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<p>Green and renewable materials are gaining increasing global attention due to their environmental benefits and sustainability. In this context, the present study investigates the morphological and tensile properties of Grewia ferruginea (GF) fibers. The fibers were extracted from the plant and treated chemically to enhance their performance characteristics. While existing research largely focused on chemically treated plant fibers, limited attention has been given to optimize treatment conditions for enhanced mechanical performance. To address this gap, the current study employs a hybrid framework integrating Taguchi L9 orthogonal experimental design, machine learning prediction, and metaheuristic optimization. Specifically, the effects of alkali, acetylation, and permanganate treatments were analyzed by varying key parameters such as concentration, treatment time, and temperature. In addition, the optimization aims to improve the morphological structure and tensile properties of GF fibers, thereby enhancing their suitability for composite applications. Scanning electron microscopy results showed that a set of alkaline treatment parameters (5%, 2.5 h, 23°C) effectively removed surface impurities and increased the surface roughness of the fibers. It was also found that alkali-treated fibers achieved the highest tensile strength of 369.29 MPa, which is 19.4% higher than that of untreated fibers, while acetylation resulted in a slight decrease (303.75 MPa, -1.8%), and permanganate treatment caused significant deterioration (193.16 MPa, -37.6%). Further studies are recommended to explore a broader range of parameters to confirm marginal optima.</p>

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Enhancing the Morphological and Tensile Properties of Chemically Treated Grewia ferruginea Fibers: An Experimental Study and Metaheuristic Optimization Integrated with Machine Learning

  • 期刊论文

  • 发表日期：

    2026年7月12日

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  • Getaw Ayay Tefera,
  • Mesfin Kebede Kassa,
  • Ermias Gebrekidan Koricho

摘要

Green and renewable materials are gaining increasing global attention due to their environmental benefits and sustainability. In this context, the present study investigates the morphological and tensile properties of Grewia ferruginea (GF) fibers. The fibers were extracted from the plant and treated chemically to enhance their performance characteristics. While existing research largely focused on chemically treated plant fibers, limited attention has been given to optimize treatment conditions for enhanced mechanical performance. To address this gap, the current study employs a hybrid framework integrating Taguchi L9 orthogonal experimental design, machine learning prediction, and metaheuristic optimization. Specifically, the effects of alkali, acetylation, and permanganate treatments were analyzed by varying key parameters such as concentration, treatment time, and temperature. In addition, the optimization aims to improve the morphological structure and tensile properties of GF fibers, thereby enhancing their suitability for composite applications. Scanning electron microscopy results showed that a set of alkaline treatment parameters (5%, 2.5 h, 23°C) effectively removed surface impurities and increased the surface roughness of the fibers. It was also found that alkali-treated fibers achieved the highest tensile strength of 369.29 MPa, which is 19.4% higher than that of untreated fibers, while acetylation resulted in a slight decrease (303.75 MPa, -1.8%), and permanganate treatment caused significant deterioration (193.16 MPa, -37.6%). Further studies are recommended to explore a broader range of parameters to confirm marginal optima.