Wood Defects Research Articles

Quantitative Identification of Defects in Lumber Based on Modal Frequencies and Artificial Neural Network

This study preliminarily discussed a new method to identify the location and size of internal wood defects using experimental modal analysis (EMA) and artificial neural network. The different defect sizes and locations were simulated by removing mass from intact wood specimens. At room temperature in the laboratory, free vibration testing was conducted to generate the frequency response functions (FRF) of intact and defective Korean Pine (Pinus koraiensis) wood specimens using fast Fourier transform (FFT) analysis system. The first three orders intrinsic frequencies were captured by picking up the location of each order peak of FRF curves. Then, two identification indexes developed by previous research were constructed based on these intrinsic frequencies, and they were used as input parameters to build the networks for localization and size determination of wood defects respectively. These two artificial neural networks were trained and tested for wood defects recognition. The research results showed that: (1) the intrinsic frequencies of defective wood were lower than those of intact wood; and (2) the constructed two identification indexes were capable to effectively detect the location and size of wood defects, which were more sensitive to large size defects than small size defects.

AVALIAÇÃO DO PERFIL RADIAL DO LENHO DE ÁRVORES DE TECA (Tectona grandis L.f.), ATRAVÉS DA TOMOGRAFIA DE IMPULSO

Tomography constitutes one of the most important non-destructive methods of non-destructive analysis, evaluating the velocity of mechanical waves through the wood and producing images of the radial profile of teak wood by means of impulse. In this paper, the impulse tomography was applied in the wood of 20 Tectona grandis trees and the images were analyzed. The results showed: i) the generation of mechanical waves in wood with mean speed of 1,087 m/s; ii) the variation coefficient of 19.81%, with low variability, demonstrating the effectiveness of the method; and iii) the internal defects in wood detected in tomography images through color difference.

Compression wood formation in Pinus strobus L. following ice storm damage in southwestern Virginia, USA1

Abstract In 1994, heavy ice storms hit southwestern Virginia, causing extensive damage to trees. Larger trees tended to experience branch breakage while smaller trees were more commonly bent under the weight of the ice. Bent trees formed compression wood in the years following the ice storm. To evaluate the influence of tree size and location on the formation of compression wood, a commercial wood defect, 47 Pinus strobus L. trees were felled and cross-sections were collected at 0.5 m above the root collar. Disks were sanded and scanned, and the cross-sectional area of compression wood within each tree ring was quantified using image analysis software. In addition, wood anatomical features were quantified in the 3 years before and after the storm. Compression wood formation was significantly related to tree diameter and the 6–9 cm diameter class formed more compression wood area than any other size class (this class was 4–8 cm at the time of the storm). Trees > 18 cm at the time of the storm did not form ...

Damped Newton based Iterative Non-negative Matrix Factorization for Intelligent Wood Defects Detection

The Non-negative matrix factorization (NMF) can be formulated as a minimization problem with bound constraints. N MF is capable to produce a region- or part- based representations of the wood images. We present an extension to the NMF and discuss the development as well as the use of damped Newton optimization approach for update matrices W and H called iterative DNNMF with good convergence property for wood defects detection by adding a diagonal correction to the stiffness matrix and employing a Newton direction in the line search until any constraints become active. We also provide algorithms for computing these new factorizations and the supporting theoretical analysis. DNNMF is tested with color wood images based on the statistical features extracted by local binary pattern (LBP) from the feature spaces. Finally, we present experimental results that explore the properties of the proposed method. After many comparative experiments, the test results show DNNMF is effectual and practical with good research values and potential applications.

Ultra Strengthening of Wooden Girders

Owing to the natural defects of wood, structural timber is currently used with conservative safety factors. This study investigates the use of Carbon Fiber Reinforced Polymer (CFRP) for improving the strength of structural timber. Four flexural test stages were carried out, the modes of failure of the three specimens in each stage were studied to determine and strengthen the suspected weak zone which caused the failure. It was found that the tensioned fibers of ordinary timber girders normally fail first especially at the sections containing knots. Using a strip of CFRP to strengthen tensioned fibers is a well known technology, but it will not prevent a premature failure due to horizontal shear. A strength increase of 56% was reached by the adaption of shear connectors in the form of screws. After treating tensile and shearing strengths, still testing specimens showed failure at the compressed fibers. By enhancing the compressed fibers with a steel strip, the prototype girders showed a remarkable increase in flexural strength of about 175% compared with the ordinary wooden girders having the same dimensions.

