Optimization of Injection Moulding Process for Multiple Quality Characteristics of Reprocessed ABS Products via the Integration of Taguchi Method and Grey Relational Analysis

64

International Journal of Engineering & Technology Research

Volume-1, Issue-1, July – September, 2013, pp. 64-76, © IASTER 2013

www.iaster.com, ISSN Online: 2347-4904, Print: 2347-8292

Optimization of Injection Moulding Process for Multiple

Quality Characteristics of Reprocessed ABS Products via the

Integration of Taguchi Method and Grey Relational Analysis

Ng Chin Fei1, Nik Mizamzul Mehat

1,2, Shahrul Kamaruddin

1

1School of Mechanical Engineering, Universiti Sains Malaysia,

Engineering Campus, 14300, Nibong Tebal, Penang, Malaysia 2Department of Mould Technology, Kolej Kemahiran Tinggi MARA,

Balik Pulau,Genting, 11000, Balik Pulau, Penang, Malaysia

ABSTRACT

Determining the optimal parameters setting is performed routinely in the plastic injection

moulding industry as it has a direct and dramatic influence on product quality and costs. This

study emphasizes on the optimization of processing parameters to enhance multiple quality

characteristics of the injection-moulded ring stopper, including hoop tensile strength, elongation

at break and shrinkage. The specimens were prepared under different injection moulding

conditions according to the design of experiments based on the Taguchi’s L9 orthogonal array, by

varying the melt temperature, packing switchover, injection pressure and packing pressure at

second progressive stroke. Grey relational analysis is integrated with main effects analysis to

obtain the optimal parameters setting. From the analysis, the optimal parameters setting which

statistically result in the maximum hoop tensile strength and elongation at break, as well as

minimum shrinkage for the ring stopper, are predicted to be the combination of melt temperature

of 240°C, 80% of packing switchover, 1600MPa of injection pressure and 800MPa of packing

pressure at second progressive stroke. On the other hand, the results also revealed that the melt

temperature was found to be the most decisive factor whereas the packing pressure at second

progressive stroke has the least influence in affecting the hoop tensile strength, elongation at

break and shrinkage of the ring stopper in this study.

Keywords: Grey Relational Analysis, Taguchi Method, Optimization, Multiple Quality

Characteristics, Hoop Tensile Strength.

1. INTRODUCTION

Injection moulding is the most important manufacturing process in the plastics industry as more

than 1/3 of all thermoplastic materials are injection moulded and more than half of all polymer

processing equipments are for injection moulding [1]. This is because the injection moulding

process has the highest efficiency, largest yield, and highest dimensional accuracy among all the

processing methods [2]. In addition, the injection-moulded products which exhibit good

dimensional accuracy and excellent surface finish, has further proven the value of injection

moulding process [3]. However, the complexity of injection moulding process has created a very

intense effort to keep the quality characteristics under control. There are many factors contributing

to the occurrence of defects that affect the quality of injection-moulded parts during the production

such as material selection, part and mould designs and processing parameters. Inappropriate

combination of material selection, part and mould design and the processing parameters can cause

numerous production problems (e.g. product defects, long lead time, much scrap, high production

costs, etc.), reduce the competitive price advantage, and decrease the company’s profitability.


Volume-1, Issue-1, July – September, 2013, www.iaster.com

ISSN (O) 2347- 4904

(P) 2347-8292

65

Therefore, determining the optimal parameters setting is performed routinely in the plastic

injection moulding industry as it has a direct and dramatic influence on product quality and costs.

Previously, the traditional method of multi-factorial experimentation is the ‘‘change-one-factor-at-

a-time’’ method. It is that only one factor is varied, while keeping all the other factors fixed at a

specific set of conditions. This method is popular due to its simplicity. However, in a fiercely

competitive market, the practice of the ‘‘change-one-factor-at-a-time’’ method is no longer

sufficient to meet the challenges of globalization especially at the point where the disadvantages

outweigh its advantages.

