Predicting moisture content and density distribution of Scots pine by microwave scanning of sawn timber

This study was carried out to investigate the possibility of calibrating a prediction model for the moisture content and density distribution of Scots pine (Pinus sylvestris) using microwave sensors. The material was initially of green moisture content and was thereafter dried in several steps to zero moisture content. At each step, all the pieces were weighed, scanned with a microwave sensor (Satimo 9,4GHz), and computed tomography (CT)-scanned with a medical CT scanner (Siemens Somatom AR.T.). The output variables from the microwave sensor were used as predictors, and CT images that correlated with known moisture content were used as response variables. Multivariate models to predict average moisture content and density were calibrated using the partial least squares (PLS) regression. The models for average moisture content and density were applied at the pixel level, and the distribution was visualized. The results show that it is possible to predict both moisture content distribution and density distribution with high accuracy using microwave sensors.     

Estimation of shrinkage coefficients in radial and tangential directions from CT images

The aim of the present work was to use the displacement information generated from the spatial alignment in order to compute wood shrinkage in the radial and tangential directions in computed tomography (CT) images, and to compare the results with those obtained with computer-aided design software on the same images. To estimate the shrinkage coefficients from tomography images, wood specimens in the green state, equilibrium moisture content 15% and 8% state and oven dry condition were scanned. Specimens were taken from Norway spruce and Scots pine logs. The root-mean-square-error calculations showed acceptable small differences between the two measuring methods, which means that the algorithm is a useful tool for estimating the shrinkage coefficients in radial and tangential direction from CT images. This provides an image processing tool to monitor the dimensional changes during the drying and heat treatment process.