


ARTICLE 

Year : 2015  Volume
: 5
 Issue : 1  Page : 5663 

MultiResponse Optimization of Die Casting Process for Lock Assembly of a Two Wheeler
Yasir Zubair, Satpal Sharma
School of Engineering, Gautam Buddha University, Greater Noida, Uttar Pradesh, India
Date of Web Publication  16Jan2015 
Correspondence Address: Yasir Zubair School of Engineering, Gautam Buddha University, Greater Noida, Uttar Pradesh India
Source of Support: None, Conflict of Interest: None  Check 
DOI: 10.4103/09768580.149491
Abstract   
Thousands of consumer, commercial, and industrial products can be produced by die casting process with high volume ranging from small to large components and hence die casting can be referred as mass production process. One such component is the lock assembly of a two wheeler which is made of ZAMAK (Zinc alloy) and produced by die casting process. Some of the products produced by die casting was defective and hence needs to improve the performance of the process. The objective of the study was to evaluate the effect of injection pressure of the molten metal, hydraulic pressure, temperature of the molten metal, and the cooling time on the dimensional stability of the lock assembly of two wheeler. In order to study the effect, the process parameters such as injection pressure of the molten metal, hydraulic pressure, temperature of the molten metal, and the cooling time Response Surface Methodology (RSM) with Face Center Design (FCD) was used. The experiments were carried out according to design matrix. Four responses namely slot diameter, pitch circle diameter, pinhole diameter, and pinhole depth were used for study. The RSM model was developed for each response. Analysis of variance (ANOVA) for each response was also carried out to find out the significant factors and their interactions. Finally, optimization was carried out and the optimized input parameters were validated by conducting experiments. The modeled and experimental results were compared and an error of 27% was observed. Keywords: Die casting, RSM, ZAMAK alloy
How to cite this article: Zubair Y, Sharma S. MultiResponse Optimization of Die Casting Process for Lock Assembly of a Two Wheeler. J Eng Technol 2015;5:5663 
How to cite this URL: Zubair Y, Sharma S. MultiResponse Optimization of Die Casting Process for Lock Assembly of a Two Wheeler. J Eng Technol [serial online] 2015 [cited 2019 Apr 18];5:5663. Available from: http://www.onlinejet.net/text.asp?2015/5/1/56/149491 
1. Introduction   
Die casting is a manufacturing process for producing metal parts by forcing molten metal under high pressure into a die cavity. The history of the die casting goes back to the eighteenth century. The commercial application of the plunger type die casting unit commenced in 1892 and the mass production of the related started in 1900. A lot of modifications and advancement since then have taken place. In a die casting machine, the molten metal melted by furnace is injected into the die cavity of required shape and size allowing the molten metal to solidify. This solid piece is ejected out and goes on the trim line for the further processing. The quality of the component coming out of a die casting unit (DCU) is an important issue. Hence, to ascertain consistent good quality of the end product, a proper monitoring of the various input process parameters of the die casting is essential.
Various researchers worked in the area of die casting process parameters to improve the quality of the die cast components such as Anastasiou ^{[1]} investigated the effects of process parameters on porosity formation in the pressure die casting process to improve casting quality using Taguchi method. Process parameters like plunger velocity and die temperature were optimized by Tsoukalas et al., ^{[2]} using Taguchi method to improve quality and to reduce the cost. Guharaja et al., ^{[3]} accomplished optimal settings of various significant process parameters like green strength, moisture content, permeability, and mould hardness for minimizing green sand casting defects using Taguchi's parameter design approach. Oktem et al., ^{[4]} developed a model using Taguchi optimization method for low surface roughness for milling an aluminum alloy.
Peng et al., ^{[5]} investigated the effects of process parameters on casting thickness and metaldie interfacial heat transfer coefficient (IHTC) in the highpressure die casting (HPDC) process. Experiments were carried out using cold chamber die casting machine with two casting alloys AM 50 and aluminum die casting (ADC 12). The IHTC was determined using an inverse approach based on the temperature measurements inside the die. Results show that the IHTC was different at different steps and changes as the solidification of the casting proceeds. Process parameters only influenced the IHTC in its peak value, and for both AM 50 and ADC 12 alloys. Results also showed that a closer contact between the casting and die could be achieved when the casting alloys is ADC 12 instead of AM 50, which consequently leads to a higher IHTC. Chiang et al., ^{[6]} proposed mathematical models for the modeling and analysis of the effects of machining parameters on the performance characteristics in the high pressure die casting process (HPDC) of AlSi alloys which were developed using the response surface methodology (RSM) to explain the influence of the three processing parameters, namely die temperature, injection pressure and cooling time on the performance characteristics of the mean particle size (MPS) of primary silicon, and material hardness (HBN) value.
