Development of A 3D Log Sawing Optimization System for Small Sawmills in Central Appalachia, US
Keywords:Heuristic, dynamic programming, grade sawing, modeling, optimization
AbstractA 3D log sawing optimization system was developed to perform log generation, opening face determination, sawing simulation, and lumber grading using 3D modeling techniques. Heuristic and dynamic programming algorithms were used to determine opening face and grade sawing optimization. Positions and shapes of internal log defects were predicted using a model developed by the USDA Forest Service. Lumber grading procedures were based on National Hardwood Lumber Association rules. The system was validated through comparisons with sawmill lumber values. External characteristics of logs, including length, large-end and small-end diameters, diameters at each foot, and defects were collected from five local sawmills in central Appalachia. Results indicated that hardwood sawmills have the potential to increase lumber value through optimal opening face and sawing optimizations. With these optimizations, average lumber value recovery could be increased by 10.01% using the heuristic algorithm or 14.21% using the dynamic programming algorithm. Lumber grade was improved significantly by using the optimal algorithms. For example, recovery of select or higher grade lumber increased 16-30%. This optimization system would help small sawmill operators improve their processing performance and improve industry competitiveness.
Bhandarkar SM, Faust TD, Tang M (2002) Design and development of a computer vision-based lumber production planning system. Image Vis Comput 20:167-189.nBhandarkar SM, Luo X, Daniels R, Tollner EW (2008) Automated planning and optimization of lumber production using machine vision and computer tomography. IEEE Trans Autom Sci Eng 5(4):677-695.nCarpenter RD, Sonderman DL, Rast ED, Jones MJ (1989) Defects in hardwood timber. Ag Hndb 678. USDA, Washington, DC. 88 pp.nChang SJ, Cooper C, Guddanti S (2005) Effects of the log's rotational orientation and the depth of the opening cut on the value of lumber produced in sawing hardwood logs. Forest Prod J 55(10):49-55.nFaaland B, Briggs D (1984) Log bucking and lumber manufacturing using dynamic programming. Mgmt Sci 30:245-247.nFunk JW, Zeng Y, Brunner CC, Butler DA (1993) SAW3D: A real shape log breakdown model. In: R Szymani, ed. Proc 5th International Conference on Scanning Technology and Process Control for the Wood Products Industry, October 25-27, 1993, Atlanta, GA. Miller Freeman, Inc, San Francisco, CA. 19 pp.nGeerts JM (1984) Mathematical solution for optimising the sawing pattern of a log given its dimensions and its defect core. N Z J For Sci 14(1):124-134.nGuddanti S, Chang SJ (1998) Replicating sawmill sawing with TOPSAW using CT images for a full-length hard-wood log. Forest Prod J 48(1):72-75.nHallock H, Galiger L (1971) Grading hardwood lumber by computer. Res Pap FPL-RP-157. USDA For Serv Forest Prod Lab, Madison, WI.nHallock H, Stern AR, Lewis DW (1976) Is there a ‘best’ sawing method. Res Pap FPL-RP-280. USDA For Serv Forest Prod Lab, Madison, WI.nHarless TEG, Wagner FG, Steele PH, Taylor FW, Yadama V, McMillin CW (1991) Methodology for locating defects within hardwood logs and determining their impact on lumber-value yield. Forest Prod J 41(4):25-30.nKlinkhachorn P, Franklin JP, McMillin CW, Connors RW, Huber H (1988) Automated computer grading of hardwood lumber. Forest Prod J 38(3):67-69.nLee SM, Abbott AL, Schmoldt DL (2001) A modular approach to detection and identification of defects in rough lumber. CP577. Review of Progress in Quantitative Nondestructive Evaluation 20:1950-1957.nLewis DW (1985) Sawmill simulation and the best opening face system: A user's guide. Gen Tech Rep FPL-GTR-48. USDA For Serv Forest Prod Lab, Madison, WI.nLin W (2011) Development of a 3D log processing optimization system for small-scale sawmills to maximize profits and yields from central Appalachian hardwoods. PhD dissertation, West Virginia University, Morgantown, WV. 182 pp.nLin W, Wang J, Thomas E (2011) A 3D optimal sawing system for small sawmills in central Appalachia. Pages 67-76 in: S Fei, JM Lhotka, JW Stringer, KW Gottschalk, GW Miller, eds. Proc. 17th central hardwood forest conference, April 5-7, 2010, Lexington, KY, Gen. Tech. Rep. NRS-P-78. US Department of Agriculture, Forest Service, Northern Research Station, Newtown Square, PA.nMalcolm FB (1965) A simplified procedure for developing grade lumber from hardwood logs. Res Pap FPL-RP-098. USDA For Serv Forest Prod Lab, Madison, WI. 13 pp.nMilauskas SJ, Anderson RB, McNeel J (2005) Hardwood industry research priorities in West Virginia. Forest Prod J 55(1):28-32.nNational Hardwood Lumber Association (2008) Rules for the measurement and inspection of hardwood and cypress. NHLA, Memphis, TN. http://www.nhla.com/pdf/2008_Rules_all.pdf'>http://www.nhla.com/pdf/2008_Rules_all.pdfnOcceña