基于振动模态分析的落叶松节子定位的无损检测

木材作为结构材使用时,节子的存在影响木材的强度,降低了木材的利用价值.为了节约木材,提高木材的使用率,人们一直在研究木材缺陷无损检测的方法.Beall(2000)首次使用表面传感器研究人造板的力学性能和内部缺陷,收到良好效果.日本学者小玉泰羲(2000)用连续小波变换分析处理利用敲击木材所获得的声信号,对气干木材的节子缺陷进行检测,研究发现,当木材含有节子缺陷时,声信号的共振频率降低、声信号共振的持续时间缩短.     

基于单片机的木材表面粗糙度激光在线检测系统

介绍一种用单片机控制激光位移传感器进行在线检测木材机械加工表面粗糙度的新方法.该系统由单片机、模数转换器和激光传感器测量系统(激光头及激光控制器)等组成,试验在木材加工CNC装置上进行.文中给出了硬件结构及软件程序设计流程图.     

木材机械加工表面粗糙度的激光在线检测系统

介绍了一种采用计算机控制激光位移传感器在线检测木材机械加工表面粗糙度的新方法。该系统由计算机、模数转换板(A/D板)、激光传感器测量系统(激光头及激光控制器)等组成,试验研究是在木材加工CNC(Computer Numerical Control)装置上进行的。文中给出了硬件结构、软件程序设计流程图及木材表面粗糙度在线检测的实验数据。     

基于激光三角测距的锯材表面缺陷检测方法

为实现自动在线检测锯材表面钝棱和裂纹等缺陷,提出了一种全新的基于激光三角测距和计算机图像处理相结合的木材表面缺陷检测方法。激光发射器发射扇形光源至传送台上的试件表面,从另一角度由相机对试件表面的激光光斑进行成像。通过图像处理,能自动识别裂纹及钝棱缺陷轮廓线,并得到外材面材宽和裂纹宽度尺寸信息。以7块含钝棱的毛边锯材和7块含裂纹的锯材进行试验,结果表明:在入射激光线与物镜光轴的夹角为60°的情况下,锯材外材面宽度和裂纹宽度检测值与实际值的误差均值都不超过±1 mm,证实了研究提出的锯材表面缺陷检测模型及构建的检测装置的高精度和可靠性。该技术能为木材加工中的自动优选下锯提供基础数据,可用于锯材优选自动化加工生产线。     

断层扫描技术在木材无损检测中的应用

为实现木材缺陷的自动、高效检测,提高木材利用率,应用自主研发的CT设备扫描原木木段横切面,并重建其二维图像和三切面视图。结果表明:通过二维图像可观察横切面年轮、节子和裂纹等信息;结合三视图和透视图可精确定位缺陷位置。为无损检测木材内部结构特征,及木材分等提供依据。     

基于人工神经网络的原木CT图像缺陷识别

以欧洲白蜡为例,利用训练好的神经网络识别原木CT图像中的各种木材缺陷.不同隐蔽层节点的神经网络可以正确地识别树皮、节子、腐朽和无疵木材;但是对于细小裂纹尚还不能准确识别.计算机快速、自动识别图像中的各种缺陷,有利于实现最优化的锯切方案.     

基于Faster R-CNN模型的木材表面节子缺陷检测

研究基于Faster R-CNN模型的木材表面节子缺陷检测方法,解决现有方法存在的特征选取困难、不能适应木材以及节子的多样性变化、检测精度不高等问题,而且能够给出节子缺陷的位置以及图像的大小。小样本集测试结果表明,该方法能够取得较高的检测精度,准确率为94.0%;对其中4个典型样本进行分析,位置检测最大误差仅为5个像素,大小检测最大误差仅为7个像素;相比OSTU方法具有更好的检测精度。     

木材缺陷检测理论及方法的发展

通过对国内外板材节子视频检测理论现状和发展的研究，对计算机视频技术在木材缺陷检测上的应用与发展进行了综述，并对视频技术在木材缺陷检测上的应用前景进行展望。     

基于MEA-BP神经网络对木材内部缺陷诊断的研究

为了提高木材内部缺陷的自动识别率,采用电阻层析成像(ERT)的方法获取电导率波动信号,通过小波包变换对采集的数据进行3层小波包分析,对八维特征向量进行提取,利用思维进化算法(MEA)优化权值和阈值,孔洞、节子、腐朽试样各45组数据,进行BP神经网络训练,每种缺陷20组作为测试集,识别木材内部缺陷。结果表明:MEA-BP神经网络对木材孔洞、节子和腐朽的识别率分别为96.92%、95.38%和92.31%,该模型解决了复杂组合的优化问题,提高了搜索效率,并且达到最佳的预测效果。     

