Tomography in standing trees: revisiting the determination of acoustic wave velocity

Abstract

Context The quality of acoustic tomographic images in standing trees is mainly function of the accuracy of the acous- tic velocity computation. Improving the acoustic velocity determination is, furthermore, of great interest because acous- tic tools are widely used in nondestructive testing of wood. • Aims Four different signal processing algorithms were used (1) to study the effect of the signal dynamic on the velocity determination, (2) to determine the validity range of each computation method, and (3) to compare the behavior be- tween a homogeneous material and wood. • Methods The experiments were performed using the con- ventional experimental protocol for the ultrasonic characteri- zation of materials in a tank (normal incidence transmission at 500 kHz). A polyurethane resin (homogeneous material) and two wood species ( Bagassa guianensis Aubl., Milicia excelsa (Welw.) C.C. Berg) were used for the experiments. • Results Computed velocity increased as the noise level increased. The Hinkley method appeared to be the most exact when the noise level exceeded 10 dB. The Fisher method was that most suitable for very noisy signals. No difference was found between the resin and the wood samples. • Conclusion A combination of the Fisher and Hinkley methods in the same algorithm would yield the most accurate acoustic velocity determinations in the tomography of stand- ing trees. Key message Wood acoustic velocity determination is affect- ed by the wavelength and the detection algorithm used. The Fisher algorithm is optimal with high signal attenuation; oth- erwise, the Hinkley algorithm should be used. (Résumé d'auteur

Validation of strength and flexibility of sengon (Paraserianthes falcataria) by experimental tree-pulling test and numerical simulation

“…A number of methods for such a purpose have been adopted worldwide. Besides the well-practiced yet inadequate visual inspection, the use of a more advanced acoustic tomography [1][2][3], ultrasonic tomography [4][5], have also been reported. Another widespread assessment method is the tree-pulling test which dates to several decades ago.…”

Ultrasonic imaging of standing trees: factors influencing the decay detection

“…The number of sensors, for example, is directly related to the spatial resolution of the image; a larger number of sensors allows a smaller defective area detection [11,12]. The excitation signal used for testing is directly related to the precision of the TOF estimation, affecting the image reconstruction method [13,14]. This image reconstruction method must be adapted to the wood anisotropy condition, in order to avoid the bias introduced by the isotropic hypothesis considering straight-line trajectories [15,16].…”

Velocity Error Correction Based Tomographic Imaging for Stress Wave Nondestructive Evaluation of Wood

“…The resolution of the image increases if the applied frequency is raised, the number of sensors is increased, or the applied algorithm is more advanced. In addition, some sonic tomography equipment (e.g., FAKOPP ArboSonic 3D, PiCUS Sonic Tomograph, and Arbotom) has been developed to conduct multipath stress wave testing, and these products can generate 2D or 3D tomograms of the tree trunk (Arciniegas et al 2015).…”

“…Their results showed that the proposed method performed well and can resist the signal interference caused by the density variation of the defective area. Arciniegas et al (2015) used four different signal processing algorithms to study the effect of the signal dynamic on the velocity determination and to determine the validity range of the signal processing techniques. They found that the computed velocity increased as the noise level increased, and they suggested that a combination of the Fisher and Hinkley methods (Fisher 1912;Hinkley 1971) in the same algorithm would yield the most accurate acoustic velocity determinations in the tomography of standing trees.…”