基于纤维角预测的针叶材抗压弹性模量建模方法
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  • 英文篇名:Modeling method for compressive elastic modulus of softwood based on fiber angle prediction
  • 作者:张怡卓 ; 侯弘毅 ; 潘屾
  • 英文作者:Zhang Yizhuo;Hou Hongyi;Pan Shen;Electrical and Mechanical College,Northeast Forestry University;
  • 关键词:抗压弹性模量 ; 管胞效应 ; 纤维角检测 ; 神经网络
  • 英文关键词:compressive modulus of elasticity;;tracheid effect;;fiber angle detection;;neural network
  • 中文刊名:BJLY
  • 英文刊名:Journal of Beijing Forestry University
  • 机构:东北林业大学机电工程学院;
  • 出版日期:2018-05-15
  • 出版单位:北京林业大学学报
  • 年:2018
  • 期:v.40
  • 基金:国家林业局948项目(2015--4--52);; 中央高校基本科研业务费专项资金项目(2572017DB05);; 黑龙江省自然科学基金项目(C2017005)
  • 语种:中文;
  • 页:BJLY201805012
  • 页数:7
  • CN:05
  • ISSN:11-1932/S
  • 分类号:107-113
摘要
【目的】针叶材的管胞不仅具有输导养分的作用,而且具有较强的支撑能力,其管胞分布状态影响着木材力学特性。探究管胞分布状态与木材力学特征的内部关系对实现木材抗压弹性模量的预测有重要意义。【方法】本研究从针叶材管胞效应入手,设计了一套集光源发射、光斑采集与分析、木材遍历为一体的纤维角检测平台,构建了木材纤维角分布与其抗压弹性模量的数值关系模型。首先,利用最小二乘法拟合投射在木材表面激光光斑的椭圆轮廓,完成纤维角测量;然后,通过分析纤维角测量误差,选用系数为20的均值滤波方法以提高纤维角测量精度;通过对木材遍历采样,完成纤维角分布的采集;最后,以木材两个面上纤维角分布的均值、潜入系数与标准差为输入,以试样的抗压弹性模量为输出,构建了6输入1输出的4层神经网络,完成抗压弹性模量的预测。按照GB/T15777—1995《木材顺纹抗压弹性模量测定方法》加工了落叶松试样100个,应用检测平台采集了相应试件的纤维角分布后,采用力学试验机得到对应力学真值,按照3∶1的比例划分训练样本与测试样本。【结果】平均滤波次数选取20时,该设备纤维角采集测量误差达到0.65°以下;分别构建了以双面纤维角分布特征、单面纤维角分布特征以及双面纤维角分布特征均值为输入,抗压弹性模量为输出的网络预测模型。实验比较发现:以双面纤维角分布特征为变量的网络模型预测精度上优于其他两组,此时网络预测的抗压弹性模量准确率达到90.80%。【结论】应用纤维角分布特征可以实现针叶材抗压弹性模量的有效预测。最小二乘拟合与均值滤波法的结合可以有效、准确地表达纤维角的特征信息。纤维角的均值、潜入系数与标准差可以有效描述纤维角的分布特征。在构建木材抗压弹性模量时,木材双面的纤维角分布特征对其抗压弹性模量预测精度最高。
        [Objective] The tracheid of softwood not only has the function as a medium of nutrient transportation,but also is a strong support to the trees,and its state has close relationship to the mechanical properties of timber. The investigation of internal relationship between the distribution of tracheid and the mechanical properties of wood is of great significance for the prediction of compressive elastic modulus of wood. [Method] This paper starts with the tracheid effect of coniferous timber and introduces a set of detection platform covering the functions of light source,spot collection,spot analysesand plate traversal to build the numeric relationship between fiber angle distribution and compressive elastic modulus. First,least square method was used to fit the ellipse contour of the spots to measure the fiber angle; second,by analyzing the measurement error of fiber angle,a filtering method with mean value of 20 was selected to improve the accuracy of fiber angle measurement,and then the collection of fiber angle was completed after a traversal sampling. Finally,taking the mean value,diving coefficient and standard deviation of fiber angle distribution on the two surfaces of the plate as input,and the compressive modulus of the sample as output,a four-layer neural network with 6 inputs and 1 output was constructed to predict the compressive elastic modulus. To testify the effect of the study,100 samples of Larix gmelini were processed in accordance with the requirement of GB/T 15777—1995,the National Standard of Compressive Modulus of Elasticity,and divided into training and testing samples with proportion of 3∶ 1 after collecting their fiber angle and mechanic truth value with the detection platform and testing machines. [Result] The results of the experiment revealed that when the average frequency of filtering was 20,the measurement error of the fiber angle acquisition was less than 0. 65 degrees and the same time,the precision of compression modulus of the network prediction could reach 90. 80%.[Conclusion]The compressive elastic modulus of the softwood can be predicted by collecting the fiber angle distribution. The combination of the least squares and filtering method can effectively express the characteristic information and ensure the measurement precision of the fiber angle. The mean value,diving coefficient and standard deviation can effectively describe the distribution characteristics of the fiber angle. By selecting different features as input,the elastic modulus prediction accuracy is directly affected. In the experiment of this paper,with the double-sided feature as input,the elastic modulus prediction accuracy is the highest.
引文
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