STUDY ON ABILITY SHRINKAGE POROSITY FORMATION OF a380 ALUMINUM BY TAGUCHI METHOD NGHIÊN cứu về KHẢ NĂNG HÌNH THÀNH độ xốp CO NGÓT của hợp KIM NHÔM a380 BẰNG PHƯƠNG PHÁP TAGUCHI

Kỷ yếu hội nghị khoa học và công nghệ toàn quốc về cơ khí - Lần thứ IV

STUDY ON ABILITY SHRINKAGE POROSITY FORMATION OF A380
ALUMINUM BY TAGUCHI METHOD.
NGHIÊN CỨU VỀ KHẢ NĂNG HÌNH THÀNH ĐỘ XỐP CO NGÓT CỦA HỢP KIM
NHÔM A380 BẰNG PHƯƠNG PHÁP TAGUCHI.
Anh Tuan Do 1,a, Tran Vung Vu 1b, Van Thuy Hoang 2c, Thi Mo Pham 2d.
1
Hung Yen University of Technology and Education
2
Vocational Intermediate School No.14, Ninh Binh
a
; ;
c
;
ABSTRACT
The current paper on shrinkage porosity formation of die casting for automobile part
product, the following issues are focused: filling simulation, defect analysis, finally the use of
the Taguchi multi quality analytical method to find the optimal parameters and factors to
increase quality and efficiency with the aluminum A380 material die casting. Experiments
were conducted by varying molten alloy temperature, die temperature, plunger velocities in
the first and second stage, and multiplied pressure in the third stage using L 27 orthogonal
array of Taguchi method. After conducting a series of initial experiments in a controlled
environment, significant factors for pressure die casting processes are selected to construct an
appropriate multivariable linear regression analysis model for developing a robust
performance for pressure die casting processes. The appropriate multivariable linear model is
a useful and efficient method to find the optimal process conditions in pressure die casting
associated with the minimum shrinkage porosity percent.
Keywords: Taguchi method, die-casting, shrinkage porosity, aluminum A380,
optimization.
TÓM TẮT

Bài báo nói về quá trình hình thành xốp co ngót của vật đúc áp lực cho một phần sản
phẩm ô tô. Các vấn đề sau đây được tập trung: mô phỏng điền đầy khuôn, phân tích các
khuyết tật, cuối cùng là sử dụng phương pháp phân tích Taguchi đa chất lượng để tìm ra các
thông số tối ưu và các yếu tố để tăng chất lượng và hiệu quả của hợp kim nhôm A380 đúc áp
lực cao. Các thí nghiệm được tiến hành bằng cách thay đổi nhiệt độ nóng chảy hợp kim, nhiệt
độ khuôn đúc, vận tốc pittông trong giai đoạn đầu tiên và thứ hai, và áp lực giữ trong giai
đoạn thứ ba sử dụng L 27 mảng trực giao của phương pháp Taguchi. Sau khi tiến hành một loạt
các thí nghiệm ban đầu trong một môi trường kiểm soát, trong đó các yếu tố ảnh hưởng đến
quá trình đúc áp lực được lựa chọn để xây dựng một mô hình phân tích hồi quy tuyến tính đa
biến phù hợp cho việc triển khai quá trình đúc áp lực cao. Mô hình tuyến tính đa biến thích
hợp là một phương pháp hữu ích và là phương pháp hiệu quả cho quá trình tối ưu trong đúc
áp lực cao liên quan đến phần trăm tối thiểu của xốp co ngót.
Từ khóa: phương pháp Taguchi, đúc áp lực, xốp co ngót, hợp kim nhôm A380, tối
ưu hóa.
1. INTRODUCTION
High-pressure die casting (HPDC) process is significantly used in the industry for its
high productivity and less post-machining requirement. Due to light weight and good
forming-ability, aluminum die casting plays an important role in the production of
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transportation and vehicle components. It has a much faster production rate in comparison to
other methods and it is an economical and efficient method for producing components with
low surface roughness and high dimensional accuracy. All major aluminum automotive
components can be processed with this technology. The development of industrial die-casting
and requirements for higher quality product, shorter development times and more complex
geometry, the use of computer aided simulation has become essential to stay competitive.
In 2001, M. Avalle et al [1] compared the production of the original parts and standard
specimens. The study of die-casting defects and fatigue strength of aluminum also found that:

