International Journal of Pure and Applied Mathematics ... · of a polymer or plastic injection...
Transcript of International Journal of Pure and Applied Mathematics ... · of a polymer or plastic injection...
OPTIMIZATION OF INJECTION MOULDING MOULD FLOW ANALYSIS USING
TAGUCHI APPROACH
1N.Subramani, 2J.Ganesh Murali, 3P.Vijaya Rajan, 4C.Godwin Jose
1Assistant Professor,
2Professor,
Karpagam College of Engineering, Coimbatore.
3Assistant Professor, Sri Sairam Engineering College, Chennai.
4Assistant Professor, PSN College of Engineering and Technology,Tirunelveli. [email protected]
Abstract: This work is intended to optimize the mould
flow of the hopper by using the ANSYS and Taguchi
method. Moulding is considered the most prominent
process for mass production. There are many kinds of
plastic moulding methods are there. In that method here
the injection moulding method is selected for the
research. Each technique has its own advantages in the
manufacturing of specific item. Among these various
plastic production technologies, injection moulding
takes up approximately 32%, because of its ability in
producing complex parts with low cost and high
productivity. Here the analysis helps us to optimize the
flow in moulding.
1. Introduction
Injection moulding has become the most important
process for manufacturing plastic parts due to its ability
to produce complex shapes with good dimensional
accuracy[1].Cad/ Cam can help designer to speed up
design for the plastic part and mould design process
and reduce the long lead time[2]
The Taguchi approach is mostly used in the
industrial environment, but it can also be used for
scientific research. The method is based on balanced
orthogonal arrays[3].In this work , the combined
effects of multi moulding process parameters are
analyzed by the combination of orthogonal experiments
and mould flow simulation tests and then the sensible
gate location and optimized parameter combination is
obtained[4].This study applies of the optimization
strategy based on Taguchi’s experimental designs [5-6]
The introduction of simulation software has made
a significant impact in the injection moulding industry.
With the increasing use of computers in design
engineering, the amount of commercially available
software on the market has also increased [7].
Traditional trial runs on the factory floor can be
replaced by less costly computer simulations. Now
days, research on optimizing the plastic injection
moulding process has developed a lot. CAD/CAE tools
are used to produce an optimal mould gating design
using CATIA and Mould flow applications. The mould
flow analysis helps in reducing costs and time and also
prevents other defects occurring in the process [8].
Injection moulding is a process of forming a product by
forcing molten plastic material under pressure into a
mould where it is cooled, solidified and subsequently
released by opening the two or three halves of the
mould. Bryce.M.D (1996) has stated that the injection
moulding is used for the formation of intricate plastic
parts with excellent dimensional accuracy. The design
of a polymer or plastic injection mould is an integral
part of plastic injection moulding as the quantity of the
final plastics part is greatly reliant on the injection
mould.
Figure 2.1 Injection moulding
International Journal of Pure and Applied MathematicsVolume 118 No. 11 2018, 241-250ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version)url: http://www.ijpam.eudoi: 10.12732/ijpam.v118i11.30Special Issue ijpam.eu
241
Figure 2.2 Injection moulding cycle
1. Factors Affecting Injection Moulding
There are several factors that are critical to the injection
moulding process. These include:
• Plastic Melt Temperatures
• Barrel Temperatures
• Nozzle Temperatures
• Plastic Flow Rates
• Plastic Pressure or Screw Back Pressure
• Plastic Cooling Rates
2. Mould Flow Analysis
Analysis is essential for designing and mould making
through simulation step-up and result interpretation to
show how changes to wall thickness, gate location,
material and geometry affects manufacturability and
also experiments with “what-if” scenarios before
finalizing a design.
Figure 2.3 Hopper
The Mould flow analysis was performed using
Autodesk Mould Flow analysis software.
3. Steps involved in Mould Flow Analysis
1. Converting the 3D model in STEP OR IGES format.
2. Meshing the model by using dual domain type of
mesh.