CONGELAMENTO DE MADEIRA – AVALIAÇÃO PRELIMINAR DE SUA UTILIZAÇÃO

O método de congelamento de madeira é conhecido há certo tempo em áreas como conservação e restauro de obras de arte e em algumas etapas de processos industriais, como a secagem de peças de madeira com pequenas dimensões. Entretanto a prática dessa técnica levou, de forma empírica, à percepção de defeitos, estéticos e físicos, em peças tratadas dessa maneira. Portanto, o objetivo deste trabalho foi estudar o efeito do congelamento de peças de madeira e sua influência em processos industriais e procedimentos de conservação e restauro de obras de arte. Os dados obtidos revelaram que as peças submetidas a esse método sofrem uma redução em sua elasticidade, deixando-as com um comportamento mais frágil e plástico. Percebeu-se também que peças com maiores teores de umidade apresentaram um percentual mais acentuado de defeitos, como rachaduras internas e trincas superficiais, e que as estruturas mais afetadas pelo método proposto são as células dos raios. Desse modo, o método de congelamento não é indicado para o tratamento de obras de arte e é aceitável em alguns processos industriais que não exijam um melhor acabamento. Palavra chave: Congelamento de madeira; rachaduras internas; trincas superficiais; redução de elasticidade. Abstract Wooden freezing – Preliminary evaluation of its use. The freezing of wood is a well known method in some study areas, such as conservation and restoration of works of art, as well as in some steps of industrial processes, such as the drying of wood pieces of small size. However, the practice of this technique led, in an empirical way, to the perception of defects (aestethic and phisical) in wood under this treatment. So, the aim of this work was to study the effect of freezing wood pieces and its influence in industrial processes and procedures of conservation and restoration of works of art. The obtained data showed that the wood pieces under this treatment had its elasticity reduced, getting a more fragile and more plastic behavior. It was also noticed that the wood pieces of higher moisture content showed a higher percentual of deffects, such as internal cracks and end checks, and the most affected elements were the ray cells. Thus the method of freezing is not indicated for the treatment of art works and is acceptable in some industrial processes which do not require a better finish.Keywords: Freezing of wood; internal cracks; end checks; elasticity reduction.

Characterization of ring shake defect in chestnut (Castanea sativa Mill.) wood in the Lazio Region (Italy)

Summary The incidence of ring shake in chestnut wood (Castanea sativa Mill.) from eight sites (237 total trees) in the Lazio territory, in central Italy, was investigated. All the areas studied are coppice managed. The aim of this study was to increase knowledge of this defect through the measurement and classification of numerous indices. using analysis of variance and correlation analysis, age was found to be the principal factor affecting the ring shake defect, the incidence of which was highest in trees aged 30 years or over. The highest risk of splitting occurred in the 12- to 14-year-old age group, although cracking started with a certain intensity at an age of 7–8 years. Comparing trees of the same age (25–30), it can be seen that diameter was not strongly related to magnitude of defect, but it had the effect of moving the ring shake location outwards. Wood defects (star shake, injuries, double core and eccentricity of core) and stem defects (cortical cancer, epicormic branches, dead branches and bark cracking) were not significantly correlated with shake: indeed, only bark cracking and injuries seem to have a slight influence on ring shake risk.

Appling a Novel Cost Function to Hopfield Neural Network for Defects Boundaries Detection of Wood Image

A modified Hopfield neural network with a novel cost function was presented for detecting wood defects boundary in the image. Different from traditional methods, the boundary detection problem in this paper was formulated as an optimization process that sought the boundary points to minimize a cost function. An initial boundary was estimated by Canny algorithm first. The pixel gray value was described as a neuron state of Hopfield neural network. The state updated till the cost function touches the minimum value. The designed cost function ensured that few neurons were activated except the neurons corresponding to actual boundary points and ensured that the activated neurons are positioned in the points which had greatest change in gray value. The tools of Matlab were used to implement the experiment. The results show that the noises of the image are effectively removed, and our method obtains more noiseless and vivid boundary than those of the traditional methods.

The Feasibility of Flash Thermography for the Examination and Conservation of Works of Art

This study investigates the feasibility of flash thermography for the examination and conservation of works of art: paintings, works on paper and sculpture. Thermography is a non-destructive technique for the identification of subsurface defects in materials. It is based on the propagation of surface-deposited heat through into the material. Differences in propagation between defect and defectfree areas result in a difference in the surface temperature of the material. The surface temperature is mapped over time by imaging with a mid-infrared digital camera. A xenon arc lamp is used to provide the initial source of radiation, and signal processing is typically applied to the collected data to reduce noise and to enhance key signal characteristics. This technique offers the possibility of investigating the structure of paintings and paper, particularly in cases where other non-destructive examination techniques do not provide sufficient information, for example subsurface delamination and layer structure. The results indicate that thermography is a good technique for detection of paint delamination and the degree of adhesion between layers, particularly in canvas paintings. It also successfully detected wood grain in situations where X-rays did not, although it was not effective for detecting voids or defects in wood.

