WOOD RESEARCH Volume 70, Number 4, 2025
JINGGUI WANG, MING LI, SAIYIN FANG, TINGTING DENG, JIAXIN GUO, ERZHUANG ZHAI, and WENTAO XU

THE STUDY ON THE DAMAGE CHARACTERISTICS OF PINUS SYLVESTRIS VAR. MONGOLICA UNDER DIFFERENT MOISTURE CONTENTS

This study investigated the effects of moisture content (0%, 12%, 20%, 30%, 40%, 50%) on the mechanical behaviour and damage evolution of Pinus sylvestris var. mongolica three-point bending tests. A Weibull distribution model quantified damage progression, extracting scale (λ) and shape (k) parameters. Results revealed moisture regulated failure mechanisms, Low moisture (≤20%) induced brittle fracture (high k, low λ) with tensile crack dominance and stress concentration. High moisture (≥30%) promoted ductility (low k, high λ) via enhanced λ, driven by shear cracks. Mixed cracks persisted across all stages. Moisture altered cell wall plasticization, inter-fibre friction, and interfacial bonding, modulating damage evolution. Weibull modelling effectively captured stage dependent damage variable trends. Increased moisture facilitated fibre slippage and interlayer shear through lubrication and cell wall softening.This work establishes quantitative links between moisture content, mechanical response, and micromechanical damage mechanisms in wood, providing insights for moisture dependent structural applications of engineered timber.

WOOD RESEARCH Volume 70, Number 3, 2025
ERZHUANG ZHAI, SAIYIN FANG, MING LI, TINGTING DENG, BOWEN ZHANG, JIAXIN GUO, KEFEI CHEN, and KUN DU

Research on clustering identification of acoustic emission events in the process of wood crack propagation using PCA

This study presents a methodology for feature extraction and identification of acoustic emission (AE) events during wood crack propagation utilizing Principal component analysis (PCA) and enhanced K-means clustering algorithm. Experimental setups included double cantilever beam (DCB) for mode I crack propagation analysis and three-point bending test for mixed-mode crack propagation assessment. Various AE parameters, such as amplitude, duration, absolute mean value, peak frequency, and frequency centroid, were computed. PCA applied for dimensionality reduction to extract principal components and eliminate redundant information. The optimal number of clusters was determined using a combination of the elbow method and the Davies-Bouldin index to classify damage modes. Results indicate that the principal components contribute to 88.5% and 92% of the variance in the two tests, respectively, yielding three distinct types of AE events in both crack propagation scenarios. Specifically, high-frequency, low-amplitude signals correspond to microcrack initiation; low-frequency, low-amplitude signals signify interface delamination; and high-amplitude, long-duration events indicate mode I opening macroscopic damage (high frequency) and mixed-mode macroscopic failure (low frequency).

WOOD RESEARCH Volume 70, Number 2, 2025
ZHIYING TIAN, SAIYIN FANG, MING LI, TINGTING DENG, ERZHUANG ZHAI, CHUMIN CHEN, and YONGCHUAN WU

THE ANISOTROPIC VELOCITY MODEL OF INTERNAL ACOUSTIC EMISSION SIGNALS IN ZELKOVA WOOD AND THE INFLUENCE OF HOLES ON THEIR PROPAGATION PROCESS

In this article, an acoustic emission (AE) source was generated through pencil-lead break (PLB). On Zelkova schneideriana specimens featuring central through-holes with diameters ranging from 8 to 16 mm, the time-difference-of-arrival (TDOA) in combination with the least squares fitting approach was utilized to establish an anisotropic model of the AE wave velocity within the wood as it varies with the detection angle. Then, a pulse signal was generated through a signal generator to analyze the influence of the holes on the peak values of AE signals at different angles. The results indicate that when the propagation angle is less than 80°, the AE wave velocity rises rapidly with the increase of the angle and eventually approximates the longitudinal wave velocity along the grain. The AE signal peak emerged in the direction approximately 30° for the specimen without holes. In the presence of holes, as the hole diameter increases, the variation trend of AE peak amplitudes within an angular range of -18° to +18° relative to the hole center becomes progressively smoother, with a concurrent reduction in their mean value. This distinct characteristic can serve as a robust indicator for identifying internal hole defects in wood

