The thermal modification of wood has become the most-commonly commercialised wood modification process globally, with the ThermoWood^® process currently being the most dominant. As with all commercial processes, there is a need to have a robust quality control system, with several small–scale studies undertaken to date investigating quality control using a range of analytical methods, culminating in a multi-year assessment of colour as a means of quality control. This study, as an extension to this multi-year assessment, further explores the colour of Norway spruce and Scots pine commercially modified by the ThermoWood^® S and D processes, respectively, along with the mechanical properties and structural characterisation by Fourier transform infrared (FT–IR) spectroscopy and principal component analysis (PCA) to ascertain further correlations between colour and other measurable properties. Infrared spectroscopy indicated modifications in the amorphous carbohydrates and lignin, whereas the use of PCA allowed for the differentiation between untreated and modified wood. Colour measurements indicated reduced brightness, and shifting toward red and yellow colours after thermal modification, hardness values decreased, whereas MOE and MOR values were similar for modified wood compared to unmodified ones. However, by combining the colour measurements and PC scores, it was possible to differentiate between the two modification processes (Thermo–S and Thermo–D). By combining the mechanical properties and PC scores, it was possible to differentiate the untreated wood from the modified ones, whereas by combining the mechanical properties and colour parameters, it was possible to differentiate between the three groups of studied samples. This demonstrates there is a degree of correlation between the test methods, adding further confidence to the postulation of using colour to ensure quality control of ThermoWood^®.

The established methods for testing the hardness of wood are of questionable value for assessing the performance of surface-densified wood, since the density profile beneath the densified surface is an important property that needs to be considered. The purpose of this study was to evaluate the influence of the density profile of surface-densified wood and the hardness test parameters, such as indenter geometry and applied load on the measured hardness. The influence of the density profile varied considerably depending on the hardness test parameters. This can make a comparison of hardness values of surface-densified wood prone to misinterpretation. The selection of hardness test parameters should either be product-specific, or the density profile itself should be used to evaluate the hardness of surface-densified wood. A strong influence of the density profile on the indentation depth development during the hardness tests indicates the possibility of predicting the density profile based on the hardness test methods.

Wood under thermo-mechanical densification behaves differently depending on the cross-sectional growth ring orientation (GRO) relative to the direction of compression. This influences the degree of cell damage, but also the shape-memory effects occurring when the compression load is released (spring-back) and when the timber is re-moistened (set-recovery). To study how the GRO influences the shape-memory effects, Scots pine specimens were separated into three distinct groups of GRO (Flat, Inclined, Hybrid) and then thermo-mechanically surface-densified. Spring-back and set-recovery were determined by thickness measurements and by digital image correlation. A GRO parallel to the densified surface, resulted in a low spring-back and a high set-recovery which were uniform over the width of the specimen. Specimens with a GRO between 15 and 45° to the densified surface showed high spring-back and low set-recovery, indicating cell-wall damage. Spring-back mainly occurred in the non-plasticised region immediately below the heated surface region and elasto-plastic rolling-shear deformation along individual growth rings occurred. The GRO of softwood subjected to thermo-mechanical densification determines if an applied load results in rolling shear-deformation or radial compression. This in turn determines where in the cross-section and when in the process the cells deform and if this deformation occurs below or above the glass-transition temperature.

The recent emphasis on the need for sustainability, fire safety, and renewability in structural materials has promoted research into the creation of fire-safe and mechanically strong wood products....

The density profile of surface-densified wood has a major influence on the indentation resistance of the material. A method that can predict the density profile in surface-densified wood from measurements of the indentation in a hardness test was established. The combined information of hardness and density profile is expected to better assess the performance of surface-densified wood. Density profile and hardness test data for surface-densified Scots pine have been subjected to a partial least squares analysis to determine the relationship between the indentation depth measured during a hardness test and the density profile measured by X-ray densitometry. Among seven different hardness tests, which varied in test force and indenter geometry, the Brinell method according to the EN 1534 standard showed the highest correlation between the indentation-versus-time curve and the density profile. The mean absolute error for the prediction of density profiles in an external test set was 5–10%, indicating that the method proposed in this study can be used to replace X-ray densitometry in process control and process design.

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.