International Journal of Engineering Trends and Technology (IJETT) – Volume 11 Number 7 - May 2014
Optimization of Fill Time for Manifold and
Coupler Body of Air Inflator Component
Prashant.V.H1 and Ramesh Babu K1
[1] Dept. of PG studies, Govt. Tool Room & Training centre, Mysore-570016, India
Abstract: Injection molding is a manufacturing technique to
produce plastic components by injecting the molten plastic
material by applying pressure between two halves of the
mould, after material gets solidified the two halves are moved
apart then component is safely ejected by ejector system. To
meet the customer requirements such as higher degree of
accuracy at lower cost, injection molding is a suitable
technique. Increasing complexity of the product design and
requirement of multi response quality characteristics, it has
made the challenging process for the process optimization. It
needs to be optimization of the process to avoid the time
consumption and wastage of trail outs. Optimization of
injection moulding process can be done by various methods
which includes mathematical models, Taguchi method,
Artificial Neural Networks (ANN), Fuzzy logic, Case Based
Reasoning (CBR), Genetic Algorithms (GA), Finite Element
Method(FEM),Non Linear Modelling, Response Surface
Methodology, Linear Regression Analysis, Grey Rational
Analysis and Principle Component Analysis (PCA). Taguchi
method is the method of obtaining the optimum results by the
systematic approach, to optimize design for quality,
performance and cost, this method is easy and effective. In
this paper approaches to optimize the results of fill time of
two components of different material and different shapes to
be produced in a single mold tool. Results of fill time are
obtained from the Mold Flow insight software.
Keywords: Fill Time ,Injection moulding, orthogonal array,
optimization, Taguchi technique.
I.
INTRODUCTION
A. Injection Molding
Nowadays plastic is one of the most used
materials in industrial and commercial life, because of its
immense properties, which makes use in these fields, is
going on increasing. Injection molding is a most commonly
used manufacturing process to produce plastic parts. In
order to meet the complexity and product with higher
accuracy, it is the one of the most effective technique. It is
similar to the die casting, material is heated and sufficient
amount of melted material is injected into the cavity which
may exceed 140 MPa. Pressure is usually maintained after
the initial filling to force the additional material into the
cavity, while material gets solidified increasing the
dimensional stability and accuracy in the product.
Solidified part is ejected by ejector assembly.
It is not only to be considering the part performance
requirements but process and material as well. For the
significant product cost saving and quick time to market,
Effective management of these constraints is very much
necessary [1]. The product quality depends on mould
design, material selection, and process parameter setting.
Plasticization, cooling, packing, injection are the phase of
ISSN: 2231-5381
injection moulding process. Incorrect input parameters
settings will cause bad quality of surface roughness,
decreases Dimensional accuracy, warpage, unacceptable
wastes, time consumption and cost [2].
There are many factors that affect the quality of an
injection molded part. These include mold cavity design,
cooling system design and molding condition selection. For
an injection molded part, it is desirable to obtain the
optimum part quality for a given cycle time. Part quality is
most readily controlled by changing the molding conditions
which include fill time, mold temperature, and melt
temperature. Determining optimal process parameter
settings critically influences productivity, quality, and cost
of production in the plastic injection moulding (PIM)
industry. Increasing complexity of the project design and
requirement of multi response quality characteristics, it has
made the challenging process for the process optimization.
Previously researchers and projection engineers were
carrying out the trial-and-error methods to determine the
process parameters setting for plastic injection molding
(PIM) which is not advisable [3].
Previously engineers and researchers have
attempted various approaches in the determination of
process parameters for injection moulding in order to
reduce the time consumption in time to market and obtain
consistent quality of moulded parts. The various
approaches includes mathematical models, Taguchi
method, Artificial Neural Networks (ANN), Fuzzy logic,
Case Based Reasoning (CBR), Genetic Algorithms (GA),
Finite Element Method (FEM), Non Linear Modelling,
Response Surface Methodology, Linear Regression
Analysis, Grey Rational Analysis and Principle Component
Analysis (PCA).
