This review article focuses on the long-term durability challenges associated with bamboo fiber-reinforced polymer composites when subjected to various environmental aging conditions such as water immersion, hygrothermal fluctuations, ultraviolet (UV) radiation, soil burial, and refrigerated storage. The primary issue addressed is the degradation of mechanical and structural performance of bamboo fiber-reinforced polymer composites due to moisture absorption, fiber swelling, and fiber–matrix interface deterioration. To mitigate these aging effects, the study evaluates and compares multiple strategies, including chemical and physical fiber surface treatments, filler additions, and fiber hybridization, which aim to enhance moisture resistance and mechanical stability. These composites are relevant in automotive interiors, construction panels, building insulation, and consumer goods due to their eco-friendly nature and potential to replace conventional synthetic composites. This review is necessary to consolidate current knowledge, identify effective enhancement approaches, and guide the development of environmentally resilient bamboo fiber-reinforced polymer composites for real-world applications.

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.

The proposed review demonstrates the effect of the surface modification process, specifically, deep rolling, on the material surface/near-surface properties of commercial steels. The present research examines the various process parameters involved in deep rolling and their effects on the material properties of AISI 1040 steel. Key parameters such as the rolling force, feed rate, number of passes, and roller geometry are analyzed in detail, considering their influence on residual stress distribution, surface hardness, and microstructural alterations. Additionally, the impact of deep rolling on the fatigue life, wear resistance, and corrosion behavior of AISI 1040 steel is discussed. Engineering components manufactured by AISI 1040 steel can perform better and last longer when deep rolling treatments are optimized with an understanding of how process variables and material responses interact. This review provides critical insights for researchers and practitioners interested in harnessing deep rolling techniques to enhance the mechanical strength and durability of steel components across diverse industrial settings. In summary, the valuable insights provided by this review pave the way for continued advancements in deep rolling techniques, ultimately contributing to the development of more durable, reliable, and high-performance steel components in diverse industrial applications. The establishment of generalized standardizations for the deep rolling process proves unfeasible because of the multitude of controlling parameters and their intricate interactions. Thus, specific optimization studies tailored to the material of interest are imperative for process standardization. The published literature on the characterization of surface and subsurface properties of deep-rolled AISI 1040 steel, as well as process parameter optimization, remains limited. Additionally, numerical, analytical, and statistical studies and the role of ANN are limited compared with experimental work on the deep rolling process.

Fiber-reinforced polymer composites are exposed to severe environmental conditions throughout their intended lifespan. It is essential to investigate how they age when exposed to cold and hot water to increase the durability of fiber-reinforced polymer composites. This work uses a hand lay-up process to create composites with different weight percentages of glass fiber, nanoclay, and epoxy. ASTM guidelines are followed for performing tensile and flexural tests. The input parameters, varying wt.% of glass fiber and nanoclay, are continuous, and the aging condition is deemed a categorical factor. The mechanical properties are considered as response variables (output). The mechanical properties are optimized using Response Surface Methodology (RSM), while Artificial Neural Networks (ANNs) provide a reliable predictive model with high correlation coefficients. The findings demonstrate that ANNs outperform RSM in flexural strength prediction, whereas RSM offers greater accuracy for tensile strength modeling. SEM analysis of the fracture surfaces reveals the causes of specimen failure under tensile load, with distinct differences between dry, cold, and boiling water-soaked specimens.