Prediction of mechanical properties of wood fiber insulation boards as a function of machine and process parameters by random forest

In this case study, machine and process variables were extracted from the process control system (Prod-IQ) and combined with tested mechanical properties of wood fiber insulation boards according to product type and time of manufacture. The boards were taken from the production line (dry process), and the internal bond strength ( σ mt ) and the compressive strength at 10% deformation ( σ 10 ) were determined according to the European Standard EN 826 and 1607. The complete data set was preprocessed and split into training and test sets using k-fold cross-validation. The performance of the random forest algorithm (RF) was evaluated with the correlation coefficient ( R ), the coefficient of determination ( R 2 ), root-mean-square error (RMSE) and mean absolute percentage error (MAPE) and compared with artificial neural networks (ANN) and support vector machines (SVM). Forward feature selection was used to reduce input dimensionality and improve the generalizability of the algorithms. All machine learning algorithms predicted the mechanical properties with high accuracy, but the RF algorithm revealed the best generalization performance ( σ mt : R = 0.960, R 2 = 0.916, RMSE = 4.05, MAPE = 12.11; σ 10 : R = 0.981, R 2 = 0.963, RMSE = 17.19, MAPE = 5.64). This work demonstrates that machine learning can be applied to predict relevant properties of wood fiber boards for an improved quality control in real time.