Relevant bibliographies by topics / Wood – Defects

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Wood – Defects'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 March 2025

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Journal articles on the topic "Wood – Defects"

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Prasmana, Muhammad Farid, Deny Andesta, and Hidayat Hidayat. "Analysis of the Causes of Defects in the Timber Production Process Using the FMEA (Failure Mode Effect Analysis) Method Approach at PT. KQW." Jurnal Sains dan Teknologi Industri 20, no. 2 (February 16, 2023): 639. http://dx.doi.org/10.24014/sitekin.v20i2.21837.

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PT. KQW is related to quality. Some problems occur during the production process and product results, which are associated with the level of defects in the production process. From historical production data for April 2022 – June 2022, glulam wood products contribute the most defects, both congenital physical defects of the material itself and during the production process. This study aims to identify the level of defects in wood products, determine the factors that cause defects in wood products, and determine the efforts made to reduce the level of damage to wood products. The formulation of the problem in this study is how to control the damage/defects of wood products faced by PT. KQW. This research method uses FMEA. This research resulted in defects in the production process of glulam wood cracks, holes and breaks. The highest types of defect are hollow wood. Identification of the causes of every kind of defect, the order of the highest RPN value for each NG defect. There are 3 solution recommendations based on the highest RPN value for each defect. For cracked wood defects, the highest RPN is because the workers are less thorough (100). Then for hollow wood defects, the highest RPN is due to poor raw materials (147). The highest RPN for broken wood defects is due to less engine pressure (224). Keywords:FMEA, Timber Production Process, RPN, Defect, Failure
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Ibrahim, Eihab Abdelkariem Bashir, Ummi Raba'ah Hashim, Lizawati Salahuddin, Nor Haslinda Ismail, Ngo Hea Choon, Kasturi Kanchymalay, and Siti Normi Zabri. "Evaluation of texture feature based on basic local binary pattern for wood defect classification." International Journal of Advances in Intelligent Informatics 7, no. 1 (March 31, 2021): 26. http://dx.doi.org/10.26555/ijain.v7i1.393.

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Wood defects detection has been studied a lot recently to detect the defects on the wood surface and assist the manufacturers in having a clear wood to be used to produce a high-quality product. Therefore, the defects on the wood affect and reduce the quality of wood. This research proposes an effective feature extraction technique called the local binary pattern (LBP) with a common classifier called Support Vector Machine (SVM). Our goal is to classify the natural defects on the wood surface. First, preprocessing was applied to convert the RGB images into grayscale images. Then, the research applied the LBP feature extraction technique with eight neighbors (P=8) and several radius (R) values. After that, we apply the SVM classifier for the classification and measure the proposed technique's performance. The experimental result shows that the average accuracy achieved is 65% on the balanced dataset with P=8 and R=1. It indicates that the proposed technique works moderately well to classify wood defects. This study will consequently contribute to the overall wood defect detection framework, which generally benefits the automated inspection of the wood defects.
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Ni, Song Yuan, Hua Dong Xu, and Li Hai Wang. "Quantitative Identification of Defects in Lumber Based on Modal Frequencies and Artificial Neural Network." Advanced Materials Research 183-185 (January 2011): 2279–83. http://dx.doi.org/10.4028/www.scientific.net/amr.183-185.2279.

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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.
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Li, Hongli, Zhiqi Yi, Zhibin Wang, Ying Wang, Liang Ge, Wei Cao, Liye Mei, Wei Yang, and Qin Sun. "FDADNet: Detection of Surface Defects in Wood-Based Panels Based on Frequency Domain Transformation and Adaptive Dynamic Downsampling." Processes 12, no. 10 (September 30, 2024): 2134. http://dx.doi.org/10.3390/pr12102134.

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The detection of surface defects on wood-based panels plays a crucial role in product quality control. However, due to the complex background and low contrast of defects in wood-based panel images, features extracted by traditional deep learning methods based on spatial domain processing often contain noise and blurred boundaries, which severely affects detection performance. To address these issues, we have proposed a wood-based panel surface defect detection method based on frequency domain transformation and adaptive dynamic downsampling (FDADNet). Specifically, we designed a Multi-axis Frequency Domain Weighted Information Representation Module (MFDW), which effectively decoupled the indistinguishable low-contrast defects from the background in the transform domain. Gaussian filtering was then employed to eliminate noise and blur between the defects and the background. Additionally, to tackle the issue of scale differences in defects that led to difficulties in accurate capture, we designed an Adaptive Dynamic Convolution (ADConv) module for downsampling. This method flexibly compressed and enhanced features, effectively improving the differentiation of the features of objects of varying scales in the transform space, and ultimately achieved effective defect detection. To compensate for the lack of data, we constructed a dataset of wood-based panel surface defects, WBP-DET. The experimental results showed that the proposed FDADNet effectively improved the detection performance of wood-based panel surface defects in complex scenarios, achieving a solid balance between efficiency and accuracy.
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Batista, Djeison Cesar, Márcio Pereira da Rocha, and Ricardo Jorge Klitzke. "COMPARISON BETWEEN WOOD DRYING DEFECT SCORES: SPECIMEN TESTING X ANALYSIS OF KILN-DRIED BOARDS." Revista Árvore 39, no. 2 (April 2015): 395–403. http://dx.doi.org/10.1590/0100-67622015000200019.

