OBRABOTKAMETALLOV MATERIAL SCIENCE Vol. 26 No. 3 2024 before the casting process. Melting and stirring parameters were optimized using the control panel. The following parameter values were set: stirring speed = 50–100 rpm, stirring time = 20 minutes, melting temperature = 800 °C, preheating temperature = 200 °C. A cylindrical mould with a diameter of 20 mm and a length of 250 mm was made for pouring molten metal. Using an orthogonal array L9, nine specimens were prepared, 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. %. The hardness of each composite was calculated via selecting three values of indentation load of 10, 20 and 30 kN, the control variables table of which is shown below. Design of Experiment Material characterization requires an optimal output response to reduce the number of variables and improve the performance and durability of composite specimens. This optimization is achieved by controlling the input parameter over the output response, and Taguchi analysis is the optimal platform for characterizing materials [14]. Here, three parameters as weight percentage of red mud, weight percentage of coconut shell ash and indentation load are selected to test the Brinell hardness response of the hybrid aluminum composite material. Three levels of input parameters were selected to evaluate the hardness response, therefore, an orthogonal array L27 was selected for ANOVA and regression analysis, which is an experimental and predictable result of regression analysis, summarized in Table 1. In this research work, ANOVA and regression analysis were performed using Minitab 17 software to check the hardness value of hybrid aluminum composite specimens. The weight percentage of coconut shell ash (CSA weight %), the weight percentage of red mud (RM weight %) and the indenter load were accepted as input parameters. The coconut shell ash and red mud levels are assumed to be equal to 5, 7.5 and 12.5 wt. % at loads of 10, 15 and 20 kN. The hardness of composite specimens with different parameters is shown in Table 2. Figure 1 shows the effect of hardness on the signal-to-noise (SN) ratio and here the response is optimized for a larger hardness value and mean of SN ratio varies from 31 to 32.6 hardness values, which shows the optimal variability of the output hardness response. The composite material with the highest percentage of red mud and coconut shell ash reinforcing with the highest indentation load has a maximum hardness of 52.44 HB, which is almost 95 % greater than the composite material with the lowest percentage of reinforcing material (5 wt. % coconut shell ash and 5 wt. % red mud) and indenter load (10 kN). Thus, the hardness value improves with increasing volume fraction of the reinforcing material under loading [19]. Hardness behavior The hardness behavior characterization of a hybrid aluminum composite material reinforced with red mud (RM) and coconut shell ash (CSA) is presented in Table 2. The hardness increases with increasing percentage of reinforcing martial because the hard and brittle phase of the reinforcing martial creates a lubricating dislocation density and while the application of a dislocation density load leads to the formation of new strain fields that resist the dislocation movement [19]. Also, the difference in melting temperatures between the reinforcing material and the aluminum matrix activates the mechanism of strain hardening due to the transfer of the strain field along the grain boundary, which creates a barrier field along the matrix and hinders the indentation load, therefore increasing the hardness of the composites [20]. Figure 2 shows that the hardness value increases with increasing load, since under high load conditions the lubricating layer (formed due to the thermal mismatch between the reinforcingmaterial and the aluminum Ta b l e 1 Level of Control variables for hardness Variable Unit Level I Level II Level III Red mud Weight % 5 7.5 12.5 CSA Weight % 5 7.5 12.5 Load kN 10 20 30
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