The present work aimed to investigate the dry sliding wear behaviors of hybrid polymer matrix composites made up of Kevlar, bamboo, palm, and Aloe vera as reinforcement materials of varying stacking sequences, along with epoxy as the matrix material. Three combinations of composite laminates with different stacking sequences such as AB, BC, and CA were fabricated by a vacuum-assisted compression molding process. The influence of composite laminates fabricated through various stacking sequences and dry sliding wear test variables such as load, sliding distance, and sliding velocity on the specific wear rate and co-efficient of friction were investigated. Experiments were designed and statistical validation was performed through response surface methodology-based D-optimal design and analysis of variance. The optimization was performed using grey relational analysis (GRA) to identify the optimal parameters to enhance the wear resistance of hybrid polymer composites under dry sliding conditions. The optimal parameters, such as composite combinations of CA, a load of 5 N, a sliding velocity of 3 m/s, and a sliding distance of 1500 m, were obtained. Furthermore, the morphologies of worn-out surfaces were investigated using SEM analysis.

Nowadays quality plays a vital role in all the products. Hence, the development in manufacturing process focuses on the fabrication of composite with high dimensional accuracy and also incurring low manufacturing cost. In this work, an investigation on machining parameters has been performed on jute-flax hybrid composite. Here, the two important responses characteristics like surface roughness and material removal rate are optimized by employing 3 machining input parameters. The input variables considered are drill bit diameter, spindle speed and feed rate. Machining is done on CNC vertical drilling machine at different levels of drilling parameters. Taguchi's L16 orthogonal array is used for optimizing individual tool parameters. Analysis Of Variance is used to find the significance of individual parameters. The simultaneous optimization of the process parameters is done by grey relational analysis. The results of this investigation shows that, spindle speed and drill bit diameter have most effect on material removal rate and surface roughness followed by feed rate.

MATEC Web of Conferences, open access proceedings in Materials science, Engineering and Chemistry

This study investigates developing and characterizing a novel hybrid composite reinforced with Prosopis juliflora bark nanoparticles, Raffia fiber, and glass fiber embedded in a polyester resin matrix. The composite was fabricated using the resin transfer molding technique to ensure uniform fiber impregnation and nanoparticle dispersion. Mechanical tests revealed a tensile strength of 165 MPa, a flexural strength of 388 MPa, an impact strength of 4 J, a Shore D hardness of 42 RHN, and an interlaminar shear strength of 24 N/mm², marking significant improvements of 35–40%, 25–30%, and 20% in tensile, flexural, and impact properties, respectively, compared to conventional composites. Thermogravimetric analysis showed enhanced thermal stability, with decomposition temperatures increasing by 15–20% due to the thermal shielding effect of nanoparticles. Scanning Electron Microscopy (SEM) confirmed uniform nanoparticle dispersion and strong fiber-matrix adhesion, contributing to the composite’s superior performance. The novelty of this research lies in the synergistic combination of natural and synthetic fibers with multifunctional nanoparticles, which optimizes mechanical and thermal properties while maintaining environmental sustainability. These hybrid composites, with their significant mechanical and thermal improvements, are a scientific achievement and a cost-effective solution for high-performance applications in automotive, structural, and aerospace industries, offering a sustainable alternative to traditional materials.