To date, Taguchi method as a robust experimental design is commonly used by the researchers to

obtain the best combination set of factors/levels with lowest cost solution to achieve the product

quality requirements. It consists of several functional elements that can provide the necessary

contribution needed to enhance the optimization implementation especially the Taguchi’s

orthogonal array. From the review of Ilzarbe et al [4], Taguchi’s orthogonal matrices achieved the

highest usage in engineering application with 31% outweighing other types of experimental design

due to its practicality. Liu and Chang [5] employed the Taguchi method to optimize seven

processing parameters, including melt temperature, mould temperature, injection speed, injection

pressure, short-shot size, gas injection pressure, gas injection delay time, to improve surface

roughness in gas assisted injection moulding. Li et al [6] also exploited the L9 experimental design

to study the effect of four processing parameters on the weld-line of the right door of copy

machine which was modelled with three gates. In another study, Wu and Liang [7] further

improved the weld-line strength of an injection-moulded part by utilizing the Taguchi optimization

method. Ozcelik et al [8] conducted experiments adopting a L9 orthogonal array to optimize the

processing parameters of ABS mouldings on multiple quality characteristics such as elasticity

module, tensile strength at yield, tensile strain at yield, tensile strain at break, flexural modules and

Izod impact strength. However, the results generated one optimal combination of processing

parameters for different performance measure, indicating that the multi-response optimization was

ineffective as it is impractical to obtain so many optimal parameters setting to improve multiple

quality characteristics simultaneously.

Taguchi’s contribution to the processing optimization has been far ranging as it provides a

considerable reduction of time and effort needed to determine the important factors affecting

product quality as well as obtain the optimal process conditions. Nevertheless, the Taguchi method

is initially designed to be used for a single response optimization. Unfortunately, it is widely

known that most industrial products manufactured today possess more than one quality response

and their quality characteristics are considered by the customers [9]. There are some obvious

shortcomings in performing the Taguchi method single-handedly when more than one quality

characteristic is considered in the optimization. An engineering judgement is required to define a

weight for each quality characteristic but it will increase uncertainty during the decision-making

process. Therefore, in view of the fact that the distinction of grey relational analysis (GRA) is

attempted to integrate multiple responses, it is feasible to combine Taguchi method with GRA to

provide an optimal constitute of processing parameters for the cases with multiple quality

characteristics to accomplish the prerequisites of optimization effectively.

The grey relational analysis (GRA) was first proposed by Deng [10] to optimize the multi-response

problem by making use of the grey relational coefficient and grey relational grade. The grey

relational coefficient can express the relationship between the desired and actual experimental

results and the grey relational grade is simultaneously computed corresponding to each quality



ISSN (O) 2347- 4904

(P) 2347-8292

66

characteristic. Few works have studied the optimization of injection moulding process parameters

by using the integration of Taguchi method and GRA. Fung [11] utilized the Taguchi based

orthogonal array and GRA to improve the wear volume losses in two sliding directions which are

parallel and perpendicular. Similar to the previous work, two studies were carried out by Fung et al

[2] and Yang [13] using the same procedures to optimize the processing parameters for multiple

quality characteristics. The former study focused on the concurrent improvement of yield stress

and elongation of PC/ABS blend whereas the latter study examined the effect of processing

parameters on the mechanical and tribological properties of PC composites, including ultimate

stress, surface roughness and friction coefficient. Kuo and Su [14] optimized the processing

parameters to improve the V cut depth and angle of a LCD light-guide plate and analyzed the

results thoroughly based on the Taguchi method. They performed the main effects analysis and

ANOVA to evaluate the effect of processing parameters on multiple quality characteristics instead

of referring to the comparability sequence as shown in GRA. It was found that the percentage

errors for the confirmation experiment values and the predicted performance values at optimal

process conditions were less than 5% for both depth and angle of the LCD light-guide plate.

Therefore, this study emphasized on the optimization of processing parameters via the integration

of the Taguchi method and grey relational analysis to improve the product quality of a ring stopper

which is made from an ABS blend comprising 40% of recyclates. Four parameters of melt

temperature, packing switchover, injection pressure and packing pressure at second progressive

stroke, each at three levels were selected as the control conditions. On the other hand, three quality

characteristics of the mechanical properties and dimensional stability are selected, including hoop

tensile strength, elongation at break and shrinkage for the injection-moulded ring stopper.

Simultaneously, the grey relational analysis was applied to determine the optimal combination of

process parameters and the most influential factor for the injection moulding process can be

determined in this study.

