Aerospace and automobile industries are facing challenges in developing lightweight materials with high corrosion and wear resistance. The magnesium (Mg) alloys are superior to their monolithics, as they have maximum strength-to-weight ratio. These challenges can be solved with application of Mg-based hybrid composites. Therefore, this study investigated the hybridizing effect of molybdenum disulphide (MoS$_2$) reinforcement on tribological performance of magnesium–boron carbide (Mg–B$_4$C) hybrid composites, fabricated by powder metallurgy technique. Wear tests under dry sliding condition were carried out on the prepared composite samples with different proportions/weight percentage (wt%), using a pin-on-disc apparatus. Mg, MoS$_2$, B$_4$C and their various composites were characterized, using X-ray diffraction, thermogravimetric analysis, scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy analysis. The experiments were conducted using L$_{27}$ orthogonal array with five factors at three levels that affected the tribological performance. The wear resistance of the hybrid Mg–B$_4$C–MoS$_2$ composites significantly increased when compared with Mg–B$_4$C and Mg–MoS$_2$ composites, due to the refined effect of both reinforcements. Analysis of variance and grey-relational analysis result showed that increase in MoS$_2$, sliding distance ($D_{Sl}$) and load ($L_{Sl}$) significantly influenced the tribological performance of the hybrid composites. Mg–10wt%B$_4$C–5wt%MoS$_2$ exhibited significant best improvement on the multi-response tribological performance. The optimum quantity of MoS$_2$ reinforcement was around 7 wt%. Beyond this threshold proportion, wear was significantly increased, due to the agglomeration of MoS$_2$ particles. Hardness of the composites increased with hybridized reinforcements. SEM micrographs depicted the homogeneous dispersion of reinforcements in the Mg matrix. Also, SEM micrographs of the worn surfaces confirmed that delamination wear mechanism was dominant on the Mg hybrid composites.

Scanning electron microscopy (SEM) is a reference approach for determining the dimensional properties of the nanocomposites or black particles (BPs). Numerous approaches can be available for extracting BPs from SEM imagesand evaluating their diameters. However, the existing models are subjective as well as very time-consuming. Moreover, they need help to collect the complete quantitative information of interest. For that reason, in this study, an automated SEM analysis with the help of image orientation detection is proposed using the adaptive density-based spatial clustering of application with noise (ADBSCAN) clustering algorithm. The detection of image orientation using the proposed approach helps to find out the material strength of BPs. Here, the images of eggshell powder with 20 ${\mu}$m having different weights, such as 1%, 2%, 3%, 4% and 5%, are taken as input. Later, pre-processing is carried out in which noise removalusing Cosine Distance induced Bilateral Filter is performed. Then, the image’s contrast is enhanced through the Gaussian distribution adapted contrast limited adaptive histogram equalization technique. Next, the canny edge detector is used,followed by segmentation with Threshold-based Otsu. Finally, clustering takes place via ADBSCAN model. After that, quantitative analysis is done using Gaussian, scatterplot and histogram for all weight percentages. The outcome revealed that the proposed work computes the orientation more accurately, and effectively estimates the material strength of the eggshell powder.