Detection of material strength of composite eggshell powders with an analysis of scanning electron microscopy

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 images and 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 µm having different weights, such as 1%, 2%, 3%, 4% and 5%, are taken as input. Later, pre-processing is carried out in which noise removal using 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.