Chiang et al., ^{[7]} proposed an algorithm, by combining the gray relational analysis with the fuzzy logic, obtains a grayfuzzy reasoning grade to evaluate the multiple performance characteristics according to the gray relational coefficient of each performance characteristic. One of the real case studies performed in the die casting process, thinwalled cover components of liquid crystal display (LCD) panel, verifies that the proposed optimum procedure was feasible and effective. Zheng et al., ^{[8]} in this work, developed an evaluation system for the surface defects of casting has been established to quantify surface defects, and artificial neural network was introduced to generalize the correlation between surface defects and die casting parameters, such as mould temperature, pouring temperature, and injection velocity. It was found that the trained network had great forecast ability.
Zhu et al., ^{[9]} developed a numerical model for predicting microporosity formation in aluminum casting, which described the redistribution of hydrogen between solid and liquid phases, the transport of hydrogen in liquid by diffusion, and Darcy flow in the mushy zone. One of the key features of the model was that a twostage approach for porosity prediction was used. In the first stage, the volume fraction of porosity was calculated based on the reduced pressure, whereas the second stage, at fractions solid greater than the liquid encapsulation point. Kumar et al., ^{[10]} carried out a study the purpose was to identify the influencing factors, causing casting defects and determination of optimum value of factors to minimize these defects in a melt shop industry, situated in north India. Percentage contribution of these factors was also estimated to develop an empirical expression between process performance and independent input variables. The outcome of this case study was to optimize the process parameters of the melt shop process, which leads to minimize the casting defects. Kittur et al., ^{[11]} demonstrated the effect of die casting machine parameters (fast shot velocity, slow shot to fast shot change over point, intensification pressure, and holding time) on the performance characteristics of die casting process using RSM.
Based on the literature survey and the subsequent analysis of gaps, the present work aims to investigate the effect of various parameters in a die casting of lock assembly on the properties of aluminum alloy and optimize the parameters using RSM. The experiments have been conducted using zinc alloy. The process parameters varied were the injection pressure, hydraulic pressure, pot temperature, and cooling time. The research was carried out on the existing die casting machine in the industry located in National Capital Region (NCR).
2. Experimental Methods   
2.1 Material selection
The raw material here used was ZAMAK3, which is an alloy with base metal of zinc and its chemical composition is shown in [Table 1]. The required percentage of these elements in the raw material should be according to the specifications which are decided according to the product requirement. To ensure that the raw material is according to the specifications, it is tested in the spectroscopy labs whenever the new lot comes. This is the most used general purpose zinc die casting alloy, providing an excellent combination of strength, ductility, and impact strength. It also provides excellent plating and finishing characteristics. The DCU under consideration comprises of raw material storage (RMS), die casting machine (DCM), and finished goods storage (FGS). A schematic diagram depicting the layout of the DCU is given in [Figure 1]. Fuel tank cover and lock assembly produced by the company is shown in [Figure 2].  Figure 1: Material flow in die casting unit. RMS: Raw material storage, DCM: Die casting machine, FGS: Finished goods storage
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2.2 Research methodology
A systematic and scientific approach is necessary to design and carry out the experimentation efficiently. A properly planned experimentation is of utmost importance for deriving clear and accurate conclusion/inferences from the experimental observations. RSM is considered to be a very useful strategy for accomplishing these tasks. In general, RSM establishes the methods for drawing inferences from observations when these are not exact but subject to variation.
The RSM is a collection of mathematical and statistical techniques for analyzing problems in which several independent variables influence a dependent variable or response. RSM has been developed by Box and Wilson (1951) to explore the potential of statistical design in industrial experiments. In many industrial situations, it is possible to represent independent input parameter in quantitative form and establish a functional relationship with the response as given below:
Where Y is the response and x _{1} , x _{2} , x _{k} are the number of input parameters. The function Φ is called the response surface or response function and ε_{R} is the residual sum of errors.
