 Open Access
 Total Downloads : 10
 Authors : Tharoon T, Praveen Rajha M, Santhosh N, Ranjith S N, Chelliah A
 Paper ID : IJERTCONV5IS07033
 Volume & Issue : ETDM – 2017 (Volume 5 – Issue 07)
 Published (First Online): 24042018
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Diminishing the Casting Defects by using Optimization Technique
Tharoon T1, Praveen rajha M2, Santhosh N3, Ranjith S N4, Chelliah A5
1, 2, 3, 4 UG Scholar, Department of Mechanical Engineering, SNS College of Technology, Coimbatore. 5Assiant Professor, Department of Mechanical Engineering, SNS College of Technology, Coimbatore.
Abstract – The main intension of the paper is to improve the quality of casting products and to produce the casting products with zero defects by using various kind of optimization techniques such as Taguchi technique. The bonding strength of the sand is very essential to increase the quality of sand casting products. The taguchi analysis is done for optimizing the process parameters such as content of diethylene glycol content of dextrin and content of cellulose. The diethylene glycol, dextrin and cellulose are sand additives which are added to sand at different levels and remaining all adding agents such bentokole, bentonite, water content which are kept constant level in all type of testing sand. The experiments have been as per taguchis L27 orthogonal array. Analysis of variance (ANOVA) performed to verify the sufficiency of the mathematical model. The Regression equation is predicted in account of process parameter, bonding strength. It can be noted that the bonding strength is increased with increase of content of cellulose in sand.
Key words: Diethylene, Glycol, Cellulose, Optimization, ANOVA, Taguchi

INTRODUCTION
Casting is a process of forming metallic products by melting the metal, pouring it into a cavity known as the mould, and allowing it to solidify. When it is removed from the mould it will be of the same shape as the mould. Almost any article may be cast with proper technique and design, and there is partially no limit as to the size and shape of the casting that may be made. The sand that is used to create the moulds is typically silica sand (SiO2) that is mixed with a type of binder to help maintain the shape of the mould cavity. Using sand as the mould material offers several benefits to the casting process. Sand is very inexpensive and is resistant to high temperatures, allowing many metals to be cast that have high melting temperatures.
Additives are the materials generally added to thesand mixture to develop special properties in the mould and consequently in castings. Binders can be classified as inorganic and organic. Inorganic binders are clays (kaolinite, illite, and bentonites), cements, gypsum, and sodium silicate. Brown coal ashes etc. Organic binders include carbohydrate (starch, dextrin, and dextrose), molasses, various types of oils, pitches, natural resins (calophony, shellac) and synthetic resins (acrylic alkyd, polystyrene, melamine, urea formaldehyde, phenolic etc. The primary purpose of binders is to influence the bonding properties of sand of all the binders, dextrin is perhaps the best. It increases airsetting strength, toughness, and collapsibility and prevents. The quality of the sand that is used also greatly affects the quality of the casting and is usually described by the following five measures. Ability of the sand to maintain its shape is known as strength. Ability to allow venting of trapped gases through the sand, a higher permeability can reduce the porosity of the mould, but a lower permeability can result in a better surface finish. Permeability is determined by the size and shape of the sand grains is known as permeability, Ability to resist damage, such as cracking, from the heat of the molten metal is called thermal stability, Ability of the sand to collapse, or more accurately compress, during solidification of the casting. If the sand cannot compress, then the casting will not be able to shrink freely in the mould and can result in cracking is collapsibility and Ability of the sand to be reused for future sand moulds is known as reusability. Optimization is the act of obtaining the best result under given circumstances. The word optimum is taken to mean maximum or minimum depending on the circumstances. In design, construction, and maintenance of any engineering system, engineers have to take many technological and managerial decisions at several stages. The ultimate goal of all such decisions is either to minimize the effort required or to maximize the desired benefit. Since the effort required or the benefit desired in any
practical situation can be expressed as a function of certain decision variables, so optimization can be defined as the process of finding the conditions that give the maximum or minimum value of a function. The optimum searching methods are also known as mathematical programming techniques and are generally studied as a part of operations research. Operations research is a branch of mathematics concerned with the application of scientific methods and techniques to decision making problems and with establishing the best or optimal solutions.

PROBLEM DEFINITION
In industry, the defects occur in sand casting due to improper of properties of sand such as insufficient bonding strength and improper mixing of sand additives. So improve the bonding strength by means of proper mixing of sand additives. This is remedy action for that kind of problem.

EXPERIMENTAL DETAILS
In the present investigation, casting sand (Green sand) is taken for testing. The other contents
Fig 1. Sand testing equipment (strength tester)
Such as bentokol, bentonite, water level and remaining items are added to the testing sand and they are kept in constant level in all kind of sand. Sand additives such as diethylene glycol, dextrin, and cellulose are added to all kind of sand but in different levels of composition like 27 various composition tabulated in table 1. The main intension of this experiment is to peruse highest bonding strength at which level of additives composition such as diethylene glycol, dextrin and cellulose. After, adding of various chemical composition such as diethylene glycol, dextrin and cellulose sands are tested by the sand testing equipments like strength tester. The values are tabulated in table 2. The equipments are shown in Fig 1.

