processing parameters effect on shrinkage or warpage
(Hakimian &
Sulong, 2012)
Blends:
(PC/ABS),
(PPE/PS),
POM
Microgears with
18 ”teeth”
5.5x0.6mm
(d x t)
Fiber glass content gave the most significant influence on the
warpage and shrinkage. Amorphous blends showed better
resistance against warpage compared to semi-crystalline.
(Huang & Tai,
2001)
Blends:
PC/ABS
Rectangular
hollow sample,
120x50x8x1mm
(L x b x h x t)
Packing pressure was the most influential factor (15.6%),
followed by mould temperature (12.1%), melt temperature
(10.8%), and packing time (9.6%). The less influential factors
were gate dimension and filling time.
(Altan, 2010) PP, PS Rectangular
hollow sample,
110x10x3.2mm
(L x h x t)
The most significant parameters for minimizing shrinkage was
packing pressure and melt temperature for PP and PS,
respectively. Injection pressure had the least effect on the
shrinkage of both materials.
(Oktem et
al., 2007)
PC/ABS Thin-shell for
Orthose.
54x26x14 mm
(L x b x h)
Packing pressure was the parameter having the greatest
influence on warpage (58.0%), followed by packing time (23.0%),
injection time (15.2%), and cooling time of (3.7%).
(Sha et al.,
2007)
PP, POM,
ABS
Plate with micro-
features of
circular pins,
“gear”, and
“fingers”.
Increasing barrel temperature, mould temperature and
injection speed improved the polymer melt fill in micro-cavities.
However, the effects of these factors on the process replication
capabilities are not consistent for different polymer materials,
and could be adverse in specific conditions.
(Erzurumlu &
Ozcelik,
2006)
PC/ABS,
POM,
PA66
Thin shell part
with varying rib
design 300x60x2
mm (L x b x t)
The PC/ABS blend showed the lowest warpage followed by PA66.
Parameter influence on warpage for “PC/ABS”, “POM” and
“PA66” showed: packing pressure (36.7%; 20.2%; 14.3%), melt
temperature (8.0%; 8.4%; 27.3%), and mould temperature (0.7%;
13.6%; 23.8%).
(Ozcelik &
Sonat, 2009)
ABS, PC,
carbon
fibres
Cell phone cover
145x60x20 mm
(L x b x h). t=0.9-
1.1mm
As a result of analyses, the parameter influencing the warpage
the most for PC/ABS material is packing pressure (85.2%)
followed by melt temperature (12.7%). Very low influence of
mould temperature and packing time was found.
(Tang et al.,
2007)
ABS Thin plate
120×50×1 mm
(L x b x h)
Melt temperature had the greatest influence on warpage (47 %),
followed by packing time (29 %), packing pressure (17 %) and
filling time (7 %). (Focus on fabrication of mould).

