WOOD RESEARCH Volume 69, Number 3, 2024
CHUMIN CHEN, MING LI, SAIYIN FANG, JIALONG ZHAO, XIN ZHANG, FANGYONG LU, TINGTING DENG, and BO ZHANG

STUDY OF STRESS WAVE PROPAGATION PATH AND DEPTH IDENTIFICATION IN CRACKED WOOD BASED ON ACOUSTIC EMISSION AND COMSOLSIMULATION

The propagation velocity models were built using AE sensors to capture stress wave on pine specimen surface.On the different specimens, cracks were made in different numbers and the depth was gradually increased from 0 mm to 90 mm at 10 mm intervals. AE experiment was combined with COMSOL to investigate propagation path.The results show that R-squared is 0.996 when fitting tangent of angle to propagation velocity.At smaller crack depths, stress wave is diffracted around crack tip and then continues to propagate in to sensor along a straight line.However, as the crack depth increases, the reflected wave at the end face will arrive at the detection location faster with significantly weaker diffraction.The area with dimensions of20×10 mm was identified about the crack tip by crack identification method

WOOD RESEARCH Volume 69, Number 2, 2024
XIN ZHANG, MING LI, SAIYIN FANG, FEILONG MAO, LONGFEI YANG, YUE ZHAO, and GEZHOU QIN

EVALUATION OF WOOD DAMAGE AND FRACTURE BEHAVIOR BASED ON ENERGY ENTROPY OF ACOUSTIC EMISSION SIGNALS

In order to assess the damage and fracture behavior of wood under continuous loading, an energy entropy and b-value associated with the acoustic emission (AE) signal were defined to quantitatively describe the release of strain energy during loading. Firstly, the acoustic emission signals of the wood in the three-point bending test were collected. This paper presents the concept of energy entropy according to the definition of information entropy. In order to further evaluate the strain energy intensity released by the damage behavior of the wood specimen, the acoustic emission b-value was defined. Finally, by jointly analysing the dynamics of these two parameters, the test process can be divided into three phases. The results show that even in the elastic phase, micro-destructive behavior occur inside the wood specimen; in the plastic phase, the wood specimen is not only subjected to macroscopic damage, but also often accompanied by fine cracks inside

WOOD RESEARCH Volume 68, Number 4, 2023
FEILONG MAO, SAIYIN FANG, MING LI, GEZHOU QIN, YUE ZHAO, and Ning Xu

Study on Acoustic Black Hole Effect of Acoustic Emission Signals in Pinus sylvestris var. mongolica litv

The difference in density and wave velocity causes distinct wave impedance between air and wood, resulting in complex acoustic emission (AE) signals due to reflection on the wood’s surface. This study explores the suppression of AE signal reflection by modifying the structure of thin wood panels, utilizing the theory of acoustic black holes (ABH). Initially, a one-dimensional ABH structure was created by forming a wedge structure on one side of the specimen. Pencil-lead break (PLB) tests simulated sudden AE sources on the specimen’s surface. AE signals were collected using three equidistant sensors on the upper surface, with a sampling frequency of 2 MHz. The AE signal was then segmented into frequency bands using the differential method and analyzed in both time and frequency domains. Comparisons were made to understand the impact of the one-dimensional ABH on AE signal propagation. Results demonstrated that the one-dimensional ABH effectively suppressed AE signal reflection on the wood’s surface, reducing the high-frequency components by 18.31%, 20.83%, and 12.09% for each sensor, respectively. Furthermore, the experimental cut-off frequency of 0.98 kHz surpassed the theoretically calculated value of 0.39 kHz due to the disparity between the ABH structure’s thickness and the theoretical prediction.

WOOD RESEARCH Volume 67, Number 6, 2022
GEZHOU QIN, MING LI, SAIYIN FANG, TINGTING DENG, Changlin Huang, Zhouling Yang, FEILONG MAO, and YUE ZHAO

Study on the dispersion characteristics of wood acoustic emission signal based on wavelet decomposition

Artificial AE sources were generated on the surfaces of Ulmus pumila, Zelkova schneideriana, Cunninghamia lanceolata, and Pinus sylvestris var. mongolica Litv. specimens. The AE transverse wave signal was decomposed into 3-layers detail signals by wavelet decomposition and reconstructed, and it was calculated based on correlation analysis. Then the longitudinal wave speed was calculated according to the time-difference-of-arrival (TDOA) method, and the wood dispersion phenomenon was studied. The results showed that the dispersion phenomenon of Ulmus pumila was obvious. The propagation speed of high-frequency signal was 2.38 times that of low-frequency signal. The ratio of high and low frequency propagation speed of soft wood was 1.72 and 1.73. The dispersion degree of Zelkova schneideriana was the weakest, and the propagation speed of the high frequency was 1.25 times of the low one. The ratios of longitudinal and transverse wave speeds of the four specimens were 4.59, 4.07, 4.24 and 4.2, respectively.

WOOD RESEARCH Volume 66, Number 4, 2021
TINGTING DENG, Shuang Ju, Minghua Wang, and MING LI

Study on propagation law of acoustic emission signals on anisotropic wood surface

In order to explore the influence of wood’s anisotropic characteristics on Acoustic Emission (AE) signals’ propagation, the law of AE signals’ propagation velocity along different directions was studied. First, The center of the specimen’s surface was took as the AE source, then 24 directions were chose one by one every 15º around the center, and 2 AE sensors were arranged in each direction to collect the original AE signals. Second, the wavelet analysis was used to denoise the original AE signals, then the AE signals were reconstructed by Empirical Mode Decomposition (EMD). Finally, time difference location method was utilized to calculate AE signals’ propagation velocity. The results demonstrate that AE signals’ propagation velocity has obvious feature of quadratic function. In the range of 90º, as the angle of propagation direction increases, the propagation velocity of the AE signals presents a downward trend.

WOOD RESEARCH Volume 66, Number 1, 2021
Minghua Wang, MING LI, TINGTING DENG, SAIYIN FANG, Xiaosong Li, and Fei Lai

Study on lamb wave propagation characteristics along the grain of thin wood sheet

Through the time-frequency analysis of the propagation waveform of the acoustic emission (AE) signal propagating in the thin sheet of Pinus sylvestris var. mongolica, the propagation characteristics of the stress wave when propagating as a lamb wave was studied. An AE source was generated on the surface of the specimen, the discrete wavelet transform method was used to achieve AE signal de-noising and reconstruct the waveform of the AE signal. On this basis, the time difference positioning method was used to calculate the propagation velocity of lamb waves, and compared with the propagation characteristics of lamb waves in the metal specimen. The results show that the high-frequency mode of lamb waves attenuated sharply as they propagate in the thin wood sheet, indicating that the microstructure of wood has a significant low-pass characteristic for lamb waves. The average attenuation rates of lamb waves in metal and thin wood sheet were 87.1% and 75.7%, and the velocity was 4447.0 m.s-1 and 1186.3 m.s-1, respectively. This shows that AE signals can travel longer distances in the thin wood sheet, but the propagation velocity is significantly reduced.