|Abstract or Summary
- In recent years, the forest products industry has been
forced to cope with a supply of raw material that is
declining in both quality and quantity. In addition, log
prices are escalating rapidly. Because log costs may
represent as much as 90 percent of the overall production
costs of lumber (Williston, 1985), it is crucial that a mill
make every attempt to maximize grade and recovery.
Quality control (QC) is a broad term used loosely to
indicate producing a product to meet the customer's
Specifications. In lumber production, quality control is
heavily influenced by meeting lumber grade requirements
while minimizing fiber use. One component of these grade
requirements is conformance to size standards.
Size control is one of the functions of a mill's QC
department. Size control for lumber manufacturing involves
minimizing the variations in the dimensions of lumber that
are introduced by the sawing process.
An adequate size control program enables a sawmill to
maximize both grade and recovery. Grade may be maximized by
reducing the amount of boards downgraded due to either
excess wane or from planer skip that can result from
excessive sawing variation. Minimization of sawing variation can also lead to
maximizing recovery. It should be made clear that the
reduction in target size that can result from minimizing
sawing variation will most often result in a slightly longer
or wider board from any one log rather than obtaining
additional boards (Darwin, 1989). There are however certain
limited log diameter breakpoints at which additional boards
may be obtained due to decreased sawing variation.
What is proposed in this project is an automated system
to collect and analyze board measurement information using
optical scanning techniques. The system is intended to
provide feedback to mill quality control personnel. This
thesis has built upon the image processing and quality
control system developed by Aslam (1990) at the Department
of Forest Products at Oregon State University. The system
is discussed in Aslam's M.S. thesis as well as in the
article by Funck et al. (1992).
The image processing system scans the edges of boards
while still held in cant form after leaving a gang edger.
The system can provide lumber thickness as well as kerf
width data. A size control program is included to analyze
measurement data and provide information regarding sawing
A far more detailed representation of sawing variation
can be obtained using this system than is possible with
standard size control practices providing measurement
accuracy is sufficient. For instance, at gang edgers
immediate feedback regarding sawing variation for both
boards and kerf is provided. Sawing variation information
is of sufficient detail for diagnosing which machine center,
and more specifically, which pocket of saws is causing
This thesis continued the previous work by quantifying
measurement accuracy using various hardware and software
configurations, and by developing the software necessary to analyze and then display the data in the most meaningful
form. Software techniques were created to avoid measurement
The first step in the project was a pilot study
conducted using several different imaging configurations,
noise filtering techniques, and edge-detection algorithms to
quantify the system's precision in measuring static objects.
The second step was to develop display software to
convey the detailed information this system can provide.
The development of measurement error trapping
mechanisms was the third step. Measurement errors may occur
through incorrectly locating the edges of boards due to the
presence of wane, bark, pitch or bark pockets, or knots.
Measurement errors may also occur when physically blocked
saw kerfs cause a failure to detect a board edge.
Lastly, the system was tested on images representing a
range of measurement difficulty. This test was performed to
determine the measurement accuracy of the system when using
combinations of three different edge-detection algorithms
and three different noise filtering techniques.
This type of automated measurement collection, data
analysis and display system will have a financial benefit to
a mill by enabling the mill to quickly identify and correct
situations leading to sub-optimal lumber or grade recovery.