基于射线分割的林木应力波断层成像算法

doi:10.11707/j.1001-7488.20210918

Abstract

Abstract:

Objective: Stress wave nondestructive testing technology has been widely applied in wood and tree defect detection. The traditional stress wave imaging algorithm has low accuracy due to the limited sensor number. To improve the accuracy of the stress wave imaging algorithm and accurately reflect the position, size and decay of the internal defects of wood, the signal distribution of the stress wave on the cross section was investigated. Method: Four log samples(Pinus spp., Sapium sebiferum) and four live trees(Cinnamomum camphora, Sabina chinensis, Salix spp.) in Jiangnan University campus were selected as the samples for experiments, and the stress wave velocities were collected using FAKOPP instrument. Firstly, the stress wave velocities were corrected, and the wave ray diagram was generated, then the cross section was divided into many grid cells. Secondly, each stress wave ray was segmented, and the velocity of multiple segments on the ray was estimated, therefore the stress wave signal amount was increased and an improved stress wave ray diagram was also obtained. The velocity values of the grid cells were estimated using the improved propagation rays and the image processing method, thus the tomographic image of wood was generated. The Resistograph micro-drilling tool was used to evaluate the internal conditions of the live trees. Result: Experimental results showed that the proposed algorithm could accurately reconstruct the tomographic images of the four log samples. The Resistograph micro-drilling tool was used to perform multi-path drilling on live trees to get the resistance curves. Compared with the resistance curves, it was demonstrated that the proposed algorithm could also generated the tomographic images with a high resolution of 4 live trees. Conclusion: Based on the ray segmentation, the proposed algorithm might improve the accuracy and correlation of the initial grid cell velocity value, which could achieve high-quality imaging of wood defects, and could be effectively applied to nondestructive testing of wood.

Key words: stress wave, non-destructive testing, ray segmentation, acoustic tomography

CLC Number:

S781.5

Tao Liu,Guanghui Li. Stress Wave Tomography Imaging Algorithm Based on Ray Segmentation for Nondestructive Testing of Wood[J]. Scientia Silvae Sinicae, 2021, 57(9): 181-192.

Figures/Tables 13

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样本 Sample	样本高度 Sample height/cm	直径 Diameter/cm	含水率 Moisture content(%)	测量高度 Measuring height/cm	缺陷尺寸 Decay size/cm	传感器数量 Number of sensors
雪松1 Cedrus deodara 1	30	27.6	12.5	25	健康Heath	10
雪松2 Cedrus deodara 2	45	30.0	12.0	35	16	12
乌桕Sapium sebiferum	25	30.0	13.1	20	9	12
红松Pinus koraiensis	40	30.0	24.3	30	10	12

样本 Sample	周长 Circumference/cm	测量高度 Measuring height/cm	含水率 Moisture content(%)	基本信息 General information
圆柏1 Sabina chinensis 1	138	100	13.3	树木表面存在一些凸起不平处，部分树皮已经枯死 There are some bumps on the surface of the tree, and part of the bark has died
圆柏2 Sabina chinensis 2	157	150	16.5	树木表面存在一条较大的裂缝 There is a large crack on the surface of the tree
香樟 Cinnamomum camphora	150	150	15.7	树木外观较为健康，仅有一条较小的裂缝 The appearance of the tree is relatively healthy, with only a small crack
柳树 Salix spp.	120	50	18.9	树干周围有真菌引起的异常 An abnormality caused by a fungus around the trunk

样本 Sample	实际缺陷面积 Actual defect area/cm²	HWPM缺陷面积 Defect area of HWPM/cm²	RSIA缺陷面积 Defect area of RSIA/cm²
雪松1 Cedrus deodara 1	0	0	0
红松 Pinus koraiensis	69	127	78
乌桕 Sapium sebiferum	46	85	59
雪松2 Cedrus deodara 2	164	193	151

树种 Tree species	路径 Path	l₀/cm	HWPM		RSIA
树种 Tree species	路径 Path	l₀/cm	l₁/cm	Δl(%)	l₂/cm	Δl(%)
圆柏1 Sabina chinensis 1	1-7	35	38	8.6	33	5.7
圆柏1 Sabina chinensis 1	4-10	26	22	15.3	23	11.5
圆柏2 Sabina chinensis 2	6-12	4	0	100.0	0	100.0
	7-12	14	9	35.7	11	21.4
	10-5	14	16	14.3	12	14.3
香樟 Cinnamomum camphora	1-7	2	2	0	1	50
	3-10	0	2	—	0	0
	6-2	3	5	66.7	1	66.7
柳树 Salix spp.	3-7	1	1	0	1	0
柳树 Salix spp.	5-9	1	0	100.0	0	100.0

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Stress Wave Tomography Imaging Algorithm Based on Ray Segmentation for Nondestructive Testing of Wood

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