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It is important to develop drying technologies for Eucalyptus grandis lumber, which is one of the most planted species of this genus in Brazil and plays an important role as raw material for the wood industry. The general aim of this work was to assess the conventional kiln drying of juvenile wood of three clones of Eucalyptus grandis. The specific aims were to compare the behavior between: i) drying defects indicated by tests with wood specimens and conventional kiln-dried boards; and ii) physical properties and the drying quality. Five 11-year-old trees of each clone were felled, and only flatsawn boards of the first log were used. Basic density and total shrinkage were determined, and the drying test with wood specimens at 100 °C was carried out. Kiln drying of boards was performed, and initial and final moisture content, moisture gradient in thickness, drying stresses and drying defects were assessed. The defect scoring method was used to verify the behavior between the defects detected by specimen testing and the defects detected in kiln-dried boards. As main results, the drying schedule was too severe for the wood, resulting in a high level of boards with defects. The behavior between the defects in the drying test with specimens and the defects of kiln-dried boards was different, there was no correspondence, according to the defect scoring method.
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Mu, Hong Bo, Da Wei Qi, and Ming Ming Zhang. "Image Segmentation of Wood with Knot Defects Based on Gray Transformation." Applied Mechanics and Materials 71-78 (July 2011): 1691–94. http://dx.doi.org/10.4028/www.scientific.net/amm.71-78.1691.

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Wood knot image obtained by X-ray was done gray transformation. The contrast of the image after gray transformation can be enhanced obviously and the position of knot can be highlighted. Binary processing was adopted for the image after gray transformation. The defects areas of the binary images are filled. Then, invert the best binary image of wood defect, and then adduct the obtained image. The result of wood defect image plus is that defects regions take apart completely with their background, which means the image segmentation is completed. Wood utilization is improved. The experiment results show that this method is feasible and effective.
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Gao, Mingyu, Fei Wang, Peng Song, Junyan Liu, and DaWei Qi. "BLNN: Multiscale Feature Fusion-Based Bilinear Fine-Grained Convolutional Neural Network for Image Classification of Wood Knot Defects." Journal of Sensors 2021 (August 17, 2021): 1–18. http://dx.doi.org/10.1155/2021/8109496.

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Wood defects are quickly identified from an optical image based on deep learning methodology, which effectively improves the wood utilization. The traditional neural network technique is unemployed for the wood defect detection of optical image used, which results from a long training time, low recognition accuracy, and nonautomatic extraction of defect image features. In this paper, a wood knot defect detection model (so-called BLNN) combined deep learning is reported. Two subnetworks composed of convolutional neural networks are trained by Pytorch. By using the feature extraction capabilities of the two subnetworks and combining the bilinear join operation, the fine-grained features of the image are obtained. The experimental results show that the accuracy has reached up 99.20%, and the training time is obviously reduced with the speed of defect detection about 0.0795 s/image. It indicates that BLNN has the ability to improve the accuracy of defect recognition and has a potential application in the detection of wood knot defects.
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Zhu, Yuhang, Zhezhuang Xu, Ye Lin, Dan Chen, Zhijie Ai, and Hongchuan Zhang. "A Multi-Source Data Fusion Network for Wood Surface Broken Defect Segmentation." Sensors 24, no. 5 (March 2, 2024): 1635. http://dx.doi.org/10.3390/s24051635.

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Wood surface broken defects seriously damage the structure of wooden products, these defects have to be detected and eliminated. However, current defect detection methods based on machine vision have difficulty distinguishing the interference, similar to the broken defects, such as stains and mineral lines, and can result in frequent false detections. To address this issue, a multi-source data fusion network based on U-Net is proposed for wood broken defect detection, combining image and depth data, to suppress the interference and achieve complete segmentation of the defects. To efficiently extract various semantic information of defects, an improved ResNet34 is designed to, respectively, generate multi-level features of the image and depth data, in which the depthwise separable convolution (DSC) and dilated convolution (DC) are introduced to decrease the computational expense and feature redundancy. To take full advantages of two types of data, an adaptive interacting fusion module (AIF) is designed to adaptively integrate them, thereby generating accurate feature representation of the broken defects. The experiments demonstrate that the multi-source data fusion network can effectively improve the detection accuracy of wood broken defects and reduce the false detections of interference, such as stains and mineral lines.
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Jackson, Marcus B., Beverly M. Bulaon, and Michael A. Marsden. "Wood Changes in Four Size Classes of Fire-Killed Western Larch." Western Journal of Applied Forestry 25, no. 2 (April 1, 2010): 62–67. http://dx.doi.org/10.1093/wjaf/25.2.62.

