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1
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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Albrektas, Darius, and Agne Styraite. "Modelling and Investigating Real-World Drying Defects in Wood." Drvna industrija 73, no. 2 (May 31, 2022): 115–23. http://dx.doi.org/10.5552/drvind.2022.2040.
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Depending upon the conditions in which they are used, wood products are required to have been produced with the necessary moisture levels for the intended job. In many cases, this means that drying is required in order to achieve these moisture levels. Wood is also often thermally modified. During the required processing, stresses often occur in wood assortments. If such stresses exceed the limits of durability in the wood, cracks will appear. Similar cracks in wood can occur prior to the drying and/or heat treatment stage. These defects are usually internal and invisible, but they can significantly alter the mechanical properties of the product. This study has shown that wood defects can often be detected using the original method, which involved transverse resonant vibrations, invisible drying, and others. It has been found that a defect of at least 12.5 % of the specimen’s overall length will change the mechanical properties of that specimen. When the defect length makes 25 % of the specimen’s overall length, or even more, the assortment sometimes behaves as a system of several bodies. In addition, when the defect reaches half the total length of the specimen, the modulus of elasticity may decrease to 20 %, and the coefficient of damping may increase to 80 %.
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Wang, Rijun, Fulong Liang, Bo Wang, and Xiangwei Mou. "ODCA-YOLO: An Omni-Dynamic Convolution Coordinate Attention-Based YOLO for Wood Defect Detection." Forests 14, no. 9 (September 16, 2023): 1885. http://dx.doi.org/10.3390/f14091885.
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Accurate detection of wood defects plays a crucial role in optimizing wood utilization, minimizing corporate expenses, and safeguarding precious forest resources. To achieve precise identification of surface defects in wood, we present a novel approach called the Omni-dynamic convolution coordinate attention-based YOLO (ODCA-YOLO) model. This model incorporates an Omni-dimensional dynamic convolution-based coordinate attention (ODCA) mechanism, which significantly enhances its ability to detect small target defects and boosts its expressiveness. Furthermore, to reinforce the feature extraction and fusion capabilities of the ODCA-YOLO network, we introduce a highly efficient features extraction network block known as S-HorBlock. By integrating HorBlock into the ShuffleNet network, this design optimizes the overall performance. Our proposed ODCA-YOLO model was rigorously evaluated using an optimized wood surface defect dataset through ablation and comparison experiments. The results demonstrate the effectiveness of our approach, achieving an impressive 78.5% in the mean average precision (mAP) metric and showing a remarkable 9% improvement in mAP compared to the original algorithm. Our proposed model can satisfy the need for accurate detection of wood surface defects.
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Jambreković, Branimir, Filip Veselčić, Iva Ištok, Tomislav Sinković, Vjekoslav Živković, and Tomislav Sedlar. "A Comparative Analysis of Oak Wood Defect Detection Using Two Deep Learning (DL)-Based Software." Applied System Innovation 7, no. 2 (April 15, 2024): 30. http://dx.doi.org/10.3390/asi7020030.
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The world’s expanding population presents a challenge through its rising demand for wood products. This requirement contributes to increased production and, ultimately, the high-quality and efficient utilization of basic materials. Detecting defects in wood elements, which are inevitable when working with a natural material such as wood, is one of the difficulties associated with the issue above. Even in modern times, people still identify wood defects by visually scrutinizing the sawn surface and marking the defects. Industrial scanners equipped with software based on convolutional neural networks (CNNs) allow for the rapid detection of defects and have the potential to accelerate production and eradicate human subjectivity. This paper evaluates the suitability of defect recognition software in industrial scanners against software specifically designed for this task within a research project conducted using Adaptive Vision Studio, focusing on feature detection techniques. The research revealed that the software installed as part of the industrial scanner is more effective for analyzing knots (77.78% vs. 70.37%), sapwood (100% vs. 80%), and ambrosia wood (60% vs. 20%), while the software derived from the project is more effective for analyzing cracks (70% vs. 65%), ingrown bark (42.86% vs. 28.57%), and wood rays (81.82% vs. 27.27%).
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Docherty, Hugh. "Historical defects in buildings – No. 5: Wood-wool formwork." Structural Engineer 102, no. 3 (March 1, 2024): 16–17. http://dx.doi.org/10.56330/yspw6016.
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Sun, Liping, Hongju Zhou, Hongwei Zhou, Guizhong Jiao, and Ling Ma. "Imaging of Internal Defects of Polymer-Modified Wood Using Total Focusing Method." Advances in Polymer Technology 2019 (June 17, 2019): 1–7. http://dx.doi.org/10.1155/2019/1045280.
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Polymer modification can improve the stability and corrosion resistance of wood, but it could create defects inside wood during the modification processing. Detection of defects inside polymer-modified wood can reduce wood losses and prevent the occurring of defects. Data simulation and tomographic imaging of polymer-modified wood internal defects were carried out using electromagnetic waves with nondestructive testing. This study constructed the polymer-modified wood models, simulated the electromagnetic scattering wave, and used the total focusing method to perform tomography of the defects in the polymer-modified wood. By analyzing the imaging characteristics of different types of defects, the effectiveness of electromagnetic waves in the detection of internal defects of polymer-modified wood was proved. This method can be extended to test internal defects of other high molecular polymers.
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Wang, Rijun, Fulong Liang, Bo Wang, Guanghao Zhang, Yesheng Chen, and Xiangwei Mou. "An Efficient and Accurate Surface Defect Detection Method for Wood Based on Improved YOLOv8." Forests 15, no. 7 (July 6, 2024): 1176. http://dx.doi.org/10.3390/f15071176.
