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.

本研究的主要目的是对不同方法的可用性进行评估，包括响应面方法、自适应神经模糊推理系统和人工神经网络模型，以预测和评估使用胡桃木层和一种天然粘合剂（氧化淀粉粘合剂），就这一事实而言，使用改性淀粉可以降低甲醛释放量。在本次调查中，作为输入数据的四个变量包括甲醛与尿素的摩尔比（1.12-1.52）、纳米纤维素含量（0%-4%，基于粘合剂的干重）、氧化的质量比淀粉粘着脲醛树脂 (30:70–70:30) 和压制时间 (4–8 分钟)。为了找到每种选定评估方法的最佳预测性能，使用了不同的隶属函数。当摩尔比、纳米纤维素含量、氧化淀粉质量比和压制时间分别设置为1.22、3%、70:30和7 min时，获得最佳结果。基于响应面方法、自适应神经模糊推理网络和人工神经网络建模获得的性能标准，包括均方根误差 (RMSE) 和平均绝对百分比误差 (MAPE)，很明显响应面方法可以以最低的错误水平提供更好的响应预测。旁，