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Abstract Four size classes (8–12, 12.1–16, 16.1–20, and 20.1+ in. dbh) of fire-killed western larch (Larix occidentalis) were monitored and dissected over a 5-year period to assess causes and rates ofpostfire wood changes. Defect and merchantable volume were assessed by a certified scaler during the first 3 years. A greater proportion of wood volume in small trees was affected by decay, wood borers, and checks than in the large trees. Half of the 8‐12 in. dbh size class wood volumewas lost to postfire defects, whereas less than 15% of the 20.1+ in. dbh size class wood volume was lost to postfire defects after 3 years.
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Gao, Mingyu, Dawei Qi, Hongbo Mu, and Jianfeng Chen. "A Transfer Residual Neural Network Based on ResNet-34 for Detection of Wood Knot Defects." Forests 12, no. 2 (February 11, 2021): 212. http://dx.doi.org/10.3390/f12020212.

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In recent years, due to the shortage of timber resources, it has become necessary to reduce the excessive consumption of forest resources. Non-destructive testing technology can quickly find wood defects and effectively improve wood utilization. Deep learning has achieved significant results as one of the most commonly used methods in the detection of wood knots. However, compared with convolutional neural networks in other fields, the depth of deep learning models for the detection of wood knots is still very shallow. This is because the number of samples marked in the wood detection is too small, which limits the accuracy of the final prediction of the results. In this paper, ResNet-34 is combined with transfer learning, and a new TL-ResNet34 deep learning model with 35 convolution depths is proposed to detect wood knot defects. Among them, ResNet-34 is used as a feature extractor for wood knot defects. At the same time, a new method TL-ResNet34 is proposed, which combines ResNet-34 with transfer learning. After that, the wood knot defect dataset was applied to TL-ResNet34 for testing. The results show that the detection accuracy of the dataset trained by TL-ResNet34 is significantly higher than that of other methods. This shows that the final prediction accuracy of the detection of wood knot defects can be improved by TL-ResNet34.
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Dissertations / Theses on the topic "Wood – Defects"

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Mugabi, Paul. "Moisture content and drying defects in kiln-dried Eucalyptus grandis poles." Thesis, Stellenbosch : Stellenbosch University, 2007. http://hdl.handle.net/10019.1/4575.

