Title page for ETD etd-12162002-104624

Type of Document Master's Thesis
Author Winn, Matthew F.
Author's Email Address mattwinn@vt.edu
URN etd-12162002-104624
Title Analysis of Red Oak Timber Defects and Associated Internal Defect Area for the Generation of Simulated Logs
Degree Master of Science
Department Forestry
Advisory Committee
Advisor Name Title
Wynne, Randolph H. Committee Chair
Araman, Philip A. Committee Member
Baumgras, John E. Committee Member
Oderwald, Richard G. Committee Member
  • log simulation
  • data generation
  • log defects
Date of Defense 2002-12-05
Availability unrestricted
Log sawing simulation computer programs can be a valuable tool for training sawyers as well as for testing different sawing patterns. Most available simulation programs rely on databases from which to draw logs and can be very costly and time-consuming to develop. In this study, a computer program was developed that can accurately generate random, artificial logs and serve as an alternative to using a log database. One major advantage of using such a program is that every log generated is unique, whereas a database is finite.

Real log and external defect data was obtained from the Forest Service Northeastern Research Station in Princeton, West Virginia for red oak (Quercus rubra, L.) logs. These data were analyzed to determine distributions for log and external defect attributes, and the information was used in the program to assure realistic log generation. An attempt was made to relate the external defect attributes to internal defect characteristics such as volume, depth, and angle. CT scanning was used to obtain internal information for the five most common defect types according to the Princeton log data. Results indicate that external indicators have the potential to be good predictors for internal defect volume. Tests performed to determine whether a significant amount of variation in volume was explained by the predictor variables proved significant for all defect types. Corresponding R2 values ranged from 0.39 to 0.93. External indicators contributed little to the explanation of variation in the other dependent variables. Additional predictor variables should be tested to determine if further variation could be explained.

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