Abstract
Air-coupled ultrasound was used for assessing natural defects in wood boards by through-transmission scanning measurements. Gas matrix piezoelectric (GMP) and ferroelectret (FE) transducers were studied. The study also included tests with additional bias voltage with the ferroelectret receivers. Signal analyses, analyses of the measurement dynamics and statistical analyses of the signal parameters were conducted. After the measurement series, the samples were cut from the measurement regions and the defects were analyzed visually from the cross sections. The ultrasound responses were compared with the results of the visual examination of the cross sections. With the additional bias voltage, the ferroelectret measurement showed increased signal-to-noise ratio, which is especially important for air-coupled measurement of high-attenuation materials like wood. When comparing the defect response of GMP and FE sensors, it was found that FE sensors had more sensitive dynamic range, resulting from better s/n ratio and short response pulse. Classification test was made to test the possibility of detecting defects in sound wood. Machine learning methods including decision trees, k-nearest neighbor and support vector machine were used. The classification accuracy varied between 72 and 77% in the tests. All the tested machine learning methods could be used efficiently for the classification.
Highlights
IntroductionUltrasound and other acoustic techniques are widely used as nondestructive testing techniques for the detection of internal defects and strength determination of wood
Ultrasound and other acoustic techniques are widely used as nondestructive testing techniques for the detection of internal defects and strength determination of wood (Bucur 2003, 2005, 2011; Chimenti 2014; Fang et al 2017; Ross and Pellerin 2002; Department of Applied Physics, University of Eastern Finland, PO Box 1627, 70211 Kuopio, FinlandFederal Institute for Materials Research and Testing (BAM), Unter den Eichen 87, 12205 Berlin, GermanyWood Science and Technology (2020) 54:1051–1064Solodov et al 2004)
This study suggests that air-coupled ultrasound and machine learning may be efficiently used to detect natural defects in wood
Summary
Ultrasound and other acoustic techniques are widely used as nondestructive testing techniques for the detection of internal defects and strength determination of wood Wood defects affect the mechanical properties of wood and the propagation of ultrasound signal in wood. Delaminations cause additional reflections, which reduce the transmission signal. Decay, bark pockets, holes and wane cause variation in ultrasound signal compared to clear wood even in fresh-cut high moisture content (MC) state (Kabir et al 2002). Internal checks and surface cracks increase the ultrasound transmission time perpendicular to grain (Fuller et al 1994). The internal cracking in wood causes scattering of the ultrasound, whereas wetwood increases the viscoelastic damping, both resulting in attenuated sound signals (Schafer et al 1999)
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