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Thesis (PhD (Forest and Wood Science))--Stellenbosch University, 2007.
ENGLISH ABSTRACT: There has been a reported reduction in the durability of creosote treated, wooden utility poles in South Africa in recent years. Several factors could have been responsible for this. In this study, Eucalyptus grandis pole drying schedules currently used, methods of measurement of moisture content (MC) after drying and drying defects were investigated. Relationships involving drying defects and MC gradient before treatment which may help in the development of simple nondestructive methods of assessing defects such as surface checking, honeycomb and collapse, and MC gradient were also explored. The long term goal of this study was to increase the durability of treated poles by avoiding unacceptable drying defects and MC values before treatment. The results of this investigation are presented in the following chapters: an introduction motivating the aims of the investigations (Chapter 1); a review of literature relevant to MC and drying defects in sawn or round wood (Chapter 2); ready for press manuscripts on MC and drying defects in kiln-dried E. grandis poles (Chapters 3 to 7) and a general conclusion that links up chapters 3 to 7, and recommendations (Chapter 8). The auger drill method gave reliable MC values when samples in increments of 25 mm depths into the pole were taken. However, when single samples of 50 mm and 75 mm depths were considered, the auger drill MC measurements were unreliable. It was concluded that SABS SM 983 (2000), which specifies taking single radial auger drill samples of depth 70±5 mm at pole mid length, is not a reliable method of measuring MC in a kiln-dried E. grandis pole. Correlation results indicated that tree growth factors such as sapwood depth, green MC and heartwood percentage were related to final drying defects and may be used as criteria for pole sorting before kiln drying. Also, the number of valleys per unit length (VPUL) of the circumference at the theoretical ground line (TGL) was positively correlated with honeycomb and closed surface checks, implying that VPUL as a parameter of the pole circumference profile can be used to assess invisible, internal defects. The dry bulb temperature (Tdb) of 80oC, used to dry poles in industry, was too high and resulted into unacceptable levels of drying defects in kiln dried E. grandis poles. In addition, the drying period of 8 days was too short to attain an acceptable MC gradient in poles. It is, therefore, recommended that: To use the auger drill method to reliably determine the moisture content of a pole, samples in increments of 25 mm should be taken. MC measurements should be made at the most critical zones of a pole such as the TGL and not higher up since there is normally considerable MC variation in the longitudinal direction. It is also important to consider sampling more than one position on the pole circumference to cater for the MC variation in the tangential direction. For poles with large sapwood depths i.e. >15mm, mild drying conditions at the beginning of a drying run should be used, since such poles may be more susceptible to surface checking. Poles with large heartwood percentages should also be dried with suitable kiln schedules, i.e. with low Tdb to minimise honeycomb and collapse. In general, Tdb lower than 80oC should be considered in order to reduce the defects to acceptable levels. Poles should be dried for longer than 8 days even at Tdb as high as 80oC to reduce the MC to acceptable values and gradients. Since only three schedules were tested, more drying schedules should be investigated to make reliable conclusions about the effect of schedule on drying defects. In addition, more poles per schedule should be considered in order to obtain statistically reliable results. The relationship of pole diameter and drying defects also requires further investigation. Since a limited sample was used to test for the effect of source of poles and drying schedules on defect correlations, further studies should be done on sufficient samples to come up with more reliable conclusions. It is also necessary to further investigate the possible variation in correlation of VPUL and internal defects on a large sample of poles from specific sources and drying schedules.
AFRIKAANSE OPSOMMING: Die afgelope paar jaar is waargeneem dat kreosootbehandelde transmissiepale 'n afname in duursaamheid toon. Verskeie faktore kan hiervoor verantwoordelik wees. In hierdie studie is huidige Eucalyptus grandis oonddrogingskedules en voggehalte- (VG) bepalingsmetodes vóór en na droging asook drogingsdefekte ondersoek. Verwantskappe tussen drogingsdefekte en VG-gradiënt vóór behandeling wat kan meehelp om nie-destruktiewe metodes te ontwikkel om defekte soos oppervlaktekrake, interne barste en instorting en VG-gradiënt te bepaal, is ook bestudeer. Die langtermyn doelwit van hierdie studie is om die duursaamheid van behandelde pale te verbeter deur onaanvaarbare drogingsdefekte en voggehaltes vóór behandeling te vermy. Die resultate van hierdie ondersoek word in die volgende hoofstukke bespreek: 'n inleiding waarin die doelwitte van die onderskeie ondersoeke motiveer word (Hoofstuk 1); 'n oorsig van die literatuur relevant tot VG en drogingsdefekte in gesaagde en rondehout (Hoofstuk 2); persklaar manuskripte oor VG en drogingsdefekte in oondgedroogde E. grandis pale (Hoofstukke 3 tot 7) en 'n algemene gevolgtrekking wat bevindings uit hoofstukke 3 tot 7 saamvat gevolg deur aanbevelings (Hoofstuk 8). Die augerboor-metode kon betroubare VG-waardes lewer indien monsters van 25mm diepteinkremente in die paal geneem is. Augerboor-voggehaltemetings met enkel 50mm en 75mm monsters was egter onbetroubaar. Daar is bevind dat SABS SM 983 (2000), wat 'n enkel radiale augerboormonster tot op 'n diepte van 70±5 mm in die middel van die lengte van die paal spesifiseer, nie 'n betroubare VG-bepalingsmetode vir oondgedroogde E. grandis pale is nie. Die resultate het aangetoon dat boomgroeifaktore soos spinthoutdiepte, groen voggehalte en die persentasie kernhout aan drogingsdefekte verwant is en as basis vir sortering van pale vóór oonddroging gebruik kan word. Ook kon die aantal valleie per eenheidslengte (e. VPUL) van die omtrek by die teoretiese grondlyn (TGL) positief met interne barste en geslote oppervlaktekrake korreleer word wat impliseer dat VPUL as parameter van die omtrekprofiel van die paal gebruik kan word om onsigbare, interne defekte mee vas te stel. Die droëbaltemperatuur (Tdb) van 80oC wat in die industrie gebruik word, was te hoog omdat dit onaanvaarbare vlakke van drogingsdefekte in oondgedroogde E. grandis veroorsaak het. Daarbenewens was die drogingsperiode van 8 dae by 'n Tdb van 80oC te kort om 'n aanvaarbare VG-gradiënt te lewer. Die volgende word derhalwe aanbeveel: Om betroubare VG-resultate d.m.v. die augerboormetode te verkry, behoort monsters in inkremente van 25mm geneem te word. VG-metings behoort in die mees kritiese dele van 'n paal gedoen te word byv. by TGL en nie hoër nie omdat daar normaalweg beduidende VG-variasie in die longitudinale rigting is. Dis ook belangrik om op meer as een posisie op die omtrek te meet om vir die variasie in die tangensiale rigting voorsiening te maak. Ligte drogingstoestande behoort aan die begin van die drogingsproses by pale met groot spinthoutdieptes, bv. > 15mm, gebruik te word omdat sulke pale meer geneigd tot oppervlaktekrake is. Pale met hoë kernhoutpersentasies moet ook met geskikte drogingskedules, bv. met lae Tdb, gedroog word om interne barste en ineenstorting te verminder. Oor die algemeen behoort Tdb laer as 80oC oorweeg te word om die omvang van defekte by aanvaarbare vlakke te hou. Selfs by Tdb so hoog as 80oC behoort pale langer as 8 dae te droog om die VG tot by aanvaarbare waardes en 'n gradiënt te bring. Omdat slegs drie skedules evalueer is, behoort meer skedules ondersoek word om betroubare gevolgtrekkings oor die effek van skedule op drogingsdefekte te verskaf. Addisioneel behoort meer pale per skedule oorweeg word om statisties betroubare resultate te verkry. Die verwantskap tussen paaldeursnee en drogingsdefekte benodig ook verdere ondersoek. Aangesien 'n beperkte aantal pale gebruik is om die herkoms en drogingskedule met defekte te korreleer, behoort verdere studie op genoeg pale gedoen te word om meer betroubare gevolgtrekkings te kry. Dit is ook noodsaaklik om die moontlike variasie in die korrelasie tussen VPUL en interne defekte op 'n groot aantal pale van spesifieke herkoms en drogingskedules vas te stel.
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van, Dyk Hermanus H. "Ultrasonic Evalutation of Defects and Moisture Content of Lumber." Fogler Library, University of Maine, 2004. http://www.library.umaine.edu/theses/pdf/vanDykHH2004.pdf.

