Process parameter optimization for enhanced mechanical and thermal properties of kenaf/jute hybrid composites using grey fuzzy logic

Research on natural fiber composites often prioritizes fiber composition over manufacturing parameters, leaving a gap in optimizing the compression molding process critical for interfacial adhesion and mechanical performance in hybrid composites. This study addresses this by applying a Grey-Fuzzy Logic approach to optimize the compression molding parameters for kenaf/Jute hybrid composites, a material chosen for its complementary strength and sustainability yet challenged by hydrophilicity and poor fiber-matrix bonding. An L16 Taguchi design was used, varying Kenaf fiber (10-25 wt%), Jute fiber (10-25 wt%), NaOH treatment (0-8 wt%), molding pressure (10-16 MPa), and temperature (100-120 °C). The results identified a singular optimal parameter set (20 wt% kenaf (KF), 25 wt% jute, 5 wt% NaOH, 10 MPa molding pressure, and a temperature of 120 °C), achieving a Grey-Fuzzy Grade of 0.888 and yielding maximum mechanical properties (48.8 MPa tensile, 90.1 MPa flexural, 33.7kJ/m² impact). Crucially, ANOVA revealed molding pressure as the second-most significant factor (29.47% contribution), a novel finding underscoring that process parameters are as vital as fiber selection. This research uniquely demonstrates that superior hybrid composite performance is not attained through fiber treatment alone but requires the synergistic optimization of material and process parameters. The validated Grey-Fuzzy model provides a robust framework for manufacturing high-performance, sustainable composites for automotive and structural applications. Material characterization further confirmed that the 5% NaOH treatment effectively removed non-cellulosic components (Fourier Transform infrared spectroscopy) (FTIR); increased crystallinity by 12% (X-Ray Diffraction); enhanced thermal stability, raising the maximum degradation temperature by 5 °C (Thermogravimetric Analysis).