Using machine learning to predict the density profiles of surface-densified wood based on cross-sectional images

Abstract

Over the past decades, the surface densification of solid wood has received increased attention. However, the inhomogeneous density distribution in the densification direction might be a challenge with regard to process control within a large-scale production process, as the density profile governs many relevant properties of surface-densified wood. Currently, the measurement of density profiles relies on sensitive X-ray equipment and is difficult to integrate into an on-line process. Hence, in this study, three machine learning approaches were applied to predict the density profiles of surface-densified Scots pine specimens, only based on visual image acquisition—a technology that is ubiquitous in the wood industry: partial least squares (PLS) regression, artificial neural networks (ANN), and convolutional neural networks (CNN). The machine learning models were trained on images of the specimen cross-sections as input data, and X-ray density profiles as output data. There were 1850 observations, and the model performance was evaluated on external test sets. The models had mean absolute percentage errors of the predicted values between 9 and 18%; the CNN achieving the smallest error (9.24%). A deeper analysis of the data revealed that the ANN approach performed inconsistently between observations. PLS regression predicted the main density peak to a high accuracy but could not model other features. Only the CNN could reliably model the main density peak, wide growth rings, and the important region between the specimen surface and the main density peak. The ability of the models to generalise to untypical new data was improved by augmentation of the training data.