Calibration of non-contact ultrasound as an online sensor for wood characterization: Effects of temperature, moisture, and scanning direction
Source abstract
Abstract
Numerous handheld moisture meters are available for measuring moisture levels of wood and building materials for a vast range of quality control and moisture diagnosis applications. However, many methods currently available require physical contact of a probe with the test material to operate. The contact requirement of such devices has limited applications for these purposes. There is a tremendous demand for dynamic online quality assessment of in-process materials for moisture content (MC) measurements. In this paper, a non-destructive non-contact ultrasound technology was used to evaluate the effects of increasing temperature in two MC levels and of increasing MC in lumber. The results show that the ultrasonic absolute transmittance and velocity parameters are directly correlated very well (R2≥0.87) with temperature for the two moisture levels in wood. At constant temperature, however, the velocity is inversely correlated with MC. It was also found that the distribution of MC along the length is marginally insignificant to both ultrasonic measurements. The transmittance measurement along the orthogonal thickness direction is insignificant above the fiber saturation MC; similarly, the velocity measurement is marginally insignificant. The study concludes a positive correlation and a good fit for this technology to advance into the development of an automated device for determining wood moisture levels, which will in turn be used to control the dynamics of wood drying/sterilization processes. Further calibration research is recommended to ascertain the constraints and limitations of the technology to specific wood species and dimension.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Wood Treatment and Properties · Ultrasonics and Acoustic Wave Propagation · Industrial Vision Systems and Defect Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (21)
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