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Hansen, Helge Johannes. "Acoustic Studies on Wood." Thesis, University of Canterbury. School of Forestry, 2006. http://hdl.handle.net/10092/1746.

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Several acoustic techniques have been used to determine elastic and damping properties of trees, logs and beams. Time of flight (TOF) measurements in the outerwood of 14-year-old Pinus radiata trees showed that pruning operations increased the outerwood stiffness by up to 25% compared with unpruned trees. However, at the most 5% to 10% of the increased stiffness can be explained by the fact that the outerwood of the pruned trees is free of knots, as TOF measurements are little affected by knots. Thus, it is not known what causes the increase of outerwood stiffness in the pruned trees. One possible explanation could be a smaller microfibril angle (MFA) in the S2 layer of the outerwood cells, which would cause a significant increase in stiffness. Thinning operations decreased the outerwood stiffness by up to 8%. In small Eucalyptus nitens and Pinus radiata logs, which had branch nodes and nodal whorls at specific locations, MOE calculations (using the resonance technique) based on different harmonics gave different results. This indicates that defects do interact with acoustic waves. Acoustic tests on laminated beams with artificial defects (holes filled with dowels) at specific locations also had a significant impact on the MOE. Moreover, it was evident that the damping ratio (evaluated from the Q- factor) of the beams increased with increasing diameter of the holes. However, it was found that holes in laminated beams decreased stiffness while branch nodes and nodal whorls increased stiffness. This shows that relatively small defects, occupying a small volume of the beam, have an impact on acoustic measurements. It is not appropriate to base the MOE calculation on a single harmonic, considering that different harmonics investigate different parts of the specimen
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Bond, Brian Jr. "Characterization of Wood Features Using Color, Shape, and Density Parameters." Diss., Virginia Tech, 1998. http://hdl.handle.net/10919/30629.

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Automated defect detection methods allow the forest products industry to better utilize its resources by improving yield, reducing labor costs, and allowing minimum lumber grades to be utilized more intelligently. While many methods have been proposed on what sensors and processing methods should be used to detect and classify wood features, there exists a lack of understanding of what parameters are best used to differentiate wood features. The goal of this research is to demonstrate that by having an in depth knowledge of how wood features are represented by color, shape, and density parameters, more accurate classification methods can be developed. This goal was achieved through describing wood features using parameters derived from color and x-ray images and characterizing the variability and interrelationships of these parameters, determining the effect of resolution and species on these relationships, and determining the importance and contribution of each parameter for differentiating between wood features using a statistical prediction model relating feature types to the parameters. Knots, bark pockets, stain and mineral streak, and clearwood were selected as features from red oak, (Quercus rubra), hard maple, (Acer saccharum), and Eastern white pine (Pinus stobus). Color (RGB and HSI), shape (eccentricity and roundness), and density (gray-scale values) parameters were measured. Parameters were measured for each wood feature from images and parameter differences between feature types were tested using analysis of variance techniques (ANOVA) and Tukey's pairwise comparisons with a=0.05. Discriminant classifiers were then developed to demonstrate that an in-depth knowledge of how parameters relate between feature types could be used to develop the best possible classification methods. Classifiers developed using the knowledge of parameter relationships were found to provide higher classification accuracies for all features and species than those which used all parameters and where variable selection procedures had been used< It was determined that differences exist between all feature types and can be characterized and classified based on two color means, one color standard deviation, the mean density, and a shape parameter. A reduction in image resolution was determined not to affect the relationship of parameters. For different species, the intensity of features was to be related to the intensity of clearwood. The ability to explain classification errors using the knowledge gained about feature parameters was demonstrated. This knowledge could be used to reduce future classification errors. It was determined that combining parameters collected using multiple sensors increases classification accuracy of wood features. Shape and density were found not to provide good classification variables for features when used separately, but were found to contribute to classification of features when used with other parameters. The ability to differentiate between the feature types examined in this research was found be equal when using the RGB or HSI colorspace.
Ph. D.
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Henman, G. S. "'Shake' defects and wood structure variations in British oaks (Quercus spp.)." Thesis, Bangor University, 1991. https://research.bangor.ac.uk/portal/en/theses/shake-defects-and-wood-structure-variations-in-british-oaks-quercus-spp(124e6162-d6f4-4e61-a985-ad5e4edef257).html.