The Application of Fractal Theory in Wood Science Research

This article introduces the fractal theory and its applications in wood fracture、wood texture、wood drying 、wood defect nondestructive detecting and other fields in wood science researching, among which many researching results are characterised by fractal dimension. While the methods in other fields which fractal were used in their researches were refered , the writer attempts to apply them in wood science researches.At last the new viewpoints and its applications were proposed.

Applying multifractal spectrum combined with fractal discrete Brownian motion model to wood defects recognition

Wood nondestructive testing technology is a new and multidisciplinary industry scientific research. It has attained fast development and achievements in recent years. X-ray computed tomography (CT) scanning technology is a kind of wood nondestructive testing technology in practice. CT scanning technology has been applied to the detection of internal defects in the logs for the purpose of obtaining prior information, which can be used to reach better wood sawing decision. Fractal geometry and its extension multifractal are used for describing, modeling, analyzing, and processing of different complex shapes and images. A method in CT image edge detection using multifractal theory combined with fractal Brownian motion is applied in the paper. First, its multifractal spectrum is estimated. Then, different types of pixels are classified by the spectrum; they are smoothing edge points and singular edge points. From the images processed by multifractal spectrum theory and compared with each image by different spectrum values, it can be seen that the larger the range of threshold is set, the more exact the edge can be detected. The paper provides a new method to recognize the defect information and to saw it in the condition of nondestructive wood.

Effective semi-supervised nonlinear dimensionality reduction for wood defects recognition

Dimensionality reduction is an important preprocessing step in high-dimensional data analysis without losing intrinsic information. The problem of semi-supervised nonlinear dimensionality reduction called KNDR is considered for wood defects recognition. In this setting, domain knowledge in forms of pairs constraints are used to specify whether pairs of instances belong to the same class or different classes. KNDR can project the data onto a set of 'useful' features and preserve the structure of labeled and unlabeled data as well as the constraints defined in the embedding space, under which the projections of the original data can be effectively partitioned from each other. We demonstrate the practical usefulness of KNDR for data visualization and wood defects recognition through extensive experiments. Experimental results show it achieves similar or even higher performances than some existing methods.

Wood defect classification based on image analysis and support vector machines

This paper addresses the issue of automatic wood defect classification. A tree-structure support vector machine (SVM) is proposed to classify four types of wood knots by using images captured from lumber boards. Simple and effective features are proposed and extracted by partitioning the knot images into three distinct areas, followed by utilizing a novel order statistic filter to yield an average pseudo color feature in each area. Excellent results have been obtained for the proposed SVM classifier that is trained by 800 wood knot images. Performance evaluation has shown that the proposed SVM classifier resulted in an average classification rate of 96.5% and false alarm rate of 2.25% over 400 test knot images. Future work will include more extensive tests on large data set and the extension of knot types.

Pattern recognition and size determination of internal wood defects based on wavelet neural networks

This paper presented the results of a preliminary study on detecting internal wood defects using an ultrasound technique coupled with wavelet transform and artificial neural networks analysis. At room temperature in the laboratory, the type and size of the wood defects in 275 Elm specimens were detected using a RSM-SY5 ultrasonic instrument. The original signals of the Elm specimens were decomposed using wavelet packets, the energy variation of each node in the fifth layer was calculated, and back-propagation artificial neural networks (BP ANN) were trained and employed for wood defect recognition. The energy variation caused by wood defects mostly depends on the degree of the defect's deterioration (i.e., the more serious the wood defect's deterioration, the larger the energy variation). By comparing the energy variation of all 32 node signals in the fifth layer wavelet packet, the variation of the node (5,0) was the largest and contained the most defect information. The node (5,0) was used as the input in back-propagation artificial neural networks in order to detect the type of defects. The accuracy rate for Elm specimens was at least 90%. The same method was used to test the size and position of the hole-defects in Elm specimens with an accuracy rate of at least 80%.

A comparative study on the velocities of stress wave propagation in standing Fraxinus mandshurica trees in frozen and non-frozen states

In order to improve the accuracy and reliability of identifying wood defects and to realize the maximum wood utilization of trees, we employed an experimental method to test the stress wave propagation velocity in standing Fraxinus mandshurica trees selected from the Harbin Forest Experimental Station in winter. Thirty standing trees in good conditions were taken as test specimens and stress wave propagation velocities were measured using a FAKOPP Microsecond Timer in trees in both fall and winter. The test data were processed with the aid of Excel and SPSS software. The results show that 1) the velocities in longitudinal and radial stress wave propagation in frozen F. mandshurica trees were much higher than those in the non-frozen trees; 2) there was a highly positive correlation between longitudinal stress wave propagation velocity in frozen and non-frozen states, with a correlation coefficient of 0.82, as well as a positive correlation between radial stress wave propagation velocity in frozen and non-frozen states with a correlation coefficient of 0.87; 3) in the frozen state, the longitudinal stress wave propagation velocity was significantly affected by the moisture content (MC) of standing tree, while it was not obvious in the non-frozen state and 4) the radial stress wave propagation velocity was not significantly affected by MC in either frozen or non-frozen state.

