Applying machine learning to predict the tensile shear strength of bonded beech wood as a function of the composition of polyurethane prepolymers and various pretreatments

The present study showed that machine learning can be used to predict the tensile shear strength of bonded beech wood as a function of the composition of polyurethane prepolymers and various pretreatments. A comprehensive experimental data set was used for training and testing the algorithms support vector machines, random forest and artificial neural networks. Within the framework of the experiments, the structure–property relationships of 1C PUR prepolymers were analyzed by systematical variation of the structural parameters urea and urethane group content, cross-link density, ethylene oxide content, and the functionality via isocyanate (NCO) or polyether component. The bonded wood joints were tested according to DIN EN 302-1. Prior to testing, the shear test specimens were pretreated according to procedures A1 and A4, five temperature steps (5, 40, 70, 150 and 200 °C) and two alternating climates. The complete data set (N = 2840) was preprocessed and split into a training set and a test set using tenfold cross-validation. The performance of the algorithms was evaluated with the coefficient of determination (R²), root-mean-square error (RMSE) and mean absolute percentage error (MAPE). All machine learning algorithms revealed a high accuracy, but the artificial neural network showed the best performance with R²= 0.92, RMSE = 0.948 and a MAPE of 9.21. The work paves the way for future machine learning applications in the field of adhesive bonding technology and may enable a fast and effective development of new adhesives and enhance the efficiency of their application.