6 Software
In this section, the different software’s used for simulations, metrological measurements and statistical analysis are
briefly explained. Simulations of injection moulding were done in Autodesk Moldflow Insight (AMI), whereupon it was
transferred to GOM Inspect, a metrological software. GOM was used to measure the flatness-error, and the results
were graphically visualized by Minitab.
In order to get an overview of the procedures and of what was done in each of the software programs, a flowchart of
the simulations, measurements and analysis is shown in Figure 5.
Figure 5 Experimental flowchart
6.1 Autodesk Moldflow Insight – Simulation setup
In order to obtain a simulation model of the injection moulded polymer parts, the model was analysed by finite
element analysis (FEA). The software used for the simulations is Autodesk Moldflow Insight 2016 (AMI).
AMI is software, which offers a vast material database with information about material properties and recommended
processing conditions. It is as well possible to design the gate, runners system, sprue and cooling channels, which
makes it possible to create very realistic simulations.
An AutoCAD model (.stl) was imported into Moldflow and a 3D mesh type was used in the simulations. The meshing
consisted of tetrahedral elements, with a total element number of 1.782.562 for Model 1 and 1.140.376 for Model 2
(the models and mesh design were given beforehand). The number of mesh elements in Model 1 is much larger than
in Model 2, even though the model is about half the area and one third of the thicknesses, which results in a much
finer mesh and allows us to get more precise results of the flatness-error.
For the simulation, a Fill + Pack + Warp analysis was run. The simulation investigated the whole injection moulding
cycle, including the filing, packing and cooling phase. Worth noticing is the degree of preciseness that can be offered
by this program and it is proportionally to the amount of meshing elements.
Results such as the polymer flow fill time, temperature, pressure and stress distribution, warpage effect and many
other parameters can be extracted as outputs of this analysis. In our case, one of the utmost important results is the
flatness-error expressed as warpage. The flatness-error was defined as the major quality criterion for the injection
moulded parts.
After finalising the analysis, the warped model was imported to the metrological software, GOM Inspect V8 SR1.
•Import CAD model to Autodesk Moldflow Insight (AMI) and set processing parameters
•Setup of Design of Experiment (DOE)
•Run 24 simulations for Model 1 and 2x4 simulation for Model 2 in AMI
•Export the warped model from AMI to GOM Inspect (GOM)
•Import the original CAD model to GOM
•Align plate surface of warped model with the "pure" CAD model
•Measure the surface deviation (flatness-error)in 12 points
•Analyse results by Minitab
•Compare Model 2 with experimental data

6.2 Metrological software, GOM Inspect
The metrological software, GOM Inspect V8 SR1 is useful software for geometrical analysis of 3D models. In this case,
we are interested in the surface alignment analysis between the warped model from AMI and the original CAD model.
After importing both models into GOM, a 3-point alignment was done to align the two upper surfaces of the plate.
The surface alignment was done by selecting points in three of the corners on the upper surface of the nominal model
(CAD model) and three equivalent point of the actual model (warped model from AMI). It is important to notice, that
we only care about the flatness of the plates and not about the distortion of the runners, gates or sprue.
The optical representation of the warped surfaces is animated on a colour map that represents the deviations
between the two surfaces. These deviations were measured in 12 evenly distributed points in three straight lines on
the upper surface of each plate. The minimum and maximum deviation was recorded and these values were
transferred to Minitab.
In Figure 6, Model 2 is shown after running warpage simulation in AMI (left figure) and after being imported into GOM
followed by a surface alignment (right figure).
Figure 6 Difference between the terms’ “warpage” (left) and “flatness-error” (right)
6.3 Statistical analysis, Minitab
Minitab is a tool for statistical analysis that can analyse data from many different sources. The software is able to
perform statistical analysis, to assess interaction effects of multiple factors and also to present these effects
graphically. The statistical analysis will define which combination of factors can create the biggest impact on the
experimental or simulated results.
In order to design the experiment (DOE), directions on how the software was used are given in the bullet points
below.
 The first step for the creation of a DOE analysis was to define the factors and factor levels and to normalize
the excel data that consist the measured values.
 The second step for the creation of DOE starts from the Stat Menu  DOE Factorial  Analyze Factorial
Design.
 The parameters, type of plots, inputs and the demanded outputs was set.
 On “Term choice”, the factors were imported. As a default, the combination of the two factors was selected
(AB) which was deleted in order to create on the statistical analysis a degree of freedom. In order to plot the
interaction, “AB” must be kept.
 On “Graphs”, the standardized Normal and Residual versus fits Plots were selected. On the Result selection
all the choices were selected. Finally, on the Storage window, the Residuals and the Coefficients sets were
selected.
 In order to find the Interaction and the “Main Effect plot, the path was Stat Menu  DOE Factorial 
Factorial Plots Graphs InteractionMain Effects. The latter two plots will be displayed above in order to
assist the further analysis of the factors and their interactions.

