In softwood species, annual ring width correlates with various timber characteristics, including the density and modulus of elasticity along with bending and tensile strengths. Knowledge of annual ring profiles may contribute to more accurate machine strength grading of sawn timber. This paper proposes a fast and accurate method for automatic estimation of ring profiles along timber boards on the basis of optical scanning. The method utilizes two 1D convolutional neural networks to determine the pith location and detect the surface annual rings at multiple cross-sections along the scanned board. The automatically extracted rings and pith information can then be used to estimate the annual ring profile at each cross-section. The proposed method was validated on a large number of board cross-sections for which the pith locations and radial ring width profiles had been determined manually. The paper also investigates the potential of using the automatically estimated average ring width as an indicating property in machine strength grading of sawn timber. The results indicated that combining the automatically estimated ring width with other prediction variables can improve the accuracy of bending and tensile strength predictions, especially when the grading is based only on information extracted from optical and laser scanning data.(C) 2022 The Author(s). Published by Elsevier Ltd.

A computer-implemented method for generating a training dataset for training an artificial neural network configured to use images of lateral faces of a timber board to provide information about structure and/or defects, the method including; a log generation step during which a virtual model of a log is generated; a sawing step of the virtual model to obtain one or more virtual timber boards; a pattern step during which a surface pattern is determined as the intersection between the virtual lateral face and the internal structure and/or defects; a rendering step during which a rendered surface image of the lateral face of the virtual timber board is created; and an input data generation step during which the rendered surface images are used to create one or more item of input data; an output data generation step during which an item of output data is generated; and a population step during which a record is added to the training dataset comprising the item of input data, in combination with the item of output data.

Knowledge of pith location is needed for modelling of sawn timber and for real time assessment of wood material in the wood working industry. However, the methods that are available and implemented in optical scanner today seldom meet customer requirements on accuracy and/or speed. In the present research data of greyscale images of the four longitudinal sides of board and a one-dimensional convolutional neural network were used to determine pith location along Norway spruce timber boards. A novel stochastic model was developed to generate thousands of virtual timber boards, with photo-realistic surfaces and known pith location, by which the network was trained before it was successfully applied to determine pith location along real boards.

Detection of pith, annual rings and knots in relation to timber board cross-sections is relevant for many purposes, such as for modelling of sawn timber and for real-time assessment of strength, stiffness and shape stability of wood materials. However, the methods that are available and implemented in optical scanners today do not always meet customer accuracy and/or speed requirements. The primary purpose of this doctoral dissertation was to gain an increased knowledge and a better understanding of how different characteristics and surface defects of timber boards can be identified automatically and robustly. The secondary purpose was to explore the possibilities of how such identified features/defects can be used to add value to the wood manufacturing industry. 

In the present study, three different methods were developed to non-destructively and automatically detect annual rings and pith location based on images obtained by optical scanning of the four longitudinal surfaces of the timber board. In the first method, a signal-processing-based approach and an optimisation algorithm were applied. In the second method, a deep-learning-based conditional generative adversarial network (cGAN) and a shallow artificial neural network (ANN) were used. In the third method, a single step deep-learning approach with a one-dimensional convolutional neural network (1D CNN) was applied. A novel stochastic model was also proposed to generate an unlimited number of virtual timber boards, with photo-realistic surfaces and known pith location, by which the proposed 1D CNN was trained before it was successfully applied to real timber boards. Concerning accuracy, all the three methods gave prediction errors of the same magnitude, between 4 mm and 6 mm. The 1D CNN method needed only 1.1 ms to locate the pith at a single section, which was 165 and 127 times faster than the signal-processing based and the cGAN based methods, respectively. Hence, the 1D CNN method proved to be the fastest, most operationally simple and robust method.

In sawn timber, the presence of knots causes the fibres to deviate from the longitudinal direction of the board, leading to a significant reduction of strength and stiffness. In the current study, a computer algorithm was proposed to detect knots on board surfaces and to reconstruct the knots in three dimensions (3D) by using the detected pith location. Moreover, a fibre modelling method was also proposed and used to produce the 3D fibre orientation within the volume of timber boards. Furthermore, the detected pith location and annual rings visible on the board surfaces were also utilised to estimate the radial annual ring profiles along the longitudinal direction of timber boards.

In the woodworking industry, detection of annual rings and location of pith in relation to timber board cross sections, and how these properties vary in the longitudinal direction of boards, is relevant for many purposes such as assessment of shape stability and prediction of mechanical properties of timber. The current work aims at developing a fast, accurate and operationally simple deep learning-based algorithm for automatic detection of surface growth rings and pith location along knot-free clear wood sections of Norway spruce boards. First, individual surface growth rings that are visible along the four longitudinal sides of the scanned boards are detected using trained conditional generative adversarial networks (cGANs). Then, pith locations are determined, on the basis of the detected growth rings, by using a trained multilayer perceptron (MLP) artificial neural network. The proposed algorithm was solely based on raw images of board surfaces obtained from optical scanning and applied to a total of 104 Norway spruce boards with nominal dimensions of 45×145×4500mm³. The results show that optical scanners and the proposed automatic method allow for accurate and fast detection of individual surface growth rings and pith location along boards. For boards with the pith located within the cross section, median errors of 1.4 mm and 2.9 mm, in the x- and y-direction, respectively, were obtained. For a sample of boards with the pith located outside the board cross section in most positions along the board, the median discrepancy between automatically estimated and manually determined pith locations was 3.9 mm and 5.4 mm in the x- and y-direction, respectively.

