Optimization of Fill Time in Multi Cavity Plastic Injection Molding Through Simulation

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International Journal of Engineering, Management & Sciences (IJEMS)
ISSN-2348 –3733, Volume-2, Issue-4, April 2015
12 www.alliedjournals.com
Abstract Injection molding is a complex process regulated
by many factors (injection speed, injection pressure, melt
temperature, mold temperature, and others). Despite, process
control and parameter optimization, it is often difficult to
replicate thermoplastic components with adequate dimensional
accuracy, especially in the case of geometries with high aspect
ratios. For these reason, there is a need for study of fill time on
other parameters . In this paper simulate the fill the effect with
the help of the simulation work and with DOE approach. Data
analysis evidenced the greater quality of work pieces obtained
by mould flow adviser.
Index Terms Injection molding,DOE approach, Mould
Flow
I. INTRODUCTION
This type of molding stages is initiated for getting of food to
polymer type to tray keeping material to gun pipe followed by
heating with enough temperature for making it moving liquid
, then the made liquid by heat soft, readily bent which was
gone slo wly will be kept within high pressure vessel for
forming whole process within one's knowledge as in original
name, after this process i.e. injection molding issent in name
for both platens moving and fixed in injection molding
machine in order to place in ship for goods the vessel for
forming person used by another together after output as result
product put to somewhat cold that helps in the solidification
process. Po lymers play an important role in injection
molding. Types of polymers: Thermoplastic, thermosets and
elastomers. Further types of Thermoplastic: crystalline and
amorphous .P olymers are the combination of numbers of
small organic repeating monomer shaving high resistivity to
chemicals. Thermoplastic materials generally soften Due its
weak inter molecules force when they are come contact to
heat and return to their previous conditio n when cooled
polymer process of forming polymer. Due recyclable property
it is used in a wide range of applications such as insulation,
Food packaging, credit cards holder and automobile bumpers.
II.LITERATURE REVIEW
Shunliang Jianget al. [15] told injection molding is one of
the most widely used manufacturing processes for
producing thin soft, readily bent parts. It is chiefly of vessel
for forming, p acking, and making somewhat cold stages.
Manuscript receivedApril 24, 2015.
Gurmeet Singh,Mechanical Engineering dept,Rajast han Institute of
Engineering and Technology Jaipur. (Raj)
Sharad shrivastava,Mechanical Engineering dept,Rajasthan Institute of
Engineering and Technology,Jaipur. (Raj)
Neeraj Kumar Sharma,Mechanical Engineering dept,Rajasthan
Institute of Engineering andTechnology,Jaipur. (Raj)
The air entrapments and weld-lines occurred in the vessel
for forming stage has one influence on the quality of
thermal product.Material used is molten polymer, which is
used for thin plastic products. Main problems occur in
filling is resin transfer molding (RTM) and vacuum assist
resin transfer mold (VARTM). Analysis and case studies
conclude that computing time and complexity solving
temperature were less. Material used is molten polymer.
According to J.G. Kovacs and B. Siklo [12] in warpage
developing many parameters influence at the corners of
injection molded plastic parts. Main cause of this
deformation is the asymmetrical cooling of the injection
mold. This study presents an injection molding analysis of the
heat flow developing in injection molds. The analysis shows
that significant temperature difference appeared between the
two sides of the mold after the hot polymer melthad filled the
cavity. It was highlighted that the unevenness of the cooling
should be considered during the mold design in order to avoid
the warpage of the parts.
T.Barriere, et al. [13] says the MIM (metal injection
moulding) process is combination of powder metallurgy
technology and IM (Injection molding) to produce small and
integrating parts to get exact same as per requirement. 3D
software is used for efficient injection in cavity. Bi-phasic
model is used to describe the flows of the metallic powder
and plastic blinder so that can predict the accurately the
powder segregation in injection. Powder segregation ins
main problem. Software permits to optimize the mould
design and processing parameters to get required
components
In this study consist of the effects of injection parameters and
weld line on the mechanical properties of polypropylene (PP)
moldings were studied. To produce weld line at PP
specimens, the obstacles having edge angle of 0°, 15° and 45°
were designed. These obstacles were located at the center of
the mold. The effects of both obstacles angles and the
injection molding parameters such as melt temperature and
packing pressure on the mechanical strength were
investigated. Mechanical prop erties such as tensile strength
and Izod impact strength (Notched) of the specimens were
measured by test methods. The effect of molecular orientation
on the mechanical properties of the specimens was discussed
by Finite Element Analysis. Weld line tensile strength of
injection molded specimens having obstacle edge angle
(OEA) of 15° was obtained higher than the other obstacle
edge angles. The cavity of the mold was manufactured in the
CNC machine. A test part was injected by a plastic injection
Optimization of Fill Time in Multi Cavity Plastic
Injection Molding Through Simulation
Gurmeet Singh , Sharad Srivastava , Neeraj Kumar Sharma
Optimization of fill time in multi cavity plastic injection molding Through Simulation
13 www.alliedjournals.com
machine. Which had a clamping force of 490 kN and an
Injection pressure of 275 MPaChing-Piao Chen et al. [5]
deals with the application of computer-aided engineering
integrating with statistical technique to reduce warpage
variation depended on injection molding process.
For this purpose, a number of Mold-Flow analyses are carried
out by utilizing the combination of process. In the meantime;
apply the design of experiments ( DOE) approach to
determine an optimal parameter setting.
In this regression models that link the controlled parameters
and the targeted outputs are developed, and the identified
models can be utilized to predict the war page at various
injection molding conditions. The melt temperature and the
packing pressure are found to be the most significant factors
in both the simulation and the practical for an inj ection
molding process of thin-shell plastic objects.
