Optimization of Injection Molding

ProcessAlexander 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.

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

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

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

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

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

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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Optimization of plastic injection molding process parameters for manufacturing a

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