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Accurate detection of wood surface defects plays a pivotal role in enhancing wood grade sorting precision, maintaining high standards in wood processing quality, and safeguarding forest resources. This paper introduces an efficient and precise approach to detecting wood surface defects, building upon enhancements to the YOLOv8 model, which demonstrates significant performance enhancements in handling multi-scale and small-target defects commonly found in wood. The proposed method incorporates the dilation-wise residual (DWR) module in the trunk and the deformable large kernel attention (DLKA) module in the neck of the YOLOv8 architecture to enhance the network’s capability in extracting and fusing multi-scale defective features. To further improve the detection accuracy of small-target defects, the model replaces all the detector heads of YOLOv8 with dynamic heads and adds an additional small-target dynamic detector head in the shallower layers. Additionally, to facilitate faster and more-efficient regression, the original complete intersection over union (CIoU) loss function of YOLOv8 is replaced with the IoU with minimum points distance (MPDIoU) loss function. Experimental results indicate that compared with the YOLOv8n baseline model, the proposed method improves the mean average precision (mAP) by 5.5%, with enhanced detection accuracy across all seven defect types tested. These findings suggest that the proposed model exhibits a superior ability to detect wood surface defects accurately.
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Meng, Wei, and Yilin Yuan. "SGN-YOLO: Detecting Wood Defects with Improved YOLOv5 Based on Semi-Global Network." Sensors 23, no. 21 (October 25, 2023): 8705. http://dx.doi.org/10.3390/s23218705.
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Object detection based on wood defects involves using bounding boxes to label defects in the surface image of the wood. This step is crucial before the transformation of wood products. Due to the small size and diverse shape of wood defects, most previous object detection models are unable to filter out critical features effectively. Consequently, they have faced challenges in generating adequate contextual information to detect defects accurately. In this paper, we proposed a YOLOv5 model based on a Semi-Global Network (SGN) to detect wood defects. Unlike previous models, firstly, a lightweight SGN is introduced in the backbone to model the global context, which can improve the accuracy and reduce the complexity of the network at the same time; the backbone is embedded with the Extended Efficient Layer Aggregation Network (E-ELAN), which continuously enhances the learning ability of the network; and finally, the Efficient Intersection and Merger (EIOU) loss is used to solve the problems of slow convergence speed and inaccurate regression results. Experimental results on public wood defect datasets demonstrated that our approach outperformed existing target detection models. The mAP value was 86.4%, a 3.1% improvement over the baseline network model, a 7.1% improvement over SSD, and a 13.6% improvement over Faster R-CNN. These results show the effectiveness of our proposed methodology.
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Ye, Ruochen, Ying Pei, Weibin Wang, and Haibin Zhou. "Scientific Computational Visual Analysis of Wood Internal Defects Detection in View of Tomography Image Reconstruction Algorithm." Mobile Information Systems 2022 (March 27, 2022): 1–15. http://dx.doi.org/10.1155/2022/6091352.
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With the development of non-destructive testing technology and computer technology, wood non-destructive testing technology will develop towards intelligence and automation. The primary condition for development is to be able to carry out the testing of various physical properties of wood without destroying the wood itself. Wood defects refer to abnormal wood structure. Its existence will affect the quality of wood, change the normal performance of wood, and reduce the utilization rate and use value of wood. The purpose of this article is to try to find an effective detection method without damaging the original structure of the wood. It can accurately and quickly determine the defect information on the wood surface. This research mainly discusses the visual analysis of wood internal defect detection based on tomographic image reconstruction algorithm. According to the analysis of the planks to be tested, this paper determines the structural characteristics, the types of defects to be tested, and the classification standards. To analyze the principle of machine vision imaging, this paper designs a hardware experiment system for wood board imaging, by observing the collected Biyun Temple building wood images and summarizing the Biyun Temple building wood defects, surface texture features, and various appearance features in the image. Based on digital image processing technology, this paper designs a complete set of real-time timber classification and detection algorithms. The algorithm realizes the extraction of Biyun Temple’s architectural wood region, texture region extraction, and the comprehensive feature vector extraction of Biyun Temple’s architectural wood. In the image preprocessing stage, the color image is converted into a grayscale image through the grayscale processing of the image. Through the equalization processing of the histogram, the defect features in the defect image are highlighted. Through image smoothing and denoising processing, the noise points that may exist in the image are removed. The signal-to-noise ratio of the Marching Cube algorithm is maintained at a relatively high level under different noise conditions, which is about 3-4db higher than the Gaussian method with OPED. This research is helpful for the continuation and inheritance of ancient architectural attainments.
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Mastap, Sushardi, Tibertius Agus Prayitno, Yustinus Suranto, and Ganis Lukmandaru. "Suitability of Teak Log Quality from Gunung Kidul and Bantul Yogyakarta Community Forest for Export Meubel Purpose." Journal of Sylva Indonesiana 4, no. 02 (August 30, 2021): 78–86. http://dx.doi.org/10.32734/jsi.v4i02.6347.
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Natural defect or poor log quality are common in community teak forests. Furniture manufacturers typically use these log types for export purposes. The aim of this study is to determine the effect of community teak forest location and teak stand age affect on teak wood quality. Three teak forest locations were Bantul and two locations in Gunung Kidul. The teak stand age class namely 6, 8, and 10 years old. The data was analyzed using SPSS 20.0 with Tukey test. The result showed that brittleness defect, sapwood defect, and different wooden knot defects were significant in all locations, while log straightness defect was found only in Dlingo. Similarly, all teak stand age (6, 8, and 10 years old) also produced the same defects such as brittleness defect, sapwood defect, and wood knot defect, while the straightness defect at the age of 10 years old was different from to other two teak stand age (6 and 8-year-old). Average teak log defects were straightness defect 1.87-3.53%, brittleness defect 1.19–6.21%, sapwood defect 1.49-4.82 cm, and wood knot defect 5.10-11.46 cm. However, the teak log quality still met the SNI 7534.2-2010 and 7535.2-2010 as raw material for exporting furniture.
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Wang, Bo, Rijun Wang, Yesheng Chen, Chunhui Yang, Xianglong Teng, and Peng Sun. "FDD-YOLO: A Novel Detection Model for Detecting Surface Defects in Wood." Forests 16, no. 2 (February 10, 2025): 308. https://doi.org/10.3390/f16020308.
