The development of the best properties in polyester composite from pineapple leaf fiber (PALF) as a reinforcing material is a subject of interest. The properties of PALF are reliant upon fiber length, wherein technical difficulties in production of long fibers and processing for better characteristics in polyester composites possess inherent challenges. The PALFs are subjected to silane treatment for altering fiber properties. This research attempts to analyze the impact of silane-treated PALF with varying fiber lengths (5, 10, 15, 20, and 25 mm) on the performance of natural fiber composites (NFC) properties. Open mold and hand lay-up techniques were employed to develop the polyester composites. The prepared PALF-based polyester composites were examined for different properties (impact, flexural, tensile strength, and wear rate). Coefficient of friction and wear studies are performed on the prepared composites subjected to different loads (10, 20, and 30 N) via a pin on disc test rig. Polymer composite fracture surfaces were analyzed to observe the interfacial bonding between fibers and matrix via scanning electron microscopy (SEM). SEM results showed that the application of silane treatment resulted in better surface topography (fiber length of 5–10 mm showed smooth surface resulted in crack proliferation possessing low fracture toughness of 15–32 MPa; whereas a 15–20 mm fiber length resulted in better fiber–matrix bonding, improving the fracture toughness from 42–55 MPa) as a result of change in chemical structure in PALF. The 20 mm length of PALF resulted in better properties (flexural, tensile, impact, and wear resistance) which are attributed to fiber–matrix interfacial bonding. These properties ensure the developed polymer composites can be applied to walls, building insulation, and artificial ceilings.

Pure aluminium poses inferior properties that limit its use in load-bearing applications. Reinforcing multiwall carbon nano-tube (solid lubricant) and graphene to aluminium matrix offers better (antifriction, hardness, and wear resistance) properties in composites for such applications. A stir casting processing route is employed to prepare the hybrid composite (aluminium-multiwall carbon nanotube-graphene Al-MWCNT-GR). The Taguchi L16 experimental matrix representing four variables (percent reinforcement of graphene, die temperature, melt temperature, and stir speed) operating at four levels were studied to analyze and obtain higher hardness and low wear rate in hybrid composites. Percent reinforcement of graphene showed maximum impact, and die temperature resulted with the least contribution towards both the responses. Criteria importance through intercriteria correlation (CRITIC) method is applied to determine the weight fractions (importance) for hardness and wear rate equal to 0.4752 and 0.5482, respectively. Grey relational analysis (GRA) and multi-objective optimization by the ratio analysis (MOORA) method converts multiple objective functions into a single objective function with weight fractions assigned to each output. Taguchi-CRITIC-MOORA outperformed the Taguchi-CRITIC-GRA method, which could result in 31.77% increase in hardness and a 36.33% decrease in wear rate compared to initial conditions. The optimal conditions ensure a dense microstructure with minimal pores, result in enhanced properties compared to that obtained for initial and average stir casting conditions. The worn-out surface results in a few thin and slender grooves between tracks with less crack propagation, ensuring self-lubrication in composites fabricated with the optimized condition. The better properties resulted in the hybrid composites correspond to optimized stir casting conditions and can be implemented in industries for large-scale applications.