7 Results
In this section, the results from the simulations of the injection-moulded Model 1 and 2 are shown. Several figures
from AMI and GOM will be presented followed by graphical illustrations from Minitab of the flatness-error as a
function of the different processing or material parameters. Finally, the influence of the processing parameters will be
discussed.
In Section Fejl! Henvisningskilde ikke fundet., the results are more thoroughly discussed and compared with results
from other studies focusing on minimizing flatness-error and warpage of injection-moulded parts.
7.1 Results for Model 1
Model 1 was simulated with one material, COC, and with four processing parameters with two to three levels. Figure 7
shows the warpage for two different simulations of Model 1 in AMI, which are giving the largest and smallest
warpage, respectively. The warpage patterns are very different from each other. Generally, the warpage pattern on
the left figure is present for mould temperatures of 115 °C and 120 °C, whereas the right figure is present for mould
temperatures of 125 °C. This tendency is also visible from the results of flatness-error.
Figure 7. Left: The largest warpage is Simulation 15 (mould temperature of 120 °C, an injection speed of 200 mm/s, a
melt temperature of 265 °C, and a packing pressure of 50 MPa). Right: The smallest warpage is simulation 20. The
processing parameters used were a mould temperature of 125 °C, an injection speed of 200 mm/s, a melt temperature
of 255 °C, and a packing pressure of 60 MPa.
The flatness-error was measured in GOM. The simulation matrix and the results from the flatness-error (output) are
shown in
Table 9. The flatness-error was measured as the deviation between the original CAD model and the model from AMI
with simulations of the warpage. Since we didn’t have enough time to run all simulations (16 out of 24 models were
simulated), some of the results are left empty.
For all the simulations with a mould temperature at 115 °C and 120 °C, the flatness-error is very similar and around
0.040-0.045 mm. When increasing the mould temperature to 125 °C, the flatness-error changes significantly. Even
though we only had time to run three simulations (out of eight possible) with a mould temperature of 125 °C, it is
tempting to conclude that the mould temperature has the greatest significance for this specific model made of COC
and with processing parameters such as injection speed, melt temperature and packing time in the range that was
used in the simulations.

 The largest flatness-error was found for simulation 15. The processing parameters used were a mould
temperature of 120 °C, an injection speed of 200 mm/s, a melt temperature of 265 °C, and a packing pressure
of 50 MPa.
 The smallest flatness-error was found for simulation 20. The processing parameters used were a mould
temperature of 125 °C, an injection speed of 200 mm/s, a melt temperature of 255 °C, and a packing pressure
of 60 MPa.
A bar chart showing the flatness-error for all simulations of Model 1 is shown in
Figure 8 and the models with the largest and smallest deviations (flatness-errors) in GOM is shown in Figure 9 and
Figure 10, respectively.
Table 9 Results of flatness-error for simulations of Model 1 measured in GOM
Model 1 – Flatness-error from simulations in AMI
Mouldtemperature
(°C)
Injectionspeed
(mm/s)
Melttemperature
(°C)
Packingpressure
(MPa)
Simulationno.
Flatness-error
(mm)
Comments
115
150
255
50 1 0.041231
60 2 - Not run
265
50 3 0.040868
60 4 0.041367
200
255
50 5 0.042109
60 6 0.043104
265
50 7 0.043484
60 8 - Not run
120
150
255
50 9 0.041915
60 10 - Not run
265
50 11 0.044183
60 12 0.044030
200
255
50 13 0.042903
60 14 0.043016
265
50 15 0.045441 Largest
60 16 0.041721
125
150
255
50 17 - Not run
60 18 - Not run
265
50 19 0.010562
60 20 0.007240 Smallest
200
255
50 21 - Not run
60 22 - Not run
265
50 23 - Not run
60 24 0.014825