3. Importing the meshed file to the solver package
specifying the boundary condition, loads such as
injection pressure, injection time, mould temperature,
melt temperature, material properties etc.
4. Building the feed system such as sprue, runner and
gate.
5. Mesh the feed system and cooling lines.
6. Run the analysis for different analysis types like fill,
flow, war page etc.
7. Study the result, interpret them.
8. Establish the optimized data for runner, gate, sprue
dimensions, coolant temperature etc. Based on the
analysis the optimal combination of part geometry,
material choice, gate location and process parameter to
produce quality finish part are determined.
Figure 2.4 Meshing of Hopper part
4. Parameters for Mould Flow Analysis
•
• Specification of the moulding material including
grade and color.
• Moulding machine specification.
International Journal of Pure and Applied Mathematics Special Issue
242
• Number of impression.
• Shrinkage of the material.
• Type of mould
• Type of runner or gate
• Parting line of mould
• Types of ejection system.
• Type of cooling system.
• Injection pressure.
• Shot weight.
• Distance between the tie bars.
• Shut height of mould.
• Shut height of machine.
• Clamping force.
• A fully detailed component drawing.
5. Air trap analysis
Figure 5.1 Air Trap Analysis at 50cm3 / sec
mould flow rate
Figure 5.2 Air Trap Analysis at 60cm3/ sec
mould flow rate
Figure 5.3 Air Trap Analysis at 75cm3/ sec
mould flow rate
Figure 5.4 Air Trap Analysis at 100cm3/ sec
mould flow rate
The ANSYS Mould flow analysis of the
occurrence of air traps in the hopper component is
performed. Further the analysis for different situations
is considered based on the variance of mould flow
mould flow rate. In the above results, the pink colour
indicates the presence of air traps. For the first case air
traps occur throughout the mould. The number of air
traps is more due to the slow mould flow mould flow
rate. In the last case also the number of air traps is
more. This is due to the high mould flow mould flow
rate. So with slower as well as higher mould flow
mould flow rates we can see there is a high occurrence
of air traps. In the case three, there is a mode mould
flow rate number of air traps due to the mode mould
flow rate mould flow mould flow rate.
5.1 Bulk Temperature Analysis
Figure 5.5 Bulk Temperature Analysis at 50cm3/ sec
mould flow rate
Figure 5.6 Bulk Temperature Analysis at 60cm3/ sec
mould flow rate
International Journal of Pure and Applied Mathematics Special Issue
243
Figure 5.7 Bulk Temperature Analysis at 75cm3/ sec
mould flow rate
Figure 5.8 Bulk Temperature Analysis at 100cm3/ sec
mould flow rate
From the above cases we have concluded that,
when the temperature with in the mould is high, freeze
time will increase. Mould temperature is directly
proportional to the freeze time. The mould temperature
will also affect the cycle time of the process. In Fig 5.8
Bulk Temperature is low, so there is a decrease in
freeze time .Likewise in Fig 5.12 the Bulk temperature
increases and the freeze time also increases.
5.2 Freeze Time Analysis
Figure 5.9 Freeze Time Analysis at 50cm3/ sec
mould flow rate
Figure 5.10 Freeze Time Analysis at 60cm3/ sec
mould flow rate
Figure 5.11 Freeze Time Analysis at 75cm3/ sec
mould flow rate
Figure 5.12 Freeze Time Analysis at 100cm3/ sec
mould flow rate
There is a balance always struck between freeze
times and feeding system. The freeze time depends up
on the wall thickness .From the above results we come
to conclude that the freeze time depends up on the
temperature of the mould and also heat transfer to the
walls. In Fig 5.18 The maximum time taken for
solidification is about 273.5 seconds and minimum
time for solidification is 266.7 seconds.
