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Hardware Processing

Hardwood Log Processing System

Recently, the economic competition and turbulent economic times have made it more difficult for sawmills to continue operating and has placed increased pressure on their operation efficiency, marketing techniques, and energy consumption efficiency. Maximizing profits from the conversion of hardwood logs into hardwood lumber is still a primary concern for both large and small forest products companies. There is a growing need for milling technology that can provide the most efficient method of optimizing the grade and yield of hardwood lumber (Zhu et al. 1996, Sarigul et al. 2001). Studies have shown that hardwood lumber value can be increased by 11 to 21% by using optimal sawing strategies that have the ability to detect internalhardwood log defects (Sarigul et al. 2001). While log defects, especially internal defects, are difficult to detect, any amount of improvement in defect detection can lead towards the recovery of higher quality lumber from hardwood logs and increased profits for sawmills (Thomas 2002). Many large sawmills have implemented the latest sawing and optimization technology to increase lumber yield and value. Smaller sawmills, however, are less able to adopt new, more efficient technologies because of initial cost, payback period, and modifications to operations (Occeña et al. 2001). For example, only 35% of all Pennsylvania hardwood sawmills employed a computer-aided headrig (Smith et al. 2004).

Small hardwood producers are a key contributor to the industry as they represent a significant share of the market. Approximately 47% of the hardwood lumber sawmills produce less than 1-million board feet of green hardwood lumber per year in West Virginia (WVDOF 2004). The widely used nondestructive scanning systems in large sawmills are very expensive, making it less viable for medium to small sawmills. The U.S. Forest Service in cooperation with Virginia Tech and Concord University has developed a full shape 3D log scanner and methods to detect severe log surface defects. Mathematical models were also developed to predict internal defect characteristics based on external defect features (Thomas 2008, Thomas et al. 2006). Using the 3D log scanning data, Lin et al. (2011a, 2011b) developed a cost-effective 3D lumber sawing, edging, and trimming optimization system for small mills to facilitate their lumber value recovery. Even though the advanced technologies are available for use, there remains a critical need for better training on the techniques for primary and secondary hardwood processing. According to two survey investigations, sawing, edging and trimming, and grading were the most important and worthy of improved education or training for sawmill operators (Hassler 2000, Denig et al. 2008). By systematic training, the sawyer and edger and trimmer operators will make better manufacturing decisions, resulting in the improvement of lumber yield, profitability, and economic competitiveness, which is especially important under current turbulent economic situations.

Marketing and Management

The forest industry is also in need of improved forest products markets and marketing techniques in order to cope with the ever changing marketing environment and economic competitiveness of their products. Diversifying product lines, establishing niche markets, providing a high level of customer service, and producing high-quality furniture are a few of the market strategies being used today (Schuler et al. 2001). Many mills have been struggling to make a profit since the economic downturn that started in 2008, in-part due to their inability to adapt efficient management and marketing strategies. With many sawmills using outdated management practices, businesses are failing as margins get increasingly slim. Most mill owners have done little to respond with the exception of laying-off employees and cutting working hours. This strategy is among the least effective means to save costs as it also poses a huge risk to employee morale (Cascio 2002). A better solution for improving profits would include implementing modern marketing strategies and lean manufacturing principles. Costs could be cut further by reducing energy consumption to effectively cut costs. Using the above cost cutting strategies, some profitability could once again be realized. The proposed research will assist small and medium sized sawmills in becoming more robust through strategy training and information transfer.

Energy Consumption and Efficiency

Energy efficiency proves to be another weak point in many Appalachian sawmills. Energy upgrades are typically limited in light of the current economy. Larger sawmills are more likely to be energy conscious. Significant utility savings can become a reality if less efficient, older motors were upgraded to new energy efficient models. Even without expensive upgrades to equipment and motors, a substantial amount of money can be saved by simply fixing air leaks throughout the mill. This should prove to be a very cost effective method to reduce energy consumption in the mills. Gopalakrishnan et al. (2005) visited nine large wood processing facilities in West Virginia and found that the energy usage per million board feet of production ranged from 86.15 kWh to as high as 660.40 kWh. However, there has been limited investigation regarding the energy consumption efficiency for small sawmills in the Appalachian region. Given this, there is a need to assess the impacts of energy costs for small sawmill operations and provide training, technology transfer and suggestions on ways to reduce energy consumption that will allow them to remain profitable and economically competitive.