In the present work, RSM was applied for developing the mathematical models in the form of multiple regression equations for the various responses. In applying the RSM, the dependent variable is viewed as a surface to which the model is fitted. Parametric effects on the response were evaluated by considering a polynomial response surface mathematical model given by:
This assumed surface Y contains linear, squared, and cross product terms of variable xi's. Where,
 b _{0} is the mean response over all the test conditions (intercept),
 b _{i} is the slope or linear effect of the input variable xi (the firstorder model coefficients),
 b _{ii} the quadratic coefficients for the variable i (linear by linear interaction effect between the input factor Xi and Xi), and
 b _{ij} is the linear model coefficient for the interaction between factor i and j.
To estimate the regression coefficients, a number of experimental design techniques are available. In this study, a fourfactor, threelevel facecentered design was used. The facecentered design (FCC) was used for study of four responses namely slot diameter, pitch circle diameter, pinhole diameter, and pinhole depth. The process parameters and their range are shown in [Table 2]. The corresponding design matrix is shown in [Table 3] along with response values. The tests were conducted randomly according to design matrix [Table 3]. The regression coefficients were calculated with the help of Design Expert V6 statistical software. After determining significance of the coefficients at a 95% confidence level, the final relationships for slot diameter, pitch circle diameter, pinhole diameter and pinhole depth in terms of the process parameters were developed.  Table 3: Facecentered composite design matrix for four variables in actual and coded form
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3. Result and Discussions   
3.1 Model development
Results and analysis of the responses are presented in this section. The appropriate selection of RSM model was carried out on the basis of sequential model sum of squares, lack of fit tests, and model summary statistics. The analysis of variance (ANOVA) was also carried out to find the significant factors and their interactions. The ANOVA for slot diameter is shown in [Table 4]. The ANOVA shows the significance of various factors and their interactions at 95% confidence level. ANOVA [Table 4] shows the "Model" as "Significant" while the "Lack of fit" is "Not significant" which are desirable for model adequacy. The probability values <0.05 in the "Prob. >F" column indicates the significant factors and interactions. The ANOVA for other responses were carried out in the same way and are not shown here. All the main factors and their significant interactions are included in the final model while the insignificant interactions are excluded from the model. Injection pressure (A), hydraulic pressure (B), pot temperature (C), and cooling time (D) were the significant factors while interactions between Injection pressure and cooling time (AD), pot temperature  cooling time (CD) were the significant interactions. Thus, finally quadratic model with main factors (A, B, C, and D) and their significant interactions was selected. The final selected model for slot diameter in terms of coded and actual formsare shown in [Table 5] and [Table 6], respectively. Similarly the developments of final models for other responses were also carried out in the same way [Table 5] and [Table 6]. [Table 5] (equations 36) shows the response models in terms of coded form while [Table 6] (equations 710) shows the response models in terms of actual factor levels.  Table 5: Response surface models in terms of coded form for various responses
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 Table 6: Response surface models in terms of actual form for various responses
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The statistical inferences which can be drawn from ANOVA test [Table 4] are given below:
 The model Fvalue 18.26 implies that the model is significant (adequate).
 The "Lack of fit Fvalue" 2.76 implies the lack of fit is not significant relative to pure error. Insignificant lack of fit is good for adequate model.
