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

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...

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Bibliographic Details
Published in:IEEE transactions on automation science and engineering 2008-10, Vol.5 (4), p.677-695
Main Authors: Bhandarkar, S.M., Xingzhi Luo, Daniels, R.F., Tollner, E.W.
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Applied sciences
Artificial intelligence
Automated lumber grading
automated lumber production
Automation
Biological and medical sciences
Computed tomography
computed tomography (CT)
Computer science
control theory
systems
Cross-disciplinary physics: materials science
rheology
Exact sciences and technology
Forestry
Fundamental and applied biological sciences. Psychology
Image analysis
Kalman filters
Lumber
lumber production optimization
Machine vision
Materials science
Materials testing
nondestructive evaluation
Optimization
Pattern recognition. Digital image processing. Computational geometry
Physics
Production planning
Production systems
Prototypes
Sawing
Three dimensional displays
Tomography
Vision systems
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Summary: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.
ISSN:1545-5955
1558-3783
DOI:10.1109/TASE.2008.925254