Effect of knot detection inaccuracy on value recovery improvement when using a CT log scanner for sawing control

• Roundwood scanners utilizing X-ray computed tomography (CT) provide the information required for individual log-sawing optimization. However, errors in the automated detection of quality-relevant internal wood features for sawing control may lead to improper log positioning at breakdown, impairing the realization of value recovery potential. It is thus of interest to have an estimation of the impact of feature detection errors on the performance of sawing optimization. A sensitivity analysis was conducted to quantify the effect of errors in knot detection on a breakdown optimization by adjustment of log rotation. Therefore, sawing simulations were performed with the geometric descriptions of log shape and internal knots extracted from the CT scans of 57 Norway spruce (Picea abies (L.) Karst.) logs. Three types of artificially set knot description errors were tested under different pricing and product scenarios, each in different magnitudes as systematic or random error. Errors in knot diameter were found to have the greatest impact for both systematic and random errors. The effect of errors in dead knot border radial position was less pronounced but still substantial for higher error levels, while errors in knot rotational position could be neglected even for the highest magnitudes of error tested. The assumed price differentiation between product qualities had a major influence on the impact of the errors. It could be observed that with errors of higher magnitudes than those reported for present knot detection algorithms, an improvement in value recovery compared with outer-shape–based optimization still resulted in the simulated rotation optimization.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Maskinteknik -- Annan maskinteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Mechanical Engineering -- Other Mechanical Engineering (hsv//eng)

Keyword

Träteknik

Wood Technology

Publication and Content Type

ref (subject category)

art (subject category)