Single-Sided Microwave Near-Field Scanning of Pine Wood Lumber for Defect Detection

Radwan, Mohamed; Thiel, David V.; Espinosa, Hugo G.

doi:10.3390/f12111486

Cited by 12 publications

(4 citation statements)

References 18 publications

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“…Reference [25] used principal component analysis to detect wood defects, and the results showed that this method was applicable and reliable to identify wood defects. Ondrejka et al [26] developed an algorithm framework based on deep learning, established a convolutional neural network composed of eight layers of networks for feature extraction, and extracted wood surface defect features from a deep learning network model, thus improving the detection efficiency and classification accuracy of wood defects.…”

Section: Research Status Of Wood Nondestructive Testingmentioning

confidence: 99%

Wood Quality Defect Detection Based on Deep Learning and Multicriteria Framework

Ping-an

2022

Mathematical Problems in Engineering

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Traditional nondestructive testing technology for wood defects has a series of problems such as low identification accuracy, high cost, and cumbersome operation, and traditional testing methods cannot accurately show the specific location and size of wood internal defects; it is urgent to explore a new nondestructive testing scheme for wood defects. Aiming at this problem, this paper designs and develops an automatic detection method for wood surface defects based on deep learning algorithm and multicriteria framework. By comparing the performance of different deep learning detection methods on the data set, the advantages and disadvantages of the detection method in this paper are proved. After a series of works, such as the development and optimization of the experimental algorithm, the algorithm proposed meets the requirements in both the detection accuracy and training time.

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Section: Research Status Of Wood Nondestructive Testingmentioning

confidence: 99%

Wood Quality Defect Detection Based on Deep Learning and Multicriteria Framework

Ping-an

2022

Mathematical Problems in Engineering

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“…To reduce heat energy loss, the practical usage of eucalyptus requires the lowest moisture content. Based on these challenges, this study reviewed the antennas that can transmit and receive signals by electromagnetic waves through eucalyptus trunks that have been cut off [13][14][15][16][17][18] and to subsequently increase the efficiency of the receiving-transmitter power with EBG. The following literature on the structure of rectangular and cylindrical waveguide antennas used with the EBG was reviewed: Several studies have explored the structure of waveguide antennas of various shapes which employed EBG [19][20][21][22][23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

An Adjusted Waveguide Antenna with A Woodpile-Shaped EBG for Eucalyptus Wood Moisture Content Measurement

Naktong,

Puangnak,

Phanthuna

et al. 2024

Trends Sci

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A rectangular waveguide antenna was tested for eucalyptus wood moisture content (MC) measurement before being used. To improve the moisture content measurement of the rectangular waveguide antenna, a woodpile-shaped Electromagnetic Band Gap (EBG) was installed by Transverse Electric (TE) arrangement. This antenna is responsible for receiving and transmitting signals which are converted from electrical energy into radio frequency energy transmitted through the air. In this research, we take advantage of the wave propagation of antennas in the air and use them to propagate waves through wood materials. To compare signal transmission power to determine moisture content in wood and the benefit of the EBG is that it acts like a convex lens to increase the strength of the waves so that they can penetrate the wood more efficiently. The results showed that woodpile-shaped EBG could increase the efficiency of receiving and transmitting signals, reduce working hours for farmers and reduce costs by about 90 %. When this antenna was tested and actually used, it was found that the frequency band that responded best to the moisture content value was 2.20 GHz. This antenna was built with an aluminum material of size 9×4×15 cm3 and 2×6 units woodpile EBG on a 9.54×4 cm2 Polyester Mylar base plate. There was a gain of 7.81 dBi from the original structure, or by 20 %. At a radius of 4 cm, the values of moisture content ranged from –9.46 to –42.19 dBm. At a radius of 6 cm, the values were –9.41 to –42.89 dBm. At a radius of 8 cm, the values ranged from –9.39 to –43.01 dBm, respectively. All 3 values were found to be efficient. The size was not less than 84 % compared to the general standard meter. HIGHLIGHTS A rectangular waveguide antenna was tested for eucalyptus wood moisture content (MC) measurement before being used. To improve the moisture content measurement of the rectangular waveguide antenna, a woodpile-shaped Electromagnetic Band Gap (EBG) was installed by Transverse Electric (TE) arrangement. This antenna is responsible for receiving and transmitting signals which are converted from electrical energy into radio frequency energy transmitted through the air. In this research, we take advantage of the wave propagation of antennas in the air and use them to propagate waves through wood materials. To compare signal transmission power to determine moisture content in wood and the benefit of the EBG is that it acts like a convex lens to increase the strength of the waves so that they can penetrate the wood more efficiently. The results showed that woodpile-shaped EBG could increase the efficiency of receiving and transmitting signals, reduce working hours for farmers and reduce costs by about 90 %. When this antenna was tested and actually used, it was found that the frequency band that responded best to the moisture content value was 2.20 GHz. This antenna was built with an aluminum material of size 9×4×15 cm3 and 2×6 units woodpile EBG on a 9.54×4 cm2 Polyester Mylar base plate. There was a gain of 7.81 dBi from the original structure, or by 20 %. At a radius of 4 cm, the values of moisture content ranged from –9.46 to –42.19 dBm. At a radius of 6 cm, the values were –9.41 to –42.89 dBm. At a radius of 8 cm, the values ranged from –9.39 to –43.01 dBm, respectively. All 3 values were found to be efficient. The size was not less than 84 % compared to the general standard meter. GRAPHICAL ABSTRACT

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“…Internal defects such as knots, hollows, and decay can reduce the utilization rate of logs and harm the health of trees and wooden buildings [1,2]. These defects are difficult to detect because they are located inside the wood, so it is important to conduct nondestructive or semi-destructive testing and the imaging of internal defects in the wood [3,4]. Researchers have tried various techniques to qualitatively evaluate the features of trees and harvested wood.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Sensor Positions in the Stress Wave Tomography of Internal Defects in Hardwood

Du,

Zheng,

Feng

2024

Forests

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Stress wave tomography technology uses instruments to collect stress wave velocity data via sensors, visualizes those velocity data, and reconstructs an image of internal defects using estimated velocity distribution. This technology can be used to detect the size, position, and shape of internal defects in hardwood, and it has increasingly attracted the attention of researchers. In order to obtain enough stress wave signals, 12 sensors are usually equidistantly positioned around the cross-section of trunks like a clock. Although this strategy is reasonable and convenient, it is obviously not the optimal signal acquisition strategy for all defects. In this paper, a novel sensor position’s optimization method for high-quality stress wave tomography is proposed. The relationship between the shape of defects and the planar distribution of sensors is established by taking the ray penetration ratio and degree of equidistant distribution of sensors as indicators. Through the construction of the fitness function and optimization conditions, the optimal strategy for the planar distribution of sensors was determined using the Genetic Algorithm. Seven samples containing simulated defects and real tree trunks were used to test the proposed algorithm, and the comparison results show that the image of internal defects in hardwood can be reconstructed with high accuracy after optimizing the sensor positions.

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Single-Sided Microwave Near-Field Scanning of Pine Wood Lumber for Defect Detection

Cited by 12 publications

References 18 publications

Wood Quality Defect Detection Based on Deep Learning and Multicriteria Framework

Wood Quality Defect Detection Based on Deep Learning and Multicriteria Framework

An Adjusted Waveguide Antenna with A Woodpile-Shaped EBG for Eucalyptus Wood Moisture Content Measurement

Optimizing Sensor Positions in the Stress Wave Tomography of Internal Defects in Hardwood

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