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Wood surface defect detection is a critical step in wood processing and manufacturing. To address the performance degradation caused by small targets and multi-scale features in wood surface defect detection, a novel deep learning model is proposed in this study, FDD-YOLO, specifically designed for this task. In the feature extraction stage, the C2f module and the funnel attention (FA) mechanisms are integrated into the design of the C2f-FA module to enhance the model’s ability to extract features of wood surface defects of various sizes. Additionally, the Dual Spatial Pyramid Pooling-Fast (DSPPF) module is developed, and the Context Self-attention Module (CSAM) is introduced to address the limitations of traditional max pooling methods, which often overlook global contextual information when extracting local features, thereby improving the detection of small-scale wood defects. In the feature fusion stage, a Dual Cross-scale Weighted Feature-fusion (DCWF) module is proposed to fuse shallow, deep, and cross-scale features through a weighted summation approach, effectively addressing the challenge of scale variation in wood surface defects. Experimental results demonstrate that the proposed FDD-YOLO model significantly improves detection performance, increasing the mAP of the baseline model YOLOv8 from 78% to 82.3%, a substantial enhancement of 4.3 percentage points. Furthermore, FDD-YOLO outperforms other mainstream defect detection models in terms of detection accuracy. The proposed model demonstrates significant potential for industrial applications by improving detection accuracy, enhancing production efficiency, and reducing material waste, thereby advancing quality control in wood processing and manufacturing.
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Yuan, Peng, Liming Lou, Yu Shi, Pengle Cheng, Lei Yan, and Lei Pang. "Motion-blurry Image Restoration Method for Detecting Surface Defects of Wood Veneer." International Journal of Circuits, Systems and Signal Processing 16 (March 11, 2022): 843–51. http://dx.doi.org/10.46300/9106.2022.16.103.
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The detection of veneer surface defects is of great significance to wood veneer material evaluation, quality control, and product classification in the wood processing. When the high-speed moving veneer image is collected on the conveyor belt, the image appears blurred, making it difficult to accurately identify the defect type and estimate the defect area. To solve this problem, this study compared three image restoration methods including unnatural L0 sparse representation (L0), multi-scale convolutional neural network (MSCNN), and scale-recurrent convolutional neural network (SRCNN). To perform the comparison analysis, a wood veneer image acquisition system was developed and it provided a wood veneer image dataset with 2,080 groups of blur-clear veneer image pairs. Analysis results showed that the SRCNN method performed better than the other two methods. At four different wood moving speeds, the peak signal to noise ratio (PSNR) of the SRCNN was 4.64%, 14.63%, 18.48%, and 25.79%, higher than the other two methods and structural similarity (SSIM) was less than 2%. The average time for this algorithm to restore a blurred wood veneer image was 13.4 s. The findings of this study can lay the foundation for the industrialized detection of wood veneer defects.
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Gao, Mingyu, Peng Song, Fei Wang, Junyan Liu, Andreas Mandelis, and DaWei Qi. "A Novel Deep Convolutional Neural Network Based on ResNet-18 and Transfer Learning for Detection of Wood Knot Defects." Journal of Sensors 2021 (August 13, 2021): 1–16. http://dx.doi.org/10.1155/2021/4428964.
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Wood defects are quickly identified from an optical image based on deep learning methodology, which effectively improves wood utilization. Traditional neural network techniques have not yet been employed for wood defect detection due to long training time, low recognition accuracy, and nonautomatical extraction of defect image features. In this work, a model (so-called ReSENet-18) for wood knot defect detection that combined deep learning and transfer learning is proposed. The “squeeze-and-excitation” (SE) module is firstly embedded into the “residual basic block” structure for a “SE-Basic-Block” module construction. This model has the advantages of the features that are extracted in the channel dimension, and it is fused in multiscale with original features. Instantaneously, the fully connected layer is replaced with a global average pooling; consequently, the model parameters could be reduced effectively. The experimental results show that the accuracy has reached 99.02%, meanwhile the training time is also reduced. It shows that the proposed deep convolutional neural network based on ReSENet-18 combined with transfer learning can improve the accuracy of defect recognition and has a potential application in the detection of wood knot defects.
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Özcan, Uğur, Kazım Kiliç, Kenan Kiliç, and İbrahim Alper Doğru. "Using Deep Learning Techniques for Anomaly Detection of Wood Surface." Drvna industrija 75, no. 3 (September 30, 2024): 275–86. http://dx.doi.org/10.5552/drvind.2024.0114.
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The study presents a novel computer-aided vision system for the detection of wood defects using deep learning techniques. Our study utilizes a dataset consisting of over 43000 labelled wood surface defects found in a comprehensive collection of 20276 wood images. To enable rapid decision-making on the production line, a binary classification approach was employed, distinguishing between defective and perfect wood samples. Only flawless wood can be used in production. Wood with one or more defects is not used in production and must be removed from the production line. Deep learning-based convolutional neural networks (CNNs) were optimized and used for the detection of defective and perfect wood. Using the transfer learning approach, experiments were performed with VGG-16, MobileNet, ResNet-50, DenseNet-121, Xception and InceptionV3 architectures. To decide the best optimization, the analysis of Adam, RMSprop, Adagrad, SGD and Adadelta optimization algorithms were tested on CNN architectures. In addition, different numbers of neurons, namely 256, 512, 1024 and 2048 neurons, were used and wood defect detection was performed with optimum parameters. As a result of the experiments, it was found that the RMSprop optimization algorithm of the Xception architecture reached 97.57 % accuracy, which is the most successful result with 512 neurons.
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Hashim, Ummi Rabaah, Siti Zaiton Mohd Hashim, Azah Kamilah Muda, Kasturi Kanchymalay, Intan Ermahani Abd Jalil, and Muhammad Hakim Othman. "Systematic feature analysis on timber defect images." International Journal of Advances in Intelligent Informatics 3, no. 2 (July 31, 2017): 56. http://dx.doi.org/10.26555/ijain.v3i2.94.
