EVALUATION OF DYNAMIC MECHANICAL PROPERTIES OF FLAX FIBER POLYPROPYLENE (FP) COMPOSITES USING THE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM

Flax fiber reinforced polypropylene (FP) composites were prepared using the compression moulding technique and their dynamic mechanical (DM) properties were studied based on the fiber content. Fiber loading was varied from 0 to 50 vol% with increments of 10 vol% and the samples were designated accordingly as neat PP, FP10, FP20, FP30, FP40, and FP50 composites. The results showed that increased fiber content in the PP matrix improved the DM properties, such as storage modulus (E’), loss modulus (E”), and damping factor (Tanδ). Amongst the fabricated FP laminates, FP40 composites exhibited better viscoelastic performance. Furthermore, the adaptive neuro-fuzzy inference system (ANFIS) was implemented to predict the DM properties, mainly, E’ and E” of the FP composites. The validation of the ANFIS model showed that experimental and predicted results were in good agreement to each other, confirming its reliability in forecasting the DM properties of flax fiber reinforced polypropylene (FP) composites. This study also investigated the thermal degradation behavior of the FP composites using thermogravimetric analysis (TGA). The results showed that thermal stability of the neat PP improved from 432.69 °C to 463.90 °C for optimal fiber loading of 40 vol% flax fiber (FP40). Fractured samples of FP composites were examined using scanning electron microscopy (SEM) to comprehend the interfacial bonding mechanism of the FP composites.