基于YOLOv4的结构用锯材表面缺陷识别

结构用锯材在使用之前进行表面质量评价、分级,对于提高木材的综合利用率具有重要作用。综合利用机器视觉技术和深度学习方法,选取国内常用的云杉结构用锯材作为研究对象,通过工业相机采集结构用锯材表面主要缺陷(节子、虫眼、裂纹),并对锯材主要缺陷进行数字化评价分析。先通过自主搭建的机器视觉图像采集装置,采集100块结构锯材正反面表面图像,共获取表面缺陷图像1 450张,其中活节缺陷图像550张、死节缺陷图像320张、裂纹缺陷图像295张、虫眼缺陷图像285张;随后搭建基于YOLOv4的深度学习缺陷检测识别框架,对缺陷图像中80%的图像进行训练,剩余20%用于测试。试验结果表明,基于YOLOv4的深度学习缺陷检测识别框架,能有效识别并准确定位锯材表面缺陷的类型和位置,平均识别率96.7%,其中活节缺陷识别率100%、死节缺陷识别率97.5%、裂纹缺陷识别率90%、虫眼缺陷识别率96.7%,可满足生产应用需求。     

Strain analysis of lumber containing a knot during tensile failure

We analyzed the strain distribution of lumber containing a knot under a tensile load. The local tensile strain near the knot was measured using the digital image correlation method. Fracture often initiated near the knot where the fiber orientation changed in a three-dimensional manner. The fiber direction in this zone was different from that in the clear part, coinciding with the thickness direction and not with the longitudinal direction of the specimen. Our results disagree with those of previous models that assumed the longitudinal direction of lumber as the direction of crack propagation. Strain analysis showed that a nonlinear region existed around the knot just before ultimate fracture occurred. The results indicated that nonlinear characterization is necessary to determine the failure mechanism of lumber containing a knot, despite the brittleness fracture at the macroscopic scale.     

Automatic detection of washboarding in bandsaws

This paper presents an on-line method for detecting washboarding generated in bandsaws. It uses a parameter based on the shape of sawing force amplitude distributions. The curve of the probability density function of the parallel sawing force amplitude obeyed a normal distribution for the normal sawn surface, but it changed to an M-shaped distribution when washboarding appeared on the sawn surface of the workpiece. The curve of probability density function of the normal sawing force amplitude obeyed the normal distribution for any sawn surface investigated. The workpiece including a knot did not influence use of the parameter for detecting the appearance of washboarding.     

基于表面主要缺陷的锯材外观质量量化评价方法

【目的】提出一种锯材外观质量量化评价方法,探索锯材外观质量数字化检测与评价分级的可行性,为实现锯材外观质量的实时在线检测提供基础理论和技术支撑。【方法】建立由密闭暗室、光源、工业相机等组成的锯材表面缺陷在线检测系统,在稳定光环境下采集样本锯材彩色图像。基于图像处理技术开发软件试验系统,实现对锯材缺陷的检测和识别。分别建立节疤、孔洞和裂缝缺陷外观质量评价模型,并据此提出外观质量综合量化评价方法;通过与国家标准对照,验证本研究提出方法的科学性和可行性。【结果】量化评价方法与锯材材质指标等级进行对照,二者线性相关系数为0.85;锯材材质指标等级除了2级和3级对应的综合量化评价值分布比较分散、等级之间数据有部分交叉外,其他等级之间对应的综合量化评价值分布几乎没有交叉。量化评价方法与集成材层板外观质量要求进行对照,二者线性相关系数为0.88;锯材材质指标等级4级对应的综合量化评价值分布比较集中,与其他等级之间没有数据交叉;锯材材质指标等级1级、2级和3级相邻等级对应的综合量化评价值分布比较分散,等级之间数据有部分交叉,不相邻等级间数据分布没有交叉。【结论】综合量化评价值与依据有关国家标准确定的锯材材质指标等级、集成材层板外观质量要求的线性相关性相对较好,可为实现锯材外观质量数字化检测与评价奠定基础;通过调整模型有关影响系数,可满足不同树种和不同应用需求,以达到较好评价效果。     

扭叶松实木板材三种动态弹性模量和静态弹性模量比较研究(英文)

采用Pundit、Metriguard、FFT等三种无损检测方法和常规弯曲法对加拿大扭叶松(lodgepole pine)蓝变与非蓝变实木板材的动态及静态弹性模量进行检测和比较研究。结果表明,蓝变材三种动态弹性模量及静态弹性模量均高于非蓝变材;对比分析表明,蓝变材和非蓝变材的动态及静态弹性模量存在差异,其中动态弹性模量差异均达到0.01显著性水平,静态弹性模量差异达到0.05显著性水平,并且心、边材及密度值不同是导致以上差异的主要原因。相关性分析表明,动态与静态弹性模量间相关性达到0.01显著性水平;尽管三种无损检测方法测量结果存在差异,但它们之间仍存在密切相关性,FFT 技术测量的准确性高于Pundit和Metriguard;板材中结子数影响木材动态和静态弹性模量,随着板材结子数增加弹性模量相应地降低。     

Shape and distribution of knots in a sample of picea abies logs

In an attempt to develop a better understanding of the effect of knottiness on the yield of logs for sawn timber products, the present study examines the position and geometrical characteristics of 429 knots found in seven Norway spruce (Picea abies (L.) Karst) middle logs from southern Finland. The raw data used in this study were in the form of coordinates of points located on the knot surface recorded in a cylindrical (R, L, T) reference frame. The data were smoothed using a non linear three‐dimensional model. A curvature analysis of the knot pith was used to differentiate the curved and straight portions of the knots. This analysis made possible calculation of knot angles, symmetry, ovality and volume. The major finding of this study is the uneven distribution of knots around the log circumference. Nearly 50% of the total knot volume was concentrated in one‐third of the log facing the south‐east. Knots were also more prominent in higher portions of the logs studied. These observations support the argument that the uneven distribution of knots offers potential for improving the grade yield of timber sawn from trees harvested in high‐latitude countries.     

