Characterization of aloe vera gel incorporated unsaturated polyester resin jute-cotton fabric composites for enhanced biodegradability, flexibility, and insulation properties

Dayan, Md Anisur Rahman; Habib, Md Mahmudul; Uddin, Md Moslem; Khatun, Mahmuda; Hossain, Md Sahadat; Rashid, Muhammad Abdur

doi:10.1016/j.heliyon.2024.e35261

Heliyon

2024

DOI: 10.1016/j.heliyon.2024.e35261

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Characterization of aloe vera gel incorporated unsaturated polyester resin jute-cotton fabric composites for enhanced biodegradability, flexibility, and insulation properties

Md Anisur Rahman Dayan,

Md Mahmudul Habib,

Md Moslem Uddin

et al.

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2025

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Mechanical and wear performance of Al-, mica-, SiO₂-filled glass fiber-reinforced composites and prediction of wear properties with artificial neural networks

Demir,

Ergün

2025

Proceedings of the Institution of Mechanical Engineers, Part E:

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This study investigates the effects of different categories of microparticles on the mechanical and tribological properties of glass fiber-reinforced composites. The tensile and wear properties of composites produced by hand lay-up technique, with Al, mica, and SiO2 added to epoxy matrix at 2, 4, and 6%, were determined. The average particle size ranges from 20 to 50 μm for Al, <15 μm for SiO2, and from 30 to 50 μm for mica. SiO2 particles have flaky shapes, and Al and mica particles have irregular shapes. The highest and lowest tensile strengths were obtained in the composites with 2% Al and 6% mica fillers, respectively. Increasing the filler content in all filled composites resulted in a decrease in mechanical properties. The best mechanical properties were observed at the 2% filler content. It was determined that SiO2, mica, and Al powders were the most effective filler types in reducing coefficient of friction of unfilled composites, in that order. An increase in filler content in the composites filled with Al and mica increased the wear rate. The lowest wear loss was observed in SiO2-filled composites. The damage mechanisms, matrix deformations, and fiber fractures of the composites after wear testing were identified through scanning electron microscopy imaging. An artificial neural network model was generated to estimate the wear rates of the filled composites, and the artificial neural network model was successful in validating the experimental results.

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Mechanical and wear performance of Al-, mica-, SiO₂-filled glass fiber-reinforced composites and prediction of wear properties with artificial neural networks

Demir,

Ergün

2025

Proceedings of the Institution of Mechanical Engineers, Part E:

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Characterization of aloe vera gel incorporated unsaturated polyester resin jute-cotton fabric composites for enhanced biodegradability, flexibility, and insulation properties

Cited by 1 publication

References 29 publications

Mechanical and wear performance of Al-, mica-, SiO₂-filled glass fiber-reinforced composites and prediction of wear properties with artificial neural networks

Mechanical and wear performance of Al-, mica-, SiO₂-filled glass fiber-reinforced composites and prediction of wear properties with artificial neural networks

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Characterization of aloe vera gel incorporated unsaturated polyester resin jute-cotton fabric composites for enhanced biodegradability, flexibility, and insulation properties

Cited by 1 publication

References 29 publications

Mechanical and wear performance of Al-, mica-, SiO2-filled glass fiber-reinforced composites and prediction of wear properties with artificial neural networks

Mechanical and wear performance of Al-, mica-, SiO2-filled glass fiber-reinforced composites and prediction of wear properties with artificial neural networks

Contact Info

Product

Resources

About

Mechanical and wear performance of Al-, mica-, SiO₂-filled glass fiber-reinforced composites and prediction of wear properties with artificial neural networks

Mechanical and wear performance of Al-, mica-, SiO₂-filled glass fiber-reinforced composites and prediction of wear properties with artificial neural networks