Figure 8 Bar chart of flatness-error results for Model 1. Empty bars are simulations which were not run.
Based on these results, it would be interesting to investigate higher mould temperatures such as 130 °C and 140 °C.
This could be done in order to determine when the mould temperature starts to have a negative effect the flatness-
error of the plate. According to (Fischer, 2013), an increase in mould temperature will often results in a larger
shrinkage and warpage.
In this specific case where we investigate a plate with a thickness of only 1 mm, a higher mould temperature would
help ensuring a better fluidity of the material which will result in a better distribution and packing. The mould
temperatures recommended by AMI are 95-110 °C, which are significantly lower than the optimal mould temperature
of 125 °C that we find in the simulations. We believe that this deviation from the recommended temperature is due to
the very thin geometry of the plate.
0.0000
0.0100
0.0200
0.0300
0.0400
0.0500
50 60 50 60 50 60 50 60 50 60 50 60 50 60 50 60 50 60 50 60 50 60 50 60
255 265 255 265 255 265 255 265 255 265 255 265
150 200 150 200 150 200
115 120 125
Flatness-error(mm)
Processing conditions
Model 1 - Flatness-error
Packing pressure
Melt temp.
Injection speed
Mould temp.

Figure 9. The largest flatness-error (mm) was found for Model 1 - simulation 15. The processing parameters used were
a mould temperature of 120 °C, an injection speed of 200 mm/s, a melt temperature of 265 °C, and a packing pressure
of 50 MPa.
Figure 10. The smallest flatness-error (mm) was found for Model 1 - simulation 20. The processing parameters used
were a mould temperature of 125 °C, an injection speed of 200 mm/s, a melt temperature of 255 °C, and a packing
pressure of 60 MPa.
Due to too large gaps in the simulation matrix (see Table 9), it was not possible to analyse the data in Minitab.

7.2 Model 2 – Simulation data
Model 2 was investigated for two different materials, the amorphous ABS and the semi-crystalline PP. Two different
processing parameters with two levels each were investigated for both materials. The matrix and result from the
simulations are shown in the table below for both materials and both plater of Model 2 (plate 1 and 2).
Model 2 - Flatness-error from simulations in AMI
Processing parameters Flatness-error [mm]
Run Order Packing Pressure [MPa] Packing Time [s] ABS-Plate 1 ABS-Plate 2 PP-Plate 1 PP-Plate 2
1 28 0.5 0.003194 0.004652 0.024012 0.021815
2 28 4 0.012595 0.019972 0.019265 0.023073
3 56 0.5 0.003393 0.005789 0.035450 0.014007
4 56 4 0.009170 0.011020 0.013903 0.029322
Figur 1 Plate 1 and plate 2 for Model 2
7.2.1 Minitab plots – ABS – Simulations
In this section, the plots of main effects and interactions which were extracted from Minitab are presented.
Figure 11 Main effects for Model 2 - ABS - plate 1 (left) and 2 (right) – Simulation
It is clearly visible that the flatness-error is decreasing for higher pressures. For increasing packing time, the flatness-
error is larger for both plates.

Figure 12 Interaction effects for Model 2 - ABS - plate 1 (left) and 2 (right) – Simulation
From the interaction plots it is seen that there is no interaction among the factors due to the non-intersecting lines.
For plate 2 with a packing time of 4 seconds, the flatness-error is dropping by 0.009 mm as the packing pressure is
increased. As for the packing time that corresponds to 0.5 sec, a slight increment of the flatness-error is observed.
7.2.2 Minitab plots – PP – Simulations
Figure 13 Main effects for Model 2 - PP - plate 1 (left) and 2 (right) – Simulation
For plate 1, an increase of the packing pressure, an increment of 0.0035 mm is noticeable. For an increase in the
packing time, a tremendous fall of the flatness error is observed, which has a positive impact for the sample.
For plate 2, an increase of the pressure a decrease of 0.001 mm is noticeable. For a rise of the packing time a
tremendous rise of the flatness error is observed, which has a clearly negative impact for the sample.

