Modeling and Optimizing the Tensile Behavior of Developed Aluminum Hybrid Composite
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Date
2022-08-05
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World Scientific
Abstract
Aluminum and its alloy arc versatilc metal materials engaged in various applications based on their high strength, corrosion resistance and light weight. Ilowever, there are rnany lilllitations to its applications when cornpared with steel. In a bid to illiprove 011 the properties, alutninurn cornposites arc dcvclopcd. In this study, Al 6111 cornpositc was developed by the blend of silica and bmnboo leaf ash (BLA) as reinforcement employing stir casting process. The input factors for the experiment were silica dosage (A), BLA proportion (B) and stirring ternperature (C). The experirnental design carried out via Box Behnken design of the response surface methodology. Composites were fabricated through stir casting process by varying the inputs according to the dictations Of the experirnental runs. Parmneters evaluated are yield strength, ultirnate tensile strength, elastic modulus and elongation. Result of the AN()VA analysis showed that the parameters had conscquential effect on the response and the developed model for each parameter are fit for predictions. From the surface plot, interaction between 5 wt.% and 10 wt.% silica and 2 wt.% and 'l wt.% BLA led to improvement in yield, ultimate tensile strength but decrease in elongation even as proportions 10 wt. % and 15 wt.% silica and 4 wt.% and 6 wt. % BLA ensued reduction in the value. Stirring ternperature of 700—800 0 C is favorable to the strength paratneters while led to strength reduction. Optimization via response surface, predicted optimum conditions of 11.62/19 wt.%, 3.957()7 wt.% and 789.()33 0 C for A, B and C, respectively. Predicted values for yield strength, ultimate tensile strength, elastic modulus and elongation are 278.'26 MPa, 378.24 MPa, 97.7885 GPa and 1().132%, respectively. Validation experiment was carried out at the optirnutn condition and the deviation in parameters between the predicted aud validated values is < 5%. Ilence, the Inodels are statistically fit for property predictions.