This comprehensive review focuses on the most recent advances in electron backscatter diffraction (EBSD) methods in the context of materials science and includes a thorough evaluation of the sample preparation procedures unique to EBSD as well as a complete examination of the important operational parameters inherent in EBSD setups. This review highlights the importance of customizing EBSD parameters for precise microstructural imaging and enhancing understanding of material behavior. While some studies have explored grain boundary characterization, stored energy, and crystallographic orientation using EBSD, there is a clear need for more comprehensive investigations to fully leverage its capabilities. Additionally, there is a significant gap in understanding the optimal choice of the reference plane in EBSD analysis, indicating the necessity for further research to improve EBSD analyses’ accuracy and efficacy. The review seeks to present a detailed and contemporary viewpoint on the many applications, sample preparation techniques, and optimal operational considerations that jointly increase the adaptability and efficacy of EBSD in materials science research by relying on the relevant literature.

This research contributes to the field of materials engineering through an analysis of the wear performance of both glass fiber-reinforced epoxy composites (GFEC) and bamboo fiber-reinforced epoxy composites (BFEC). This study aims to assess the wear performance, defined by mass loss, of the composites under various factors: load, speed, time, nanoclay content, and composite type. Specimens are subjected to wear tests by a pin-on-disc tribometer. Composite wear performance is studied through Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) as predictive models. Experimental runs are planned based on the Box–Behnken design of RSM to present a regression model that is then checked with an ANOVA analysis; afterwards, training and testing are performed using an ANN model to improve predictive accuracy. As per the results, GFEC exhibits lower mass loss compared to BFEC. For both of the composites, the mass loss is drastically reduced by the addition of nanoclay. The addition of nanoclay has more pronounced effects on BFECs than on GFECs. ANN predictions are found to be better in agreement with the experimental values compared to those derived from the RSM model. Scanning Electron Microscopy (SEM) analysis provides insight into wear mechanisms. This study demonstrates the effectiveness of a statistical and machine learning approach in optimizing wear performance in composite materials.