Development and Validation of ANN Models for Water Absorption in Sawdust and Kenaf Fiber-Reinforced Polystyrene Composites

Abstract

This research paper involved modeling the water absorption behavior of polystyrene (PS) composites with sawdust and kenaf fiber (KF) reinforcement using the Artificial Neural Network (ANN) method. The composites were made by manual mixing combined with the hand lay-up process at room temperature (25°C ± 2°C) and cured in an open mold over 7 days at ambient temperature. The water absorption measurements were done according to the ASTM D1037-99. The findings were that the water uptake was enhanced by filler content as well as immersion duration in the sawdust composite and KF. The ANN model also had good accuracy; the coefficients of determination (R²) in all the ANN models were more than 0.98 in all the training, validating, and test sets of both types of materials. Also, values of root mean square error (RMSE) were low (less than 1 wt%), indicating that this model was very accurate in forecasting the behavior of water absorption. Parity plots indicated that there was a good balance of the performance of the predictions, which captured the low and high values of absorption. Moreover, the p value was lower than 0.05, which showed ANOVA results are statistically significant.