defects in material fatigue strength are lower than the materials containing defects in castings.
M. Avalle made a total of three defects in a sample: the porosity, the cold fills and the
aluminum oxide film. They make the results: in particular, the tensile strength decreases
linearly with the porosity range and other defects, the fatigue strength is not only related to
defects with the nature of the material itself. So that, improve the quality of the casting from
reducing casting defects and materials selection.
M. Avalle in another study [2] for static and fatigue strength of a die cast aluminum
alloy under different feeding conditions indicated that three batches of different samples
conducted - neither the same gate nor flow channel design, would be porosity and impact
casting defects, thereby affecting the static and fatigue strength.
The HPDC castings production process has many defects, such as: shrinkage porosity,
misrun, cold-shut, blister, scab, hot-tear… Several previous studies of defects in aluminum
alloy by the method of HPDC and disability solutions. Techniques such as cause-effect
diagrams, design of experiments (DOE), casting simulation, if-then rules (Fuzzy Logic
Controller), genetic algorithms (GA) and artificial neural networks (ANN) are used by various
researchers for analysis of casting defects. Dargusch et al [3] used pressure sensor in the
cavity to make a confident statement of aluminum that molten metal velocity increases and
porosity development with high pressure die- casting. G.O. Verran [4] used the design of
experiments (DOE) to find out the best parameters in production and notice that: porosity low
indices are related with low speeds from slow and fast shots and high upset pressures. M.
Anijdan et al [5] used genetic algorithm (GA) methods to determine the optimum conditions
leading to minimum porosity in aluminum alloy die casting. V.D. Tsoukalas [6,7] used the
design of experiments (DOE) and genetic algorithm (GA) methods to determine the optimum
conditions leading to minimum porosity in aluminum alloy die castings. G.P. Syrcos [8] used
Taguchi method to determine the optimum conditions leading to casting density in aluminum
alloy die castings.
In this paper, the ProCAST® Software commercial is used for analysis casting defects
and die filling simulation to enhance the quality and efficiency of die casting. The Taguchi
method control with design of experiments will be developed to improve aluminum die
casting quality and productivity in the cold chamber die casting method. After conducting a

series of initial experiments in a controlled environment, significant factors for die casting
processes are selected to find the optimal parameters to increase the aluminum die casting
quality and efficiency.
2. EXPERIMENTAL PROCEDURE
2.1. Die-casting body design
In order to understand how the casting generated defects start its source, casting design
began to proceed from the casting wall thickness, holes, fillets, draft angles and to find out the
ways designing better and faster. Die casting of this study is provided through aluminum diecasting factory, so the casting body no changes. The casting is designed on CATIA software,
shown in Fig. 1.
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Kỷ yếu hội nghị khoa học và công nghệ toàn quốc về cơ khí - Lần thứ IV

Fig. 1. Part product is designed
Moreover, the die casting material selection is very important; the nature of the material
will directly affect the quality of the casting and die-casting parameters configuration, taking
into account the casting and casting pressure, this study selects casting material as the
aluminum alloy A380. The chemical composition of the aluminum alloy used in the
experimental procedure is given in Table 1.
Table 1. Chemical composition of the alloy A380 used