LG, Rayner TJ, Schmoldt DL, Abbott AL (2001) Cooperative use of advanced scanning technology for low-volume hardwood processors. Pages 83-91 in Proc First International Precision Forestry Cooperative Symposium, June 17-20, 2001, Seattle, WA. College of Forest Resources, University of Washington.nOcceña LG, Schmoldt DL, Thawornwong S (1997) Examining the use of internal defect information for information-augmented hardwood log. Proc ScanPro—7th International Conference on Scanning Technology and Process Optimization for the Wood Products Industry, November 12-14, 1997, Charlotte, NC. Miller Freeman, Inc, San Francisco, CA. 8 pp.nOcceña LG, Tanchoco JMA (1988) Computer graphics simulation of hardwood log sawing. Forest Prod J 38(10):72-76.nRast ED, Sonderman DL, Gammon GL (1973) A guide to hardwood log grading. Gen Tech Rep NE-GTR-1. USDA For Serv Northeastern Forest Exp Stn, Broomall, PA. 31 pp.nRichards DB (1973) Hardwood lumber yield by various simulated sawing methods. Forest Prod J 23(10):50-58.nRichards DB, Adkins WK, Hallock H, Bulgrin EH (1979) Simulation of hardwood log sawing. Res Pap FPL-RP-355. USDA For Serv Forest Prod Lab, Madison, WI. 8 pp.nRichards DB, Adkins WK, Hallock H, Bulgrin EH (1980) Lumber values from computerized simulation of hardwood log sawing. Res Pap FPL-RP-356. USDA For Serv Forest Prod Lab, Madison, WI. 28 pp.nSamson M (1993) Method for assessing the effect of knots in the conversion of logs into structural lumber. Wood Fiber Sci 25(3):298-304.nSarigul E, Abbott AL, Schmoldt DL (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.nSmith PM, Dasmohapatra S, Luppold WG (2004) A profile of Pennsylvania's hardwood sawmill industry. Forest Prod J 54(5):43-49.nSteele PH (1984) Factors determining lumber recovery in sawmilling. Gen Tech Rep FPL-GTR-39. USDA For Serv, Forest Prod Lab, Madison, WI.nSteele PH, Harless TEG, Wagner FG, Kumar L, Taylor FW (1994) Increased lumber value from optimum orientation of internal defects with respect to sawing pattern in hardwood sawlogs. Forest Prod J 44(3):69-72.nSteele PH, Wagner FG, Kumar L, Araman PA (1993) The value versus volume yield problem for live-sawn hardwood sawlogs. Forest Prod J 43(9):35-40.nThawornwong S, Occeña LG, Schmoldt DL (2003) Lumber value differences from reduced CT spatial resolution and simulated log sawing. Comput Electron Agric 41:23-43.nThomas E (2008) Predicting internal yellow-poplar log defect features using surface indicators. Wood Fiber Sci 40(1):14-22.nThomas E (2011) Internal hardwood log defect prediction model validation. Pages 77-82 in S Fei, JM Lhotka, JW Stringer, KW Gottschalk, GW Miller, eds. Proc. 17th central hardwood forest conference, April 5-7, 2010, Lexington, KY. Gen. Tech. Rep. NRS-P-78. US Department of Agriculture, Forest Service, Northern Research Station, Newtown, PA.nThomas L (2002) Analysis of 3-D hardwood log surface data using robust estimation and filtering methods. Virginia Polytechnic Institute and State University, Blacksburg, VA.nThomas L (2006) Automated detection of surface defects on barked hardwood logs and stems using 3-D laser scanner data. PhD dissertation, Virginia Polytechnic Institute and State University, Blacksburg, VA. 131 pp.nThomas L, Mili L, Schaffer CA, Thomas E (2004) Defect detection on hardwood logs using high resolution three-dimensional laser scan data. http://www.ari.vt.edu/people/Mili_Docs/Thomas-ICIP04%20paper.pdf'>http://www.ari.vt.edu/people/Mili_Docs/Thomas-ICIP04%20paper.pdfnTodoroki CL, Rönnqvist EM (1997) Secondary log break-down optimization with dynamic programming. J Oper Res Soc 48:471-478.nTodoroki CL, Rönnqvist EM (1999) Combined primary and secondary log breakdown optimisation. J Oper Res Soc 50(3):219-229.nTsolakides JA (1969) A simulation model for log yield study. Forest Prod J 19(7):21-26.nWang J, Liu J, LeDoux CB (2009) A three-dimensional bucking system for optimal bucking of central Appalachian hardwoods. International Journal of Forest Engineering 20(2):26-35.nWang J, Wu J, DeVallance DB, Armstrong JP (2010) Appalachian hardwood product exports: An analysis of the current Chinese market. Forest Prod J 60(1):94-99.nWest Virginia Division of Forestry (2004) Green lumber production directory. http://www.wvforestry.com/Green%20Lumber.DIR.pdf'>http://www.wvforestry.com/Green%20Lumber.DIR.pdfnWoo M, Neider J, Davis T, Shreiner D (2000) OpenGL programming guide: The official guide to learning OpenGL, Version 1.2. Addison-Wesley, Reading, MA. 730 pp.nZeng Y (1995) Integration of an expert system and dynamic programming approach to optimize log breakdown using 3-dimensional log and internal defect shape information. PhD dissertation, Oregon State University, Corvallis, OR.nZhu D, Conners RW, Schmoldt DL, Araman PA (1996) A prototype vision system for analyzing CT imagery of hardwood logs. IEEE T Syst Man Cyb 26(4):522-532.n
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