1. Optimization of Injection Molding
Process
Alexander Larsh
Injection molding is best suited for mass-producing objects with specific
dimensional requirements. The general process can be broken down into three
basic parts: filling, post filling, and mold opening. As the plastics exhibit extremely
complicated thermo-viscoelastic material properties, the complexity of the molding
process makes it very challenging to attain desired part properties and thus causes
difficulty in maintaining part quality during production. In the actual operations, the
molding process conditions are often selected from references or handbooks, and
then adjusted subsequently by a trial-and-error approach. This approach is very
costly and time consuming, as well as highly dependent on the experience of the
molding operators.
One way researchers have found to improve the efficiency of this process is
through Computer Aided Engineering (CAE). CAE has made a major impact on the
design and manufacturing process in the injection molding industry in terms of both
quality improvement and cost reduction based on applications of various computer
simulation techniques. However, even more advanced techniques are demanded
from this progressive industry [1].
ANN and GA are two of the most promising natural computation techniques.
In recent years, ANN has become a very powerful and practical method to model
very complex non-linear systems [2, 6]. GA can be found in various research fields
for parameter optimization [7]. These two techniques have been the most widely
accepted methods of optimizing the injection molding process.

2. Traditional modeling methods are mostly relied on assumptions for model
simplifications, and thus may lead to inaccurate results. On the other hand, the
characteristic of the ANN technique make it suitable for modeling the quality
prediction of injection molded parts. Genetic algorithms are search algorithms
designed to mimic the principles of biological evolution in natural genetic system.
GAs are also known as stochastic sampling methods, and they can be used to solve
difficult problems in terms of objective functions that possess ‘bad’ properties [1].
The outline of the combining ANN/GA optimization algorithm is given in Fig. 1.
Fig. 1.
Flow chart of combining ANN/GA optimization.
The primary objective of the present research is to study the possibility of
modeling and predicting the quality of injection molded parts and optimizing the
process conditions so as to improve the part quality by using the combing ANN/GA
method. CAE simulations are used to replace real experiments for the sake of cost
saving. The ANN technique has been shown as an effective method to model the
complex relationship between the process conditions and the quality index of
injection molding parts. The GA is especially appropriate to obtain the global
optimization solution of the complex non-linear problem. The combining ANN/GA

3. method proposed in this paper gives satisfactory result for the optimization of the
injection molding process. An ANN model of volumetric shrinkage variation versus
process conditions for injection molding with a 5–9–1 configuration has been
developed. The optimized results by GA have been verified by the numerical
experiments. The modeling and optimization methods proposed in this paper show
the great potential in complicated industrial applications.
Because injection molding has the ability to produce such a high volume of
products in such a short period of time, traditional processes of manufacturing at
times cause bottlenecks in the production line. Thus, layout optimization plays a
crucial role in this type of problem in terms of increasing the efficiency of the
production line. In this regard, a novel computer simulation–stochastic data
envelopment analysis (CS-SDEA) algorithm is proposed in this paper to deal with a
single row job-shop layout problem in an injection molding process.
Layout problems often occur, and there is a lack of data to find solutions to
this problem. Layout design in manufacturing systems is a crucial task in
redesigning, expanding, or designing the system for the first time. Major
considerations in designing a manufacturing layout can be minimizing material
handling costs, frequency of products and employees among workstations,
smoothing production, and providing a safe workplace for employees. The layout
problem in manufacturing systems involves determining the location of machines,
workstations, rest areas, inspection rooms, clean rooms, heat treatment stations,
offices, and tool cribs to achieve the following objectives: minimization of the
transportation costs of raw material, parts, tools, work-in-process, and finished
products among the facilities [9]and [10], facilitate the traffic flow and minimization
the costs of it [11], maximization of the layout performance [12], minimization of
the dimensional and form errors of products depending on the fixture layout
[13]and [14], minimization of the total number of loop traversals for a family of
products [15] increasing the employee morale, minimization of the risk of injury of