5.3 Fill Time Analysis
Figure 5.13 Fill Time Analysis at 50cm3/ sec
mould flow rate
Figure 5.14 Fill Time Analysis at 60cm3/ sec
mould flow rate
International Journal of Pure and Applied Mathematics Special Issue
244
Fig 5.15 Fill Time Analysis at 75cm3/ sec
mould flow rate
Figure 5.16 Fill Time Analysis at 100cm3/ sec
mould flow rate
Fill time analysis is used to evaluate the time for
filling the mould cavity with molten metal. If the mould
flow rate of flow of molten metal is high, then the fill
time is low. Thus the mould flow rate of flow of molten
metal is inversely proportional to the fill time. In Fig
5.19, the mould flow mould flow rate is low and so the
fill time is increased as 6.882 seconds. In Fig 5.17, the
mould flow mould flow rate is 100cm3 / second and the
fill time reduces to 1.666 seconds. If mould flow mould
flow rate increases the air trap increases for the optimal
process the correct mould flow rate should be selected.
5.4 Filling Pressure Analysis
Figure 5.17 Filling Pressure Analysis at 50cm3/ sec
mould flow rate
Figure 5.18 Filling Pressure Analysis at 60cm3/ sec
mould flow rate
Figure 5.19 Filling Pressure Analysis at 75cm3/ sec
mould flow rate
Figure 5.20 Filling Pressure Analysis at 100cm3/ sec
mould flow rate
From the results shows that the pressure is still
nonexistence at the initial stages and increases
somewhat due to the filling of small amount of
feedstock in to the mould. Filling pressure is inversely
proportional to the filling time and directly proportional
to mould flow rate. Increase in pressure leads to slight
increase in temperature.
5.5 Weld Line Analysis
International Journal of Pure and Applied Mathematics Special Issue
245
Figure 5.21 Weld Line Analysis at 50cm3/ sec
mould flow rate
Figure 5.22 Weld Line Analysis at 60cm3/ sec
mould flow rate
Figure 5.23 Weld Line Analysis at 75cm3/ sec
mould flow rate
Figure 5.24 Weld Line Analysis at 100cm3/ sec
mould flow rate
Weld lines are also a mouldings defects, Weld
lines are generally formed when two melt fronts come
in contact with each other so that they do not bond
perfectly. weld lines are also formed due to presences
of pins ,cores and multiple gates are one of the most
significant defects from both performance and
appearances point of u This can cause a weak area in
the part which can cause breakage when the part is
under stress. In the results we conclude that weld line
of different mould flow mould flow rate are studied and
the defects are highlighted in different colour. The fig
5.25 shows that there was an maximum defect in the
corners. Therefore increasing rate of mould flow results
in formation of weld line.
6. Optimisation Using Taguchi
There are a number of parameters that have influences
on an injection moulding process, which are types of
material used, types of mould base material, types of
cavity insert material, types of machine, the profile of
the parts, selection of coolant runners as well as
selection of the coolant liquid. However in this study,
only a few major factors are taken into considerations
as to make sure the result can be achieved.
Assumptions to be made
• Gate dimension factor is neglected because of its
design is not identical for every part.
• The temperature of the environment is assumed
constant.
• The coolant is assumed as pure water.
• The effects of other minor factors (Other than
melting temperature, mould temperature, filling and
packing processes) are not to be under the topic of
discussion.
• The layout of the cooling channels is assumed to
maintain a constant temperature.
• The effects due to the shape and size of the mould
and product are neglected due to various shapes of
product.
• The plastic material used in all of the simulations
is amorphous thermoplastic PC/ABS blend, Cycoloy
C2950HF from GE. Its viscosity is between 102 and
104poise
Where the shear rate is in 102-103 s-1 range. The
range of melt temperature is between 220 OC and 400 OC approximately.