III.PROBLEM IDENTIFICATION
In this study the problem focused to improve pro ductivity of
thick plastic prod ucts and this is done by applying CAE
methods in plastic injection molding process. Five controlling
factors named mold temperature, melt temperature, injection
pressure, packing pressure and runner diameter were used
with three levels. For design of experiment Taguchi tables
were used which are discussed in detail in the next chapter. In
the analysis of the product main responses from all
experiments were volumetric shrinkage and fill time. The
design set up of the product consisting of sprue, gate, cooling
channels and runner used in this study is shown in figure 1 and
2.
Fig 1 design set up of the product
Fig. 2 design consisting of sprue, gate, cooling channels
and runner
IV.DESIGN OF EXPERIMENT
In Plastic injection molding process quality of the product is
always affected by its process parameters like injection
pressure, injection speed, mold temperature, melt
temperature, packing pressure, packing time, cooling time
and many more. During the last decade the effects of these
parameters were studied by various researchers. For such kind
of studies DOE (Design o f Experiment) is a scientific
technique which is being generally used by researchers now
days. By using DOE the important factors which affect the
output results are identified. For a particular experiment DOE
finds.
One simple plastic product i.e. car handle with sprue and
runner arrangement was used in this study which is shown in
figure 4.1 and DOE was applied to find the optimum value of
input parameters that affects the output results for plastic
injection molding product. It was very difficult to choose
proper technique for experiments design as DOE is very vast
and complicated subject. From literature review it was
revealed that DOE based on Taguchi methods was used by
various researchers and for this reason that this technique was
also used in this study and the detailed discussion related to
this technique is given in the next section.
Research problem plays a vital role in the preparation DOE
experiments. Some important steps which were followed by
researchers during DOE designing were shown below.
Define the problem Statement. Developing a good problem
statement plays an important role in the analysis and make
sure that r esearchers are studying the right variables in the
right direction. At this step, identify the logical questions and
that will be answered rightly.
Define the objective of study. A well-defined and planned
objective will make sure that the designed experiment
answers the right questions and yields practical and useful
information. At this step, the goals of the experiment are
being defined.
Develop an experimental plan that will provide meaningful
and worth full information. Much relevant and useful
background information, such as theoretical principles and
knowledge are gained through literature of previous research
papers. The factors or process conditions which affect process
performance and contribute to process variab ility need to be
identified by Researchers. Once the process is already
established and the factors influencing the process have been
identified, you need to determine optimal process conditions.
Fig. 3 Sprue Location for the Car Handle Design
International Journal of Engineering, Management & Sciences (IJEMS)
ISSN-2348 –3733, Volume-2, Issue-4, April 2015
14 www.alliedjournals.com
V.FACTORS AND LEVELS
By selection of proper factors and their levels makes the
possibility of Design of DOE table. In this study five factors
were selected with three levels for each product and were
shown in table 1.
Table 1 Summary Table of Factors and Levels
Le
vel
s
P1
(Mo
uld
Tem
p)
P2
(Melt
Temp
)
P3
(Inj.
Pr.
%)
P4
(Packin
g Pr.
%)
P5
(Runne
r Dia .)
(mm)
1
70
280
30
20
10
2
80
290
40
25
12
3
90
300
50
30
14
VI.RESULT AND DISCUSSION:
Taguchi Method
To determine the effect of fill time in this study the Taguchi
method was applied and five process parameters used as input
variables. Fill time and volumetric shrinkages simulated
values are shown in table 2.
Table 2 Shrinkage and Fill Time Values
N
o.
of
E
x
p.
P2
(Mel
t
Tem
p)
P3
(Inj.
Pr.
%)
P4
(Pac
king
Pr)
P5
Run
ner
Typ
e
Fill
Time
1
280
30
20
10
1.668
2
280
30
20
12
1.667
3
280
30
20
14
1.502
4
290
40
25
10
1.489
5
290
40
25
12
1.332
6
290
40
25
14
1.333
7
300
50
30
10
1.329
8
300
50
30
12
1.165
9
300
50
30
14
1.167
10
280
40
30
10
1.165
11
280
40
30
12
1.658
12
280
40
30
14
1.658
13
290
50
20
10
1.502
14
290
50
20
12
1.5
15
290
50
20
14
1.337
16
300
30
25
10
1.332
17
300
30
25
12
1.332
18
300
30
25
14
1.167
19
280
50
25
10
1.825
20
280
50
25
12
1.66
N
o.
of
E
x
p.
P1
(Mol
d
Tem
p)
P2
(Mel
t
Tem
p)
P3
(Inj.
Pr.
%)
P4
(Pac
king
Pr)
P5
Run
ner
Typ
e
Fill
Time
21
90
280
50
25
14
1.66
22
90
290
30
30
10
1.494
23
90
290
30
30
12
1.495
24
90
290
30
30
14
1.495
25
90
300
40
20
10
1.335
26
90
300
40
20
12
1.336
27
90
300
40
20
14
1.337
Regression Equation
In this study regression equations for responses fill time were
developed.
Regression Equation for Fill Time
Fill time = 6.096 - 0.00547*A
-0.01646*B-0.00004*C-0.000620*D-0.0134*E
Fill Time Contours
The timewhich requires filling the resin in cavity is called Fill
time. Fill time basically d epends on properties of material,
injection pressure limit and gating system for resins. It was
not possible to show here all 27 experiments fill time
contours, so only few good and worst combinations of process
parameters based results are discussed here.
Fig. 3 Fill Time for Experiment 3
Fig. 4 Fill Time for Experiment 9
Taguchi Analysis: Fill Time versus P1 (Mold Tem, P2
(Melt Tem, P3 (Inj. Pr., ...
Response Table for Signal to Noise Ratios
Smaller is better