4. personnel and damage to property, providing supervision and face-to-face
communication [16].
This particular study analyzes the layout of a refrigerator company. A main
process in manufacturing refrigerators is the injection in which the foam is injected
between the metal body and plastic tub. The molded part is then cooled and forms
the final product. The case of injection molding process under study is used for
producing four dissimilar types of refrigerator with different technical specifications
in a feeder-line before transporting them to the assembly line. The injection molding
process is composed of a sequence of manual and automated operations. This
process comprises five stages including mold closing, filling, packing–holding,
cooling and mold opening are preceded repeatedly for each product model [8].
The goal of the company being studied is to improve their efficiency by
preventing bottlenecks in the injection molding process. To do this, the processing
time must be minimalized by implementing the best layout of the process stations. A
novel algorithm has been found to help achieve this goal. This algorithm has been
based off of discrete-event-simulation and stochastic data envelopment analysis
(SDEA). The algorithm consists of two main steps: First, simulation is used to model
the process of foam injection. Discrete-event-simulation is known as a powerful and
flexible tool for modeling, visualizing, and manipulating complex systems. With the
aid of the proposed discrete-event-simulation model, key performance indicators of
the system can be simply evaluated. In the second step, SDEA-output oriented model
is utilized to rank different layout formations with respect to a set of key
performance indicators obtained from the simulation models in order to determine
optimum solutions. In this SRFLP, each layout is considered as a decision-making
unit (DMU). Queue length (QL), machine utilization (MU), and time in system (TIS)
are defined by the decision-makers of the company as primary evaluation measures.
These indicators are considered as outputs of the SDEA model. The proposed SDEA
approach specifies the strength and weakness of each layout formation in terms of

5. technical efficiency. This in turn, helps the decision-makers to make right decisions
regarding to various layouts and find the optimal one.
The results are stacked up against two other conventional algorithms
previously mentioned in this literature review, Genetic Algorithm (GA) and Artificial
Neural Network (ANN). The results show that the CS-SDEA efficiency scores fall into
the same range as the other two algorithms, which is between 1.001 and 1.007
efficiency. This can be seen in Table 1 below.
Table 1.
Performance comparison with GA and ANN.
Layout
alternative
The proposed CS-
SDEA
ANN GA
Efficiency Rank Efficiency Rank Efficiency Rank
#01 (1234) 1.002 2 1.00588 2 1.00604 2
#02 (1243) 1.003 5 1.00443 13 1.00427 12
#03 (1342) 1.003 5 1.00301 19 1.00318 18
#04 (1324) 1.003 5 1.00400 10 1.00433 11
#05 (1423) 1.005 19 1.00360 14 1.00387 14
#06 (1432) 1.002 2 1.00476 7 1.00480 4
#07 (2134) 1.004 14 1.00506 3 1.00516 3
#08 (2143) 1.003 5 1.00472 6 1.00476 6
#09 (2314) 1.003 5 1.00415 9 1.00447 9
#10 (2341) 1.004 14 1.00438 4 1.00478 5
#11 (2413) 1.003 5 1.00370 15 1.00382 15
#12 (2431) 1.004 14 1.00334 16 1.00353 16
#13 (3124) 1.003 5 1.00416 8 1.00461 8

6. Layout
alternative
The proposed CS-
SDEA
ANN GA
Efficiency Rank Efficiency Rank Efficiency Rank
#14 (3142) 1.003 5 1.00406 11 1.00434 10
#15 (3241) 1.004 14 1.00425 12 1.00424 13
#16 (3214) 1.003 5 1.00239 18 1.00305 19
#17 (3412) 1.004 14 1.00291 20 1.00257 20
#18 (3421) 1.001 1 1.00700 1 1.00700 1
#19 (4123) 1.005 19 1.00163 21 1.00208 21
#20 (4132) 1.006 21 1.00138 24 1.00100 24
#21 (4213) 1.002 2 1.00447 5 1.00471 7
#22 (4231) 1.007 24 1.00112 22 1.00112 22
#23 (4312) 1.006 21 1.00100 23 1.00103 23
#24 (4321) 1.006 21 1.00359 17 1.00330 17
Table 2 shows the features of each algorithm and shows the advantages of the CS-
SDEA algorithm over the other two.
Table 2.
The features of the simulation–stochastic DEA algorithm versus other methods.
Method Feature

7. Multiple
outputs
Stochastic
outputs
High
precision
and
reliability
Multi-
variate
decision-
making
through
new
output-
oriented
Stochastic
DEA
Practicability
in real world
cases
Simulation–
stochastic
DEA
algorithm
✓ ✓ ✓ ✓ ✓
Genetic
algorithm
✓ ✓ ✓ ✓
Neural
network
model
✓ ✓ ✓
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