Taguchi Optimization of Process Parameters on the Hardness and Impact Energy of Aluminium Alloy Sand Castings
^{1 }Prototype Engineering Development Institute Ilesa, Osun State. Nigeria.
^{2}Nigerian National Petroleum Corporation, Abuja. Nigeria.
^{3}Department of Materials Science & Engineering, Obafemi Awolowo University, IleIfe, Nigeria.
Email: ojiortega@gmail.com; pamtoks4u@gmail.com; layopet01@yahoo.com; aderade2004@yahoo.com
* Corresponding author: Phone: +234(0)7084089009
Abstract
An optimization technique for sand casting process parameters based on the Taguchi method is reported in this paper. While keeping other casting parameters constant, aluminium alloy castings were prepared by sand casting technique using three different parameters, namely the mould temperature, pouring temperature and runner size. Hardness and impact energy tests were done for the resulted castings. The settings of parameters were determined by using the Taguchi experimental design method. The level of importance of the parameters on the hardness impact energy was determined using the analysis of variance (ANOVA). The optimum parameter combination was obtained by using the analysis of signaltonoise (S/N) ratio. Analysis of the results shows that 100°C mould temperature and 700°C pouring temperatures are optimal values for hardness and impact energy. However 200 mm^{2} and 285 mm^{2} runner sizes are the optimal values for hardness and impact energy respectively. The mould temperature was the most influential parameter on the hardness impact energy of the castings.
Keywords
Taguchi method; Optimization; Sand casting; Aluminium alloy; Process parameters; Hardness; Impact energy; ANOVA.
Introduction
The wide range of the application of aluminium alloys is very obvious. Their desirable characteristics of light weight, excellent resistance to corrosion in the atmosphere and water, strength [1] and high thermal conductivity gives them an edge over other metals in the electrical, aviation, marine, aerospace, construction and automotive industries just to mention but a few [2]. This increased usage creates the need for a deeper understanding of their mechanical behaviour and the influences of processing parameters, [3, 4]. This knowledge enables the designer to ensure that the casting will achieve the desired properties for its intended application [5, 6].
There is no doubt that casting as a process involves so many parameters [79]such as melting temperature of the charge, temperature of the mould, pouring speed, pouring temperature, composition, microstructure, size of casting, runner size, composition of the alloy and solidification time just to mention but a few. [712] just to mention but a few have successfully carried out studies on the varying effects of casting process parameters on the mechanical properties of casted metals and their alloys.
One of the recent most important optimization processes is the Taguchi method [13] conceived and developed by Japanese scholar Engr. Dr. Genichi Taguchi in 1950. Taguchi technique is a powerful tool for the design of high quality systems [14, 15]. It provides a simple efficient and systematic approach to optimize design for performance, quality and cost. The methodology is valuable when design parameters are qualitative and discrete. Taguchi parameter design can optimize the performance characteristic through the setting of design parameters and reduce the sensitivity of the system performance to source of variation [16, 17]. The Taguchi approach enables a comprehensive understanding of the individual and combined from a minimum number of simulation trials. This technique is multi – step process which follow a certain sequence for the experiments to yield an improved understanding of product or process performance [18].
The objective of this study is to determine the optimal settings of sand casting process parameters using Taguchi’s experimental design method. Orthogonal arrays of Taguchi, the signaltonoise (S/N) ratio, the analysis of variance (ANOVA), and regression analyses are employed to find the optimal levels and to analyze the effect of the casting process parameters on hardness and impact energy values.
Experimental Design
The steps applied for Taguchi optimization in this study are as follows.
Ø Select noise and control factors (process parameters in this case)
Ø Select Taguchi orthogonal array
Ø Conduct Experiments
Ø Hardness and Impact Energy measurements
Ø Analyze results; (Signal to noise ratio)
Ø Predict optimum performance
Ø Confirmation experiment
The standard Taguchi L_{9} Orthogonal Array (OA) format is chosen from preliminary works [10, 19, 20] by the authors which identified three parameters namely the mould temperature, pouring temperature and runner size as important casting process variables which affect the mechanical properties. Sufficient details of the effect of different parameter values on experimental results can be obtained by choosing three levels for each parameter to investigate. The criteria used for choosing the three parameter levels are to explore a maximum range of experimental variables. In addition, it is unnecessary to have uniformly spaced levels because of the counterbalance property of the Orthogonal Array [21]. Previous work by [11] has shown that the optimum pouring temperature range is 700750°C so for this study the temperature level range is 700750°C. The three levels for mould temperature parameter and runner size parameter are selected also according to literature reviews and previous casting experiences.
Taguchi methods which combine the experiment design theory and the quality loss function concept have been used in developing robust designs of products and processes and in solving some taxing problems of manufacturing/production [22]. The degrees of freedom for three parameters in each of three levels were calculated as follows [14].
Degree of Freedom (DOF) = number of levels 1. For each factor, DOF equal to: For (Mould temperature); DOF = 3 – 1 = 2; For (Pouring Temperature); DOF = 3 – 1 = 2; For (Runner size); DOF = 3 – 1 = 2
In this study nine experiments were conducted at different parameters. For this Taguchi L_{9} orthogonal array was used, which has nine rows corresponding to the number of tests, with three columns at three levels. L_{9} OA has eight DOF, in which 6 were assigned to three factors (each one 2 DOF) and 2 DOF was assigned to the error. For the purpose of observing the degree of influence of the process parameters, three factors, each at three levels, are taken into account, as shown in Tables 2.
Analysis of the S/N Ratio
Taguchi method uses the (signal – to – noise (S/N) ratio, because it minimizes quality characteristic variation due to uncontrollable parameter. The hardness and impact energy is the objective function so that "the largerthebetter" S/N ratio is chosen. The S/N ratio used for this type of response is given by [14]. The S/N ratio for the largerthebetter is:
S/N_{LTB} = 10log[MSD] 
(1) 
_{} 
(2) 
where: n is the number of measurements in a trial/row, in this case, n=1 and y is the measured value in a run/row. The S/N ratio values are calculated by taking into consideration equation. 1. The S/N ratio values obtained for this experiment are as shown in table 6 and 7 respectively.
Analysis of variance (ANOVA)
Analysis of Variance (ANOVA) is a computational technique to quantitatively estimate the relative contribution, which each controlled parameter makes to the overall measured response and expressing it as a percentage. ANOVA uses the S/N ratio responses for these calculations. The basic idea of ANOVA is that the total sum of squares of the standard deviation is equal to the sum of squares of the standard deviation caused by each parameter. The total sum of squared deviations SS_{T} from the total mean S/N ratio η_{m} can be calculated as [23]:
_{} 
(3) 
where n is the number of experiments in the orthogonal array and η_{i} is the mean S/N ratio for the i^{th} experiment.
The percentage contribution, P of the process parameters on the hardness and impact energy (shown as table 8 and 9) can be calculated as:
_{} 
(4) 
Results and Discussions
Table 1 shows the chemical composition of the aluminium alloy employed in the foundry process for this analysis.
Table 1. Chemical composition of aluminium alloy
Element 
Al 
Fe 
Si 
Weight Percentage (W %) 
97.2 
0.7 
2.1 
Concentration (mgl) 
972 
7 
21 
A standard Taguchi L_{9} Orthogonal Array (OA) is chosen for this study as it can operate three parameters, each at three levels and presented in Table 2 and 3.
Table 2. Process parameters and their values at 3 levels
Process Parameters 
LEVELS 

