Study On The Wood Physical Properties And Defect Detection Based On CT And Fractal Characteristics

Forest is very important resources for human. Our forest has been seriously harvesting over the years. Due to the reduction of forest resources, timber supply can not meet the actual demand. In order to achieve the sustainable development of timber, we must understand the tree growth law, actively explore silvicultural measures to strengthen the structure and properties of wood, improve the comprehensive utilization rate, recycling rate and reasonable selection, and save timber.With the development of non-destructive testing technology and computer technology, wood nondestructive testing technology developed towards intelligent and automated direction. The first condition of development was to test various physical properties of wood without destroying wood in any conditions. In the article a new method of quickly testing the physical properties of wood was explored without damaging itself. X-ray computed tomograghy (CT) technology was used to test the physical properties of wood. Combined with the fractal theory CT technology was used to detect internal log defects. Based on this study the following researches were done:(1) Wood was tested with the CT machine:After exposuring several times, scanning image of log cross-sectional ware obtained, and then scanning conditions for detecting timbers by CT machine were determined:scanning voltage 80KV-100KV, scanning current 30mA-80mA, scanning thickness according to actual needs:5mm-10mm. CT number was got from clear timber CT images. The upper and lower limits were determined according to the CT number with the statistical mathematical method. The sizes of window width and window level were determined, and the average CT number was calculated.(2) The 15 species (14 representative north species and one is south tree species) wood were selected such as Pinus koraiensis, Fraxinus mandshurica, Larix gmelini etc.. They were processed into be tested, and then divided them into two groups. One group is for experiment, another group for checking. The specimens were scanned by CT machine, and the average CT number of each sample was calculated. Air-dry density of each of specimens was measured with hydrostatic balance method. Let the average air-dry density of each sample match with their average CT numbers, and mathematical equations was obtained with the practical statistical software STATISTICA 6.0. This mathematical model was used to predict the density of another specimens group. The results showed that:predicted value and actual measurements were in good agreement. The correlation coefficients were above 0.9. It is indicated that CT number of each specimens is significantly correlated to the average air-dry density.(3) The four species, a northern common and widely spread with the north representative were selected. They were Fraxinus mandshurica, Pinus koraiensis, Betula platyphylla and Ulmus pumila. They were processed into specimens from four species. Each species was one group. They were divided into four groups. Each tested specimens was divided into two parts. Half was for the experiment, the other for the validation. First, the quality of the sample was measured. Then samples were scanned by CT to calculate the average CT number. Finally, each sample was measured with moisture content compared with the traditional method of measuring moisture content. Let the moisture content of each sample matched with their average CT numbers, and mathematical equations were obtained with the practical statistical software STATISTICA6.0. Four species, the ash, elm, birch and pine, their CT number and moisture content models were obtained. The results showed good fitting with quadratic equations with correlation coefficients above 0.9. Calibration blocks were used to do experiment. The results showed that:predicted value and actual measurements moisture content were in good agreement. The results illustrate that the mathematical equation can be used to predict the moisture content.(4) Log CT image processing system was researched and developed in WINDOWS environment. In addition to the traditional image processing functions included, the system loads the fractal characteristics of detection, including those based on the fractal dimension of Brownian motion and the fractal intercept characteristics of detection. The greatest advantage of this system is:It can demonstrate the maximum fractal characteristics of log defects from the CT images.In the testing of log defects, three defect images are processed inside the logs with the three typical defects:cracks, knots, insect eye. Three CT images were respectively processed by Brown fractal dimension feature parameters, fractal intercept characteristic parameter and the joint formation of Brown fractal dimension with fractal intercept characteristic parameters. Experimental results showed that three characteristic parameters could be used to detect the defects. And, the contrast was enhanced in the defect edges and background by Brown fractal dimension combined with fractal intercept features. Images of edge detections were clearer than the other testing operators, such as the method of Robert operator, Sobel operator and other traditional operator detections or separate detection of fractal parameters. The defect edges were continuous and few pseudo-edges. It showed that the combination of fractal dimension and fractal intercept parameter could well reflect the features of the defect edge from the CT image. The method provides a new effective measure to detect the defects of the edge in the case of preserving the shape, structure and dynamic state of the log. It provides reasonable and accurate information for select of wood.In a word, the results show that:selecting appropriate parameters of CT machine can get clear wood CT image when testing wood. Wood density and wood moisture content can be predicted from the CT number. The predicted results were consistent with the measured results. CT images were with more clear edge of wood defects after processed fractal parameters. Therefore, the following conclusions we can get from experimental results:Detection of wood density, moisture content and image processing are successful based on CT technology and fractal parameter. Besides by detecting wood density and moisture content without destroying the wood, we can also get clearer images of the inside log defect. The article provides a new method for the wood products processing, reasonable selection, effective using of wood.