Table 6.1 Physical and Mechanical Properties
Specific heat , Cp (J/kgoC) 1871
Glass t ransit ion temperature,
Tg (oC)
112
Thermal expansion coefficient,
α (mm/moC)
74
Elastic modulus, E (MPa) 2.63 x 103
Poisson's ratio, Ʋ 0.23
Thermal conductivity, K (w/moC) 0.27
The length of weld line X of the hopper obtained
from the experiment is used to calculate the signal-to-
noise (S/N) ratio to obtain the best parameter setting
International Journal of Pure and Applied Mathematics Special Issue
246
arrangement. From this technique, the percentage of
contribution is calculated in determining which of the
factor has significant effect on part’s war page. Taguchi
method is again applied where there are three factors
identified to be controlled; Pressure (A), Temperature
(B),Fill time(C). Each factor is downsized to five levels
where an orthogonal array of L9 is chosen and all
parameters have been identified.
Table 6.2 The Three Level of Effective Factor
For Experiment Variance
Factors Levels
1 2 3
Pressure , A (MPa) 6.2 7.1 7.8
Bulk temperature ,B
(oc) 230.0 230.5 231.06
Filling time ,C(s) 6.8 3.4 2.2
Table 6.3 L9 Orthogonal Array Variance
Trail Control factor
A B C
1 1 1 1
2 1 2 2
3 1 3 3
4 2 1 2
5 2 2 3
6 2 3 1
7 3 1 3
8 3 2 1
9 3 3 2
Table 6.4 The Combination Parameters for the Control
Factors
TRAIL
CONTROL FACTOR
A B C
1 6.2 230.0 6.8
2 6.2 230.5 3.4
3 6.2 231.1 2.2
4 7.1 230.0 3.4
5 7.1 230.5 2.2
6 7.1 231.1 6.8
7 7.8 230.0 2.2
8 7.8 230.5 6.8
9 7.8 231.1 3.4
The length of weld lines data obtained from the
simulation process are also analysed using Analysis of
Variance (ANOVA) and the level of confidence is set
at 0.05. The results are then taken and compared with
the results obtained from the SN ratio method. The
interaction effect of factors is identified and the
contribution of each factor towards the total effect is
analysed. The percentage contribution calculated
determines which of the factors mainly affect the length
of weld lines. The length of weld line is then measured
and S/N ratios are calculated. In this case, ‘the smaller
the better quality’ equation from Taguchi method is
chosen as far as weld line is concerned.
able 6.5 Summary of the Results Length of Weld Line
NO
CONTROL FACTOR LENGTH OF WELD LINE
S/N FOR D PRESSURE TEPERATURE FILLTIME
A B C D
1 6.2 230 6.8 6.14 -15.763
2 6.2 230.5 3.4 6.06 -15.649
3 6.2 231.1 2.2 6.42 -16.150
4 7.1 230 3.4 5.98 -15.534
5 7.1 230.5 2.2 6.21 -15.861
6 7.1 231.1 6.8 6.26 -15.931
7 7.8 230 2.2 6.34 -16.041
8 7.8 230.5 6.8 6.14 -15.763
9 7.8 231.1 3.4 6.1 -15.706
The data in Table 6.5 also analyzed using Analysis
of Variance (ANOVA) where the relative percentage
contribution of all factors is determined by comparing
the relative variance. The ANOVA then computes the
degrees of freedom, variance, F-ratio, sums of squares,
pure sum of square and percentage contribution. The
examples of calculations are shown below and the
results of S/N ratio for length of weld line in thin plate
are listed in Table 6. Only weld line at hole X is
considered because length of weld line formation at
International Journal of Pure and Applied Mathematics Special Issue
247
both of holes that shows the similar pattern under
different parameter settings.
Table 6.6 Response Table of S/N Ratio For
Length of Weld Line
Level Pressure Temperature Fill Time
1 -15.78 -15.78 -16.02
2 -15.78 -15.76 -15.63
3 -15.93 -15.93 -15.82
Figure 6.1 Main effects plot for SN ratios
From the S/N ratio response in Table VI, the
highest value from each factor is considered the best
and chosen as the finest grouping of parameters.
Table 6.7 Best Setting of Combination Parameters
Factors Paramerers
Pressure 7.1
Temperature 230.5
Fill Time 3.4
Furthermore the difference between levels in
Table 6.6 also shows which factor is more significant
that give effects on length of weld line in thin plate.
