Minimum Porosity Formation in Pressure Die Casting by Taguchi Method

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Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2013, Article ID 920865,9pages http://dx.doi.org/10.1155/2013/920865

Research Article

Minimum Porosity Formation in Pressure Die Casting by

Taguchi Method

Quang-Cherng Hsu and Anh Tuan Do

Department of Mechanical Engineering, National Kaohsiung University of Applied Sciences, 415 Chien-Kung Road, 80778 Kaohsiung City, Taiwan

Correspondence should be addressed to Anh Tuan Do; 1099403120@kuas.edu.tw Received 17 September 2013; Accepted 15 October 2013

Academic Editor: Teen-Hang Meen

Copyright © 2013 Q.-C. Hsu and A. T. Do. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Die casting process is significantly used in the industry for its high productivity and less postmachining requirement. Due to light weight and good formability, aluminum die casting plays an important role in the production of transportation and vehicle components. In the current study of die casting for Automobile starter motor casing, the following issues are focused: shot piston simulation, defect analysis, and, finally, the use of the Taguchi multiquality analytical method to find the optimal parameters and factors to increase the aluminium ADC10 die casting quality and efficiency. 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 L₂₇orthogonal 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.

1. Introduction

High pressure die casting for nonferrous casting applications is increasingly used in the foundries today as an economically viable casting process. High pressure die casting (HPDC) process has been widely used to manufacture a large variety of products with high dimensional accuracy and productivities. 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 aluminium automotive components can be processed with this technology.

High Pressure Die Casting process is rapid and depends on many factors. So, to capture the problem it requires a lot of time and experience including testing and simulation. The conventional trial and error based die design and process development is expensive and time consuming. Such a procedure also might lead to higher casting rejections. 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 (Shen et

al. 2007 [1], Dargusch et al. 2006 [2], Verran et al. 2006

[3], Mousavi Anijdan et al. 2006 [4], Tsoukalas et al. 2004,

2008 [5,6]). However, the study to optimize aluminum alloy

casting process in the condition of production casting factory is essential. This study focused on analysis of shrinkage porosity defect with mold design and put into production casting by foundry factory conditions.

Shrinkage porosity is one of the most common defects leading to rejection of aluminium die casting, often only showing up after much value has been added to the casting via operations such as machining, polishing, and coating. The added value of the casting at the point of rejection can be very high. If you find out the causes and how to reduce the defects of castings will be of great significance in reducing the production cost of die casting. However, optimizing the conditions to render aluminium die castings of minimum

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2 Mathematical Problems in Engineering

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Figure 1: Casting image.

porosity percent is costly and time consuming, because many experiments are necessary to find the optimal parameters.

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. Taguchi’s parameter design offers a systematic approach for optimization of various parameters with regard to

perfor-mance, quality, and cost (Syrcos 2003 [7], Taguchi 1986 [8]).

2. Materials and Methods

The die casting part product of this study is provided through aluminium die casting factory, so the casting body no changes. A major factor in the successful development of castings is the design of the die and design of gates, biscuit, and runner system. A well-designed gating and runner system should avoid turbulence in metal flow and to reduce incidence of inclusions and air entrapment in the casting. The die design is required to avoid solidification related defects like shrinkage, micro-porosities, hot-tear and so forth. Die design process is very much dependent on the experience and skill of the design engineer. The die for this study is the result of collaboration between the foundry factory and Department of Mechanical Engineering-National Kaohsiung University of Applied Sciences. The casting with full of the

gating, runner system and biscuit, is shown inFigure 1. The

die casting is designed in CATIA V5R19 software, shown in Figure 2. 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 configura-tion, this study selects casting material as the aluminium alloy

Table 1: Chemical composition of the alloy ADC10 used in the experiment.

Element Si Fe Cu Mg Mn Ni Zn Sm

wt% 7.5∼9.5 1.3 3.0∼4.0 0.1 0.5 0.5 3 0.35

ADC10. The chemical composition of the aluminum alloy

used in the experimental procedure is given inTable 1.

