Skip to Main Content
Lumber defect detection abilities of furniture rough mill employeesAuthor(s): Henry A. Huber; Charles W. McMillin; John P. McKinney
Source: Forest Products Journal 35(11/12):79-82
Publication Series: Miscellaneous Publication
PDF: View PDF (939 KB)
DescriptionTo cut parts from boards, rough mill employees must be able to see defects, calculate the proper location of cuts, manually position the board, and remain alert. The objective of this study was to evaluate how well rough mill employees perform the task of recognizing, locating, and identifying surface defects independent of the calculation and positioning process. Using a scoring procedure developed for this study, it was found that six rough mill employees in three plants performed at about 68 percent of perfect. Thus, a computer vision system now under development need not be perfect to improve on current practice. The economic potential is considerable for such equipment if only a small yield improvement can be obtained.
- You may send email to firstname.lastname@example.org to request a hard copy of this publication.
- (Please specify exactly which publication you are requesting and your mailing address.)
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationHuber, Henry A.; McMillin, Charles W.; McKinney, John P. 1985. Lumber defect detection abilities of furniture rough mill employees. Forest Products Journal 35(11/12):79-82
- Impact of board-marker accuracy on lumber yield
- Performance of Color Camera Machine Vision in Automated Furniture Rough Mill Systems
- A comprehensive defect data bank for no. 2 common oak lumber
XML: View XML