@Article{cmc.2026.087255,
AUTHOR = {Nashat Nawafleh, Faris M. Al-Oqla},
TITLE = {Intelligent Characterization of Natural Fibers: Integrating Grey Wolf Optimization and Fuzzy Logic for Thermal Performance Prediction},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/cmc/online/detail/28053},
ISSN = {1546-2226},
ABSTRACT = {In order to mimic the thermal properties of various natural fibers, this research presents a novel prediction framework that combines Fuzzy Logic (FL) with Grey Wolf Optimization (GWO). While the GWO technique ensures mathematical correctness by fine-tuning membership function parameters, this research uses a hybrid fuzzy model to outline nonlinear relationships between fiber components and thermal performance, which significantly reduces the need for extensive, trial-and-error laboratory testing. In this study, moisture, cellulose, and hemicellulose levels are predicted to be used to identify the finest natural fibers for biomaterial uses. An optimization methodology is seen by the proposed technique as the calibration of membership function (MF) parameters, and the best possible scenarios are found by applying the Grey Wolf Optimization (GWO) algorithm. The GWO-FL model provides a consistent and reliable tool for evaluating thermal performance, as confirmed by validation using fiber thermal conductivity measurement, which shows that the model significantly corresponds with real data. The development of environmentally friendly, thermally stable materials for uses may be accelerated with the help of this proposed work. Ultimately, it will contribute to the development of bio-products that are both more sustainable and more effective.},
DOI = {10.32604/cmc.2026.087255}
}