Full textAbstract:
Feature extraction is unquestionably an important process in a pattern recognition system. A defined set of features makes the identification task more efficiently. This paper addresses the extraction and analysis of features based on statistical texture to characterize images of timber defects. A series of procedures including feature extraction and feature analysis was executed to construct an appropriate feature set that could significantly separate amongst defects and clear wood classes. The feature set aimed for later use in a timber defect detection system. For Accessing the discrimination capability of the features extracted, visual exploratory analysis and confirmatory statistical analysis were performed on defect and clear wood images of Meranti (Shorea spp.) timber species. Results from the analysis demonstrated that there was a significant distinction between defect classes and clear wood utilizing the proposed set of texture features.
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Huang, Guo Xiang, Supapan Chaiprapat, and Kriangkrai Waiyagan. "A Probabilistic Model of Wood Defects." Applied Mechanics and Materials 799-800 (October 2015): 217–21. http://dx.doi.org/10.4028/www.scientific.net/amm.799-800.217.
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Although widely used in construction and industrial applications, wood is more prone to defects of different kinds than other materials. These defects are unpredictable and differing randomly from plank to plank. This uncertain nature of the defects complicates establishment of manufacturing plans. In this study, a probabilistic model of wood defects was constructed as a function of three variables which were quantity of defects, position of defects and size of defects. The Kolmogorov-Smirnov hypotheses testing on distributional forms of these variables were carried out. Results showed that Poisson, uniform, and log-normal distributions were suitable to represent the variables statistically. Being knowledgeable of how the defects are distributed on the plank will be of benefit in profitability justification of a cutting plan.
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Shi, Jiahao, Zhenye Li, Tingting Zhu, Dongyi Wang, and Chao Ni. "Defect Detection of Industry Wood Veneer Based on NAS and Multi-Channel Mask R-CNN." Sensors 20, no. 16 (August 6, 2020): 4398. http://dx.doi.org/10.3390/s20164398.
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Wood veneer defect detection plays a vital role in the wood veneer production industry. Studies on wood veneer defect detection usually focused on detection accuracy for industrial applications but ignored algorithm execution speed; thus, their methods do not meet the required speed of online detection. In this paper, a new detection method is proposed that achieves high accuracy and a suitable speed for online production. Firstly, 2838 wood veneer images were collected using data collection equipment developed in the laboratory and labeled by experienced workers from a wood company. Then, an integrated model, glance multiple channel mask region convolution neural network (R-CNN), was constructed to detect wood veneer defects, which included a glance network and a multiple channel mask R-CNN. Neural network architect search technology was used to automatically construct the glance network with the lowest number of floating-point operations to pick out potential defect images out of numerous original wood veneer images. A genetic algorithm was used to merge the intermediate features extracted by the glance network. Multi-Channel Mask R-CNN was then used to classify and locate the defects. The experimental results show that the proposed method achieves a 98.70% overall classification accuracy and a 95.31% mean average precision, and only 2.5 s was needed to detect a batch of 50 standard images and 50 defective images. Compared with other wood veneer defect detection methods, the proposed method is more accurate and faster.
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Klement, Ivan, Tatiana Vilkovská, Miroslav Uhrín, Jacek Barański, and Aleksandra Konopka. "Impact of high temperature drying process on beech wood containing tension wood." Open Engineering 9, no. 1 (August 28, 2019): 428–33. http://dx.doi.org/10.1515/eng-2019-0047.
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AbstractThe technology of high temperature drying has a great influence on dimensional and selected physical changes in tension wood. Article is focused on the measurement properties such as moisture content, color changes and longitudinal warping. The quality of beech wood is determined based on structure and properties of wood, frequency of defects in wood material. The tension wood is considered as an important wood defect causing negative alterations in solid wood quality and limits industrial application of wood. The different values of longitudinal warping which were measured after drying were higher in tension wood than in normal wood. Impact of radial and tangential angle of growth rings is non-significant factor.
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Chun, Teo Hong, Ummi Raba'ah Hashim, Sabrina Ahmad, Lizawati Salahuddin, Ngo Hea Choon, Kasturi Kanchymalay, and Nur Haslinda Ismail. "Identification of wood defect using pattern recognition technique." International Journal of Advances in Intelligent Informatics 7, no. 2 (April 19, 2021): 163. http://dx.doi.org/10.26555/ijain.v7i2.588.
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This study proposed a classification model for timber defect classification based on an artificial neural network (ANN). Besides that, the research also focuses on determining the appropriate parameters for the neural network model in optimizing the defect identification performance, such as the number of hidden layers nodes and the number of epochs in the neural network. The neural network's performance is compared with other standard classifiers such as Naïve Bayes, K-Nearest Neighbours, and J48 Decision Tree in finding their significant differences across the multiple timber species. The classifier's performance is measured based on the F-measure due to the imbalanced dataset of the timber species. The experimental results show that the proposed classification model based on the neural network outperforms the other standard classifiers in detecting many types of defects across multiple timber species with an F-measure of 84.01%. This research demonstrates that ANN can accurately classify the defects across multiple species while defining appropriate parameters (hidden layers and epochs) for the neural network model in optimizing defect identification performance.
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Nefjodovs, Vadims, Laura Andze, Martins Andzs, Inese Filipova, Ramunas Tupciauskas, Linda Vecbiskena, and Martins Kapickis. "Wood as Possible Renewable Material for Bone Implants—Literature Review." Journal of Functional Biomaterials 14, no. 5 (May 10, 2023): 266. http://dx.doi.org/10.3390/jfb14050266.
Full textAbstract:
Bone fractures and bone defects affect millions of people every year. Metal implants for bone fracture fixation and autologous bone for defect reconstruction are used extensively in treatment of these pathologies. Simultaneously, alternative, sustainable, and biocompatible materials are being researched to improve existing practice. Wood as a biomaterial for bone repair has not been considered until the last 50 years. Even nowadays there is not much research on solid wood as a biomaterial in bone implants. A few species of wood have been investigated. Different techniques of wood preparation have been proposed. Simple pre-treatments such as boiling in water or preheating of ash, birch and juniper woods have been used initially. Later researchers have tried using carbonized wood and wood derived cellulose scaffold. Manufacturing implants from carbonized wood and cellulose requires more extensive wood processing—heat above 800 °C and chemicals to extract cellulose. Carbonized wood and cellulose scaffolds can be combined with other materials, such as silicon carbide, hydroxyapatite, and bioactive glass to improve biocompatibility and mechanical durability. Throughout the publications wood implants have provided good biocompatibility and osteoconductivity thanks to wood’s porous structure.
