Nowadays, the natural fibers reinforced thermoplastic polymers have been increasingly used in various automotive, construction, and packaging industries because of their high modulus, strength, and renewability. In this work, the thermoplastic polymer is reinforced by surface‐modified Bauhinia Vahlii (BV) fiber. The fabrication of composite at 10, 20, and 30 wt% BV fiber loading is done by compression molding technique and characterization of the composite is carried out. The tensile and flexural strength is found to be maximum at 10 wt% BV fiber content, indicating a strong interfacial bond between fiber and matrix as supported by scanning electron microscope of fracture surfaces of the composite. The impact strength and hardness of the BV/polypropylene (PP) composite increases with BV fiber loading. According to the dynamic mechanical analysis results, with rise in BV fiber content in the composite, the storage and loss modulus improved, but tan delta reduced. The thermogravimetric analysis and derivative thermogravimetry inferred that the composite's thermal stability lies between modified BV fiber and PP matrix. The differential scanning calorimetry curve studies the melting and crystallization behavior of the composite. The higher crystallinity index of composite is obtained at 10 wt% BV fiber content supported by tensile strength result. Both hot and cold‐water immersion processes studied the water absorption behavior of the composite. The composite with a higher percentage of fiber absorbs more water and takes less time to reach saturation. Compared to other produced composites and neat PP, it is observed that composites fabricated with 10% BV fiber content have superior characteristics.

The dry sliding wear behaviour of LM 24 aluminum alloy composites reinforced with garnet particles was evaluated. Stir casting technique was used to fabricate the composites. A pin-on-disc wear-testing machine was used to evaluate the wear rate, in which an EN 24 steel disc was used as the counterface. Results indicated that the wear rates of the composites were lower than that of the matrix alloy and further decreased with the increase in garnet content. However, in both unreinforced and reinforced composites, the wear rate increased with the increase in load and the sliding speed. Increase in the applied load increased the wear severity by changing the wear mechanism from abrasion to particle cracking-induced delamination wear. It was found that with the increase in garnet content, the wear resistance increased monotonically. The observations have been explained using scanning electron microscopy analysis of the worn surfaces and the subsurface of the composites. In this work, the most influencing input and output parameters have been performed and the process parameters have been prioritized using genetic algorithm. Genetic algorithm is used to optimize the most influencing input as well as output process parameters. The practical significance of applying genetic algorithm to dry sliding wear behavior process has been validated by means of computing the deviation between predicted and experimentally obtained wear behavior of metal matrix composite.

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