DESIGN OF EXPERIMENTS
DOE is a powerful tool for identifying a set of process factors (parameters) which are most important to the process and then determine at what levels these factors must be kept to optimize the response (or quality characteristic) of interest. It derives its power from the fact that it helps maximize the information gained from a given number of experiments whilst using a minimum of resources. This is obtained through factorial design, a structured approach based on statistical methods that supports the simultaneous changing of more than one factor at a time. For developing models on the basis of experimental data, careful planning of experimentation is essential. The factors considered for the experimentation and analysis are content of diethylene glycol (g), content of Dextrin (g), and content of Cellulose (g). The design of experiments have major effect on the number of experiments needed. Therefore it is essential to have a welldesigned set of experiments. Design of experiments (DOE) is a systematic method, to determine the relationship between factors affecting a process and the output of that process. In this paper, the factors are considered such as content of diethylene glycol (g), content of Dextrin (g), and content of Cellulose (g) in sand mixing.

Experimental Parameters and Levels
Table 1. Parameters and levels
The content of diethylene glycol (g), Dextrin (g) and Cellulose (g) improvethe bonding strength of sand, So the parameters are taken for the current investigation are content of diethylene glycol (g), content of Dextrin (g), and content of Cellulose (g). The parameters and levels set at three different levels, namely low, medium and high as shown in Table 1.

Taguchi L27 Orthogonal array
The Taguchi method has been proposed to overcome these limitations by simplifying and standardizing the fractional factorial design. The methodology involves identification of controllable
Table 2. Uncoded values
Test No
Uncoded Values
Bonding
Strength (kg/cm2)
DE
(g)
DT (g)
CE
(g)
1.
100
150
100
920
2.
100
150
250
950
3.
100
150
450
970
4.
100
250
100
1090
5.
100
250
250
1075
6.
100
250
450
1080
7.
100
350
100
1350
8.
100
350
250
1380
9.
100
350
450
1400
10.
200
150
100
1080
11.
200
150
250
1070
12.
200
150
450
1070
13.
200
250
100
1300
14.
200
250
250
1275
15.
200
250
450
1250
16.
200
350
100
960
17.
200
350
250
970
18.
200
350
450
980
19.
300
150
100
1260
20.
300
150
250
1200
21.
300
150
450
1280
22.
300
250
100
908
23.
300
250
250
925
24.
300
250
450
920
25.
300
350
100
1050
26.
300
350
250
1025
27.
300
350
450
1050
uncontrollable parameters and the establishment of a series of experiments to find out the optimum combination of the parameters which has the greatest influence on the performance and the least variation from the target of the design. Based on this, Taguchi L27 orthogonal array has been selected each having a combination of different levels of factors, as shown in Table 2. The variables are coded by taking into account level of content of diethylene glycol (g), Dextrin (g) and Cellulose (g).


RESULTS AND DISCUSSION

Determination of the Regression model and Evaluation of Statistical
The Regression equation, ANOVA and Graph is generated by using Minitab software. The regression equation is give the relationship among
the content of Diethylene glycol (g), content of Dextrin (g), and content of Cellulose (g).

Regression Analysis for Bonding strength (Response) The regression equation for bonding strength is given by, Bonding Strength (Kg/cm2) = 846 + 0.332 DE + 0.203 DT
+ 1.02 CE
Table 3. Regression analysis for bonding strength
The goodness of fit was clarified by the determination coefficient (R2).In this study, the value of determination coefficient is 0.966 which is indicated that 4% of the total variations were not explained by the regression model. The adjusted determination coefficient is 0.982. So we noticed that the adjusted determination coefficient is closer to the determination coefficient which means a good correlation between the responses and the experimental results in response as bonding strength.

ANOVA Analysis for bonding strength (Response) ANOVA was performed by the Minitab Software. Which give the effective values. The percentage of contribution was calculated. From ANOVA table we understand the content of Cellulose plays an important role in bonding strength of sand because its percentage of contribution is 93.40%. The secondary contribution factor is content of diethylene glycol and its percentage of contribution factor is 3.20%. Third contribution factor is content of dextrin and its percentage of contribution is 1.49%.
Table 4. ANOVA table for bonding strength

Taguchi Analysis for bonding strength (Response) Taguchi analysis is done as above we got signal
to noise ratio graph and mean plot graph as shown in the figure. From which a major influencing factor is obtained.

Normal Probability plot Graph for bonding strength (Response)
The normal probability plot graph is obtained by using the regression equation and the experimental values. It is a graphical representation for assessing whether data set is normally distributed or not. The graph should give approximately in a line. So the errors are distributed normally.
Fig 2 Signal to noise ratio graph
Fig 4. Normal probability plot
5.6. Confirmation Test
Table 7. Confirmation test for bonding strength
Fig 3. Mean plot graph
Response Table for Signal to Noise Ratios Larger is better
Table 5. Taguchi analysis for bonding strength (SN
ratio)
Table 6. Taguchi analysis for bonding strength (Mean)
The L27 array were conducted which means 27 experiments were conducted from which the percentage of error is calculated and tabulated at different conditions such as content of diethylene glycol, content of dextrin, content of cellulose for bonding strength (response).