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Nurlaelah, Azis, U. Sudjadi, J. Hatmoko, and H. A. Rusdi. "The Detailed Description of Residential Defects in Years 2010 – 2011 of Citra Garden Residence in Indonesia." Applied Mechanics and Materials 584-586 (July 2014): 288–92. http://dx.doi.org/10.4028/www.scientific.net/amm.584-586.288.
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The detailed description of residential defects in years 2010 – 2011 of Citra Garden Residence in Indonesia was studied. Defect data were collected from interviews that have been carried out to the field supervisor and to the customer service in Citra Garden Residence. The defects observed were mainly on leakage that comes from the tile, natural stone walls, concrete roof, while the defects in addition to leakage is the wall, the painting, the floor and the ceramic wall, the plafond, the stairs, the roof, wood window & door frame, aluminum window & door frame, fence door & garage, wood stone, railing/grill, the floor and the ceramic wall spotted and sanitary. The data were processed and analyzed by using SPSS (Statistical Program for Social Science) software program to obtain statistical overview of the many types of defects for each category (minor, moderate, and serious) and the timing of the defects (Before Hand Over, During Hand-Over and After Hand-Over). The results showed that for the year 2010, the occurrence of defects in Citra Garden Residence were of moderate non-structural defects = 40.95%, of moderate structural defects = 23.334%, of minor non-structural defects = 21.904%, and of minor structural defect = 13.809%. There were neither serious structural defects nor serious non-structural defects occurred. The defects for the year 2011 were of moderate non-structural defects = 37.582%, of minor non-structural defects = 26.813%, of moderate structural defects = 21.538%, and of minor structural defects = 14.067%. Neither serious structural defects nor serious non-structural defects were found.
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Urbonas, Augustas, Vidas Raudonis, Rytis Maskeliūnas, and Robertas Damaševičius. "Automated Identification of Wood Veneer Surface Defects Using Faster Region-Based Convolutional Neural Network with Data Augmentation and Transfer Learning." Applied Sciences 9, no. 22 (November 15, 2019): 4898. http://dx.doi.org/10.3390/app9224898.
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In the lumber and wood processing industry, most visual quality inspections are still done by trained human operators. Visual inspection is a tedious and repetitive task that involves a high likelihood of human error. Currently, new automated solutions with high-resolution cameras and visual inspection algorithms are being tested, but they are not always fast and accurate enough for real-time industrial applications. This paper proposes an automatic visual inspection system for the location and classification of defects on the wood surface. We adopted a faster region-based convolutional neural network (faster R-CNN) for the identification of defects on wood veneer surfaces. Faster R-CNN has been successfully used in medical image processing and object tracking before, but it has not yet been applied for wood panel surface quality assurance. To improve the results, we used pre-trained AlexNet, VGG16, BNInception, and ResNet152 neural network models for transfer learning. The results of the experiments using a synthetically augmented dataset are presented. The best average accuracy of 80.6% was obtained using the pretrained ResNet152 neural network model. By combining all the defect classes, a 96.1% accuracy of finding wood panel surface defects was achieved.
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Xie, Yonghua, and Jiaxin Ling. "Wood defect classification based on lightweight convolutional neural networks." BioResources 18, no. 4 (September 27, 2023): 7663–80. http://dx.doi.org/10.15376/biores.18.4.7663-7680.
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Different types of wood defects correspond to different processing methods. Good classification means can transform defective boards into practical boards after appropriate processing. The detection accuracy of the wood surface defects is particularly important for improving the utilization rate and speed of processing the boards. The RegNet stands out in the field of computer vision. It automatically designs the network model based on the design space and applies it to wood defect detection, which can improve the classification accuracy. When the convolutional structure of the RegNet network is applied to industrial detection and classification, the problems of long real-time detection time and large algorithm parameters persist. This study focuses on collecting wood material images of common coniferous and broad-leaved trees in Northeast China with three types of defects: wormholes, slip knots, and dead knots. To improve the allocation of computing resources, based on the RegNet network model, an attention mechanism module was added, and the Ghostconv structure was introduced. The structure quickly and accurately highlighted the types of wood defects, improved the classification accuracy, reduced the parameters of the network, and exhibited generalization ability. To verify the performance of the improved network, MobileNet-v2, EfficientNet, and Vision-Transformer networks were introduced for comparative analysis. The improved RegNet network had smaller weight and higher accuracy, with a classification accuracy of 96.58%.
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Dhanamathi, A., K. Ajith, V. Balamurugan, and S. Sridhar. "A framework for wood quality assessment using DenseNet algorithm." i-manager’s Journal on Pattern Recognition 11, no. 1 (2024): 30. http://dx.doi.org/10.26634/jpr.11.1.21060.
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Wood defect detection is a critical aspect of quality control in the woodworking industry. This work introduces Deep Wood Inspect, a pioneering system that leverages the capabilities of deep learning for the precise identification and classification of defects in wooden materials. The proposed methodology utilizes densely connected Convolutional Neural Networks (CNNs), specifically DenseNet, to analyze high-resolution images of wood surfaces, providing an automated and efficient solution for defect detection. By integrating advanced image processing techniques with machine learning algorithms, Deep Wood Inspect not only enhances the accuracy of defect identification but also accelerates the inspection process, reducing manual labor and minimizing human error. The system's adaptability to various types of wood and defect categories further contributes to its robustness, making it a valuable tool for both large- scale manufacturers and smaller woodworking enterprises seeking to uphold high standards of quality control.
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Jackson, M. R., R. M. Parkin, and N. Brown. "Waves on wood." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 216, no. 4 (April 1, 2002): 475–97. http://dx.doi.org/10.1243/0954405021520175.