B.Mold Flow
In daily uses we see rarely where we are avoiding
the plastic use, because of its tremendous applications due
to that of its properties which can replace in almost all the
field of engineering and daily uses. There are thousands of
variety of plastic grades of commercial of plastics materials
with widely varying process parameters, complex part and
complex design of mold made constantly pushing the limits
of the process. The production of injection molded parts is
complex process where, without the right combination of
material, part and mold design and process parameters, a
number of manufacturing defects can occur, thus leads to
higher costs. The relationship between process variables
and product quality is extreamly complex. It is very
difficult to understand the relation between them. For this
reason the simulation of molding is very important. The
http://www.ijettjournal.org
Page 344
International Journal of Engineering Trends and Technology (IJETT) – Volume 11 Number 7 - May 2014
above factors bring a level of complexity to injection
moulding that makes it necessary to use CAE tools to
predict and solve potential problems before they occur.
With the development of CAE technology, especially the
mould flow software made easy to simulate and analyze the
mould design to predicting the defects, which can raise the
success rate of testing mould, thus reduce the product cost
and cut down the exploit cycle[4,5].
C.Taguchi Technique
Dr. Taguchi of Nippon Telephones and telegram
Company, Japan has developed a method based on
“ORTHOGONAL ARRAY” experiments which gives
much reduced variance for the experiment with optimum
settings of control parameters. In Taguchi Method to obtain
best results the combination of optimum control parameters
and design of experiments is carried out.
Taguchi technique recommends to use of
orthogonal array experiments. It is used to optimize the
performace characterstics within the combination of design
paramerters.
Following are basically three steps involved in design of
Taguchi Technique,
1. System design
2. Parameter design
3. Tolerance design
Two important tools are also used in parameters design are
orthogonal array and signal-to-noise (S/N) ratio.
Orthogonal array (OA) provides a set of well balanced
(minimum) number of experiments and signal-to-noise
ratios(S/N), which is log functions of desired output, serve
as objective functions for optimization, help in data
analysis[6-9].
II.
EXPERIMENTAL STUDY
Studies in this project was carried out, where the
two components are of different material and of almost
similar volumes and the two components are produced on
the same tool but different material input.
A.Materials
The material selected are Polyoxymethylene (POM) and
Polyamide-6 (PA-6) 15% glass fibered for Manifold and
Coupler Body respectively.
POM is characterized by its high strength, hardness and
rigidity to -40 °C. It is intrinsically opaque white, due to its
high crystalline composition, but it is available in all
colors. It has a density of ρ = 1.410–1.420 g/cm³.
For polyamide-6 it is 15% glass fiber reinforced because, to
enhance strength and stiffness.
B.Injection Molding Process
The parts are injection molded using 60 Ton machine
(Angel 60 TL).
ISSN: 2231-5381
C. Experimental design
Three levels of processing parameters and L9 orthogonal
array are selected. The process parameters and levels are
shown in table 3.
Table I
Experimental design for Manifold
Sl
No
1
2
Factors
Mold
Temperature(A)
Melt
Temperature(B)
Level
1
70
Level
2
80
Level
3
90
200
210
220
TableII
Experimental design for Coupler Body
Sl
No
1
2
Factors
Mold
Temperature(A)
Melt
Temperature(B)
Level
1
60
Level
2
70
Level
3
80
220
230
240
TableIII
L9 orthogonal array
Trial No
1
2
3
4
5
6
7
8
9
III.
Mold temperature (°C)
1
1
1
2
2
2
3
3
3
Melt temperature (°C)
1
2
3
1
2
3
1
2
3
RESULTS AND DISCUSSION
Experimental data for the two components are
shown in the following tables. In this study optimum value
of fill time is expected to be obtained. Thus, for S/N ratio
characteristic the lower-the-better is applied in the analysis
of experimental result.
A.Manifold
1) Fill Time Values For Manifold
TableIV
Fill Time values for Manifold
Trial
No
1
2
Mold
temperature (°C)
70
70
http://www.ijettjournal.org
Melt
temperature (°C)
200
210
Fill time
(Sec)
4.977
4.443
Page 345
International Journal of Engineering Trends and Technology (IJETT) – Volume 11 Number 7 - May 2014
3
4
5
6
7
8
9
70
80
80
80
90
90
90
220
200
210
220
200
210
220
3.906
5.175
4.641
4.106
5.584
4.946
4.415
2) General Linear Model For Fill Time
Type
Fixed
Fixed
Levels
3
3
1.00
2
2.05
1
Above Table shows the Response for Signal to Noise
Ratios of the given parameters. For this experiment, the
input parameters that are influencing the Output parameter
(Fill Time) in their decreasing order are Melt Temperature
and Mold Temperature.