L1 
L2 
L3 

Mould Tempt (°C) 
100 
150 
170 
Pouring Tempt (°C) 
700 
720 
750 
Runner Size (mm^{2}) 
180 
200 
285 
Table 3.Standard L_{9} array for hardness and impact energy
Expt No 
Mould Tempt (°C) 
Pouring Tempt (°C) 
Runner Size (mm^{2}) 
Hardness (HRB) 
Impact Energy (Joule) 
100 
700 
180 
15.08 
46 

2 
100 
720 
200 
18.22 
31 
3 
100 
750 
285 
16.47 
44 
4 
150 
700 
200 
17.60 
32 
5 
150 
720 
285 
17.40 
21 
6 
150 
750 
180 
14.00 
25 
7 
170 
700 
285 
12.10 
18 
8 
170 
720 
180 
10.02 
12 
9 
170 
750 
200 
11.42 
14 
The hardness and impact energy values measured from the experiments and their corresponding S/N ratio values are listed in Table 4 and 5.
Table 4. Hardness values and S/N ratio values for experiments
Expt. no 
Hardness (HRB) 
S/N Ratio (dB) 
1 
15.08 
23.57 
2 
18.22 
25.21 
3 
16.47 
24.33 
4 
17.60 
24.91 
5 
17.40 
24.81 
6 
14.00 
22.92 
7 
12.10 
21.66 
8 
10.02 
20.02 
9 
11.42 
21.15 
Table 5. Impact energy values and S/N ratio values for experiments
Expt No 
Impact Energy (Joule) 
S/N Ratio (dB) 
1 
46 
33.26 
2 
31 
29.83 
3 
44 
32.87 
4 
32 
30.10 
5 
21 
26.44 
6 
25 
27.96 
7 
18 
25.10 
8 
12 
21.58 
9 
14 
22.92 
The response table by factor level for the mould temperature, pouring temperature and runner size was created in the integrated manner and the results are given in Table 6 and 7.
Table 6. S/N ratio values for hardness by factor level
LEVEL 
Mould Tempt (°C) 
Pouring Tempt (°C) 
Runner Size (mm^{2}) 
1 
24.37* 
23.38* 
21.17 
2 
24.21 
23.35 
23.76* 
3 
20.94 
22.80 
23.60 
Delta 
3.43 
0.58 
2.59 
Rank 
1 
3 
2 
*Optimum Level
Table 7. S/N ratio values for impact energy by factor level
LEVEL 
Mould Tempt (°C) 
Pouring Tempt (°C) 
Runner Size (mm^{2}) 
1 
31.99* 
29.49* 
27.60 
2 
28.17 
25.95 
27.68 
3 
23.20 
27.92 
28.14* 
Delta 
8.79 
3.54 
0.54 
Rank 
1 
2 
3 
*Optimum Level
Statistically, an F test, named after Fisher [16] is used to determine design parameters which have a significant effect on the quality characteristic. In the analysis, the Fratio is a ratio of the mean square error to the residual error, and is traditionally used to determine the significance of a factor. Percent (%) is defined as the significance rate of the process parameters on the hardness and impact energy. The percent numbers depict that the mould temperature, pouring temperature and runner size have significant effects on the hardness and impact energy values. It can also be observed from Table 8 that within the selected experimental design, the pouring temperature, mould temperature, and runner size affect the hardness by 2.27%, 77.46% and 16.55% respectively.
Table 8. ANOVA results for hardness of aluminium alloy castings
Source of Variation 
Degree of freedom (DOF) 
Sum of squares (SS) 
Variance (V) 
Fratio (F) 
Percentage Contribution (P) 
Mould Tempt (°C) 
2 
21.48 
10.74 
20.85 
77.46% 
Pouring Tempt (°C) 
2 
0.63 
0.31 
0.61 
2.27% 
Runner Size (mm^{2}) 
2 
4.59 
2.29 
4.46 
16.55% 
Error 
2 
1.03 
0.51 

