Automated Planning and Optimization of Lumber Production Using Machine Vision and Computed Tomography

Abstract

An automated system for planning and optimization of lumber production using Machine Vision and Computed Tomography (CT) is proposed. Cross-sectional CT images of hardwood logs are analyzed using machine vision algorithms. Internal defects in the hardwood logs pockets are identified and localized. A virtual in silico 3-D reconstruction of the hardwood log and its internal defects is generated using Kalman filter-based tracking algorithms. Various sawing operations are simulated on the virtual 3-D reconstruction of the log and the resulting virtual lumber products automatically graded using rules stipulated by the National Hardwood Lumber Association (NHLA). Knowledge of the internal log defects is suitably exploited to formulate sawing strategies that optimize the value yield recovery of the resulting lumber products. A prototype implementation shows significant gains in value yield recovery when compared with lumber processing strategies that use only the information derived from the external log structure.