Advanced lumber manufacturing model for increasing yield in sawmills using GPR-based defect detection system

  • Udaya B. Halabe
  • Bhaskaran Gopalakrishnan
  • Jayrajsinh Jadeja
ORIGINAL ARTICLE

Abstract

This paper proposes an advanced lumber manufacturing model for real-time process control in saw mills in order to increase the yield of high value defect-free lumber. Detecting subsurface defects by scanning canted logs and generating a process plan to cut the logs can increase the yield of high-grade lumber in a saw mill industry. The defect detection process is performed using the ground-penetrating radar (GPR) system. More recently, a defect detection algorithm was developed to process GPR scanned data using the MATLAB® software. This research uses the distance and depth coordinates generated by the defect detection algorithm to develop the process plan that generates a cutting sequence for the resaw machine. The process plan is in the form of an algorithm written in MATLAB® with a simple user interface. The generated cutting sequence was validated by comparing to the conventional sawing sequence, where the operator of the resaw machine randomly performs the cutting of boards. An increase in the yield (in terms of dollar value) of about 27% was noticed using the GPR-based detection system which can map interior defects prior to sawing the log and enable an optimal sawing pattern.

Keywords

Saw mills Canted logs Wood GPR Ground-penetrating radar NDT Nondestructive testing Subsurface wood defects Knots Decays Pith Split Wane 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agrawal S (2005) “Nondestructive Evaluation of Wooden Logs Using Ground Penetrating Radar” M.S. Thesis, Department of Civil and Environmental Engineering, West Virginia University, Morgantown, WV, USAGoogle Scholar
  2. 2.
    Halabe UB, Agrawal S, Gopalakrishnan B (2009) Nondestructive evaluation of wooden logs using ground penetrating radar. Nondestr Test Eval 24(4):329–346CrossRefGoogle Scholar
  3. 3.
    Devaru D (2006) “Ground Penetrating Radar (GPR) based System for Nondestructive Detection of Interior Defects in Wooden Logs”, M.S. Thesis, Department of Industrial and Management Systems Engineering, West Virginia University, Morgantown, WV, USAGoogle Scholar
  4. 4.
    Devaru D, Halabe UB, Gopalakrishnan B, Agrawal S (2008) Ground penetrating radar (GPR) based system for nondestructive detection of interior defects in wooden logs. Int J Manufact Res 3(4):425–451CrossRefGoogle Scholar
  5. 5.
    Malcom, FB (2000) “A Simplified Procedure for Developing Grade Lumber from Hardwood Logs,” Forestry Products Laboratory, Forest Service, U.S. Department of Agriculture, Research Note FPL–RN–098Google Scholar
  6. 6.
    Bhandarkar SM, Faust TD, and Tang M (1998) “A Computer Vision System for Lumber Production Planning,” Proceedings of 4th IEEE Workshop on Applications of Computer Vision (WACV’98), p. 134Google Scholar
  7. 7.
    Sarigul E, Abbott AL, Schmoldt DL, and Araman PA (2005) “An Interactive Machine-Learning Approach for Defect Detection in Computed Tomography (CT) Images of Hardwood Logs,” Proceedings of ScanTech 2005—The Eleventh International Conference on Scanning Technology and Process Optimization in the Wood Industry, p. 15–26Google Scholar
  8. 8.
    Canpolar, Inc. (1987) “Preliminary Assessment of Impulse Radar to Detect Decay in Hardwood” Joint Publication of Canadian Forestry Service and the Alberta Forest Service pursuant to the Canada-Alberta Forest Resource Development Agreement, Edmonton, Alberta, CanadaGoogle Scholar
  9. 9.
    Muller W (2002) Trial of ground penetrating radar to locate defects in timber bridge girders. Queensland Department of Main Roads Brisbane, AustraliaGoogle Scholar
  10. 10.
    Detection Sciences, Inc. (1994) “Inspection of Wood with Impulse Radar” Research Joint Venture Agreement FP-94-2326, USDA Forest Products Laboratory, Madison, WI, USAGoogle Scholar
  11. 11.
    Geophysical Survey System, Inc., http://www.geophysical.com/
  12. 12.
    West Plains Resaw Systems, Inc., http://www.wpresaw.com/

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Udaya B. Halabe
    • 1
  • Bhaskaran Gopalakrishnan
    • 1
  • Jayrajsinh Jadeja
    • 1
  1. 1.CEE and IMSE Departments, College of Engineering and Mineral ResourcesWest Virginia UniversityMorgantownUSA

Personalised recommendations