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The overall aim was to gain information required for improvement of wood quality in British oaks (Quercus robur and petraea). Specific aims were 1) to investigate the nature and incidence of shake in British oaks; 2) to quantify variations in oak wood structure and properties, identifying which characteristics are under genetic control and which associated with growth rate; 3) to make prescriptions for selection and silviculture of oak, with a view to improving wood quality in future British crops. Site surveys (including soil analyses) of 42 woodlands were carried out. An index of severity was devised to standardise shake assessment within individual trees. Wood structure was analysed in oaks from shake-prone and sound woodlands. Associations of wood structure and property variations with genotype and growth rate were analysed in oaks from a seed origin trial. Environmental factors were strongly associated with shake. High shake incidence occurred on sites which gave poor rooting conditions for oak: shallow, nutrient poor soils with low clay content and low calcium availability, and with soil texture and/or site topography leading to seasonal droughtiness or waterlogging. Woodland type (shake-prone or sound) was a stronger influence than tree condition (shaken or sound) on wood structure: oaks from shake-prone woodlands had wider rings, smaller earlywood percentage, larger wide rays and larger earlywood vessel radial diameters. Density, sapwood width, earlywood vessel frequency and proportions of wide rays and of latewood vessels and fibres varied between seed origins; these variables therefore have selection potential for improvement of oak wood quality. Many parameters also varied with growth rate, and earlywood vessel radial diameter was strongly associated with current ring width. A model of shake development is proposed in which various predispositions (structural weaknesses) and triggers (mechanical stresses) influence a tree over time. It is proposed that predispositions are caused by environmental factors at the time of wood formation, resulting in physiological stress or cambial damage. Genotype may modify response or susceptibility to environment. Predispositions may be extended by secondary weakening. Triggers of shake are thought to be natural internal growth stresses supplemented by the action of external forces (such as wind). Recommendations are made for site choice and silviculture of future British oak crops.
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Kawamoto, Sumire. "Use of Acoustic Emission/Acousto-ultrasonic Techniques for Monitoring Wood Drying Defects." Kyoto University, 2002. http://hdl.handle.net/2433/149511.

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Jadeja, Jayrajsinh. "A model for increasing yield in sawmills based on detection of subsurface defects in canted logs using ground penetrating radar (GPR) system." Morgantown, W. Va. : [West Virginia University Libraries], 2007. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=5449.

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Thesis (M.S.)--West Virginia University, 2007.
Title from document title page. Document formatted into pages; contains ix, 106 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 75-76).
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Lawday, Geoffrey. "An analytical use of stress waves for the detection of defects in trees." Thesis, Brunel University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.246163.

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Devaru, Dayakar. "Ground penetrating radar (GPR) based system for nondestructive detection of interior defects in wooden logs." Morgantown, W. Va. : [West Virginia University Libraries], 2006. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4955.

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Thesis (M.S.)--West Virginia University, 2006.
Title from document title page. Document formatted into pages; contains v, 128 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 106-107).
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Woxblom, Lotta. "Warp of sawn timber of Norway spruce in relation to end-user requirements : quality, sawing pattern and economic aspects /." Uppsala : Swedish Univ. of Agricultural Sciences (Sveriges lantbruksuniv.), 1999. http://epsilon.slu.se/avh/1999/91-576-5860-9.pdf.

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Books on the topic "Wood – Defects"

1

Carpenter, Roswell D. Defects in hardwood timber. Washington, DC: U.S. Dept. of Agriculture, Forest Service, 1989.

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Erkki, Verkasalo, and Forest Products Laboratory (U.S.), eds. Properties related to drying defects in red oak wetwood. Madison, WI (One Gifford Pinchot Dr., Madison 53705-2398): U.S. Dept. of Agriculture, Forest Service, Forest Products Laboratory, 1993.

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Solomon, J. D. Using hardwood lumber with insect, fungus, and bird defects as character-marked woods. New Orleans, La: U.S. Dept. of Agriculture, Forest Service, Southern Forest Experiment Station, 1985.

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D, Solomon J. Using hardwood lumber with insect, fungus, and bird defects as character-marked woods. New Orleans, La: U.S. Dept. of Agriculture, Forest Service, Southern Forest Experiment Station, 1985.

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D, Solomon J. Using hardwood lumber with insect, fungus, and bird defects as character-marked woods. New Orleans, La: U.S. Dept. of Agriculture, Forest Service, Southern Forest Experiment Station, 1985.

