OBRABOTKAMETALLOV MATERIAL SCIENCE Vol. 26 No. 3 2024 matrix) creates a strong dislocation strain field along the aluminum grain boundaries, which supports the tendency of increasing hardness. Figure 2 also shows that hardness increases as the percentage of the reinforcing material increases, since the combination of both reinforcing components can refine grain the structure of the composite, and the presence of a hard and brittle phase of silicon oxide, aluminum oxide and iron oxide leads to the formation of a strong inter-atomic bond between the aluminum matrix and the reinforcing material. At the same time, a larger indentation load is required to facilitate scratching hence improves the hardness [21]. According to Table 2, nine specimens were used, while the weight percentage of red mud and coconut shell ash was selected separately in the amount of 5, 7.5 and 12.5 wt. %. In addition, three loads were selected to assess the hardness behavior: 10, 20 and 30 kN. The results of the hardness behavior assessment are given below (Figure 2). Result and Discussion ANOVA Table 3 presents the output hardness response data and shows that the weight percentage of red mud possesses higher rank than load and coconut shell ash. This is a very useful tool for testing the effect of an input parameter on the output response. Table 4 shows the results of ANOVA, which is a very valuable tool for testing the relevance of various input variables to the output results. The contribution of the weight percentage of red mud reaches 48.80 %, the weight percentage of coconut shell ash is 10.41 % and the indenter load is 23.01 %. The same type of results is presented in the response table. The effect of weight percentage of red mud on hardness is superior to the influence of weight percentage of coconut shell ash and indenter load because red mud contains industrial compounds such as Al2O3, Fe2O3, SiO2, TiO2, etc., which support the hardening mechanism of aluminum composite materials [14]. The value of the determination coefficient R2 and the adjusted value R2 drop by 97.02 % and 90.31 %, respectively, which shows the variability of the output response depending on different input parameters. Both R values are within a good range of variability and this analysis is also used to further verify the mechanical hardness of the hybrid aluminum composite material. Regression Analysis A linear regression equation was drawn for the hardness value using the parameters for red mud, coconut shell ash and indentation load taken as input parameters and Brinell hardness response was analyzed by Fig. 2. Hardness variation with respected to load Ta b l e 3 Response Table for Hardness Level Red Mud (%) CSA (%) Load (kN) 1 36.20 37.69 37.25 2 38.77 39.38 38.82 3 42.90 40.81 41.56 Delta 6.70 3.11 4.56 Rank 1 3 2
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