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The rotary machining process as applied to timber is introduced and compared with the milling and grinding of metals. The emphasis of this work is on the waviness surface quality of the machined timber and initially focuses on a review of the techniques applied to improve surface quality at higher workpiece feed velocities—typically 120 m/min. The main work concentrates on mathematical and computer-based modelling of surface waviness defects generated by two classical woodworking machine engineering science phenomena, caused primarily by forced structural vibration. Surface assessment of machined timber is discussed, with results from contact and non-contact methods highlighted. The causes of surface waviness defects are presented and possible solutions are outlined.
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Rahiddin, Rahillda Nadhirah Norizzaty, Ummi Rabaah Hashim, Nor Haslinda Ismail, Lizawati Salahuddin, Ngo Hea Choon, and Siti Normi Zabri. "Classification of wood defect images using local binary pattern variants." International Journal of Advances in Intelligent Informatics 6, no. 1 (March 29, 2020): 36. http://dx.doi.org/10.26555/ijain.v6i1.392.
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This paper presents an analysis of the statistical texture representation of the Local Binary Pattern (LBP) variants in the classification of wood defect images. The basic and variants of the LBP feature set that was constructed from a stage of feature extraction processes with the Basic LBP, Rotation Invariant LBP, Uniform LBP, and Rotation Invariant Uniform LBP. For significantly discriminating, the wood defect classes were further evaluated with the use of different classifiers. By comparing the results of the classification performances that had been conducted across the multiple wood species, the Uniform LBP was found to have demonstrated the highest accuracy level in the classification of the wood defects.
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Wang, Rijun, Yesheng Chen, Fulong Liang, Bo Wang, Xiangwei Mou, and Guanghao Zhang. "BPN-YOLO: A Novel Method for Wood Defect Detection Based on YOLOv7." Forests 15, no. 7 (June 25, 2024): 1096. http://dx.doi.org/10.3390/f15071096.
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The detection of wood defect is a crucial step in wood processing and manufacturing, determining the quality and reliability of wood products. To achieve accurate wood defect detection, a novel method named BPN-YOLO is proposed. The ordinary convolution in the ELAN module of the YOLOv7 backbone network is replaced with Pconv partial convolution, resulting in the P-ELAN module. Wood defect detection performance is improved by this modification while unnecessary redundant computations and memory accesses are reduced. Additionally, the Biformer attention mechanism is introduced to achieve more flexible computation allocation and content awareness. The IOU loss function is replaced with the NWD loss function, addressing the sensitivity of the IOU loss function to small defect location fluctuations. The BPN-YOLO model has been rigorously evaluated using an optimized wood defect dataset, and ablation and comparison experiments have been performed. The experimental results show that the mean average precision (mAP) of BPN-YOLO is improved by 7.4% relative to the original algorithm, which can better meet the need to accurately detecting surface defects on wood.
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Xi, Honglei, Rijun Wang, Fulong Liang, Yesheng Chen, Guanghao Zhang, and Bo Wang. "SiM-YOLO: A Wood Surface Defect Detection Method Based on the Improved YOLOv8." Coatings 14, no. 8 (August 7, 2024): 1001. http://dx.doi.org/10.3390/coatings14081001.
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Wood surface defect detection is a challenging task due to the complexity and variability of defect types. To address these challenges, this paper introduces a novel deep learning approach named SiM-YOLO, which is built upon the YOLOv8 object detection framework. A fine-grained convolutional structure, SPD-Conv, is introduced with the aim of preserving detailed defect information during the feature extraction process, thus enabling the model to capture the subtle variations and complex details of wood surface defects. In the feature fusion stage, a SiAFF-PANet-based wood defect feature fusion module is designed to improve the model’s ability to focus on local contextual information and enhance defect localization. For classification and regression tasks, the multi-attention detection head (MADH) is employed to capture cross-channel information and the accurate spatial localization of defects. In addition, MPDIoU is employed to optimize the loss function of the model to reduce the leakage of detection due to defect overlap. The experimental results show that SiM-YOLO achieves superior performance compared to the state-of-the-art YOLO algorithm, with a 9.3% improvement in mAP over YOLOX and a 4.3% improvement in mAP over YOLOv8. The Grad-CAM visualization further illustrates that SiM-YOLO provides more accurate defect localization and effectively reduces misdetection and omission issues. This study highlights the effectiveness of SiM-YOLO for wood surface defect detection and offers valuable insights for future research and practical applications in quality control.
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Wieruszwski, Marek, Radosław Mirski, Adrian Ttrociński, and Jakub Kawalerczyk. "Effect of sawn zone on the quality of lumber in the evaluation of selected pine wood defects." Annals of WULS, Forestry and Wood Technology 114 (June 28, 2021): 26–32. http://dx.doi.org/10.5604/01.3001.0015.2370.
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Effect of sawn zone on the quality of lumber in the evaluation of selected pine wood defects. In the course of research on the sawn pine raw material with defined distribution of the defects, a variable level of change in the presence of knots was assessed. Initially, the experimental material was classified in terms of the general-purpose timber, and then the strength classes of wood for structural applications were assigned. The proportion of sound knots increased in case of wood obtained from the middle and top zones. In the case of butt-end logs, an increase in the share of the rotten knots having an average diameter of 2-4 cm was observed. The intensity of the defect’s occurrence corresponded with the zone of origin along the large-sized roundwood length.
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PEDREIRA, Paula A., Eduardo A. PENON, A. Cecilia GOZZI, Nicolás PENON-SOBERO, and Mariela BORGNIA. "Damage to the wood of forest species caused by the debarking of Pallas´s squirrel introduced into Argentina." Forest Systems 32, no. 2 (July 3, 2023): e012. http://dx.doi.org/10.5424/fs/2023322-20098.
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Aim of study: Pallas´s squirrel (Callosciurus erythraeus) is a harmful and generalist invasive mammal species that causes different problems in the forestry sector. The aim of this study was to evaluate the damage on the wood in three commercial tree species in Argentina, Eucalyptus dunnii, Populus deltoides and Pinus elliottii, due to debarking caused by this squirrel species.