3.2 Single factor effects on slot diameter, pitch circle diameter, pinhole diameter, and pinhole depth
The effect of single factors on the responses can be explained with either equations 36 (coded form, since all the factors in coded form at the same level + 1, 0, 1) or with the help of single factor graphs [Figure 3], [Figure 4], [Figure 5], [Figure 6]. As the injection pressure increases the slot diameter also increases, while it increases with the increase in hydraulic pressure up to a certain limit afterwards tends to decrease [[Figure 3]ab]. Similarly, the slot diameter decreases in the initial state of increase of pot temperature afterwards it increases while with the increase in cooling time the slot diameter increases [[Figure 3]cd]. The single effects on other responses can be explained in the same way.  Figure 3: Single factor effect on slot diameter (a) injection pressure, (b) hydraulic pressure, (c) pot temperature, and (d) cooling time
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 Figure 4: Single factor effect on pitch circle diameter (a) injection pressure, (b) hydraulic pressure, (c) pot temperature, and (d) cooling time
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 Figure 5: Single factor effect on pinhole diameter (a) injection pressure, (b) hydraulic pressure, (c) pot temperature, and (d) cooling time
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 Figure 6: Single factor effect on pinhole depth (a) injection pressure, (b) hydraulic pressure, (c) pot temperature, and (d) cooling time
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3.3 Interaction effect on slot diameter, pitch circle diamamter, pinhole diameter, pinhole depth
The interaction 3 D graphs are shown in [Figure 7], [Figure 8], and [Figure 9] for slot diameter, pinhole diameter, and pinhole depth, respectively. The interaction graphs [[Figure 7]a] show that with the increase in injection pressure (A) and cooling time (D), the slot diameter increases (interaction AD) and same trend is shown by pot temperature  cooling time (CD) interaction on the slot diameter. Similarly, other interactions for other responses can be explained on similar lines.  Figure 7: Threedimensional plot showing the effect of (a) injection pressure  cooling time (AD) and (b) pot temperature  cooling time (CD) on slot diameter
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 Figure 8: Threedimensional plot showing the effect of (a) injection pressure  cooling time (AD) and (b) hydraulic pressure  cooling time (BD) on pinhole diameter
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 Figure 9: Threedimensional plot showing the effect of injection pressure  pot temperature (AD) on pinhole depth
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3.4 Process optimization
To investigate the influencing tendency of the process parameters on the responses, 3D graphs were plotted under certain processing conditions. The 3D response surface and 2D plots are the graphical representations of the regression equations used to determine the optimum values of the variables within the ranges considered. Equation 3 (slot diameter) was used to plot the [Figure 3] (ad) (one factor plot) and [Figure 7] (ab) (surface plot). Equation 4 (pitch circle diameter) was used to plot the [Figure 4] (ad) (one factor plot) and there was no interaction of factors were observed in this response. Equation 5 (pinhole diameter) was used to plot the [Figure 5] (one factor plot) and [Figure 8] (ab) (surface plot). Equation 6 (pinhole depth) was used to plot the [Figure 6] (one factor plot) and [Figure 9] (surface plot). The optimization module in designexpert searches for a combination of factor levels, which simultaneously satisfy the requirements placed (i. e., optimization criteria) on each of the responses and process factors (i. e., multiresponse optimization) ^{[12],[13],[14],[15]} . Numerical and graphical optimization methods were used in this study by choosing the desired goals for each factor and response. The optimization process aims to combine the goals into an overall desirability function. The numerical optimization finds a point or more that maximize this function.
In the case of dealing with many responses, it is recommended to perform numerical optimization first; otherwise, one may find it impossible to uncover a feasible region. The graphical optimization displays the area of feasible response values in the factor space. In the numerical optimization part, a criterion was adopted. The criterion is to optimize slot diameter, pitch circle diameter, pinhole diameter, and pinhole depth, which produces the target value. The target value for these responses was 2, 4, 1.5, and 0.8 mm, respectively. Equal weightage was given to each response for finding the optimum solution. The optimum conditions are shown in [Table 7] having a desirability of 1.
3.5 Validity of the response models under different operating conditions
The validity of various models such as slot diameter, pitch circle diameter, pinhole diameter, and pinhole depth was evaluated by conducting tests at optimum conditions [Table 7] of various experimental factors such as applied injection pressure, hydraulic pressure, pot temperature, and cooling time. The optimum conditions correspond to the obtained from design expert software. But it was not possible to set the machine at these conditions. Hence, the confirmation tests were conducted at conditions shown in [Table 7]. The variations between the experimental and the calculated values are of the order of 27% [Table 8].  Table 8: Comparison of modeled and experimental results of various responses
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4. Conclusions   
In the die casting process of ZAMAK3 alloy, the mathematical models of the various responses have been developed to correlate the dominant process parameters, including the injection pressure, hydraulic pressure, pot temperature, and cooling time. The FCD technique plan based on the RSM has been employed to carry out the experimental study. The conclusions of research are as follows:
 RSM can be effective tool used for modeling and multiresponse optimization in die casting process.
 The slot diameter, pitch circle diameter, pinhole diameter, and pinhole depth were modeled in terms of die casting process parameters. The confirmation tests results an error of 27%.
