Identification of selected defects in beech timber by CT scanning

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Abstract

This study deals with the application of computed tomography (CT) for the design, application, and analysis of a methodology for identifying selected qualitative characteristics of unedged beech timber ( Fagus sylvatica L.). In the context of growing interest in the use of hardwood in load-bearing structures, a methodology has been proposed that enables non-contact assessment of internal defects such as knots, cracks, and fibre deviation. CT scanning has demonstrated the ability to visualize local density variations that correlate with the occurrence of quality defects. Calibration using seven wood species of varying density confirmed a linear relationship between CT image intensity and wood density (r = 0.998). The results show that CT technology has potential not only for sorting roundwood but also for evaluating sawn timber after cutting, thereby contributing to the optimization of production and increasing the added value of wood products. Density-based defect identification provides new opportunities for automated grading using machine learning.

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