Impact of human error on lumber yield in rough mills

Abstract

Rough sawn, kiln-dried lumber contains characteristics such as knots and bark pockets that are considered by most people to be defects. When using boards to produce furniture components, these defects are removed to produce clear, defect-free parts. Currently, human operators identify and locate the unusable board areas containing defects. Errors in determining a defect and its location, known as operator error, lead to lower lumber yield and increased product cost. Technology exists that would alleviate these problems and is a viable option to avoid wasting lumber because' of human error. This study was performed in a rough mill collecting data on the errors made by humans when marking defects. Computer-based simulation tools were used to assess the significance of these errors. It was found that three-quarters of the decisions made by human operators are erroneous in some way resulting in an absolute yield loss of approximately 16.1 %. Thus, automated defect detection systems that perform more accurately than do humans could have a payback period of 1 year or less. Published by Elsevier Science Ltd.

Economical interest of a X-rays vision system in a planing mill production chain

“…The family of finished products susceptible to be made in the thickness of the board. The evaluation of these criteria for every board is subject, because of the human operator, to errors of judgment [Buehlmann U., on 2002] linked to the hardness and the repetitiveness of the task without counting a very short time of analysis and decision. The study presented here, estimates the qualitative and quantitative impact that a system of internal vision can have in the definition of the zones of homogeneous quality.…”

Modeling Quality knowledge to design log sorting system by X rays tomography

“…System performances help human decisions and sometimes enable to replace him. They give a better reliability, repeatability, accuracy or/and production rate [Buehlmann U., 2002]. In another hand, those systems make it possible to access to information that naked eyes cannot detect.…”

A review of the automated timber defect identification approach

“…A review of the automated timber defect identification approach (Teo Hong Chun) 2157 yield [3]. A related study on the ability of furniture rough mill workers to spot wood defects also showed that the precision of the human operators was capped at an average of 68%.…”