2. GREY RELATIONAL ANALYSIS

2.1 Data Pre-processing

Let the original reference sequence and comparability sequences are represented as and

, i = 1, 2,…, m; k = 1, 2,…, n, respectively. A data preprocessing is required in view of the

fact that the range and unit in one data may differ from the others. Moreover, it is necessary when

the sequence scatter range is too large or the target sequence directions are different. The data

preprocessing involves the transfer of the original sequence to a comparable sequence. There are

four methodologies of data preprocessing available for the grey relational analysis based on the

characteristics of the data sequence as follows:

The-larger-the better characteristic:

(1)

The-smaller-the-better characteristic:

(2)



ISSN (O) 2347- 4904

(P) 2347-8292

67

The-nominal-the-better characteristic:

(3)

Alternatively, the original sequence can be normalized using the simplest methodology in which

the values of the original sequence can be divided by the first value of the sequence.

(4)

where = the original sequence

= the sequence after the data preprocessing

= the largest value of

= the smallest value of

2.2 Grey Relational Coefficient and Grey Relational Grade

Following data pre-processing, a grey relational coefficient can be calculated with the pre-

processed sequences. The grey relational coefficient is defined as follows:

0 < 1 (5)

where is the deviation sequence of the reference sequence, and the comparability

sequence, , namely

= distinguishing coefficient .

On the other hand, the grey relational grade is a weighting sum of the grey relational coefficient

and is defined as follows:

(6)

Here, the gray relational grade represents the level of correlation between the reference

sequence and the comparability sequence. If the two sequences are identically coincidence, then

the value of grey relational grade is equal to one. The grey relational grade also indicates the

degree of influence that the comparability sequence could exert on the reference sequence.

Therefore, if a particular comparability sequence is more important to the reference sequence than

the other comparability sequences, the grey relational grade for that comparability sequence and

reference sequence will exceed that for other grey relational grades. Grey relational analysis is

actually a measurement of absolute value of data difference between sequences and could be used

to measure approximation correlation between sequences.



ISSN (O) 2347- 4904

(P) 2347-8292

68

3.0 EXPERIMENTAL

3.1 Part Description

In this study, a ring-shaped part namely ring

stopper with dimensions of 40mm outer

diameter, 32mm inner diameter and height of

9.3mm, is used in this research as shown in Fig.

1. The ring stopper is designed to hold a metal

yoke where the metal yoke is pressed into the

holder to form a steadfast grip with an

interference fit. In order to create an

interference fit, the diameter of the metal yoke

has to be slightly larger than the holder’s

diameter to create high degree of tightness for

the ring stopper to grip the metal yoke.

Fig. 1: The Ring Stopper

Considering the application of the ring stopper, the hoop tensile properties, a hard and tough

material would be capable of absorbing enormous amounts of energy where high hoop tensile

strength and high elongation at break are desired in the product characteristics to be able to sustain

the tensile stress created by the metal yoke against its interior wall to prevent structural failure.

Despite of the hoop tensile properties, the dimensional stability is also a crucial quality issue to be

evaluated. The shrinkage of the ring stopper will increase the degree of tightness and it has high

probability to crack the park due to the dimensional deformation. Multiple quality characteristics

including the hoop tensile strength, elongation at break and shrinkage of the ring stopper will be a

direct reflection on the product quality for its end use and are picked up in this investigation.

3.2 Material

The acrylonitrile butadiene styrene (ABS) material was an injection grade, denoted Novodur

P2MT and supplied by Lanxess. The material was used as received. The general properties of the

virgin ABS are shown in Table 1.

Table 1: General properties of ABS Novodur P2MT

Properties

Density (g/cm3) 1.04

Melt Flow Index (220°C/10 kg) 8

Ultimate Tensile Strength (MPa) 48

Tensile Strain (%) 2.4

Modulus of Elasticity (GPa) 2.5

Izod/RT, Notched (J/m) 220

Hardness (HK) 100

The recycled ABS pellets are the same grade as the virgin material where they were sourced by

crushing the existed products and the post industrial scraps via a granulator. In this study, the

recycled ABS were subjected up to 10 reprocessing cycles and a blending compound, which is

known as R-40 was prepared by mixing ratios of 40% (by volume) of 10-time-reprocessed ABS

with the virgin resins via a proportional mixing valve, Motan Metromix 38.



