No abstract

Statistical methods enable the use of portable industrial scanners with sparse measurements, suitable for fast on-site whole-core X-ray computed tomography (CT), as opposed to conventional (medical) devices that use dense measurements. This approach accelerates an informed first-stage general assessment of core samples. To that end, this novel industrial tomographic measurement principle is feasible for rock-sample imaging, in conjunction with suitable forms of priors in Bayesian inversion algorithms. Gaussian, Cauchy, and total variation priors yield different inversion characteristics for similar material combinations. An evaluation of the inversion performance in rock samples considers, in a discrete form, conditional mean estimators, via Markov Chain Monte Carlo algorithms with noise-contaminated measurements. Additionally, further assessment indicates that this statistical approach better characterizes the attenuation contrast of rock materials, compared with simultaneous iterative reconstruction techniques. Benchmarking includes X-ray CT from numerical simulations of synthetic and measurement-based whole-core samples. To this end, we consider tomographic measurements of fine- to medium-grained sandstone core samples, with igneous-rich pebbles from the Miocene, off the Shimokita Peninsula in Japan, and fractured welded tuff from Big Bend National Park, Texas. Bayesian inversion results confirm that with only 16 radiograms, natural fractures with aperture of less than 2 mm wide are detectable. Additionally, reconstructed images found approximately spherical concretions of 6 mm diameter. To achieve similar results, filtered back projection techniques require hundreds of radiograms, only possible with conventional laboratory scanners.

Tracking timber in the sawmill environment from the raw material (logs) to the end product (boards) provides various benefits including efficient process control, the optimization of sawing, and the prediction of end-product quality. In practice, the tracking of timber through the sawmilling process requires a methodology for tracing the source of materials after each production step. The tracing is especially difficult through the actual sawing step where a method is needed for identifying from which log each board comes from. In this paper, we propose an automatic method for board identification (board-to-log matching) using the existing sensors in sawmills and multimodal encoder-decoder networks. The method utilizes point clouds from laser scans of log surfaces and grayscale images of boards. First, log surface heightmaps are generated from the point clouds. Then both the heightmaps and board images are converted into "barcode" images using convolutional encoder-decoder networks. Finally, the "barcode" images are utilized to find matching logs for the boards. In the experimental part of the work, different encoderdecoder architectures were evaluated and the effectiveness of the proposed method was demonstrated using challenging data collected from a real sawmill.

Stem shapes and wood properties are typically unknown at the time of harvesting. To date, approaches that integrate information about past tree growth into the harvesting and bucking process are rarely used. New models were developed and their potential demonstrated for stem bucking procedures for cut-to-length harvesters that integrate information about external and internal stem characteristics detected during harvesting. In total 221 stems were sampled from nine Scots pine (Pinus sylvestris L.) stands in Finland. The widths of rings 11−20 from the pith were measured using images taken from the end face of each butt log. The total volume of knots in each whorl was measured by using a 4D X-ray log scanner. In addition, 13 stems were test sawn, and the diameters of individual knots were measured from the sawn boards. A model system was developed for predicting the horizontal diameter of the thickest knot for each whorl along a stem. The first submodel predicts the knot volume profile from the stem base upwards, and the second submodel converts the predicted knot volume to maximum knot diameter. The results showed that the knottiness of stems of a given size may vary greatly depending on their early growth rate. The developed system will be used to guide logging operations to achieve more profitable bucking procedures.

We consider geometry parameter estimation in industrial sawmill fan-beam X-ray tomography. In such industrial settings, scanners do not always allow identification of the location of the sourcedetector pair, which creates the issue of unknown geometry. This work considers two approaches for geometry estimation. Our first approach is a calibration object correlation method in which we calculate the maximum cross-correlation between a known-sized calibration object image and its filtered backprojection reconstruction and use differential evolution as an optimiser. The second approach is projection trajectory simulation, where we use a set of known intersection points and a sequential Monte Carlo method for estimating the posterior density of the parameters. We show numerically that a large set of parameters can be used for artefact-free reconstruction. We deploy Bayesian inversion with Cauchy priors for synthetic and real sawmill data for detection of knots with a very low number of measurements and uncertain measurement geometry.