Figure 14 Interaction effects for Model 2 - PP - plate 1 (left) and 2 (right) – Simulation
There is no interaction among the factors due to the non-intersecting lines.
7.3 Model 2 - experimental data
Experimental data on flatness-error was only available for Model 2 only. In this section, the flatness-error results are
shown and compared with simulation data.
Table 10 Results of flatness-error for injection moulding of Model 2
Model 2 - Flatness-error from experimental data
Processing parameters Flatness-error [mm]
No. Packing Pressure [MPa] Packing Time [s] ABS-Plate 1 ABS-Plate 2 PP-Plate 1 PP-Plate 2
1 28 0.5 0.11688 0.18658 0.20214 0.28555
2 28 4 0.09902 0.08194 0.05521 0.06539
3 56 0.5 0.10428 0.11987 0.24135 0.21603
4 56 4 0.04912 0.0297 0.09294 0.04750
7.3.1 Minitab plots – ABS – Experimental data
Figure 15 Main effects for Model 2 - ABS - plate 1 (left) and 2 (right) – Experimental

For plate 1, an increase of the pressure a decrease of 0.4 mm is noticeable. For a rise of the packing time a rise of the
flatness error is observed, which has a negative impact for the sample.
For plate 2, an increase of the pressure a decrease of 0.1 mm is noticeable. For a rise of the packing time a slight fall of
the flatness error is observed, which has almost no impact for the sample.
Figure 16 Interaction effects for Model 2 - ABS - plate 1 (left) and 2 (right) – Experimental
There is a clear interaction of the factors. For both plates, the packing time of 0.5 seconds, the flatness-error is
dropping radically as the packing pressure is increased. As for the packing time of 4 seconds, a slight decrement of the
error is observed but it cannot be taken into consideration due to its extreme low value. For plate 2, the decrement of
flatness-error is two times bigger than for plate 1.
7.3.2 Minitab plots – PP – Experimental data
Figure 17 Main effects for Model 2 - PP - plate 1 (left) and 2 (right) – Experimental
For plate 1, for an increase of the pressure a great decrease of 0.16 mm is noticeable, which has a clearly positive
impact for the sample. For a rise of the packing time no variation of the flatness error is observed.
For an increase of the pressure a decrease of 0.2 mm is noticeable, which has a clearly positive impact for the sample.
For a rise of the packing time a fall of the flatness error is observed.

Figure 18 Interaction effects for Model 2 – PP - plate 1 (left) and 2 (right) – Experimental
For both plates, there is a clear interaction of the factors. For the packing time of 0.5 seconds, the deviation of the
flatness error is dropping radically as the packing pressure is increased. As for the packing time that corresponds to
the 4 seconds, an almost similar trend is observed with slightly bigger angle.
7.3.3 Discussion of Minitab results - Model 2
On the plots of Main effect and Interaction, the scale is different, thus it is not possible to compare their results. The
best approach would be to observe the graph with the more digits in order to get the higher precision measurements.
On the interaction plots, when the lines are intersected, there is interaction of the factors. The bigger the
corresponded angle is, the bigger the interaction of the parameters.
On the simulation plots for the ABS, an increase of the pressure and a decrease of the packing time present a fall of
the flatness error that has a positive impact on the final surface. In contrary on the PP plots, a higher packing pressure
combined with a lower packing time can increase the flatness-error of plate 1. Concerning plate 2, the results follow
the ABS trend.
On the simulation plots for the ABS an increase of the pressure and a decrease of the packing time present a fall of the
flatness error that has a positive impact on the final surface. In contrary, on the PP plots a higher packing pressure
combined with a lower packing time can increase the flatness error on the first plate. Concerning plate 2, the results
follow the ABS trend.
On the experimental data, the main effect plots present a fixed correlation among the pressure and the flatness-error,
meaning that an increase in the packing pressure will lead to a smaller flatness-error but this trend is not monitored
on the time plots. An increase of the packing time is able to create all the possible combinations concerning the
flatness behavior (higher, lower of steady values of flatness-error).
One of the utmost important observation of the comparison among the experimental and the simulated results is that
the interaction plots of the experimental data show every time strong interaction among the parameters.
On the above plots, it was quite often the phenomenon of increased packing pressure to offer bigger flatness error
(e.g. Simulation ABS plate 2) which is incompatible with (Ozcelik & Sonat, 2009) and also with other measurements.
This possibly occurs due to different selection of PP and to the different process parameters that had been followed
on the referred study.
On the experimental results, for the Polypropylene plots, the optimum combination for minimizing the flatness error
is the packing pressure which is clear from the Main effects plots. Moreover for ABS, the factor with the biggest
influence on minimizing the flatness error is the same, which is highly in accordance with other studies (Ozcelik &
Sonat, 2009),(Hakimian & Sulong, 2012). On the other hand on the simulation results the packing time has the biggest
impact on the flatness value.