ISSN (O) 2347- 4904

(P) 2347-8292

69

3.3 Experimental Parameters and Design

A preliminary experiment was carried out by using Cadmould 3D-F to determine adequate values

of the processing parameters that are able to produce a part without any aesthetic defect such as

short shot, flash, sink mark, silver streaks and etc. Based on the simulation results, the adequate

values of the processing parameters were set and it was found out that using two progressive

strokes in packing process can significantly lower the probability of the sink marks defect. The

optimization experiment was carried out using an Arburg injection moulding machine model

270M Allrounder 350-90. Four processing parameters including melting temperature, packing

switchover, injection pressure, packing pressure at second stroke, were selected for optimization

with the aim of enhancing the hoop tensile strength, elongation at break and shrinkage of

reprocessed ABS products. Other parameters such as mould temperature (60 °C), packing pressure

at first stroke (600 bars), packing time at first and second stroke (2 and 3 seconds), cooling time

(10 seconds) and stroke distance (200 mm) were kept constant during the experimentation. The

processing parameters and their effects on the performance characteristics, each parameter was set

at three levels. Table 2 shows the injection moulding parameters and their levels.

Table 2: Processing parameters and parameter levels

Column Parameter Level 1 Level 2 Level 3

A Melting temperature (°C) 240 250 260

B Packing Switchover (%) 75 80 85

C Injection Pressure (bar) 1400 1500 1600

D Packing Pressure – Stroke 2 (bar) 600 700 800

After determining the number of processing parameters and their levels, an appropriate orthogonal

array has to be established for laying out the design of experiments that need to be carried out. For

the optimization experiment of four processing parameters each at three levels, a L9 OA was

chosen to allocate the processing parameters including melting temperature, packing switchover,

injection pressure, holding pressure at second stroke. Table 3 shows the layout of L9 OA in the

design of experiment.

Table 3: Design of experiment using L9 orthogonal array

Trial No. A B C D

1 1 1 1 1

2 1 2 2 2

3 1 3 3 3

4 2 1 2 3

5 2 2 3 1

6 2 3 1 2

7 3 1 3 2

8 3 2 1 3

9 3 3 2 1

As illustrated in Table 3, melting temperature was designated as factor A, packing switchover was

assigned as factor B, injection pressure and packing pressure at second stroke were designated as

factor C and D correspondingly. There are total nine experimental trials to be conducted under

different combination of process conditions as shown in the L9 OA, in order to obtain the optimal

processing parameters for the injection-moulded ring stopper.



ISSN (O) 2347- 4904

(P) 2347-8292

70

3.4 Quality Testing

3.4.1 Hoop Tensile Test

Due to the limitations of the dimension and shape of the component, the hoop tensile strength and

elongation at break, were measured in accordance with ASTM D2290-04, using an Instron 3367

series table-mounted universal testing machine. A split-disc test fixture was fabricated accordingly,

as shown in Fig. 2. The test specimen was mounted on the test fixture where the reduced sections

were placed at the side of split. This ensured that the specimen was centered on the line joining the

fixture’s points of attachment with the test machine (refer Fig. 3). The crosshead speed was 2.54

mm/min and five specimens were tested for each reprocessing cycle. The hoop tensile strength at

yield of the specimens was calculated using the appropriate equation as stated in ASTM D2290-04:

1 1 2 2/ ( )a bP d b d b (7)

where: σa = hoop yield or ultimate tensile stress of the specimen, MPa

Pb = maximum or breaking load, N

d1, d2 = thickness at reduced or test section, mm

b1, b2 = width of reduced or test section, mm

On the other hand, the elongation at break of the specimens was computed by taking the

percentage of the elongated displacement at maximum load over the inner diameter of the test

specimen.

ez = (lz – lo)/lo x 100% (8)

where ez is elongation at break in percentage, lz is the final length of the specimen after the hoop

tensile test and lo is the original length of the specimen.

Fig. 2: Split disk test fixture Fig. 3: Setup of split disk test fixture

3.4.2 Shrinkage Test

Rax Vision DC 3000 Mitutoyo profile projector was used to measure the outer diameter of the part.

