Pan Shen, Chang Zhanyuan
College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China.
College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China.
Sensors (Basel). 2024 Aug 28;24(17):5572. doi: 10.3390/s24175572.
As a mature non-destructive testing technology, near-infrared (NIR) spectroscopy can effectively identify and distinguish the structural characteristics of wood. The Wood Defect One-Dimensional Visual Geometry Group 19-Finite Element Analysis (WD-1D-VGG19-FEA) algorithm is used in this study. 1D-VGG19 classifies the near-infrared spectroscopy data to determine the knot area, fiber deviation area, transition area, and net wood area of the solid wood board surface and generates a two-dimensional image of the board surface through inversion. Then, the nonlinear three-dimensional model of wood with defects was established by using the inverse image, and the finite element analysis was carried out to predict the elastic modulus of wood. In the experiment, 270 points were selected from each of the four regions of the wood, totaling 1080 sets of near-infrared data, and the 1D-VGG19 model was used for classification. The results showed that the identification accuracy of the knot area was 95.1%, the fiber deviation area was 92.7%, the transition area was 90.2%, the net wood area was 100%, and the average accuracy was 94.5%. The error range of the elastic modulus prediction of the three-dimensional model established by the VGG19 classification model in the finite element analysis is between 2% and 10%, the root mean square error (RMSE) is about 598. 2, and the coefficient of determination (R2) is 0. 91. This study shows that the combination of the VGG19 algorithm and finite element analysis can accurately describe the nonlinear defect morphology of wood, thus establishing a more accurate prediction model of wood mechanical properties to maximize the use of wood mechanical properties.
作为一种成熟的无损检测技术，近红外（NIR）光谱能够有效识别和区分木材的结构特征。本研究采用木材缺陷一维视觉几何组19-有限元分析（WD-1D-VGG19-FEA）算法。1D-VGG19对近红外光谱数据进行分类，以确定实木板材表面的节疤区域、纤维偏差区域、过渡区域和净木区域，并通过反演生成板材表面的二维图像。然后，利用反演图像建立含缺陷木材的非线性三维模型，并进行有限元分析以预测木材的弹性模量。实验中，从木材的四个区域各选取270个点，共1080组近红外数据，并使用1D-VGG19模型进行分类。结果表明，节疤区域的识别准确率为95.1%，纤维偏差区域为92.7%，过渡区域为90.2%，净木区域为100%，平均准确率为94.5%。VGG19分类模型在有限元分析中建立的三维模型弹性模量预测误差范围在2%至10%之间，均方根误差（RMSE）约为598.2，决定系数（R2）为0.91。本研究表明，VGG19算法与有限元分析相结合能够准确描述木材的非线性缺陷形态，从而建立更准确的木材力学性能预测模型，以最大限度地利用木材力学性能。
