In the present research study, Structural aluminium containing 0.8% - 1.2% magnesium and 0.4% - 0.8% silicon as the major constituents is selected as it is widely applicable in the fields of construction and automotive. Figure 1 shows the image of the structural aluminium.

Boron Carbide, a ceramic material and Red mud, a waste by product from the production of alumina are selected as reinforcements, shown in Figure 2(a) and Figure 2(b). From the chemical analysis report, Composition of Structural aluminium, boron carbide and Red mud are shown in Tables 1-3.

Stir casting is an effortless and economical method, in which reinforcements are added along with the base matrix material with the aid of a mechanical stirrer to produce hybrid castings. Raw matrix materials were stacked first in the

graphite crucible as per the calculations and then melted in an electrical induction furnace, which is shown in Figure 3. Once the melt temperature reaches 750˚C the degassing of molten metal was administered using hexachloro ethane tablets. At the same instant boron carbide (2%, 4%, 6%, volume) and Red mud particles (2% volume) were heated in a separate muffle furnace at 1000˚C to remove water content. The added reinforcements were blended continuously by using a mechanical stirrer for about 10 - 12 minutes at an impeller speed of 300 rpm. The melted metal is then spouted into the preheated die. Test specimens are displayed in Figure 4.

Wear is an essential factor in deciding the life span and consistency of the

engineering systems. Frictional resistance between the contact surfaces causes wear on the surfaces which results in the cumulative loss and deformation of the material. Pin-on-disk tribometer, as shown in Figure 5 is the standard test instrument (VER02 Computerized Pin on Disc Wear Testing Machine, Bangalore) for the assessment of friction, wear rate and wear resistance. Wear samples (10 mm × 10 mm) and testing were carried out according to ASTM G99 standard. A pin is loaded against a flat revolving EN31 steel disc and the wear track diameter is set to 60 mm.

The specific wear rate of 2% B₄C and 2% Red mud aluminium hybrid composite, 4% B₄C and 2% Red mud aluminium hybrid composite, 6% B₄C and 2% Red mud aluminium hybrid composites were investigated. Tests were realized with different values of Speed at 600, 1200 and 1500 rpm, under normal loads of 10 N, 20 N, and 30 N.

The Genichi Taguchi’s Design of Experiments utilizes orthogonal array (OA), signal-to-noise (S/N) ratios, main effects, and Analysis of Variance (ANOVA). L-9 orthogonal array which is more efficient than other statistical methods is selected, which has 9 rows and 3 columns. The Input control factors considered here are Wt%. Reinforcement, Load and Speed, each at three different levels, represented in Table 4. Experiments were carried out for 9 tests according to the

standard Taguchi method. SWR is considered as the output response in the present investigation. The well-balanced experiments were implemented for the distinct combinations of input control parameters and SWR is tabulated as the output response. Minitab-17 is employed to measure the standard characteristics of S/N quantitative, which is then remodeled from the experimental value. The specific wear rate is studied as the output response to S/N ratio, considering the smaller the better-quality characteristic. The focus of the present investigation is to obtain minimal SWR. The input and output factors and the respective S/N ratios are shown in Table 5.

The investigation of S/N ratio reveals the most significant statistic decisive factor.

Table 6 shows the response for S/N ratios and according to it the most influential decisive Control factor and the optimal factor for SWR is determined.

Main effect of control factors will be directly represented by examining how each individual factor affects the SWR and is shown in Figure 6. SWR found to decrease with increase in the% of Reinforcement and Load.

In Figure 6, the effect of the three factors, which are Wt%. Reinforcement, Load & Speed for S/N ratio for the SWR is represented. From Table 6 & Figure 5, optimization of factor variant is obtained by considering the highest S/N ratio among the control factors.

From the study of the results, the lowest SWR is obtained from the factor combination having a load value of 30 N, Speed at 1200 rpm, and 6 Wt%. Reinforcement.

According to the ranking from Response table (Table 7), the most influential factors can be determined, which are in the order of Wt%. Reinforcement, Load, and

then Speed. Levels of the optimal parameter variant are M3L3S2, which means that the Wt%. Reinforcement and Load are on the third level and the Speed is on the second level.

Statistical parametric ANOVA test was employed for the inference of the experimental results. ANOVA was used to review the combination of control factors which considerably affects the output response, i.e., SWR, without lubrication. ANNOVA for specific wear rate is represented in Table 8. Analysis was conducted at a level of significance of 5% (i.e., confidence interval of 95%). The factor is highly statistically significant when the corresponding p-value is lower than the significance level of 0.05.

From the results it can be discerned that Wt%. Reinforcement was the most substantial factor having the highest statistical effect (38.3%) followed by Load (34.83%) and then Speed (25.50%). Outcomes of ANOVA for SWR obtained by using the MINITAB 17 software indicates that Wt%. Reinforcement is the most significant factor that influences the SWR, as the p value for the obtained model was less than 0.05.