Abstract: A method for characterizing undebarked wooden logs and computing optimal debarking parameters in real time is provided. The method comprises a scanning device upstream of a debarker for providing data, usually in the form of images, to a deep learning algorithm model. The model may be trained with human assistance or not to detect and identify, with an acceptable amount of certainty, characteristics of undebarked logs. The characteristics are used in an optimization software and classified in an index table. The index table is used to determine optimized parameters for debarking the log.

Abstract: A system and method for detecting the origin of wooden planks in a sawmill is provided. The method scans surfaces of processed planks and, with the help of an AI algorithm comprising a deep-learning algorithm, determines the origin of said planks based on analysed parameters of the planks. The parameters used in the analysis are mainly properties of tool marks and the resulting analysis provides tools and equipment used. The deep learning algorithm may be in a self-learning mode or in a supervised training mode.

Abstract: The present invention relates to a phase shifting debarker. The shifting debarker generally comprises a phase shifting mechanism being powered by a main motor and being connected to an operative assembly by the mean of timing belts. The main motor is being configured to control, by the mean of a belt, the movement of the phase shifting mechanism which is being adapted to control the movement of the operative assembly by the mean of the timing belts. The operative assembly generally comprises an actuator ring and a main ring. The activation of the shifting mechanism creates a shift phase between the actuator ring and the main ring while rotating at the same speed.