2.2. Taguchi design
Taguchi method is one of the efficient problems solving tools to upgrade the
performance of products and processes with a significant reduction in cost and time involved
[4, 6, 7, 8]. Shrinkage porosity formation in pressure die casting is the result of a so much
number of parameters. Fig. 2 shows a cause and effect diagram that was constructed to
identify the casting process parameters that may affect die casting porosity. In this case,
holding furnace temperature, die temperature, plunger velocity in the first stage, plunger
velocity in the second stage and multiplied pressure in the third stage were selected as the

most critical in the experimental design [7, 8]. The other parameters were kept constant in the
entire experimentation. The range of holding furnace temperature was selected as 640÷700°C,
the range of die temperature as 180÷260° C, the range of plunger velocity in the first stage as
0.05÷0.35 m/s and in the second stage as 1.5÷3.5 m/s, the range of multiplied pressure in the
third stage was chosen as 200–280 bars. The selected casting process parameters, along with
their ranges, are given in Table 2.
Table 2. Parameters with their ranges and values at three levels
Process parameters
Holding furnace temperature ( °C )
Die temperatute ( °C )
Plunger velocity, 1st stage (m/s)
Plunger velocity, 2nd stage (m/s)
Multiplied pressure (bars)

Parameters range
640–700
180–260
0.05–0.35
1.5–3.5
200–280

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Level 1
640
180
0.05
1.5
200

Level 2
670
220
0.2
2.5
240

Level 3
700
260
0.35
3.5
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Kỷ yếu hội nghị khoa học và công nghệ toàn quốc về cơ khí - Lần thứ IV

Fig. 2. Cause and effect diagram
The experimental layout plan with five factors and three levels using L 27 orthogonal
array, 27 experiments were carried out to study the effect of simulation input parameters,
shown in Table 3.
Table 3. Experimental layout using an L 27 orthogonal array
Trials

Plunger

Plunger

Multiplied

furnace Temperature velocity

velocity

pressure

Holding

Die

temperature

1st stage

2nd stage

A

B

C

D

E

1

1

1

1

1

1

2

1

1

2

2

2

3

1

1

3

3

3

4

1

2

1

2

2

5

1

2

2

3

3

6

1

2

3

1

1

7

1

3

1

3

3

8

1

3

2

1

1

9

1

3

3

2

2

10

2

1

1

2

3

11

2

1

2

3

1

12

2

1

3

1

2

13

2

2

1

3

1

14

2

2

2

1

2

15

2

2

3

2

3

16

2

3

1

1

2

17

2

3

2

2

3

18

2

3

3

3

1

19

3

1

1

3

2

20

3

1

2

1

3

21

3

1

3

2

1

22

3

2

1

1

3

23

3

2

2

2

1

24

3

2

3

3

2

25

3

3

1

2

1

26

3

3

2

3

2

27

3

3

3

1

3

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Kỷ yếu hội nghị khoa học và công nghệ toàn quốc về cơ khí - Lần thứ IV
2.3. Die-casting process analysis
Analysis software is used as a ProCAST® commercial using finite element method
analysis for a casting process. In this study, all parameters can be able to affect the analysis
process, choice of material aluminum A380 alloy die casting, cold chamber die casting
method with H13 material molding. The ProCAST with VIEWCAST module can provide
temperature field, thermal cracking, flow field, solidification time, shrinkage analysis. This

paper focused on analysis of shrinkage porosity base on parameters input form Table 4, each
experiment was repeated five times in order to reduce experimental errors.
Table 4. Shrinkage porosity results of the L 27 array design

2.3.1. Analysis of casting defects
The analysis of defects simulated by ProCAST software with modules VIEWCAST can
detect many types of disabilities casting. Defective products do not necessarily reflect the loss
of the original function, for example, the internal pore trims acceptable. The casting with the
gating system and biscuit is show in Fig. 3.