References

  • Cascio, W.F. 2002. Responsible restructuring. San Francisco, CA: Berett-Koehler Publishers Inc.
  • Denig, J, P. Scott, Y. Su, K. K. Martinson. 2008. An assessment of training needs for the lumber manufacturing industry in the eastern United States. Research Paper FPL-RP-646. Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory.
  • Dillman, D. A. 2000. Mail and Internet Surveys: The Tailored Design Method. 2nd ed. John Wiley & Sons, New York.464 pp.
  • Gopalakrishnan, B., A. Mate, Y. Mardikar, D.P. Gupta, R.W. Plummer, and B. Anderson. 2005. Energy efficiency measures in the wood manufacturing industry. 2005 ACEEE Summer Study on Energy Efficiency in Industry:68-76.
  • Hassler, C. 2000. Training/education needs assessment survey for the primary wood products industry. Appalachian Hardwood Center, West Virginia University, Morgantown, WV.
  • Lin, W., J. Wang, and E. Thomas. 2011a. A 3D optimal log sawing system for small     sawmills in central Appalachia.Wood and Fiber Science (in review).
  • Lin, W., Wang J., Sharma B. 2011b. Development of an optimal 3D visualization system for rough lumber edging and trimming and its application in central Appalachia. Forest Products Journal (in review).
  • Lin W., and Wang J. 2011. Combined Primary and Secondary Log Breakdown Optimization for small sawmills in Central Appalachian. Computers and Electronics in Agriculture (in review).
  • McCoy, Gilbert A., J.A. Rooks, and V.C. Tutterow. 1997. “MotorMaster+™: An energy-efficient motor selection and energy management tool for the pulp and paper industry.” In IEEE Conference Record of Annual Pulp and Paper Industry Technical Conference.
  • Occeña, L.G., Rayner, T.J., Schmoldt, Daniel L., and Abbott A. L. 2001. Cooperative use of advanced scanning technology for low-volume hardwood processors. Proceedings, The First International Precision Forestry Cooperative Symposium. 83-91
  • Sarigul, E., A.L. Abbott, and D.L. Schmoldt. 2001. Nondestructive rule-based defect detection and identification system in CT images of hardwood logs. CP557, Review of Progress in Quantitative Nondestructive Evaluation 20: 1936-1943.
  • Schuler, A., R. Taylor, and P. Araman. 2001. Competitiveness of U.S. wood furniture manufacturers. Forest Products Journal. 51(7/8): 14-20.
  • Smith, P.M., S. Dasmohapatra, and W.G. Luppold. 2004. A profile of Pennsylvania’s hardwood sawmill industry. Forest Products Journal. 54(5): 43-49.
  • Thomas, E. 2008. Predicting internal yellow-poplar log defect features using surface indicators. Wood and Fiber Science. 40(1): 14-22.
  • Thomas E., Thomas L., Shaffer C.A., Mili L. 2006. Using External High-Resolution Log Scanning to Determine Internal Defect Characteristics. Proceedings of the 15th Central Hardwood Forestry Conference, Knoxville,Tennessee, February 27 - March 1.
  • Thomas, L. 2002. Analysis of 3-D hardwood log surface data using robust estimation and filtering methods. Project report: Virginia Polytechnic Institute and State University, Blacksburg, VA. URL:csgrad.cs.vt.edu/~lithomas/robustestimation/.
  • WVDOF. 2004. Green lumber production directory. URL: www.wvforestry.com/Green%20Lumber.DIR.pdf.
  • Zhu, D., R.W. Conners, D.L. Schmoldt, and P.A. Araman. 1996. A prototype vision system for analyzing CT imagery of hardwood logs. IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics 26(4): 522-532.