Authors:
Mehieddine Derbas, Timothy Mark Young, Stephan Frömel-Frybort, Hans- Christian Möhring & Martin Riegler
Contents:
- The moisture content in wood has a strong impact on the optimal process parameters during milling.
- In this work, wood milling was monitored by a novel optical microphone to distinguish between moisture classes (dried, conditioned, wet and frozen).
- Machine learning was applied to the selected spectral data to determine both the moisture classes and milling speed. The best model achieved an accuracy of 97.2%.
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