Models of Internal Knot Diameter for Pinus sylvestris

The objective of this investigation was to develop models describing the vertical variation in internal knot size of Scots pine (Pinus sylvestris L.) stems. The sawlog portion of 198 mature trees was scanned using computed tomography in order to measure internal knot size. The variation in knot diameter at the sound/loose knot border was described in terms of a non-linear segmented (hyperbolic-quadratic-quadratic) model, and as a function of stand variables, tree variables and height above ground. A second, simpler (hyperbolic-quadratic) model which was deemed easier to implement was also presented. Although no validation data set was available, the models performed adequately for trees with different growth rates, growing on different sites and being at different stages of development. These results are intended to be integrated in a model system for conversion simulation studies applicable to mature trees.     

黑龙江东部地区落叶松人工林节子大小的研究

以不同年龄、不同密度、不同立地条件的落叶松人工林作为研究对象,基于10块标准地中50株样木所获得的死枝和节子大小数据,利用数据统计分析模型和模拟技术,分析并研究了节子大小在树干上的分布规律,并构建了节子大小的预测模型。研究结果表明:1)节子直径随着节子着生高度的增加而持续增加,在树冠基部达到一渐进值,可用Mitscherlich方程对节子直径进行预测;2)在树高约40%处以下的健全节长度随着节子着生位置的增加而逐渐增加,然后再向上有减小的趋势;3)疏松节随着胸径的增大而增大,随着节子着生角度的增大而减小。     

Defect detection in lumber including knots using bending deflection curve: comparison between experimental analysis and finite element modeling

A new method has been developed for detecting localized defects such as edge knots using a bending deflection curve. The coordinates of a bottom edge (edgeline) of an unloaded piece of lumber are extracted from a digital image, and a bending deflection curve is obtained from the displacement of the edgeline of the lumber using a digital image correlation (DIC) technique. Depending on the knots within the beam, the bending deflection curve is shifted from the curve of a defect-free beam. The measured bending deflection curve is regressed to a theoretical curve by elementary beam theory. A finite element method (FEM) model of the beams including defects as simplified knot structure has been performed. Comparison between the bending experiment and FEM analysis shows that cross-sectional reductions cause characteristic variations in the bending deflection curves depending on the position of encased knots, and local grain distortions cause variations in the curves depending on the direction of spike knots. Using the residual variance between the measured deflection curve and a polynomial regression curve, it is possible to detect knots at which failures initiate. Part of this article was presented at the 57th Annual Meeting of the Japan Wood Research Society, Hiroshima, Japan, August 2007     

Predicting Log Type and Knot Size Category Using External Log Shape Data from a 3D Log Scanner

The use of new technology such as 3D scanners for measuring log shape at sawmills is increasing. These scanners can provide a detailed model of log shape and evenness of the log mantle area. These data can be used for optimizing yield but also for predicting quality of the sawn goods. This report presents a model for quality - related log features based on data from a 3D log scanner. The model includes routines for development of variables related to four categories of log properties: surface unevenness, log taper, cross - sectional out - of - roundness and straightness. In total, 230 Scots pine (Pinus sylvestris L.) logs are used for developing and validating logistic regression models for sorting logs into classes of log type and knot size. The models are tested on validation data: 97% of the logs were sorted correctly according to log type and 79% were sorted correctly according to knot size.     

Predicting knottiness of Pinus sylvestris for use in tree bucking procedures

Abstract

The quality and accompanying value of Scots pine (Pinus sylvestris L.) lumber varies markedly in the Nordic countries. Consequently, tree bucking has a great impact on the economic result. The objectives of this study were (1) to analyse which knot characteristics usually reduce pine lumber sawn from the butt-logs to the next quality grade, (2) to determine the most appropriate characteristics that could be used in predicting lumber quality, and (3) to develop models that could be used in practice for predicting the probability of certain quality grades. The study was based on field experiments and test sawing data on 100 Scots pine stems from south-western Finland. Since the results showed that the maximum dead knot is the most crucial knot characteristic when the first cross-cutting point is determined, models were created that predict the height of the first dead knot that lowers grade A to B (B-grade dead knot). Both early growth rate and dead branch height should be measured to predict pine butt-log quality. Early growth rate seems to be appropriate in predicting between-stand variation, while dead branch height is appropriate for predicting within-stand variation.