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Sam, Williams R., and Forest Products Laboratory (U.S.), eds. Acoustic emission and acousto-ultrasonic techniques for wood and wood-based composites: A review. Madison, WI: U.S. Dept. of Agriculture, Forest Service, Forest Products Laboratory, 2002.

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Huang, Jinshan. The effect of wood fibre defects on fibre flexibility measurement. Ottawa: National Library of Canada, 1994.

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Weingartner, D. H. Variations in the growth and defect of aspen (Populus tremuloides Michx.) clones in Northern Ontario. Ontario: Ministry of Natural Resources, 1985.

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Farkaš, Jozef. Emission affected spruce wood. Bratislava: Výskumný ústav papieru a celulózy, 1988.

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Mark, Knaebe, and Forest Products Laboratory (U.S.), eds. Diagnostic guide for evaluating surface distortions in veneered furniture and cabinetry. Madison, Wis: U.S. Dept. of Agriculture, Forest Service, Forest Products Laboratory, 2004.

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Book chapters on the topic "Wood – Defects"

1

Zobel, Bruce J., and Jackson B. Jett. "Grain, Fibril Patterns, and Internal Defects." In Springer Series in Wood Science, 148–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-79514-5_7.

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Luo, Dan, Joseph M. Gattas, and Poah Shiun Shawn Tan. "Real-Time Defect Recognition and Optimized Decision Making for Structural Timber Jointing." In Proceedings of the 2020 DigitalFUTURES, 36–45. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4400-6_4.

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AbstractNon-structural or out-of-grade timber framing material contains a large proportion of visual and natural defects. A common strategy to recover usable material from these timbers is the marking and removing of defects, with the generated intermediate lengths of clear wood then joined into a single piece of full-length structural timber. This paper presents a novel workflow that uses machine learning based image recognition and a computational decision-making algorithm to enhance the automation and efficiency of current defect identification and re-joining processes. The proposed workflow allows the knowledge of worker to be translated into a classifier that automatically recognizes and removes areas of defects based on image capture. In addition, a real-time optimization algorithm in decision making is developed to assign a joining sequence of fragmented timber from a dynamic inventory, creating a single piece of targeted length with a significant reduction in material waste. In addition to an industrial application, this workflow also allows for future inventory-constrained customizable fabrication, for example in production of non-standard architectural components or adaptive reuse or defect-avoidance in out-of-grade timber construction.
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Gu, Irene Y. H., Henrik Andersson, and Raúl Vicen. "Automatic Classification of Wood Defects Using Support Vector Machines." In Computer Vision and Graphics, 356–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02345-3_35.

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Afaf, Soukaina, Anas Abou El Kalam, and Yassine Bouslimani. "Wood Surface Defects Detection Based on AI Algorithm Yolov7." In International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD'2023), 9–17. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54288-6_2.

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Boscaini, Davide, Fabio Poiesi, Stefano Messelodi, Ayman Younes, and Donato A. Grande. "Localisation of Defects in Volumetric Computed Tomography Scans of Valuable Wood Logs." In Pattern Recognition. ICPR International Workshops and Challenges, 692–704. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68799-1_50.

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Du, Xiaochen, Hailin Feng, Mingyue Hu, Yiming Fang, and Jiajie Li. "Three Dimensional Stress Wave Imaging Method of Wood Internal Defects Based on TKriging." In Communications in Computer and Information Science, 416–27. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7299-4_34.

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Chacon, Mario I., and Graciela Ramirez Alonso. "Wood Defects Classification Using a SOM/FFP Approach with Minimum Dimension Feature Vector." In Advances in Neural Networks - ISNN 2006, 1105–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11760191_161.

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Du, Xiaochen, Jiajie Li, Hailin Feng, and Heng Hu. "Stress Wave Tomography of Wood Internal Defects Based on Deep Learning and Contour Constraint Under Sparse Sampling." In Intelligence Science and Big Data Engineering. Big Data and Machine Learning, 335–46. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36204-1_28.

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Richter, Christoph. "Wood Characteristic or Defect?" In Wood Characteristics, 7–9. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07422-1_2.

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Taormina, Tom. "From Defects to Tort." In Is Quality Just a Word We Use?, 28. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003535621-8.

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Conference papers on the topic "Wood – Defects"

1

Tian, Cong, Xiwen Wei, and Tianwei Zhang. "Intelligent detection of wood defects based on 3D scanning technology." In Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), edited by Qinghua Lu and Weishan Zhang, 97. SPIE, 2024. http://dx.doi.org/10.1117/12.3049726.

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Tse, Ki Tung, Ka Hei Tsang, and K. W. Sum. "Acoustic Tomography of Wood Internal Defects with Convolutional Neural Network." In 2024 IEEE/ACIS 24th International Conference on Computer and Information Science (ICIS), 100–106. IEEE, 2024. https://doi.org/10.1109/icis61260.2024.10778350.