The profile projector is used for measuring two-dimensional contours of the specimens. With large

magnifications and micrometer readouts, this profile projector could ensure fairly accurate

measurements compared to vernier calliper and micrometer. The geometrical measurement was

made by a micrometer with the assistance of a magnifier lens of a ten-time magnification using



ISSN (O) 2347- 4904

(P) 2347-8292

71

diascopic illumination. In order to ensure the integrity of the comparison procedure, four injection-

moulded parts which come from the same batch were measured to determine the repeatability of

the part geometry. Finally, the relative shrinkage of the part was calculated with the following

equation:

S = (D – Dm)/Dm x 100% (9)

where S is shrinkage, D is the reading of diameter measurement using profile projector and Dm is

the mould dimension.

4. RESULTS AND DISCUSSION

4.1 The best experimental run

The experimental run and experimental results from on-line performance were listed in Table 4.

Typically, higher values of the hoop tensile strength and elongation at break in the ring stopper as

the target values are desirable. Thus, the data sequences have the-larger-the-better characteristic.

On the other hand, the lower values of the shrinkage in the ring stopper as the target values are

desirable and the data sequences have the-smaller-the-better characteristic.

Table 4: Experimental results of multiple quality characteristics for ring stopper

Experimental

run

Factor

Result

A B C D

X (MPa) Y (%) Z (%)

1 240 75 1400 600 49.701 3.829 0.184

2 240 80 1500 700 50.333 3.772 0.052

3 240 85 1600 800 49.307 3.832 0.015

4 250 75 1500 800 49.486 3.831 0.075

5 250 80 1600 600 49.636 3.759 0.005

6 250 85 1400 700 48.506 3.731 0.147

7 260 75 1600 700 48.392 3.768 0.116

8 260 80 1400 800 48.405 3.758 0.160

9 260 85 1500 600 48.008 3.688 0.133

The values of the hoop tensile strength, elongation at break and shrinkage are set to be the

reference sequence , k = 1–3. Moreover, the results of nine experiments were the

comparability sequences , i = 1, 2, 3,…, 9, k = 1–3. Table 5 lists all of the sequences

following data preprocessing using Equation (1) and (2), where the reference and comparability

sequences are denoted as and , respectively.

The deviation sequences can be calculated as follows:

So, = (0.2718, 0.0208, 1.0000)

The same calculating method was performed for i = 1–9 and the results of all for i = 1–9 are

listed in Table 5.



ISSN (O) 2347- 4904

(P) 2347-8292

72

Table 5: The sequences after data preprocessing

Reference/Comparability

Sequence X (MPa) Y (%) Z (%)

Reference sequence 1.0000 1.0000 1.0000

Comparability sequences

Experimental No. 1 0.7282 0.9792 0.0000

Experimental No. 2 1.0000 0.5833 0.7374

Experimental No. 3 0.5587 1.0000 0.9441

Experimental No. 4 0.6357 0.9931 0.6089

Experimental No. 5 0.7002 0.4931 1.0000

Experimental No. 6 0.2142 0.2986 0.2067

Experimental No. 7 0.1652 0.5556 0.3799

Experimental No. 8 0.1708 0.4861 0.1341

Experimental No. 9 0.0000 0.0000 0.2849

Investigating the data presented in Table 5, we can find that and are as follows:

The distinguishing coefficient f can be substituted for the grey relational coefficient in Equation

(5). Given that all the process parameters have equal weighting, the value of f is defined as 0.5.

Table 6 lists the grey relational coefficient and grade for all nine comparability sequences.

Table 6: The grey relational coefficient and grey relational grade for nine comparability sequences

Experimental

run

(Comparability

Sequence)

Factor Grey relational

coefficient Grey

relational

grade A B C D

X Y Z

1 240 75 1400 600 0.6478 0.9600 0.3333 0.6470

2 240 80 1500 700 1.0000 0.5455 0.6557 0.7337

3 240 85 1600 800 0.5312 1.0000 0.8995 0.8102

4 250 75 1500 800 0.5785 0.9863 0.5611 0.7086

5 250 80 1600 600 0.6252 0.4966 1.0000 0.7072

6 250 85 1400 700 0.3889 0.4162 0.3866 0.3972

7 260 75 1600 700 0.3746 0.5294 0.4464 0.4501

8 260 80 1400 800 0.3762 0.4932 0.3661 0.4118

9 260 85 1500 600 0.3333 0.3333 0.4115 0.3594

This investigation employs the main effects analysis of the Taguchi method to calculate the

average grey relational grade for each factor level, as illustrated in Table 6. In view of the fact that

the grey relational grade represents the level of correlation between the reference and the

comparability sequences, the larger grey relational grade means the comparability sequence

exhibits a stronger correlation with the reference sequence. Therefore, the comparability sequence

has a larger value of grey relational grade for the hoop tensile strength, elongation at break and

shrinkage. Based on this premise, this study selects the level that provides the largest average

response.

