Quantitative detection of log defects based on stress wave propagation velocity model

Wei Xiwen; Sun Liping; Xu Shuzheng; Yang Yang; Du Chunxiao

doi:10.12171/j.1000-1522.20190420

Journal of Beijing Forestry University > 2020 > 42(5): 143-154. > DOI: 10.12171/j.1000-1522.20190420

Wei Xiwen, Sun Liping, Xu Shuzheng, Yang Yang, Du Chunxiao. Quantitative detection of log defects based on stress wave propagation velocity model[J]. Journal of Beijing Forestry University, 2020, 42(5): 143-154. DOI: 10.12171/j.1000-1522.20190420

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Quantitative detection of log defects based on stress wave propagation velocity model

1.
College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, Heilongjaing, China
2.
Heilongjiang University of Technology, Jixi 158100, Heilongjiang, China
3.
Harbin University, Harbin 150040, Heilongjaing, China
4.
Harbin Veterinary Research Institute, Harbin 150040, Heilongjaing, China

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Received Date: November 03, 2019
Revised Date: December 01, 2019
Available Online: May 03, 2020
Published Date: June 30, 2020

Graphical Abstract

Abstract

Abstract

ObjectiveThis paper aims to study the variation of stress wave propagation velocity in the longitudinal section of trees at different angles, and to establish the corresponding propagation velocity model, so as to further understand the propagation law of stress wave in the longitudinal section of trees at different angles, and to provide theoretical and experimental basis for the two-dimensional imaging technology of internal defects of logs.
MethodFirstly, through theoretical analysis, the propagation velocity model of stress wave in longitudinal section of logs with different directions was established. Then, four representative tree species in the northeastern region of China were taken as test samples, and the propagation velocity of stress wave in longitudinal section of logs with different directions, angles of different sections and angles of different directions was measured by stress wave wood nondestructive testing instrument. The relationship of healthy samples between stress wave propagation velocity $v\left(\alpha \right)$ and direction angle α, propagation velocity $v\left(\beta \right)$ and longitudinal section angle β, $v\left({\alpha,\beta } \right)$ and α, β were obtained, respectively by regression analysis.
ResultIn the same longitudinal section, the propagation velocity of stress wave increased with the increase of direction angle, and the velocity of the horizontal direction was the smallest. At the same direction angle, the propagation velocity of the stress wave increased with the increase of longitudinal section angle, and the propagation velocity of radial direction was the greatest. The fitting results of the health sample test data were in good agreement with the theoretical mathematical model. The determination coefficient R² was all greater than 0.87, and the significance P was less than 0.01. The models had higher goodness of fit. For the larch log samples, the cavity defects with diameters of 7.5 cm were designed artificially, and the two-dimensional imaging was performed using the healthy multiple regression model $v\left({\alpha,\beta } \right) = 109.2{\alpha ^2} - 182.1{\beta ^2} +$ $36.78{\alpha ^2}{\beta ^2} - 34.76{\alpha ^2}{\beta ^4} + 1 \; 627$ with correlation coefficient R²of 0.97 and root mean square error RMSE of 17.81. When the propagation path of stress wave was located in the healthy area of the wood, the variation trend of the propagation velocity with the direction angle and longitudinal section angle fitted the model; but when the stress wave passed through the defective area of the wood, the propagation velocity was significantly reduced, no longer in normal condition. Based on the results of two-dimensional imaging, the fitness of the images was 92.06%, and the error rate of measuring defect cavities was 8.63%.
ConclusionThe analysis showes that the regression model of stress wave propagation in different longitudinal sections of healthy logs is in good agreement with the theoretical model proposed in this paper, and further verifies that the velocity model has a good guiding role in the detection of internal defects of healthy logs.
- nondestructive testing of wood,
- stress wave,
- longitudinal section with different direction angles,
- imaging technology

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References (20)

References

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Quantitative detection of log defects based on stress wave propagation velocity model