Moisture, Clay And Additive Effect In Aluminum Strength And Surfaces Roughness Using Sand Casting
The Aluminum alloys have reveled in a huge expansion over the past few decades as a result of their accessible casting temperatures, lightweight and low melting temperature compared to cast iron. The automotive industry is the largest market for aluminum casting. However, the wet sand has a high moi...
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| Format: | Thesis |
| Language: | English English |
| Published: |
2019
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/24953/1/Moisture%2C%20Clay%20And%20Additive%20Effect%20In%20Aluminum%20Strength%20And%20Surfaces%20Roughness%20Using%20Sand%20Casting.pdf http://eprints.utem.edu.my/id/eprint/24953/2/Moisture%2C%20Clay%20And%20Additive%20Effect%20In%20Aluminum%20Strength%20And%20Surfaces%20Roughness%20Using%20Sand%20Casting.pdf http://eprints.utem.edu.my/id/eprint/24953/ https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=117959 |
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| Summary: | The Aluminum alloys have reveled in a huge expansion over the past few decades as a result of their accessible casting temperatures, lightweight and low melting temperature compared to cast iron. The automotive industry is the largest market for aluminum casting. However, the wet sand has a high moisture content, low strength, and air permeability; the castings can easily have the porosity, coarse, sticky sand and expansion defect. The problem of surface roughness is the major problem during the production process in internal-combustion engines and greatly affects the quality of the product. Using RSM with the box-bennken model in order to identify the correlations between response parameters and the total of 17 experiments were conducted. The result collected was optimized using response surface (RSM) and p-value and R-square; calculated using analysis of variance (ANOVA). According to the result, best mixing ratio, the optimized parameters values were 40 ml water, 45.76 g clay, and 34.65 g corn husk. These optimized parameters have 0.7078 on desirability and achieve the maximum value of tensile strength and the minimum value of surface roughness. From the optimized set of parameters, the predicted value of achievable tensile strength was equal to 122.296 kg/mm² and surface roughness was equal to 1.5770 μm. From the result of the experimental, it was found that the most influential parameters were water, followed by corn husk for surface roughness response. Meanwhile, for tensile strength response corn husk largely influence the outcome where the relation of tensile strength increase with the increasing of corn husk value. |
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