 Slot diameter increases with increase in the process parameters (injection pressure and cooling time), but in case of hydraulic pressure it decreases initially and then increases.
 Injection pressure  Cooling time (AD) and (b) pot temperature  Cooling time (CD) on slot diameter, injection pressure  Cooling time (AD) and hydraulic pressure  Cooling time (BD) on pinhole diameter, and injection pressure  Pot temperature (AD) on pinhole depth were the main significant interactions observed.
References   
1.  Anastasiou KS. Optimization of the aluminium die casting process based on the Taguchi method. J Eng Manuf 2002;216:96977. 
2.  Tsoukalas VD, Mavrommatis SA, Orfanoudakis NG, Baldoukas AK. A study of porosity formation in pressure die casting using the Taguchi approaches. J Mater Sci Eng 2004;218:7786. 
3.  Guharaja S, Noorul HA, Karuppannan KM. Optimization of green sand casting process parameters by using Taguchi's method. Int J Adv Manuf Technol 2006;30:10408. 
4.  Oktem H, Erzurumlu T, Col MA. Study of the Taguchi optimization method for surface roughness in finish milling of mold surfaces. Int J Adv Manuf Technol 2006;28:694700. 
5.  Guo ZP, Xiong SM, Liu BC, Mei LB, Jhon AB. Effect of process parameter, casting thickness and alloys on the interfacial heat transfer coefficient in the high pressure die casting process. Metallurgical and material transactions A. Minerals Metals Mater Soc ASM Int 2008;39A:2896. 
6.  Chiang KT, Liu NM, Tsai TC. Modeling and analysis of the effects of processing parameters on the performance characteristics in the high pressure die casting process of AlSI alloys. Int J Adv Manuf Technol 2009;41:107684. 
7.  Chiang KT, Liu NM, Chou CC. Machining parameters optimization on the die casting process of magnesium alloy using the greybased fuzzy algorithm. Int J Adv Manuf Technol 2008;38:22937. 
8.  Zheng J, Wang Q, Zhao P, Wu C. Optimization of highpressure diecasting process parameters using artificial neural network. Int J Adv Manuf Technol 2009;44:66774. 
9.  Zhu JD, Cockcroft SL, Maijer DM. Modelling of micro porosity formation in A356 Aluminum alloy casting. Metallurg Mater Trans A 2006;37A:1075. 
10.  Kumar S, Satsangi PS, Prajapati DR. Optimization of process factors in melt shop using taguchi method. Int J Qual Reliab Manag 2012;30. 
11.  Kittur JK, Herwadkar TV, Parappagoudar MB. Modeling and analysis of the pressure die casting using response surface methodology. International conference on modeling, optimization, and computing (ICMOS 2010). AIP Conf Proc 1298:73541. 
12.  Box GE, Wilson KB. On the experimental attainment of optimum conditions. J Royal Stat Soc 1951;B13:1. 
13.  Evans M. Optimization of manufacturing processesA response surface approach. 1 ^{st} ed. London: Maney Publishing; 2003. 
14.  Montogomery DC. Design and analysis of experiments. New York: John Willey and Sons; 2004. 
15.  Hines WW, Montogomery DC. Probability and statistics in engineering and management sciences. 3 ^{rd} ed. Hoboken: Willey Publications; 1990. 
Authors   
Yasir Zubair,
Yasir Zubair was a student of Gautam Buddha University in Mechanical Engineering.
Dr. Satpal Sharma received his B. E. and M. Tech. from NIT, Kurukshetra. He did his Ph.D. from IIT, Roorkee, Uttrakhand, India. He has a teaching experience of more than 16 years plus 1.25 years of industrial experience. Presently he is with the Department of Mechanical Engineering in School of Engineering, Gautam Buddha University, Greater Noida, U. P., India as an Assistant Professor. His area of research is tribological properties of coatings, weld surfacing and thermal spraying, welding, composite materials and machining. He has published more than 28 research papers in refereed international journals and 08 papers in national journals. Besides this 08 papers were published in international conferences and 04 paper in national conference. Supervised 16 M. Tech. students for dissertation. Presently 06 research scholars (Ph. D. students) are working under my supervision.
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7], [Figure 8], [Figure 9]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7], [Table 8]
