Prediction of Bonding Strength of Heat-Treated Wood Based on an Improved Harris Hawk Algorithm Optimized BP Neural Network Model (IHHO-BP)

• Published in: Forests Vol. 15; no. 8; p. 1365
• Main Authors: He, Yan, Wang, Wei, Cao, Ying, Wang, Qinghai, Li, Meng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2024
• Subjects: Accuracy; Algorithms; Back propagation networks; Bond strength; Bonding strength; Chemical properties; Cutting speed; Feed rate; Grit; hawks; Heat; Heat treatment; Machine learning; Mechanical properties; Neural networks; Optimization; Optimization algorithms; Parabuteo unicinctus; prediction; Predictions; Production costs; Root-mean-square errors; temperature; Thermal properties; Wood; China
• ISSN: 1999-4907; 1999-4907
• DOI: 10.3390/f15081365

In this study, we proposed an improved Harris Hawks Optimization (IHHO) algorithm based on the Sobol sequence, Whale Optimization Algorithm (WOA), and t-distribution perturbation. The improved IHHO algorithm was then used to optimize the BP neural network, resulting in the IHHO-BP model. This model was employed to predict the bonding strength of heat-treated wood under varying conditions of temperature, time, feed rate, cutting speed, and grit size. To validate the effectiveness and accuracy of the proposed model, it was compared with the original BP neural network model, WOA-BP, and HHO-BP benchmark models. The results showed that the IHHO-BP model reduced the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) by at least 51.16%, 40.38%, and 51.93%, respectively, while increasing the coefficient of determination (R2) by at least 10.85%. This indicates significant model optimization, enhanced generalization capability, and higher prediction accuracy, better meeting practical engineering needs. Predicting the bonding strength of heat-treated wood using this model can reduce production costs and consumption, thereby significantly improving production efficiency.