Therefore, it is understood that the most major factor
that affects on length of weld line in hopper is Pressure
(A), Temperature (B), Fill time(C).The data in Table
6.5 is also analyzed using Analysis of Variance
(ANOVA) that computes the sums of squares, degrees
of freedom, variance and percentage contribution. The
examples of calculations for these quantities are shown
below and the results lengths of weld line in thin plate
are summarized in Table 6.8.
Table 6.8 Anova Table For Hooper
SOURCE DF S V F P
Pressure 2 0.01027 0.005137 0.72 0.582
Temperature 2 0.05229 0.026147 3.66 0.215
Fill Time 2 0.22595 0.112977 15.81 0.046
Residual Error 2 0.01430 0.007148
Total 8 0.30282
7. Conclusion
There are several factors such as feed systems, cooling
channel positions, gate sizes that need to be determined
first in order to design a plastic injection mould.
Simulation software can help us reducing time taken to
test. From the above experiment we conclude that the
optimised parameters for injection moulding of
hooper is pressure 7.1 MPa, Temperature 230.5oC ,Fill
time 3.4s and Mould flow rate of 50cm3/s . In this stage
the formation of the weld line is low than compared to
the other cases where we get optimum weld line this
optimisation increase the product life and cycle time.
The Ansys mould flow results also shows that air traps
are minimum at these optimised condition.
International Journal of Pure and Applied Mathematics Special Issue
248
References
[1] Bown, J., “Injection Moulding of Plastic
Components”, McGraw-Hill, 1979.
[2] Technical Directory on design and Tooling for
plastics, CIPET, Guindy, Chennai.
[3] R.K. Roy, Design of Experiments Using the
Taguchi Approach; John Willey & Sons, Inc.: New
York, 2001.
[4] Liu, J., 2005. Application of Mold flow
Technology in buckling analysis of plastic part, Mould
Industry, the 33: 32-34
[5] G. Taguchi, “Performance Analysis Design.”
Int. Journal of Production Research, 16 (1978) 521-
530.
[6] Technical Directory on design and Tooling for
plastics, CIPET, Guindy, Chennai.
[7] Manzione,L.T. Applications of Computer
Aided Engineering in Injection Moulding, Hanser,
NewYork,1987.
[8] Saman, A.M. Abdullah, A.H, Nor, MAM.
Computer Simulation apportunity in Plastic
injection Mould Development for Automotive part. Inte
rnational conference on Computer Technology and
Development.
[9] N.Subramani, C.Krishnaraj “Comparison
Analysis Of A6061-O And A6061-T6 Composites
Produced By Using Friction Stir Process” International
Journal of Pure and Applied Mathematics, Volume 116
No. 21 2017, 61-67
[10] S.V.Manikanthan and D.Sugandhi
“Interference Alignment Techniques For Mimo
Multicell Based On Relay Interference Broadcast
Channel”International Journal of Emerging Technology
in Computer Science & Electronics (IJETCSE) ISSN:
0976-1353 Volume- 7 ,Issue 1 –MARCH 2014.
[11] Rajesh, M, and J. M. Gnanasekar. "
Constructing Well-Organized Wireless Sensor
Networks with Low-Level Identification." World
Engineering & Applied Sciences Journal 7.1
(2016).
[12] T. Padmapriya, V.Saminadan, “Performance
Improvement in long term Evolution-advanced network
using multiple imput multiple output technique”,
Journal of Advanced Research in Dynamical and
Control Systems, Vol. 9, Sp-6, pp: 990-1010, 2017.
[13] S.V.Manikanthan and K.Baskaran “Low Cost
VLSI Design Implementation of Sorting Network for
ACSFD in Wireless Sensor Network”, CiiT
International Journal of Programmable Device Circuits
and Systems,Print: ISSN 0974 – 973X & Online: ISSN
0974 – 9624, Issue :November 2011, PDCS112011008.
International Journal of Pure and Applied Mathematics Special Issue
249
250