5) Graph Showing The Main Effects Plot For S/N Ratios Of
Fill Time:
TableV
General Linear model for Fill time
Factor
Mold Temperature(°C)
Melt Temperature(°C)
Delta
Rank
Values
70,80,90
200,210,220
Main Effects Plot for SN ratios
Data Means
Mold Temperature
3) Analysis Of Variance For Fill Time:
TableVI
Analysis of Variance for Fill Time
Mold
Temperature
(°C)
Melt
Temperature
(°C)
Error
D
F
2
2
-13.5
-14.0
Seg
SS
0.446
99
Adj
SS
0.446
99
Adj
MS
0.223
49
F
P
194.
83
0.0
00
1.825
50
1.825
50
0.912
75
795.
70
0.0
00
-14.5
70
80
90
200
210
220
Signal-to-noise: Smaller is better
Figure 1: S/N ratio values for Fill Time
6) Graph Showing The Main Effects Plot For Means Of
Fill Time:
Main Effects Plot for Means
0.004 0.004 0.001
59
59
15
Total
8 2.277
08
S = 0.0338690 R-Sq = 99.80% R-Sq(adj) = 99.60%
Data Means
4
For this experiment, Analysis of Variance (ANOVA) was
performed (Shown in Table 6) to identify the influence of
Machining parameters on the output Responses using
MINITAB software. Input parameters considered were
Mold Temperature and Melt Temperature. Output
parameter was Fill Time.
4) Response Table For Signal To Noise Ratios For Fill
Time:
Smaller Is Better
TableVII
Response Table for Signal to Noise Ratios for Fill Time
Level
1
2
3
-13.0
Mold Temperature(°C)
-12.91
-13.29
-13.91
ISSN: 2231-5381
Melt Temperature(°C)
-14.39
-13.39
-12.33
Mold Temperature
5.4
Melt Temperature
5.2
Mea n of Me an s
Source
Melt Temperature
-12.5
Mea n o f SN rat ios
General liner model for Fill time is shown in the above
table 5. Input parameters for this experiment are Mold
Temperature and Melt Temperature at 3 levels and values
are shown.
5.0
4.8
4.6
4.4
4.2
4.0
70
80
90
200
210
220
Fig 2: Main Effects Plot for Means
From Figure 1, it can be observed that, Fill Time Decreases
as the Mold Temperature increases. Further Surface
Roughness increases with increase in Melt Temperature.
The optimum values of machining parameters to get
Optimum Fill Time are 70 (A1) and 220 (B3).
B.COUPLER BODY
1) Fill Time Values For Manifold
http://www.ijettjournal.org
Page 346
International Journal of Engineering Trends and Technology (IJETT) – Volume 11 Number 7 - May 2014
TableVIII
Fill Time values for Manifold
Trial
No
1
2
3
4
5
6
7
8
9
Mold
temperature (°C)
60
60
60
70
70
70
80
80
80
Melt
temperature (°C)
220
230
240
220
230
240
220
230
240
TableXI
Response Table for Signal to Noise Ratios for Fill Time
Fill time
(Sec)
0.7677
0.6637
0.9783
0.7921
0.6561
0.8266
0.8424
0.6453
0.7896
2) General Linear Model For Fill Time
TableIX
General Linear model for Fill time
Level
1
2
3
Delta
Rank
Mold Temperature(°C)
2.016
2.446
2.449
0.433
2
Melt Temperature(°C)
1.937
3.675
1.299
2.376
1
Above Table shows the Response for Signal to Noise
Ratios of the given parameters. For this experiment, the
input parameters that are influencing the Output parameter
(Fill Time) in their decreasing order are Melt Temperature
and Mold Temperature.