7.4 Comparison of simulation data and experimental data
As seen in the previous sections, the experimental data does not correspond properly with the simulations. In the bar
charts below, the flatness-error is plotted for both plates, both experimental data and simulations for ABS and PP,
respectively. It is observed that the experimental data gives significantly larger flatness-error than the simulated date.
The simulation values are therefore way too conservative for the model.
Figure 19 Flatness-error of Model 2 – ABS for both experimental data and simulation data.
Figure 20 Flatness-error of Model 2 – PP for both experimental data and simulation data.
Some of these very large deviations could be caused by the way we imported the AMI models to GOM and how we
did the surface alignment. When we are operating with so low values of the flatness-error, it would be easy to make
an inadequate surface alignment even though we used the same three corners for the surface alignment each time.
In order to make the models less heavy to run, an idea would be to decrease the number of meshing elements. Since a
warpage analysis is very complex, it would probably not give an adequate precision of the warpage and flatness-error
data.
0
0.05
0.1
0.15
0.2
0.25
0.3
1 2 3 4
Flatness-error(mm)
Run number
Model 2 - Flatness-error for ABS
Experimental data - ABS plate 1
Simulation data - ABS plate 1
Experimental data - ABS plate 2
Simulation data - ABS plate 2
0
0.05
0.1
0.15
0.2
0.25
0.3
1 2 3 4
Flatness-error(mm)
Run number
Model 2 - Flatness-error for PP
Experimental data - PP plate 1
Simulation data - PP plate 1
Experimental data - PP plate 2
Simulation data - PP plate 2

8 Conclusion
Two different thin-walled plate-shaped plastic models were investigated by injection moulding simulations in
Autodesk Moldflow Insight (AMI). Processing parameters such as melt temperature, mould temperature, injection
speed, packing pressure and packing time were investigated in order to find the optimal combination and the
influence of the different parameters. The simulation results were analysed with the flatness-error as the major
quality criteria.
Model 1 was made of COC and simulations showed that the mould temperature had the greatest influence on the
flatness-error. The highest temperature of 125 °C gave the smallest flatness-error. Neither packing pressure, melt
temperature nor did injection speed have a strong influence on the flatness-error. For future investigations it is
recommendable to investigate higher mould temperatures. Because of too heavy AMI simulations of the models
(especially Model 1), not all simulation were run, why Model 1 couldn’t be analysed in Minitab.
Model 2 was made of either PP or ABS, and the simulations showed that an increase in packing pressure resulted in a
decrease in flatness-error for both plates of ABS but only for plate 2 of PP. The opposite was present for plate 1 of PP.
Regarding the analysis of the experimental data of Model 2, the packing pressure had a great influence for both
materials and both plate 1 and 2. The packing time had a very low influence.
A comparison of the flatness-error found from the experimental data and the simulations data showed that the
simulations gave much more conservative results due to the significantly lower flatness-error.

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