Table 7: The main effects analysis for grey relational grade

Column Parameter Level 1 Level 2 Level 3

A Melt temperature 0.7303 0.6043 0.4071

B Packing switchover 0.6019 0.6176 0.5223

C Injection pressure 0.4853 0.6006 0.6558

D Packing pressure 2 0.5712 0.5270 0.6435



ISSN (O) 2347- 4904

(P) 2347-8292

73

As seen in the main effect plot in Fig. 4, A1, B2, C3, and D3 show the largest value of grey

relational grade for factors A, B, C, and D, respectively. As a result, the optimal parameters setting

which statistically result in the maximum

hoop tensile strength and elongation at break,

as well as minimum shrinkage for the ring

stopper, are predicted to be A1B2C3D3. As a

result, the optimal parameters setting represent

a melt temperature of 240°C, 80% of packing

switchover, 1600MPa of injection pressure

and 800MPa of packing pressure at second

progressive stroke.

Fig. 4: Main effects plot for grey relational grade

4.2 The Most Influential Factor

Other than analysis of variance (ANOVA) of the Taguchi method, grey relational analysis can be

applied to examine the extent to which injection moulding process parameters influence product

performance. The values of parameter level in the nine experimental runs are set to the

comparability sequences for four processing parameters in this study. Data preprocessing is

performed using Equation (4), the data are normalized based on initial value, and Table 8 lists the

results. Subsequently, the deviation sequences are calculated using the method previously

mentioned. The deviation sequences and distinguishing coefficient then are substituted into

Equation (5) to obtain the grey relational coefficient. Furthermore, the grey relational coefficients

are averaged using an equal weighting to obtain the grey relational grade. Table 9 shows the grey

relational coefficient and grade of hoop tensile strength for the reference sequence and

comparability sequences. On the other hand, Tables 10 and 11 list the grey relational coefficient

and grade of elongation at break and shrinkage for the reference sequence with the same

comparability sequences, respectively.

Table 8: The sequences after data preprocessing for reference sequences

and comparability sequences

Experimental

run

Comparability sequences

Reference sequences

A B C D

X (MPa) Y (%) Z (%)

1 1.000 1.000 1.000 1.000 1.000 1.000 1.000

2 1.000 1.067 1.071 1.167 1.013 0.985 0.283

3 1.000 1.133 1.143 1.333 0.992 1.001 0.082

4 1.042 1.000 1.071 1.333 0.996 1.001 0.408

5 1.042 1.067 1.143 1.000 0.999 0.982 0.027

6 1.042 1.133 1.000 1.167 0.976 0.974 0.799

7 1.083 1.000 1.143 1.167 0.974 0.984 0.630

8 1.083 1.067 1.000 1.333 0.974 0.981 0.870

9 1.083 1.133 1.071 1.000 0.966 0.963 0.723

0.2

0.4

0.6

0.8

1.0

240 250 260 75 80 85 1400 1500 1600 600 700 800

Mea

n

Main effects plot

Melt temperature Packing pressure 2Injection pressurePacking switchover



ISSN (O) 2347- 4904

(P) 2347-8292

74

Table 9: The grey relational coefficient and grey relational grade for experimental factors

