Diminishing the Casting Defects by using Optimization Technique

DOI : 10.17577/IJERTCONV5IS07033

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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

  1. 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 air-setting 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.

  2. 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.

  3. 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.

  4. 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 well-designed 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.

      1. 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.

      2. 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).

  5. RESULTS AND DISCUSSION

      1. 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).

      2. 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.

      3. 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

      4. 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.

      5. 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).

  6. 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.

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