CONCLUSION
In this paper, Taguchi L27 Orthogonal is used to optimize the process parameter. The following conclusions are done by this experiment,
In Regression analysis, the adjusted determination
coefficient is very closer to the determination coefficient so evaluation of bonding strength of sand is done by effectively and efficiently.
Content of cellulose is found to be the first influencing
factor on bonding strength of sand.
The second influencing factor is content of diethylene
glycol on bonding strength of sand.

The third influencing factor is content of dextrin on bonding strength of sand.

The normal probability plot graph is obtained in the form of straight line so the errors are distributed normally.
REFERENCES

Achamyeleh A. Kassie, Samuel B. Assfaw Minimization of Casting Defects. EISSN: 2250 3021, pISSN: 22788719 Vol. 3, Isse 5 (May. 2013), V1 PP 3138.

Afazov. S.M, Becker. A.A, Hyde. T.H FE prediction of residual stresses of investment casting in a Bottom Core Vane under equiaxed cooling. Journal of Manufacturing Processes 13 (2011) 30 40. doi:10.1016/j.jmapro.2010.10.001.

Alastair Long, David Thornhill, Cecil Armstrong, David Watson. Predicting die life from die temperature for high pressure dies casting aluminium alloy. Applied Thermal Engineering 44 (2012) 100e107. DOI:10.1016/j.applthermaleng.2012.03.045.

Alexander V. Lotov, George K. Kamenev, Vadim E. Berezkin, Kaisa Miettinen. Optimal control of cooling process in continuous casting of steel using a visualizationbased multicriteria approach. Applied Mathematical Modelling 29 (2005) 653672. DOI: 10.1016/j.apm.2004.10.009.

Anglada. E, MelÃ©ndez. A, Maestro. L, Dominguez. I Adjustment of Numerical Simulation Model to the Investment Casting Process. Procedia Engineering 63 (2013) 75 83. DOI: 10.1016/j.proeng.2013.08.272.

Avalle. M, Belingardi. G, Cavatorta. M.P, Doglione. R Casting defects and fatigue strength of a die cast aluminium alloy: a comparison between standard specimens and production components. International Journal of Fatigue 24 (2002) 1 9. PII: S01421123(01)001128.

Avinash Juriani. Casting Defects Analysis in Foundry and Their Remedial Measures with Industrial Case Studies. EISSN: 22781684, pISSN: 2320334X, Volume 12, Issue 6 Ver. I (Nov. – Dec. 2015), PP 43 54 DOI: 10.9790/168412614354.

Benardos. P.G, Vosniakos. G.C. Optimizing feed forward artificial neural network architecture.
Engineering Applications of Artificial Intelligence 20 (2007)365382. DOI: 10.1016 /j.engappai.
2006.06.005.

Campatelli .G, Scippa. A. A heuristic approach to meet geometric tolerance in High Pressure Die Casting. Simulation Modelling Practice and Theory 22 (2012) 109122. DOI: 10.1016/j.simpat.2011.11.003.

Carlos A. Santos, Jaime A. Spim Jr., Maria C.F. Ierardi, Amauri Garcia. The use of artificial intelligence technique for the optimisation of process parameters used in the continuous casting of steel. Applied Mathematical Modelling 26 (2002) 1077 1092. PII: S0307904X (02)000628.

Cheung. N, Garcia. A. The use of a heuristic search technique for the optimization of quality of steel billets produced by continuous casting. Engineering Applications of Artificial Intelligence 14 (2001) 229 238. . PII: S09521976(00)000750.

ChiBin Cheng, Cheng. CJ, Lee. E.S. NeuroFuzzy and Genetic Algorithm in Multiple Response ptimization. Computers and Mathematics with applications 44 (2002) 15031514.

Casting Design and Simulation of Cover Plate using Auto CASTX Software for Defect Minimization with Experimental Validation. Procedia Materials Science 6 (2014) 786 797 DOI: 10.1016/j.mspro.2014.07.095.

Collini. L, Pirondi. A, Bianchi. R, Cova. M, Melilla.
P.P Influence of casting defects on fatigue crack initiation and fatigue limit of ductile cast iron. Procedia Engineering10 (2011)28982903. DOI:10.1016/j.proeng.2011.04.481.

Janagarathinam. P, Tharoon. T, Senthilkumar.K. The Assessment of Delamination in the Drilling of EN8 Steel by using Taguchi Method.International Journal of Engineering Research & Technology (IJERT).
ISSN: 22780181 Vol. 5 Issue 09, September2016

Janik. M, Diya. H Modelling of three dimensional temperature field inside the mould during ontinuous casting of
steel. Journal of Materials Processing Technology 157 158 (2004) 177182. DOI:
10.1016/j.jmatprotec.2004.09.026.

Kai Yang, EeChon Teo, Franz Konstantin Fuss. Application of Taguchi method in optimization of cervical ring cage. Journal of Biomechanics 40 (2007) 32513256. DOI: 10.1016 /j.jbiomech. 2006.12.016.