Comparison of different modeling methods toward predictive capability evaluation of the bonding strength of wood laminated products

Abstract

The main aim of this study is usability evaluation of different approaches, including response surface methodoloy, adaptive neuro-fuzzy inference system, and artificial neural network models to predict and evaluate the bonding strength of glued laminated timber (glulam) manufactured using walnut wood layers and a natural adhesive (oxidized starch adhesive), with respect to this fact that using the modified starch can decrease the formaldehyde emission. In this survey, four variables taken as the input data include the molar ratio of formaldehyde to urea (1.12–1.52), nanocellulose content (0%–4%, based on the dry weight of the adhesive), the mass ratio of the oxidized starch adhesive to the urea formaldehyde resin (30:70–70:30), and the press time (4–8 min). In order to find the best predictive performance of each selected evaluation approach, different membership functions were used. The optimal results were obtained when the molar ratio, nanocellulose content, mass ratio of the oxidised starch, and press time were set at 1.22, 3%, 70:30, and 7 min, respectively. Based on the performance criteria including root mean square error (RMSE) and mean absolute percentage error (MAPE) obtained from the modeling of response surface methodology, adaptive neuro-fuzzy inference network, and artificial neural network, it became evident that response surface methodology could offer a better prediction of the response with the lowest level of errors. Beside, artificial neural network and adaptive neuro-fuzzy inference system support the response surface methodology approach to evaluate bonding strength response with high precision as well as to determine the optimal point in fabrication of laminated products.

An Improved Optimization Model to Predict the MOR of Glulam Prepared by UF-Oxidized Starch Adhesive: A Hybrid Artificial Neural Network-Modified Genetic Algorithm Optimization Approach

“…Optimization of the application of the modified plant protein together with the MUF resin in the production of the polyurethane-core-based sandwich panels indicated that the application of the ANN optimization methods could offer an effective estimate of the mechanical properties of bio-based composites [49]. The comparison of different optimization and modeling methods showed that these methods can estimate the mechanical properties of the laminated products connected by the modified starch, even at high values of UF resin replacement [50].…”

“…methods could offer an effective estimate of the mechanical properties of bio-based composites [49]. The comparison of different optimization and modeling methods showed that these methods can estimate the mechanical properties of the laminated products connected by the modified starch, even at high values of UF resin replacement [50].…”