5) Graph Showing The Main Effects Plot For S/N Ratios Of
Fill Time:
Main Effects Plot for SN ratios
Data Means
Type
Fixed
Fixed
Levels
3
3
Values
60,70,80
220,230,240
3.0
2.5
2.0
1.5
1.0
60
Analysis of Variance for Fill Time
80
220
SS
MS
F
P
0.00397
0
0.00198
5
0.4
2
0.68
5
0.06935
3
0.03467
7
7.2
6
Main Effects Plot for Means
Data Means
0.04
7
4
0.01909 0.00477
9
5
Total
8
0.09242
3
S = 0.0690994 R-Sq = 79.34% R-Sq(adj) = 58.67%
For this experiment, Analysis of Variance (ANOVA) was
performed (Shown in Table 10) to identify the influence of
Machining parameters on the output Responses using
MINITAB software. Input parameters considered were
Mold Temperature and Melt Temperature. Output
parameter was Fill Time.
4) Response Table For Signal To Noise Ratios For Fill
Time:
Smaller Is Better
ISSN: 2231-5381
240
6) Graph Showing The Main Effects Plot For Means Of
Fill Time:
Mold Temperature
2
230
Figure 3: S/N ratio values for Fill Time
Melt Temperature
0.85
M e a n o f M e a ns
Mold
Temperature(°
C)
Melt
Temperature(°
C)
Error
70
Signal-to-noise: Smaller is better
TableX
D
F
2
Melt Temperature
3.5
General liner model for Fill time is shown in the above
Table 9. Input parameters for this experiment are Mold
Temperature and Melt Temperature at 3 levels and values
are shown.
3) Analysis Of Variance For Fill Time:
Source
Mold Temperature
4.0
Mean of SN ratios
Factor
Mold Temperature(°C)
Melt Temperature(°C)
0.80
0.75
0.70
0.65
60
70
80
220
230
240
Fig 4: Main Effects Plot for Means
From Figure 3, it can be observed that, Fill Time increaes
as the Mold Temperature increases. Further Fill time first
increases with increase in Melt Temperature then
decreases. The optimum values of machining parameters to
get Optimum Fill Time are 80 (A3) and 230 (B2).
http://www.ijettjournal.org
Page 347
International Journal of Engineering Trends and Technology (IJETT) – Volume 11 Number 7 - May 2014
IV.
CONCLUSION
Taguchi method is used to investigate the effects of
Melting Temperature and Mold Temperature on Fill Time.
S/N ratios were used for determine the optimum condition
for Fill Time. The results shown that for the component
Manifold, the optimum fill time 3.906 Sec, for input
parameters are Mold Temperature and Melt Temperature
70°C and 220°C and for Component Coupler Body
respectively optimum input parameters are 80°C and
230°C. Mold Temperature has the least effective as
compare to the Melt Temperature. From the result , it can
be stated that Taguchi Method is powerful tool for
evaluating the effective Fill Time In Injection Molding.
REFERANCES
[1] Kurt A. Beiter, James M. Cardinal, and Kos Ishii “Design for Injection
Moulding: Balancing Mechanical Requirements, Manufacturing Costs,
and Material Selection”
[2] Kalpit Jain, Deepak Kumar, Sanjay Kumawat “Plastic Injection
Moulding with Taguchi Approach - A Review”
[3] P.K Bharati, M.I. Khan “Recent Methods for Optimization of Plastic
Injection Moulding Process –A Retrospective and Literature Review”.
[4] K.poornima, M.N.M Ansari “Cooling channel Design for Multi-Cavity
Plastic injection Moulds”
[5] Vishnuvarthanan, M,Rajesh Panda, Ilangovan.S and Nayak>S.K “
Optimization of Injection Molding Cycle time Using Moldflow Analysis”.
[6] Sajjan Kumar Lal, Dr. Hari Vasudevan “ Optimization Of Injection
Moulding Process Parameters In The Moulding Of Low Density
Polyethylene (LDPE)”
[7] K. Prashantha, J. Soulestin, M. F. Lacrampe, E. Lafranche, P.
Krawczak, G. Dupin,M. Claes “Taguchi analysis of shrinkage and
warpage of injection-moulded polypropylene/multiwall carbon nanotubes
nanocomposites”
[8] M. V. Kavade, S. D. Kadam “Parameter Optimization of Injection
Moulding of Polypropylene by using Taguchi Methodology”
[9] Shaik Mohamed Mohamed Yusoff, Jafri Mohd. Rohani, Wan Harun
Wan Hamid & Edly Ramly “A Plastic Injection Moulding Process
Characterisation Using Experimental Design Technique: A Case Study”
ISSN: 2231-5381
http://www.ijettjournal.org
Page 348