Area of study: ¨Liebres Fue¨ forest plantation, located in Luján District (Province of Buenos Aires, Argentina).
Material and methods: We analyzed affected tissues and internal defects of wood associated with debarking signs. We randomly collected 74 stems of the three forest species with (N=62) and without debarking (N=12) between October 2016 and December 2017. Transversal cuttings (N=37) and longitudinal cuttings (N=37) of the stems were analyzed.
Main results: The defects inside the wood related to the damage due to the debarking caused by Pallas´s squirrels are described. All the damaged samples presented affected wood tissues, with unfavorable healing forming ribbed cracks and ram`s horn scars and/or presence of some internal defect (crack, crack with abnormal coloration, crack with kino/resin or crack with bark included). None of the damaged pieces, according to the rules of visual classification of sawn woods, showed the highest quality grade (Premium).
Research highlight: Pallas´s squirrel action causes wounds on the trees, leading to different responses by the trees that are transferred internally, showing abnormalities in the wood which diminish its value from a commercial point of view.
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Wang, Jun, Changsen Zhang, Maocheng Zhao, Hongyan Zou, Liang Qi, and Zheng Wang. "A Composite Pulse Excitation Technique for Air-Coupled Ultrasonic Detection of Defects in Wood." Sensors 24, no. 23 (November 26, 2024): 7550. http://dx.doi.org/10.3390/s24237550.
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To overcome the problems of the low signal-to-noise ratio and poor performance of wood ultrasonic images caused by ring-down vibrations during the ultrasonic quality detection of wood, a composite pulse excitation technique using a wood air-coupled ultrasonic detection system is proposed. Through a mathematical analysis of the output of the ultrasonic transducer, the conditions necessary for implementing composite pulse excitation were analyzed and established, and its feasibility was verified through COMSOL simulations. Firstly, wood samples with knot and pit defects were used as experimental samples. We refined the parameters for the composite pulse excitation technique by conducting A-scan measurements on both defective and non-defective areas of the samples. Moreover, two stepper motors were employed to control the path for C-scan imaging to detect wood defects. The experiment results showed that the composite pulse excitation technique significantly enhanced the precision of nondestructive ultrasonic testing for wood defects compared to the traditional single-pulse excitation method. This technique successfully achieved precise detection and location of pit defects, with a detection accuracy rate of 90% for knot defects.
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Rozi, Fahrul, and Widya Setiafindari. "Analisis Pengendalian Kualitas pada Pengolahan Produk Lemari Tipe MC11 01 dengan Metode Statistical Process Control pada PT Alis Jaya Ciptatama." JURNAL TEKNIK INDUSTRI 3, no. 1 (May 19, 2022): 1–15. http://dx.doi.org/10.37366/jutin0301.0115.
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Abstrak
PT Alis Jaya Ciptatama is a manufacturing company that produces furniture made of mahogany and teak wood into furniture items. From the production data for the MC11 01 type wardrobe in August to September 2021, it reached 800 products with a percentage of cracking defects of 5% per year then knot defects of about 4% per year, and color defects of 1% per year, far from the defect tolerance limit of 1% at PT Alis Jaya Ciptatama resulting in a re-production process and additional production costs. In this study, the method used is Statistical Quality Control where the method is used to analyze what types of defects occur in the production of MC11 01 type cabinets, then look for factors causing defects in the product and provide suggestions for improvement. This method consists of Check Sheet, Stratification, Control Map, Pareto Diagram, Cause and Effect Diagram, Scatter Diagram, and Histogram.The results of this study indicate that the factors that occur in the MC11 01 type wardrobe product are human factors, machines, methods and materials. the biggest defect is crack with a total damage of 60 products or about 50%. Then followed by wood eye defects as much as 40 or about 33%, and color defects as much as 20 or about 16.7%. Proposed improvements should be made by reviewing the machine operation process, conducting employee training, implementing a reward and punishment system and working according to the SOP for the production process.
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Mohsin, Mazhar, Oluwafemi Samson Balogun, Keijo Haataja, and Pekka Toivanen. "Convolutional neural networks for real-time wood plank detection and defect segmentation." F1000Research 12 (March 23, 2023): 319. http://dx.doi.org/10.12688/f1000research.131905.1.
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Background: Defect detection and segmentation on product surfaces in industry has become one of the most important steps in quality control. There are many sophisticated hardware and software tools used in the industry for this purpose. The need for the real-time classification and detection of defects in industrial quality control has become a crucial requirement. Most algorithms and deep neural network architectures require expensive hardware to perform inference in real-time. This necessitates the design of architectures that are light-weight and suitable for deployment in industrial environments. Methods: In this study, we introduce a novel method for detecting wood planks on a fast-moving conveyor and using a convolutional neural network (CNN) to segment surface defects in real-time. A backbone network is trained with a large-scale image dataset. A dataset of 5000 images is created with proper annotation of wood planks and defects. In addition, a data augmentation technique is employed to enhance the accuracy of the model. Furthermore, we examine both statistical and deep learning-based approaches to identify and separate defects using the latest methods. Results: Our plank detection method achieved an impressive mean average precision of 97% and 96% of global pixel accuracy for defect segmentation. This remarkable performance is made possible by the real-time processing capabilities of our system, which can run at 30 frames per second (FPS) without sacrificing accuracy. Conclusions: The results of our study demonstrate the potential of our method not only in industrial wood processing applications but also in other industries where materials undergo similar processes of defect detection and segmentation. By utilizing our method, these industries can expect to see improved efficiency, accuracy, and overall productivity.
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Kamperidou, Vasiliki, Efstratios Aidinidis, and Ioannis Barboutis. "Impact of Structural Defects on the Surface Quality of Hardwood Species Sliced Veneers." Applied Sciences 10, no. 18 (September 9, 2020): 6265. http://dx.doi.org/10.3390/app10186265.
