Natural-fiber-reinforced composites, especially bamboo, are an alternative material to compete with conventional materials. Their environmentally friendly, renewable, low-cost, low-density, non-toxic, and fully biodegradable properties are concerning for researchers because of their advantages over synthetic polymers. This comprehensive review presents the results of work on bamboo fiber composites with special reference to bamboo types, thermoplastic and thermoset polymers matrices, hybrid composites, and their applications. In addition, several studies prove that these properties are very good and efficient in various applications. However, in the development of composite technology, bamboo fiber has certain constraints, especially in moisture conditions. Moisture is one of the factors that reduces the potential of bamboo fiber and makes it a critical issue in the manufacturing industry. Therefore, various efforts have been made to ensure that these properties are not affected by moisture by treating the surface fibers using chemical treatments.

This study investigates the mechanical, thermal, and chemical properties of basalt/woven glass fiber reinforced polymer (BGRP) hybrid polyester composites. The Fourier transform infrared spectroscopy (FTIR) was used to explore the chemical aspect, whereas the dynamic mechanical analysis (DMA) and thermomechanical analysis (TMA) were performed to determine the mechanical and thermal properties. The dynamic mechanical properties were evaluated in terms of the storage modulus, loss modulus, and damping factor. The FTIR results showed that incorporating single and hybrid fibers in the matrix did not change the chemical properties. The DMA findings revealed that the B7.5/G22.5 composite with 7.5 wt% of basalt fiber (B) and 22.5 wt% of glass fiber (G) exhibited the highest elastic and viscous properties, as it exhibited the higher storage modulus (8.04 × 10⁹ MPa) and loss modulus (1.32 × 10⁹ MPa) compared to the other samples. All the reinforced composites had better damping behavior than the neat matrix, but no further enhancement was obtained upon hybridization. The analysis also revealed that the B22.5/G7.5 composite with 22.5 wt% of basalt fiber and 7.5 wt% of glass fiber had the highest T_g at 70.80 °C, and increased by 15 °C compared to the neat matrix. TMA data suggested that the reinforced composites had relatively low dimensional stabilities than the neat matrix, particularly between 50 to 80 °C. Overall, the hybridization of basalt and glass fibers in unsaturated polyester formed composites with higher mechanical and thermal properties than single reinforced composites.

Natural and synthetic fibres have emerged in high demand due to their excellent properties. Natural fibres have good mechanical properties and are less expensive, making them a viable substitute for synthetic fibers. Owing to certain drawbacks such as their inconsistent quality and hydrophilic nature, researchers focused on incorporating these two fibres as an alternative to improve the limitations of the single fibre. This review focused on the interply hybridisation of natural and synthetic fibres into composites. Natural fibres and their classifications are discussed. The physical and mechanical properties of these hybrid composites have also been included. A full discussion of the mechanical properties of natural/synthetic fibre hybrid composites such as tensile, flexural, impact, and perforation resistance, as well as their failure modes, is highlighted. Furthermore, the applications and future directions of hybrid composites have been described in details.

Utilizing agro-waste material such as rice husk (RH) and coco peat (CP) reinforced with thermoplastic resin to produce low-cost green composites is a fascinating discovery. In this study, the effectiveness of these blended biocomposites was evaluated for their physical, mechanical, and thermal properties. Initially, the samples were fabricated by using a combination of melt blend internal mixer and injection molding techniques. Increasing in RH content increased the coupons density. However, it reduced the water vapor kinetics sorption of the biocomposite. Moisture absorption studies disclosed that water uptake was significantly increased with the increase of coco peat (CP) filler. It showed that the mechanical properties, including tensile modulus, flexural modulus, and impact strength of the 15% RH—5% CP reinforced acrylonitrile-butadiene-styrene (ABS), gave the highest value. Results also revealed that all RH/CP filled composites exhibited a brittle fracture manner. Observation on the tensile morphology surfaces by using a scanning electron microscope (SEM) affirmed the above finding to be satisfactory. Therefore, it can be concluded that blend-agriculture waste reinforced ABS biocomposite can be exploited as a biodegradable material for short life engineering application where good mechanical and thermal properties are paramount.

The Mahalanobis–Taguchi System (MTS) is a pattern recognition tool employing Mahalanobis Distance (MD) and Taguchi Robust Engineering philosophy to explore and exploit data in multidimensional systems. The MD metric provides a measurement scale to classify classes of samples (Abnormal vs. Normal) and gives an approach to measuring the level of severity between classes. An accurate classification result depends on a threshold value or a cut-off MD value that can effectively separate the two classes. Obtaining a reliable threshold value is very crucial. An inaccurate threshold value could lead to misclassification and eventually resulting in a misjudgment decision which in some cases caused fatal consequences. Thus, this paper compares the performance of the four most common thresholding methods reported in the literature in minimizing the misclassification problem of the MTS namely the Type I–Type II error method, the Probabilistic thresholding method, Receiver Operating Characteristics (ROC) curve method and the Box–Cox transformation method. The motivation of this work is to find the most appropriate thresholding method to be utilized in MTS methodology among the four common methods. The traditional way to obtain a threshold value in MTS is using Taguchi’s Quadratic Loss Function in which the threshold is obtained by minimizing the costs associated with misclassification decision. However, obtaining cost-related data is not easy since monetary related information is considered confidential in many cases. In this study, a total of 20 different datasets were used to evaluate the classification performances of the four different thresholding methods based on classification accuracy. The result indicates that none of the four thresholding methods outperformed one over the others in (if it is not for all) most of the datasets. Nevertheless, the study recommends the use of the Type I–Type II error method due to its less computational complexity as compared to the other three thresholding methods.