Fig. 3. Casting product
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Kỷ yếu hội nghị khoa học và công nghệ toàn quốc về cơ khí - Lần thứ IV
However, with large structural castings, defect analysis of this study focuses on
maximum porosity in the selection casting, and the important parts of the casting shrinkage
analysis (an important component), casting defect analysis are described as follows:
- Solid fraction may be available shrinkage prediction casting position, the present study
is in accordance with the theory prediction of defect, and ProCAST manual [9] referred to in
the final period of solidification. Shrinkage solid fraction prone is greater than 0.7, here as the
reference value of 0.7 solid fractions. When the solid fraction area below this value and the
area around the solid phase rate rather than this value, we can predict this area shrinkage
porosity occurred.
- The maximum porosity analysis using the Shrinkage Porosity function ViewCAST
comes defined in the manual, in accordance with the ProCAST user manual Shrinkage [9], a
volume fraction of 1% (0.01) or less shrinkage (naked eyes not visible micropores) and 1%
(0.01) as compared to the above shrinkage porosity (visible to the naked eyes).
According to the above definition and with the solid fraction, it can be used to analyse
basis of the maximum porosity.

Shrinkage analysis:

Fig. 4. Casting measurement area
For the amount of inspection shrinkage casting part used for the ViewCast module
function for quantitative analysis, as shown in Fig. 4.
2.3.2. The analysis of variance (ANOVA)
The responding graph show in Fig. 5 learned that the best combination for this study,
aluminum die casting shrinkage porosity defects: A 3 B 3 C 3 D 1 E 3 .

Fig. 5. S/N Response graphs
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Kỷ yếu hội nghị khoa học và công nghệ toàn quốc về cơ khí - Lần thứ IV
2.3.3. Process parameter optimization using MVLR
The objective of the process optimization is to select the optimal control variables in
aluminum die casting process in order to obtain the minimum porosity. In this work, the
fitness function used in the optimization procedure was based on the MVLR (Multivariable
linear regression) model.
In most case, the form of the relationship between the response and the independent
variables is usually unknown. Multiple linear regression (MLR) is a method used to model the
linear relationship between a dependent variable and one or more independent variables. MLR
is based on least squares: the model is fitted such that the sum-of-squares of differences of
observed and predicted values is minimized.
Let x 1 ; x 2 ; …; x r be a set of r predictors believed to be related to a response variable Y.
The linear regression model for the jth sample unit has the form:
Y j = β 0 +β 1 x j1 +β 2 x j2 +… +β r x jr +ε j

(1)

Where ε is a random error and the β i , i=0, 1,…, r are unknown regression coefficients.
In this paper, there are five independent variables and one dependent variable. The
relationships between these variables are of the following form:
F(x)=β 0 +β 1 A+β 2 B+β 3 C+β 4 D+β 5 E

(2)

Where:
F(x) - dependence variable
A (°C) - holding furnace temperature
B (°C) - die temperature
C (m/s) - plunger velocity 1st stage
D (m/s) - plunger velocity 2nd stage
E (bars) - multiplied pressure during the third phase
The results after analysing by Intercooled Stada 8.2 Software. The final MVLR model
equation for porosity after substituting regression coefficients is as follows:
F(x)= 3.054569 – 0.8844*10-3A – 0.83*10-3B - 0.03059C + 0.01754D – 0.00201E

(3)

Fig. 6. Experimental and predicted values of shrinkage porosity
Fig. 6 show that: there is convincing agreement between experimental values and
predicted values for Shrinkage porosity percent.
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Kỷ yếu hội nghị khoa học và công nghệ toàn quốc về cơ khí - Lần thứ IV
3. RESULTS AND DISCUSSION
Matlab code for finding optimization shrinkage porosity value.
Program in Matlab

clc;
clear all;
close all;
f = @(x)3.054569-0.8844e-3*x(1)-0.83e-3*x(2)-...
0.03059*x(3)+0.01754*x(4)-0.00201*x(5);
options = optimset('GradObj','on');
[x,fval,exitflag,output] =...
fmincon(f,[670;220;0.2;2.5;240],[],[],[],[],[600;180;0.05;1.5;200],[700;260;0.35;3.5;280],[],optimset('Dis
play','iter'));
x
fval

