Prediction of Veneer Bonding Strength of Wood-Based Composites Through Soft-Computing Models

An artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) are used to predict the bonding strength of different wood-based composites and veneers. The dataset used for model creation is obtained from experimental setups. The experiments involved measuring the bonding strength of wood-based composites (MDF, OSB) and veneer (beech, oak, pine) using different cutting directions and adhesive types. A total of 540 experiments are conducted. The main objective of this study is to propose AI-based models (ANN and ANFIS) that could reduce the cost of experiments and computational time. The ANN model achieved correlation coefficients (R2) of 0.91 and 0.94 for training and testing, respectively. High R² values for both training and test datasets indicate that the ANN model is well-designed. On the other hand, the ANFIS model yielded R2 values of 0.88 and 0.85 for training and testing, respectively. Based on these results, the ANN models exhibited a stronger correlation compared to the ANFIS models. Overall, this study demonstrates the effectiveness of using artificial intelligence models, specifically ANN and ANFIS, to predict the bonding strength of wood-based composites and veneer. By employing these models, researchers can reduce the need for extensive experimentation and save computational time, making the process more efficient and cost-effective.