Shrinkage porosity formation in pressure die casting is

the result of a so much number of parameters. Figure 3

shows a cause and effect diagram that was constructed to identify the casting process parameters that may affect die

casting porosity (Tsoukalas et al. 2004, 2008 [5,6]). 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. 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, and the range of multiplied pressure in the third stage was chosen as 200∼ 280 bars. The selected casting process parameters, along with

its ranges, are given inTable 2.

Taguchi method based design of experiment has been used to study the effects of five casting process parameters (holding furnace temperature: A, die temperature; B, plunger velocity in the first stage; C, plunger velocity in the second stage; D, multiplied pressure in the third stage; E, on an

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8 Mathematical Problems in Engineering 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(‘Display’, ‘iter’)); x fval Algorithm 1 1.5 1.55 1.6 1.65 1.7 1.75 1.8 1.85 1.9 1.95 2 Number of tests Sh ri n k ag e p o ro si ty (%) Predicted Experimental Std. dev. = 0.014537; 1 2 3 4 5 6 7 8 9 _{10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27} R2 = 97.22%; adjustedR2 = 96.55%

Figure 6: Experimental and predicted values of shrinkage porosity percent.

Program in Matlab (seeAlgorithm 1).

Results after running in Matlab are as follows:

𝑥 = 700.0000 so that󳨀→ 𝐴 = 700∘C

260.0000 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%.

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By the Program in Matlab, we are known as the best combination in the 27 experimental configurations.

This result is similar to quality characteristics and is the

best combination for this study𝐴₃𝐵₃𝐶₃𝐷₁𝐸₃.

4. Conclusion

In this paper, the optimum process parameters values predicted for casting of minimum shrinkage porosity

(1.6725%) and 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 in 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.

The experiments which are conducted to determine the best levels are based on “Orthogonal Arrays,” and are bal-anced with respect to all control factors and yet are minimum in number. This in turn implies that the resources (materials, saving time, and money) required for the experiments are also minimized.

References

[1] C. Shen, L. Wang, and Q. Li, “Optimization of injection mold-ing process parameters usmold-ing combination of artificial neural network and genetic algorithm method,” Journal of Materials Processing Technology, vol. 183, no. 2-3, pp. 412–418, 2007. [2] M. S. Dargusch, G. Dour, N. Schauer, C. M. Dinnis, and G.

Savage, “The influence of pressure during solidification of high pressure die cast aluminium telecommunications components,” Journal of Materials Processing Technology, vol. 180, no. 1–3, pp. 37–43, 2006.

[3] G. O. Verran, R. P. K. Mendes, and M. A. Rossi, “Influence of injection parameters on defects formation in die casting Al12Si1,3Cu alloy: experimental results and numeric simula-tion,” Journal of Materials Processing Technology, vol. 179, no. 1– 3, pp. 190–195, 2006.

[4] S. H. Mousavi Anijdan, A. Bahrami, H. R. Madaah Hosseini, and A. Shafyei, “Using genetic algorithm and artificial neural network analyses to design an Al-Si casting alloy of minimum porosity,” Materials and Design, vol. 27, no. 7, pp. 605–609, 2006. [5] V. D. Tsoukalas, S. A. Mavrommatis, N. G. Orfanoudakis, and A. K. Baldoukas, “A study of porosity formation in pressure die casting using the Taguchi approach,” Journal of Engineering Manufacture, vol. 218, no. 1, pp. 77–86, 2004.

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Mathematical Problems in Engineering 9 [6] V. D. Tsoukalas, “Optimization of porosity formation in

AlSi₉Cu₃ pressure die castings using genetic algorithm analy-sis,” Materials and Design, vol. 29, no. 10, pp. 2027–2033, 2008. [7] G. P. Syrcos, “Die casting process optimization using Taguchi

methods,” Journal of Materials Processing Technology, vol. 135, no. 1, pp. 68–74, 2003.

[8] G. Taguchi, Introduction to Quality Engineering, Asian Produc-tivity Organization, UNIPUB, 1986.

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