Results: x =
700.0000
260.0000

→

A= 700°C
B = 260°C

0.3500

C = 0.35 m/s

1.5000

D = 1.5 m/s

280.0000

E = 280 bar

fval = 1.6725

Shrinkage porosity: 1.6725 %

By Program in Matlab we are known as the best combination in the 27 experimental
configurations.
This result is similar with ANOVA, the best combination for this study: A 3 B 3 C 3 D 1 E 3 .
CONCLUSIONS
In this paper, the optimum process parameters values predicted for casting of minimum
shrinkage porosity (1.6725%), the best combination parameters given as follows:
Holding furnace temperature
700 °C
Die temperature

260 °C

Plunger velocity, 1st stage

0.35 m/s

Plunger velocity, 2nd stage

1.5 m/s

Multiplied pressure

280 bar

The model proposed in this paper gives satisfactory results for the optimization of
pressure die casting process. The predicted values of the process parameters and the
calculated are in convincing agreement with the experimental values.
REFERENCES
[1]. M. Avalle, G. Belingardi, M.P. Cavatorta, R. Doglione, Casting defect and fatigue
strength of a die cast aluminum alloy：a comparison between standard specimens and
production components. International Journal of Fatigue 24, 2002, p. 1∼9.
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Kỷ yếu hội nghị khoa học và công nghệ toàn quốc về cơ khí - Lần thứ IV
[2]. M. Avalle, G. Belingardi, M.P. Cavatorta, Static and fatigue strength of a die cast
aluminum alloy under different feeding conditions. Presented at EUROMAT 2001,
Rimini, p. 10∼14.
[3]. Dargusch, M.S., Dour, G., Schauer, N., Dinnis, C.M. & Savage, G, The influence of
pressure during solidification of high pressure die cast aluminum telecommunications
components. Journal of Materials Processing Technology, 2006, Vol. 180, p. 37∼43.
[4]. G.O. Verran, Influence of injection parameters on defects formation in die casting
Al12Si1.3Cu alloy: Experimental results and numeric simulation. Journal of Materials
Processing Technology, 2006, Vol. 179, p. 190∼195.
[5]. Mousavi Anijdan, S.H., Bahrami, A., Madaah Hoseini, H.R. & Shafyei, A. Using genetic
algorithm and artificial neural network analyses to design an Al–Si casting alloy of
minimum porosity. Materials and Design, 2006, Vol. 27, p. 605∼609.
[6]. Tsoukalas, V.D, A study of porosity formation in pressure die casting using the Taguchi
approach. Journal of Engineering Manufacture, 2004, Vol. 218, p. 77∼86.
[7]. Tsoukalas, V.D. Optimization of porosity formation in AlSi9Cu3 pressure die castings
using genetic algorithm analysis. Materials and Design, 2008, Vol. 29, p. 2027∼2033.
[8]. Syrcos, G.P. Die casting process optimization using Taguchi methods. Journal of
Materials Processing Technology, 2003, Vol. 135, p. 68∼74.

[9]. ProCAST User Manual, ESI Group, Version 2010.
AUTHOR’S INFORMATION
1. Anh Tuan Do. Department of Mechanical Engineering, Hung Yen University of
Technology and Education, Dan Tien-Khoai Chau-Hung Yen, Vietnam.
E-mail: Phone number: 0936631999.
2. Tran Vung Vu. Department of Mechanical Engineering, Hung Yen University of
Technology and Education, Dan Tien-Khoai Chau-Hung Yen. E-mail:
Phone number: 0982426620.
3. Van Thuy Hoang. Department of Mechanical Engineering, Vocational Intermediate
School No.14, Yen Son-Tam Diep-Ninh Binh, Vietnam. Email:
Phone number: 0976110086.
4. Thi Mo Pham. Department of Mechanical Engineering, Vocational Intermediate School
No.14, Yen Son-Tam Diep-Ninh Binh, Vietnam. Email:
Phone number: 0918590388.

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