WOOD RESEARCH Volume 70, Number 2, 2025
YONGCHUAN WU, SAIYIN FANG, MING LI, TINGTING DENG, CHUMIN CHEN, and ZHIYING TIAN

ANISOTROPIC PROPAGATION CHARACTERISTICS OF ACOUSTIC EMISSION SIGNALS IN WOOD

This study investigates the propagation characteristics of acoustic emission (AE) signals in Zelkova schneideriana and Pinus sylvestris var. mongolic along different directions, with a focus on amplitude and frequency variations. Sinusoidal signals ranging from 10 to 400 kHz, along with pulsed signals of 1 μs width and 1 s period, were generated using an arbitrary waveform generator to simulate the AE source. Experiments were conducted on 80 mm cubic wood specimens, with the AE source and sensors positioned at the geometric centers of each surface. AE signals were recorded at a sampling rate of 2 MHz. The results indicate that, at the same frequency, the Zelkova schneideriana exhibits higher signal amplitude and energy than the Pinus sylvestris var. mongolic. Frequency response analysis further reveals that wood enhances the propagation of signals below 75 kHz, while significantly attenuating signals above 200 kHz in the transverse direction

WOOD RESEARCH Volume 70, Number 1, 2025
JINGGUI WANG, MING LI, SAIYIN FANG, TINGTING DENG, ERZHUANG ZHAI, CHUMIN CHEN, and KEFEI CHEN

RESEARCH ON WOOD DAMAGE FRACTURE CHARACTERISTICS BASED ON ACOUSTIC EMISSION RA-AF VALUE AND ENERGY CONCENTRATION

To study the acoustic emission (AE) characteristics and fracture properties of wood at different stress stages, three-point bending tests and real-time AE monitoring were carried out on Zelkova schneideriana and Pinus sylvestris var. in this paper. Different stress stages were classified according to AE ringing counts-cumulative AE ringing counts-load curves, damage modes of wood at different stages were identified based on distribution characteristics of RA-AF data, and fracture behavior of wood was predicted by energy concentration k. Results show that distribution characteristics of AE RA-AF data can characterize the types of cracks generated in each stress stage of wood. The crack modes generated by both specimens during three-point bending loading are tension shear composite cracks, and the proportion of tensile cracks is significantly higher than that of shear cracks, but during the elastic-plastic stage, Zelkova schneideriana specimens will produce a large number of shear cracks, whereas Pinus sylvestris var. specimens have predominantly tensile cracks, with only a small number of shear cracks produced before and after fracture. The sudden change in the energy concentration k curve between elastic-plastic deformation stage and fracture stage can be used as a precursor of damage for both specimens under three-point bending test conditions

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 68, Number 1, 2023
SAIYIN FANG, MING LI, Wei Li, Chang Lin Huang, TINGTING DENG, and KUN DU

Experimental analysis of acoustic emission propagation velocities and energy attenuation law of p and s waves in wood using improved TDOA measurements

To explore the propagation law of AE signal in wood, the propagation velocity of P-wave and S-wave and the energy attenuation law of different frequency components were studied By PLB (pencil-lead break) tests. Firstly, an improved time-difference-of-arrival (TDOA) method was designed to determine the arrive time. The propagation velocities of P-wave and S-wave were calculated. Then, the Young’s modulus was estimated by P-wave velocity. Finally, on the basis of eliminating the influence of standing wave, the energy attenuation models were obtained by numerical fitting and wavelet decomposition. The results showed that the improved TDOA algorithm can calculate the propagation velocity of P-wave and S-wave at the same time through one test, and the P-wave velocity can be used to estimate the Young’s modulus. P-wave propagated faster in soft wood, while S-wave propagated faster in hard wood. The higher the frequency of AE signal, the faster the energy attenuation.

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 67, Number 4, 2022
SAIYIN FANG, MING LI, TINGTING DENG, and KUN DU

Study on the time-frequency characteristics and propagation law of acoustic emission longitudinal waves in wood grain direction

To study the propagation law of acoustic emission (AE) longitudinal waves in wood, the relationship among wave velocity, standing wave fundamental frequency and Young’s modulus of elasticity was studied, and the energy decay model of AE longitudinal waves along the grain direction was established. Firstly, the propagation velocity of the longitudinal wave was calculated using the time-difference method. Then, the relationship between the wave velocity and Young’s modulus of elasticity was analyzed and the method of calculating the longitudinal wave velocity using the fundamental frequency was proposed. Finally, using different levels’ pulse strings as AE sources, the attenuation law of AE signal energy with distance was studied. The results show that the longitudinal wave velocity can be estimated more accurately by using the standing wave fundamental frequency. The influence of Poisson’s ratio needs to be considered when calculating the Young’s modulus of elasticity by using the longitudinal wave velocity.

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.