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Microwave testing of moist and oven-dry wood to evaluate grain angle, density, moisture content and the dielectric constant of spruce from 8 GHz to 12 GHz

Aichholzer, Andreas, Schuberth, Christian, Mayer, Herwig, Arthaber, Holger
European journal of wood and wood products 2018 v.76 no.1 pp. 89-103
Picea, anisotropy, dielectric properties, industry, ovens, regression analysis, temperature, water content, wood, wood moisture, wood processing
The scope of this work is to discuss the challenges and demonstrate the potential of microwave testing for applications in the wood processing industry. Microwave technology benefits from the anisotropic dielectric properties of wood to simultaneously identify grain angle, density, and moisture content of wood. Therefore, the theory of free space transmission measurement is thoroughly discussed with emphasis on the characteristics of (and how to deal with) reflections occurring in real measurements. A more sophisticated calculation method for the derivation of the desired physical wood properties is presented. The advantages of a modern laboratory style setup are shown and its possible transition in an industrial-style application is discussed. Moist (moisture content 7.6–14%) and oven-dry spruce samples are tested. The detection of grain angle for moist and oven-dry wood yields an RMSE (root-mean-squared-error) of 0.14° and 0.4°, respectively. Moisture content is evaluated with density- and thickness-independent methods. Adapted regression models are proposed yielding an RMSE for moisture content of 0.45% for a single frequency measurement. The promising advantages of wood moisture estimation with frequency sweeps instead of fixed frequency signals are discussed and demonstrated for all samples (RMSE 0.39%). The dielectric constant of moist and oven dry spruce in the range from 8 to 12 GHz is evaluated in respect to density, moisture content and temperature. The respective constants ε′, ε′′, and tan(δ) are formulated in a general form via a non-linear regression and compared to existing data in literature.