3.72% 
Total 
8 
27.73 


100% 
Table 9 shows also that within the selected experimental design, the pouring temperature, moulding temperature, and runner size affect the impact energy by 13.32%, 86.12% and 0.38% respectively.
Table 9. ANOVA results for impact energy of aluminium alloy castings.
Source of Variation 
Degree of freedom (DOF) 
Sum of squares (SS) 
Variance (V) 
Fratio (F) 
Percentage Contribution (P) 
Mould Tempt (°C) 
2 
115.55 
57.76 
444.42 
86.12% 
Pouring Tempt (°C) 
2 
17.87 
8.94 
68.73 
13.32% 
Runner Size (mm^{2}) 
2 
0.51 
0.26 
1.96 
0.38% 
Error 
2 
0.26 
0.13 

0.18% 
Total 
8 
134.19 


100% 
Regardless of the category of the performance characteristics, a greater S/N value corresponds to a better performance. Therefore, the optimal level of the casting process parameters is the level with the greatest S/N value. Based on the analysis of the S/N ratio, the optimal hardness was obtained at 100°C mould temperature (level 1), 700°C pouring temperature (level 1) and 200mm^{2} runner size (level 3). The varying effects of process parameters on the hardness values are shown in figure 1a, 1b, and 1c.
The hardness decreases with increase in mould temperature (above 100°C) and pouring temperature (above 700°C). An increase in runner size however produced increase in the hardness. Also, the optimal impact energy was obtained at 100°C mould temperature (level 1), 700°C pouring temperature (level 1) and 285mm^{2} runner size (level 3). The varying effects of the processing parameters on the impact energy values are shown in figure 2a, 2b, and 2c. The hardness decreases with increase in mould temperature (above 100°C) and pouring temperature (above 700°C but begins to rise on reaching 720°C). However an increase in runner size produced increase in the impact energy.
Figure 1a. Main effects plot for mould temperature S/N ratio values on hardness
Figure 1b. Main effects plot for pouring temperature S/N ratio values on hardness
Figure 1c. Main effects plot for runner size S/N ratio values on hardness
Figure 2a. Main
effects plot for mould temperature S/N ratio values on impact energy
Figure 2b. Main effects plot for pouring temperature S/N ratio values on impact energy
Figure 2c. Main effects plot for runner size S/N ratio values on impact energy
Conclusions
This study has discussed an application of the Taguchi method for investigating the effects of sand casting process parameters on the hardness and impact energy values of aluminium alloy castings. From the analysis of the results using the conceptual signaltonoise (S/N) ratio approach, regression analysis, analysis of variance (ANOVA), and Taguchi’s optimization method, the following can be concluded from the present study:
· A statistically designed experiment based on Taguchi method was performed using L_{9 }orthogonal array to analyze the hardness and impact energy as response variables.
· Within the experimental level ranges, the most significant influencing parameter on hardness is the mould temperature, which accounts for 77.46% of the total effect, followed by the runner size (16.55%), and mould temperature (2.27%) respectively. On the other hand the most significant influencing parameter on the impact energy is the mould temperature, which accounts for 86.12% of the total effect, followed by the pouring temperature (13.32%), and runner size (0.38%) respectively.
Acknowledgement
The authors are grateful to Engr S.I. Yakubu of the National Metallurgical Development Centre (NMDC) Jos, Nigeria for the synthesis of this work.
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