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Chen, Hongyang, Xiangwei Mou, and Rijun Wang. "Wood surface defect detection algorithm based on improved YOLOv5s." In International Conference on Cloud Computing, Performance Computing, and Deep Learning, edited by Wanyang Dai and Xiangjie Kong, 64. SPIE, 2024. http://dx.doi.org/10.1117/12.3051245.

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Zhang, Haoran, Zhezhuang Xu, Yuhang Zhu, and Ye Lin. "Wood Surface Discoloration Defect Detection Based on Improved HRNet." In 2024 China Automation Congress (CAC), 6738–42. IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10864787.

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Fang, Yaxuan, Wanjie Huang, Chunwei Zheng, Xi Huang, Ming-Tzau Lin, and Jung-Kuei Yang. "Comparative Analysis of Object Detection Algorithms for Wood Defect Detection." In 2024 IEEE 7th International Conference on Electronic Information and Communication Technology (ICEICT), 991–96. IEEE, 2024. http://dx.doi.org/10.1109/iceict61637.2024.10670842.

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Ghali, Rayen, Zhor Benhafid, and Sid Ahmed Selouani. "Real-time defect detection systems for steel and wood inspection." In 2024 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), 577–82. IEEE, 2024. http://dx.doi.org/10.1109/ccece59415.2024.10667164.

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Guo, Yuqi, and Wenming Cao. "Wood Surface Defect Detection Using Improved Deep Learning Algorithm:FRCE-YOLO." In 2024 6th International Conference on Electronic Engineering and Informatics (EEI), 324–28. IEEE, 2024. http://dx.doi.org/10.1109/eei63073.2024.10696054.

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Zheng, Kunxin, Song Zheng, Zhezhuang Xu, Ye Lin, and Yuhang Zhu. "Semi-supervised Learning with GAN for Semantic Segmentation of Wood Defect Image." In 2024 China Automation Congress (CAC), 6760–64. IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10865192.

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Zhang, Zhao, Ning Ye, Dongyang Wu, and Yuhui Wang. "Automatic Wood Defects Recognition Comparative Research." In 2008 International Workshop on Geoscience and Remote Sensing (ETT and GRS). IEEE, 2008. http://dx.doi.org/10.1109/ettandgrs.2008.307.

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Tatari, Sehran, and Willi Haettich. "Automatic Recognition Of Defects In Wood." In Hague International Symposium, edited by Andre J. Oosterlinck and Andrew G. Tescher. SPIE, 1987. http://dx.doi.org/10.1117/12.941319.

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Reports on the topic "Wood – Defects"

1

Tanaka, Eri, Regina Schwerd, Wolfgang Hofbauer, and Daniel Zirkelbach. Laboratory tests on decay of natural fibre insulation materials suggest a more differentiated evaluation and higher RH thresholds. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau541651346.

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To reduce CO2 emissions and save grey energy, natural materials like wood and wooden materials are becoming more and more important. However, these products are particularly sensitive to moisture, as they can be attacked by mould or decay fungi. In contrast to mould growth, which typically is associated with visual impairment and health problems, the growth of decay fungi may result in structural defects which clearly must be excluded. Up to now it is mostly assumed that wooden materials are more sensitive to such attack than solid wood. Therefore, different wood fibre insulation materials were inoculated with decay fungi and exposed to different climates to determine the requirements for the decay process and to compare them with the requirements of decay by the same fungi of solid wood. The results prove that some natural fibre materials are equally or even more resistant to decay fungi than solid wood, while others are less. The resistant products can therefore be assessed like solid wood – for which already temperature dependent thresholds and in part also transient decay prediction models are available. Maybe even specific higher moisture levels can be acceptable. However, the results also suggest a differentiated view on natural fibre insulations, as they have a very different susceptibility to wood decay. Uniform and significantly lower limits than for solid wood are not justified.
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Neu, Vania, Victoria Jupp Kina, and Lucas Mota Batista. Water Security and Dignified Sanitation in Rural Amazonia Furo Grande, Belem, Brazil. Institute of Development Studies, December 2024. https://doi.org/10.19088/slh.2024.009.

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This case study documents experiences from a 10-year project working with a remote island community along the Furo Grande tributary, just off the coast of Belem in the Amazonian region of Brazil. The residents living along Furo Grande experience extreme poverty and social marginalisation, and mainly use latrines which empty directly into the river, or defecate in the forest, which is dangerous particularly for women and girls at night. Successful programme interventions included community engagement and collaborative processes to build trust and relationships with the people, and enable development of designs that respected local cultures, beliefs and routines of the traditional population. Adaptations included development of ecological toilets and rainwater harvesting systems, redesigning the structure to ensure suitability for high tides and flooding, changes to materials, for example the type of wood to prevent rotting, and adaptations to the construction process to utilise local building techniques. Ongoing monitoring enabled challenges to be identified and rectified, and provision of support to communities to strengthen understanding on how to maintain the new facilities.
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