Estimation of Physical and Mechanical Properties of Composite Board via Adaptive Neural Networks, Polynomial Curve Fitting, and the Adaptive Neuro-Fuzzy Inference System

Abstract

Several physical and mechanical properties of particle board were investigated using estimation modeling. Particleboards (0.65 g/cm3) were produced for five experimental groups, in which lavender plant waste, red pine chips, and urea formaldehyde (UF) resin were mixed in different proportions. After immersing the particleboards in water for 24 h, several properties including thickness swelling (TS), modulus of rupture (MOR), modulus of elasticity (MOE), and internal bond strength (IBS) were determined. The statistical relevance of the experimental results was evaluated using multi-variance analysis (ANOVA), and the homogeneity between experimental groups was evaluated using Duncan tests. With the use of variable inputs and experimental results, estimation models using polynomial curve fitting (CF), adaptive neural networks (ANN), and an adaptive neuro-fuzzy inference system (ANFIS) were generated. The results obtained from the estimation models and experiments were then compared via root-mean-square error (RMSE) and R2 values. The ANFIS estimation model was the best alternative to the costly, long-term experimental methods, as it produced more economical and reliable results in a shorter period of time.