Abstract

This study explores the investigations of bamboo fiber-reinforced polyester composites with chopped glass fiber (CGF) filler, focusing on addressing the challenges of low mechanical properties, limited thermal stability, and high moisture absorption. The two types of composites were fabricated using the hand layup method, that is, long unidirectional 0° bamboo fiber (BF) and randomly oriented short bamboo fiber (BP) reinforced a polyester matrix with chopped glass fiber (CGF) filler. By incorporating CGF filler, significant improvements in mechanical properties were achieved across both types of bamboo fiber, surpassing the limitations of unfilled composites. Notably, the composite formulation consisting of 40% wt. of unidirectional 0° BF and 5% wt. of CGF filler exhibited superior ultimate tensile strength, flexural strength, impact strength, water absorption, and thermal stability. This composite demonstrated remarkable enhancements, with increases of up to 131.22 MPa, 128.76 MPa, 113.3 kJ/m2, 1.94% water absorption, and up to 255°C (representing a 10% improvement) in thermal stability compared to the unfilled composite. Statistical analysis revealed quadratic models for the mechanical properties of long unidirectional 0° bamboo fiber composites, while water absorption exhibited a linear two-factor interaction model. For randomly oriented short bamboo fiber, the models for tensile, flexural, and water absorption properties were linear, while the impact energy model showed a quadratic relationship. These statistical models provide valuable insights into predicting the properties of bamboo fiber-reinforced polyester composites. This research underscores the significance of bamboo fiber-reinforced polyester composites in wall partition systems. This study paves the way for improved performance in these areas. The findings highlight the potential of incorporating CGF filler, enabling enhanced mechanical strength, increased thermal stability, and improved resistance to moisture-related issues. The derived statistical models offer valuable guidance for predicting the properties of these composites, facilitating their application and adoption in the construction industry.

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