Knowledge of annual ring width and location of pith in relation to board cross-sections, and how these properties vary in the longitudinal direction of boards, is relevant for many purposes, such as assessment of shape mechanical properties and stability of sawn timber. Hence, the present research aims at developing a novel method and an algorithm, based on data obtained from optical surface scanning, by which the pith location along the length of sawn timber boards can be determined accurately and automatically. The first step of the method is to identify clear wood sections, free of defects along boards. Then time-frequency analysis, using the continuous wavelet transform, is applied to detect the surface annual ring width distribution of the four sides of the selected sections. Finally, the pith location is estimated by comparing annual ring width distributions on the different surfaces, and assuming that annual rings are concentric circles with the pith in the centre. The proposed algorithm was applied to a total sample of 104 Norway spruce boards. Results indicate that optical scanners and the suggested automatic method allow for accurate detection of annual ring width and location of pith along boards. For a sample of boards with the pith located within the cross-section, a mean error of 2.6 mm and 3.2  mm in the depth and thickness direction, respectively, was obtained. For a sample of boards of which 60% with pith located outside the cross-section, a mean discrepancy between automatically and manually determined pith locations of 3.9 mm and 5.8 mm in depth and thickness direction, respectively, was obtained.

Different mechanical and physical properties of wood are related to the location of pith. Norway spruce wood from the centre of logs, close to the pith, is characterized by lower longitudinal MOE, larger spiral grain angle, and larger longitudinal shrinkage coefficient than what wood farther away from the pith is [1]. Thus, knowledge of pith location along timber boards may play an important role in both appearance grading and in assessment of mechanical properties such as strength [2]. The current work aims to develop an algorithm which is capable of automatically estimating the pith location of Norway spruce boards, along the boards’ length direction, by utilizing optical scanning of longitudinal surfaces. The initial step of the algorithm is to identify defect free sections along the timber board. This is done by utilizing data from tracheid effect scanning of the four sides of the timber board. Thereafter, a continuous wavelet transform (CWT), similar to fast Fourier transform, is applied on grey scale images from scanning, to analyse the variation of light intensity across the four surfaces at selected positions along the board. Obtained local frequencies correspond to the local annular ring pattern on surfaces. Then, assuming that annular growth rings are concentric circles with the pith in the centre, detected local annular ring wavelengths (using CWT) and artificial annual ring wavelengths corresponding to different hypothetical locations of pith are compared, and an optimization procedure is used to identify the location of pith that minimizes the discrepancy between the detected and artificial sets of annular ring wavelengths. Figure 1 shows grey scale images of short segments of longitudinal surfaces, graphs of the detected local annual ring widths, and a photograph of the board cross section where the determined location of pith is marked out. Preliminary results reveal that data from optical scanners and the suggested method allow for accurate detection of annular ring width and location of pith along boards.

Optical scanners are used in the woodworking industry to detect various defects, such as dead and live knots, cracks, and fibre distortions, which are important for the visual appearance grading of wood. Data from scanning is also used to assess mechanical properties such as bending and tensile strength, for the purpose of machine strength grading of sawn timbers. Knowledge of annular ring width and location of pith in relation to board cross-sections, and how these properties vary in the longitudinal direction of boards, is relevant for many purposes, such as assessment of shape stability and mechanical properties of timber. Therefore, the purpose of the present research is to evaluate possibilities to determine annular ring width and location of pith on the basis of scanning of surfaces parallel to the longitudinal board direction. The first step of this novel method is to identify clear wood sections, free of defects along boards. Then time-frequency analysis is applied to assess the variation of light intensity over surfaces of these sections, such that local wavelengths, related to the annular ring width patterns are detected on all four surfaces around the board. Finally, the location of pith is calculated by comparing annular ring width distributions on the different surfaces, and assuming that annular rings are concentric circles with the pith in the centre. Results indicate that optical scanners and the suggested method allow for accurate detection of annular ring width and location of pith along boards.

At times dowelled glulam timber connections experience crack development in the fibre direction. The main reason for this is moisture variation in the timber elements which induces a stress perpendicular to the fibre direction. The aim of this paper is to study the influence of different moisture conditions and vertical dowel spacing on crack development through numerical simulations by use of the finite element method in three dimensions. A transient non-linear Fickian moisture diffusion model is implemented to simulate the moisture state within the glulam beam. The moisture gradient in the diffusion model was created by adopting a physical scenario by assuming what conditions the considered glulam beam will go through, from the factory up to installation. Further, an extended finite element method (XFEM) for two different vertical dowel spacing, 100 mm and 300 mm, with a linear elastic fracture mechanics (LEFM) approach was applied for the crack simulation. The results reveal that the moisture variation in combination with unfavourable placement of dowels can cause a crack to develop in the glulam timber beam. Moreover, it was shown that a moisture induced crack development may be modelled successfully by use of an Extended Finite Element Method (XFEM) approach.