to experimental result of hoop tensile strength

A B C D

Grey relational

coefficient

1.0000 1.0000 1.0000 1.0000

0.9339 0.7691 0.7537 0.5386

0.9578 0.5599 0.5438 0.3449

0.7962 0.9765 0.7035 0.3473

0.8070 0.7256 0.5549 0.9928

0.7322 0.5331 0.8820 0.4851

0.6210 0.8722 0.5151 0.4822

0.6216 0.6596 0.8733 0.3333

0.6049 0.5177 0.6301 0.8407

Grey relational grade 0.7861 0.7349 0.7174 0.5961


to experimental result of elongation at break

A B C D

Grey relational

coefficient

1.0000 1.0000 1.0000 1.0000

0.9220 0.6833 0.6709 0.4921

0.9956 0.5703 0.5532 0.3460

0.8105 0.9970 0.7127 0.3458

0.7459 0.6744 0.5220 0.9059

0.7234 0.5254 0.8730 0.4778

0.6393 0.9170 0.5256 0.4907

0.6333 0.6737 0.9047 0.3333

0.5942 0.5084 0.6191 0.8269



to experimental result of shrinkage

A B C D

Grey relational

coefficient

1.0000 1.0000 1.0000 1.0000

0.4659 0.4439 0.4424 0.4145

0.4053 0.3731 0.3710 0.3333

0.4968 0.5138 0.4853 0.4034

0.3816 0.3758 0.3594 0.3915

0.7205 0.6518 0.7568 0.6299

0.5802 0.6288 0.5498 0.5386

0.7454 0.7605 0.8275 0.5744

0.6345 0.6039 0.6423 0.6931


The grey relational grades in Tables 9, 10, and 11 can be further arranged in matrix form, as

follows:

(10)



ISSN (O) 2347- 4904

(P) 2347-8292

75

By comparing Row 1, Row 2 and Row 3, the magnitude of the grey relational grade of four

processing parameters on multiple quality characteristics of the ring stopper is determined. In Row

1, γ(X, A) > γ(X, B) > γ(X, C) > γ(X, D), which means that the sequential order of significance of

the processing parameters to the hoop tensile strength was parameter A, B, C and D in sequence.

In Row 2, it is also γ(X, A) > γ(X, B) > γ(X, C) > γ(X, D). The sequential order of significance of

the processing parameters to the elongation at break was found to be similar to the previous

performance measure. On the other hand, in Row 3, the result shows that γ(X, C) > γ(X, A) > γ(X,

B) > γ(X, D). Therefore, the significance ranking of the processing parameters to shrinkage was

determined as factor C, A, B and D in order.

The degree of influence of the processing parameters on the multiple quality characteristics can be

calculated and the most decisive factor will be given the maximum value of the row for each quality

characteristics. As shown in grey relational grade matrix (10), it can be observed that the melt

temperature is the most influential processing parameter which demonstrates the strongest

comparability sequence among the injection moulding processing parameters, with the highest values

for overall grey relational grades, [γ (X, A) > γ(Y, A) > γ(Z, A)] = (0.7861, 0.7849, 0.6034). The

analysis revealed that the melt temperature had the strongest correlation to the hoop tensile strength,

elongation at break and shrinkage. In addition, γ (X, A) = 0.7861 is also the maximum entries of the

matrix, indicating that it is the most evident that the injection moulding parameters changed for hoop

tensile strength effect. Nevertheless, packing pressure at second progressive stroke was found to have

the least importance on hoop tensile strength, elongation at break and shrinkage simultaneously with

the lowest grey relational grades [γ (X, D) > γ(Y, D) > γ(Z, D)] = (0.5961, 0.5798, 0.5532). As a result,

the melt temperature was the most influential parameter to the product quality of the ring stopper

whereas the result was vice versa for the packing pressure at second progressive stroke.

5. CONCLUSION

The grey relational analysis based on the Taguchi method’s orthogonal array and main effects

analysis was proposed as a way of effectively optimizing the processing parameters for injection

moulding process to the ring stopper of a case study. Considering the functionality of the ring

stopper, the mechanical properties of hoop tensile strength and elongation at break and the

dimensional stability of shrinkage are selected to be the performance measure of the part. Through

a series of analysis and optimization, the results are summarized as follows:

1. From the main effects analysis of the average grey relational grade, it was found that the largest

value of grey relational grade for melt temperature, packing switchover, injection pressure and

packing pressure at second progressive stroke was 240°C, 80%, 1600MPa and 800MPa,

respectively. Therefore, the optimal combination of processing parameters for the ring stopper

to achieve maximum hoop tensile strength and elongation at break, as well as minimum

shrinkage was determined as A1B2C3D3 when multiple quality characteristics are

simultaneously considered.

2. Grey relational analysis can be applied to determine the significance of the processing

parameters and arrange them according to the sequential order of importance. For hoop tensile

strength and elongation at break, the significance of the processing parameters lies in the

sequential order of melt temperature, packing switchover, injection pressure and packing

pressure at second progressive stroke for both performance measures. On the other hand, the

importance of the processing parameters to shrinkage, in sequence, is injection pressure, melt

temperature, packing switchover and packing pressure at second progressive stroke.