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The surface roughness constitutes one of the most critical properties of wood and wood veneers for their extended utilization, affecting the bonding ability of the veneers with one another in the manufacturing of wood composites, the finishing, coating and preservation processes, and the appearance and texture of the material surface. In this research work, logs of five significant European hardwood species (oak, chestnut, ash, poplar, cherry) of Balkan origin were sliced into decorative veneers. Their surface roughness was examined by applying a stylus tracing method, on typical wood structure areas of each wood species, as well as around the areas of wood defects (knots, decay, annual rings irregularities, etc.), to compare them and assess the impact of the defects on the surface quality of veneers. The chestnut veneers presented the smoothest surfaces, while ash veneers, despite the higher density, recorded the highest roughness. In most of the cases, the roughness was found to be significantly lower around the defects, compared to the typical structure surfaces, probably due to lower porosity, higher density and the presence of tensile wood. The results reveal that the presence of defects does not affect the roughness of the veneers and increases neither the processing requirements of the veneer sheets before finishing, nor the respective production cost of veneers and the veneer-based wood panels. The high utilization prospects of the examined wood species in veneer production, even those bearing various defects, is highlighted.
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Lee, Sang-Joon, Sangdae Lee, Sung-Jun Pang, Chul-Ki Kim, Kwang-Mo Kim, Ki-Bok Kim, and Jun-Jae Lee. "Indirect Detection of Internal Defects in Wooden Rafter with Ultrasound." Journal of the Korean Wood Science and Technology 41, no. 2 (March 25, 2013): 164–72. http://dx.doi.org/10.5658/wood.2013.41.2.164.
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Li, Meng, Yong Xiang Kang, Van Quy Nguyen, Xu Zhen Gao, Li Li Zhang, Jin Ling Wang, Xiao Qiang Zhou, and Gang Zhen Ma. "3D reconstruction of the tree internal decay based on radar waves." BioResources 17, no. 4 (September 21, 2022): 6277–92. http://dx.doi.org/10.15376/biores.17.4.6277-6292.
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TRU Tree Radar is a new tool for the non-destructive testing of trees. In this work, two-dimensional images of the internal defect cross-section of a tree were reconstructed to provide a theoretical basis for accurately judging the shape and position of the internal decay, rationally using wood, and improving the utilization rate of wood. Two-dimensional images of different height defect sequences of tree trunk obtained by TRU tree radar were preprocessed and interpolated. Finally, the three-dimensional reconstruction of internal defects of tree trunk was realized by the surface rendering method and volume rendering method under the Matlab environment. The surface rendering displayed the rough 3D model quickly and effectively. However, the important internal information was incomplete, and the reconstructed model was not intuitive. Volume rendering was used to process each voxel in the data set, so as to describe the physical reality of the internal defects of the tree relatively accurately. An algorithm was proposed to improve the visualization level of internal defects, which can enhance the understanding of the three-dimensional image.
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Anoop, E. V., Gayathri Mukundan, Comath Shibu, and Anish Mavila Chathoth. "Development of Coconut Palm Wood Seasoning Schedules." CORD 40 (January 6, 2025): 11–18. https://doi.org/10.37833/cord.v40i.451.
Full textAbstract:
Coconut palm is a versatile and commercially important palm in the tropical and sub-tropical regions. This study attempts to standardise moisture content-based kiln seasoning schedules for high-density and medium-densitycoconut palm wood and also understand relationship between Pilodyn Penetration Depth (PPD) and basic density for three density classes (high, medium and low). Quick drying test was conducted to study the degree and type of drying defects viz., surface cracking, end splitting, honeycombing and deformation. Defects were graded according to the Terasawa (1965) scale. The baseline parameters such as initial dry bulb temperature, final dry bulb temperature and the wet bulb depression for high and medium-density coconut palm wood were chosen by considering the major seasoning defects. The samples were subjected to different seasoning schedule treatments in a convection kiln to determine the best treatment based on grading of defects. The ideal drying period obtained for high-density coconut palm wood was 11 days, whereas for medium-density coconut palm wood it was 12 days. The schedule developed has good potential for industrial application in seasoning of coconut palm wood lumber with reduced defects in coconut growing regions of the world. Keywords: Coconut palm wood, seasoning schedule, kiln drying, Terasawa scale
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Pham, D. T., and S. Sagiroglu. "Neural network classification of defects in veneer boards." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 214, no. 3 (March 1, 2000): 255–58. http://dx.doi.org/10.1243/0954405001517649.
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Learning vector quantization (LVQ) networks are known good neural classifiers which provide fast and accurate results for many applications. The aim of this work was to test if this network paradigm could be employed for the classification of wood sheet defects. Experiments conducted with LVQ networks have shown that they provide a high degree of discrimination between the different types of defects and potentially can perform defect classification in real time.
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Sun, Ping’an. "Wood Quality Defect Detection Based on Deep Learning and Multicriteria Framework." Mathematical Problems in Engineering 2022 (May 26, 2022): 1–9. http://dx.doi.org/10.1155/2022/4878090.
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Traditional nondestructive testing technology for wood defects has a series of problems such as low identification accuracy, high cost, and cumbersome operation, and traditional testing methods cannot accurately show the specific location and size of wood internal defects; it is urgent to explore a new nondestructive testing scheme for wood defects. Aiming at this problem, this paper designs and develops an automatic detection method for wood surface defects based on deep learning algorithm and multicriteria framework. By comparing the performance of different deep learning detection methods on the data set, the advantages and disadvantages of the detection method in this paper are proved. After a series of works, such as the development and optimization of the experimental algorithm, the algorithm proposed meets the requirements in both the detection accuracy and training time.
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Miao, Y., M. Zhong, and Z. Liu. "Locating Wood Defects Based on Vibration Modes." Journal of Testing and Evaluation 46, no. 2 (October 31, 2017): 20150511. http://dx.doi.org/10.1520/jte20150511.
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Forrer, J. B., and J. W. Funck. "Dielectric properties of defects on wood surfaces." Holz als Roh- und Werkstoff 56, no. 1 (January 1998): 25–29. http://dx.doi.org/10.1007/s001070050259.
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