Fuzzy Logic Prediction Model for Mechanical and Absorption Behavior of Treated Woven Sisal Composites

Ilham Essaket, Mhammed El Bakkali, Anas El Maliki and Omar Cherkaoui

Data and Metadata, 2025, vol. 4, 1068

Abstract: The transition towards environmentally friendly and sustainable materials has intensified interest in natural fiber-reinforced composites, with sisal fibers standing out due to their biodegradability and mechanical performance. Despite these advantages, their practical use remained hindered by poor interfacial adhesion and high moisture uptake, largely attributed to their hydrophilic nature and surface impurities. In this study, a dual chemical treatment using sodium hydroxide (NaOH) followed by potassium permanganate (KMnO₄) was applied to three types of woven sisal fabrics (plain, twill, and satin) to enhance fiber–matrix interaction and overall composite properties. Twenty-seven composite variants were produced and evaluated to investigate the influence of weave structure, treatment concentration, and immersion time on tensile strength and water absorption. To capture the intricate relationships between these variables, a fuzzy logic-based predictive model was developed. This model effectively forecasted material behavior, achieving low average absolute errors of 1.77% for tensile strength and 3.46% for water absorption, demonstrating its robustness and value as a tool for process optimization. This study contributed not only to the development of high-performance, bio-based textile composites, but also introduced an intelligent, cost-effective predictive framework capable of reducing experimental demands while guiding sustainable material development.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:4:y:2025:i::p:1068:id:1056294dm20251068

DOI: 10.56294/dm20251068

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