Using non-destructive testing to assess modulus of elasticity of Pinus sylvestris trees

This study assessed variation in modulus of elasticity of trees and logs of Scots pine (Pinus sylvestris) trees. The study used 192 sample trees (c. 90–150 years) selected from 24 clear-felling forests in central and southern Sweden. Modulus of elasticity (MOE) assessed with transit-time technology on standing tree stems at 0.5–2.0m on the southern and northern side of each tree varied from 8.6 to 17.6 GPa. No systematic MOE difference was found between the southern and northern side of tree stems. The sometimes large MOE variations seen in some individual trees are probably a result of wood variation and wood defects. MOE assessed with resonance-based technology varied between 7.4 and 14.1 GPa for logs cut at similar height (<6.0 m). Models of MOE variation were derived from factors related to growth conditions at stand and tree level, with an R 2 adj of c. 0.46–0.62. The models indicate that growth and tree attributes associated with and/or creating less stem taper would yield trees with higher MOE.

Automated visual inspection system for wood defect classification using computational intelligence techniques

This article presents improvements in the segmentation module, feature extraction module, and the classification module of a low-cost automated visual inspection (AVI) system for wood defect classification. One of the major drawbacks in the low-cost AVI system was the erroneous segmentation of clear wood regions as defects, which then introduces confusion in the classification module. To reduce this problem, we use the fuzzy min–max neural network for image segmentation (FMMIS). The FMMIS method grows boxes from a set of seed pixels, yielding ideally the minimum bounded rectangle for each defect present in the wood board image. Additional features with texture information are considered for the feature extraction module, and multi-class support vector machines are compared with multilayer perceptron neural networks in the classification module. Results using the FMMIS, additional features, and a pairwise classification support vector machine on a 550 test wood image set containing 11 defect categories show 91% of correct classification, which is significantly better than the original 75% of the low-cost AVI system. The use of computational intelligence techniques improved significantly the overall performance of the proposed automated visual inspection system for wood boards.

Inspeção Automática de Defeitos em Madeiras de Pinus usando Visão Computacional

This paper addresses the issue of detecting defects in Pine wood using features extracted from grayscale images. The feature set proposed here is based on the concept of texture and it is computed from the co-occurrence matrices. The features provide measures of properties such as smoothness, coarseness, and regularity. Comparative experiments using a color image based feature set extracted from percentile histograms are carried to demonstrate the efficiency of the proposed feature set. Two different learning paradigms, neural networks and support vector machines, and a feature selection algorithm based on multi-objective genetic algorithms were considered in our experiments. The experimental results show that after feature selection, the grayscale image based feature set achieves very competitive performance for the problem of wood defect detection relative to the color image based features.

Determination of Localized Defects in Wood by the Transfer Time of Ultrasonic Waves

The effect of rupture on the speed of ultrasonic wave was investigated with the PUNDIT and the effective detecting method of defects, moving transmitter and receiver. The speed of ultrasonic wave according to the course of rupture was not affected by transmitter and receiver and in the course of parallel rupture, but only in the course of vertical rupture. When rupture was not located on the ultrasonic wave progressing course, rupture was not detected. However, if rupture was formed perpendicularly to the ultrasonic wave progressing course but located on the ultrasonic wave progressing direction, rupture can be detected. Also, the rupture can be detected, when one transmitter and receiver was placed at top and the other at bottom. After detecting rupture in a part, rupture was determined accurately, placing and moving two transmitters and receivers at width.

Assessment of the effect of quality factors on the assortment structure in poplar plantations

Quality factors affecting the assortment quality structure in poplar plantations were researched on nine temporary sample plots in the period 1997-2004, from the aspect of the implementation of roundwood standards. Sample plots were selected depending on the factors, the effect of which was studied. The following factors were researched: clonal species, planting pattern, flood risk, pruning of branches, plantation position and age. The study was performed by the original methodology. Based on the study results, it is concluded that knots have the highest effect on the assortment quality structure in poplar plantations, regardless of the site and stand conditions. Their effect rises significantly with the increase of tree diameter. Also, the analyses show that the number of large and rotten knots on the tree trunk increases considerably with the older age. In the flooded area, the sweep of the lower part of the stem is more intensive and multiple curvatures are frequent on the trees growing immediately along the river, whereas curvature decreases with the distance from the river bank. Based on the study results, it is concluded that the percentage of best quality assortments is considerably reduced by the above wood defects.