Machine learning prediction and optimization of anisotropic mechanical and machining behavior of biocomposites

Abstract

Natural fiber composites are chosen for their environmental sustainability, biodegradability, affordability, and lower energy requirements compared to traditional synthetic fibers. These sustainable materials match the ecological criteria of development by providing renewable and green alternatives to petroleum-based fibers. Scientists categorize banana fiber as a natural fiber due to its abundance, high tensile strength, lightweight nature, and resistance to pests and microorganisms. Agricultural waste produces banana fibers, an eco-friendly material used by manufacturers to create useful products and reduce waste. Progressive communities should explore the use of banana fibers in their materials to create new economic opportunities for farmers, ultimately fostering regional income growth. Using banana fiber composites helps the environment by reducing landfill waste and promoting sustainable resource practices, which lessen pollution and landfill expansion. The broader use of banana fiber composites is hindered by weak bonding between the fibers and matrix, uneven fiber distribution, and complex machining needs. The study assesses the mechanical performance, machining characteristics, and degradation behavior of banana epoxy composites using machine learning predictive optimization models. newline