ISSN (O) 2347- 4904

(P) 2347-8292

76

3. The melt temperature showed the strongest comparability sequence among the four important

injection moulding process parameters investigated in this study whereas the result is vice versa

for the packing pressure at second progressive stroke. It means that the melt temperature had

the strongest correlation to hoop tensile strength, elongation at break and shrinkage while the

packing pressure at second progressive stroke showed the least correlation to all the

performance measures. The melt temperature was determined as the most influential factor to

the product quality of the ring stopper.

REFERENCES

[1] C. Shen, L. Wang, W. Cao, L. Qian, Investigation of the Effect of Molding Variables on

Sink Marks of Plastic Injection Molded Parts Using Taguchi DOE Technique, Polymer-

Plastics Technology and Engineering, 46, 2007, 219-225.

[2] F.C. Kung, Y.J. Weng, Optimizations of the Processing Parameters of High-Performance

Engineering Plastic in Injection Molding, Polymer-Plastics Technology and Engineering, 47,

2008, 1154-1161.

[3] H.Y. Wong, K.T. Fung, F. Gao, Development of a transducer for in-line and through cycle

monitoring of key process and quality variables in injection molding, Sensors and Actuators

A: Physical, 141(2), 2008, 712-722.

[4] L. Ilzarbe, M.J. Álvare, E. Viles, M. Tanco, Practical Applications of Design of

Experiments in the Field of Engineering: A Bibliographical Review, Quality and Reliability

Engineering International, 24, 2008, 417-428.

[5] S.J. Liu, J.H. Chang, Application of the Taguchi Method to Optimize the Surface Quality of

Gas Assist Injection Molded Composites, Journal of Reinforced Plastics and Composites,

19, 2000, 1352-1362.

[6] H. Li, Z. Guo, D. Li, Reducing the effects of weldlines on appearance of plastic products by

Taguchi experimental method, International Journal of Advanced Manufacturing

Technology, 32(9-10), 2007, 927-931.

[7] C.H. Wu, W.J. Liang, Effects of Geometry and Injection-Molding Parameters on Weld-Line

Strength, Polymer Engineering and Science, 45, 2005, 1021-1030.

[8] B. Ozcelik, A. Ozbay, E. Demirbas, Influence of injection parameters and mold materials on

mechanical of ABS in plastic injection molding, International Communications in Heat and

Mass Transfer, 37, 2010, 1359-1365.

[9] H.C. Liao, Y.K. Chen, Optimizing multi-response problem in the Taguchi method by DEA based

ranking method, International Journal of Quality and Reliability Management, 19(7), 2002, 825-837.

[10] J.L. Deng, Introduction to Grey System Theory, The Journal of Grey System, 1, 1989, 1-24.

[11] C.P. Fung, Manufacturing process optimization for wear property of fiber-reinforced polybutylene

terephthalate composites with grey relational analysis, Wear, 254, 2003, 298-306.

[12] C.P. Fung, C.H. Hwang, J.L. Doong, The Study on the Optimization of Injection Molding

Process Parameters with Gray Relational Analysis, Journal of Reinforced Plastics and

Composites, 22, 2003, 51-66.

[13] Y.K. Yang, Optimization of Injection-Molding Process for Mechanical and Tribological

Properties of Short Glass Fiber and Polytetrafluoroethylene Reinforced Polycarbonate

Composites with Grey Relational Analysis: A Case Study, Polymer-Plastics Technology

and Engineering, 45, 2006, 769-777.

[14] C.F.J. Kuo, T.L. Su, Optimization of Injection Molding Processing Parameters for LCD Light-Guide

Plates, Journal of Materials Engineering and Performance, 16(5), 2007, 539-548.

[15] S. Kedr, Y. Aaachi, Y. Fghdi, and N. Ishii, Application of logic theory to writer recognition of English

Characters, International Journal of Modelling and Simulation, 13 (1), 1999, 112-116.

Optimization of Injection Moulding Process for Multiple Quality Characteristics of Reprocessed ABS Products via the Integration of Taguchi Method and Grey Relational Analysis

Documents