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DEVELOPMENT of a 3D LOG SAWING OPTIMIZATION SYSTEM for SMALL SAWMILLS in CENTRAL APPALACHIA, US Wenshu Lin{ Jingxin Wang*{ Edwar

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DEVELOPMENT of a 3D LOG SAWING OPTIMIZATION SYSTEM for SMALL SAWMILLS in CENTRAL APPALACHIA, US Wenshu Lin{ Jingxin Wang*{ Edwar CORE Metadata, citation and similar papers at core.ac.uk Provided by Wood and Fiber Science (E-Journal) DEVELOPMENT OF A 3D LOG SAWING OPTIMIZATION SYSTEM FOR SMALL SAWMILLS IN CENTRAL APPALACHIA, US Wenshu Lin{ Graduate Research Assistant E-mail: [email protected] Jingxin Wang*{ Professor West Virginia University Division of Forestry and Natural Resources Morgantown, WV 26506 E-mail: [email protected] Edward Thomas Research Scientist USDA Forest Service, Northern Research Station Princeton, WV 24740 E-mail: [email protected] (Received April 2011) Abstract. A 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 program- ming 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. Keywords: Heuristic, dynamic programming, grade sawing, modeling, optimization. INTRODUCTION (Zhu et al 1996; Lee et al 2001). This process is limited by the decision-making ability of the Maximizing profits gained from the conversion operators. Because most log defects are at of hardwood logs into lumber is a primary con- unknown internal locations within the log, it is cern for both large and small forest product difficult to give an optimal decision (Sarigul companies. Increased operating costs caused by et al 2001). Problems that arise from manual higher fuel and electricity prices coupled with log defect detection and conventional log saw- lower lumber prices are forcing wood processors ing practices include low lumber yields, less to become more efficient in their operations. than adequate lumber quality with respect to Conventional log sawing practices rely heavily grade, and slow production, all of which result on manual inspection of external log defects and in inefficient use of forest resources (Thomas are based on maximizing either volume or grade 2002). * Corresponding author Log scanning and optimization systems can { SWST member be used in lumber production (Thomas et al Wood and Fiber Science, 43(4), 2011, pp. 379-393 # 2011 by the Society of Wood Science and Technology 380 WOOD AND FIBER SCIENCE, OCTOBER 2011, V. 43(4) 2004). Preliminary studies have shown that implemented the latest sawing and optimization implementing an automated log scanning inspec- technologies to increase lumber yield and value. tion system has the potential to improve produc- However, small hardwood sawmills are less able tivity and quality, or grade, of the hardwood to implement advanced technologies because of lumber being produced (Zhu et al 1996). Hard- high initial cost, long payback period, and mod- wood lumber value can be increased 11-21% by ifications to current operations (Occen˜aetal using optimal sawing strategies gained through 2001). To survive in the highly competitive mar- the ability to detect internal log defects (Sarigul ketplace under current turbulent economic con- et al 2001). Although internal defects are difficult ditions, developing appropriate cost-effective to detect, any improvements in defect detection milling technology for these smaller mills is can contribute to recovery of higher quality lum- essential for them to improve profitability and ber, increased profits, and better use of forest competitiveness. Therefore, the objectives of resources (Thomas 2002). Currently, most avail- this study were to 1) design optimization algo- able scanning systems are based on external rithms to determine opening face and log sawing models that use a laser-line scanner to collect patterns to improve lumber value recovery; rough log profile information. These systems 2) develop a 3D visualization computer-aided were typically developed for softwood (pine, log sawing simulation system for small hard- spruce, fir) log processing and for gathering wood sawmills to implement optimal computer information about external log characteristics algorithms; and 3) validate the optimal sawing (Samson 1993) but are becoming more common system by comparing computer-generated results in hardwood mills. The USDA Forest Service in with actual output at existing sawmills. cooperation with Concord University and Vir- ginia Tech has developed a full shape 3D log scanner to detect severe log surface defects using LITERATURE REVIEW relatively low-cost equipment ($30,000 plus inte- Since the 1960s, there have been ongoing efforts gration labor cost) (Thomas 2002, 2006). In addi- to improve lumber value or volume recovery tion, internal log defect prediction models also through either computer simulation or mathemat- were developed based on measurements of exter- ical programming (Tsolakides 1969; Hallock and nal defects (Thomas 2006, 2008). A recent study Galiger 1971; Richards 1973; Hallock et al 1976; has shown that the models can accurately pre- Richards et al 1979, 1980; Lewis 1985; Occen˜a dict about 81% of internal knot defects for red and Tanchoco 1988; Harless et al 1991; Steele oak (Thomas 2011). There are also several inter- et al 1993, 1994; Occen˜a et al 1997, 2001; nal log scanning technologies being developed Guddanti and Chang 1998; Chang et al 2005). (X-ray, computed tomography, MRI, etc); how- For example, Tsolakides (1969) reconstructed a ever, none of them are efficient and cost-effective log as a cylinder and developed a digital com- for small-scale hardwood sawmills. puter analytical technique to study effects of alternative sawing methods. Hallock and Galiger Small hardwood producers are key contributors (1971), Hallock et al (1976), and Lewis (1985) to the hardwood industry in the central Appala- developed Best Opening Face (BOF) systems to chian region. In West Virginia, 68.52% of the maximize the volume of lumber produced from hardwood sawmills produce less than four mil- small-diameter softwood logs. The BOF program lion board feet (9440 m3) of green hardwood is a computer simulation model of the sawing lumber per year (West Virginia Division of process for recovering dimension lumber from Forestry 2004). In Pennsylvania, 50% of respon- small, straight logs. The sawing algorithm deter- dents in a hardwood sawmill profile survey mines the initial opening face to produce the produced less than 3.2 Mm3 of lumber per smallest acceptable piece from a given log. Once year (Smith et al 2004). Most large softwood the opening face is found, consecutive cuts are mills and many large hardwood mills have made and the yield for the log is determined. Lin et al—3D LOG SAWING OPTIMIZATION SYSTEM FOR SMALL SAWMILLS 381 Then, the opening face is moved toward the sawing system for optimal lumber production. center of the log by arbitrarily selected incre- The system provides optimal opening face deter- ments and the sawing process repeated. At last, mination, mathematical models, and heuristic and all possible sawing results are compared and the dynamic programming algorithms for lumber BOF determined. The program was widely production optimization. External log defect adopted during the 1980s, and many softwood information is used to obtain internal log defect sawmills still use it today to produce lumber. data, which are used to maximize lumber value However, application of BOF in hardwood saw- recovery. The system can be used in decision- mills is very limited. making for hardwood lumber production and as a training tool for novice sawyers. Richards et al (1979, 1980) designed a computer simulation program for hardwood log sawing. In this program, a log was represented by a trun- MATERIALS AND METHODS cated cone and each knot was simulated as a cone with its apex of 24 at the pith. Occen˜a Data Collection and Tanchoco (1988) used a graphic log sawing Log sawing practices for five small hardwood simulator as an analytical tool for automated sawmills in North Central West Virginia were hardwood log breakdown. Log sawing optimiza- studied between October 2009 and August 2010 tion can be defined as a dynamic programming (Lin et al 2011). These mills were typical small- problem, and recursive equations were scale hardwood sawmills for this state in terms established to find optimum total lumber value of equipment and sawing methods. All the saw- or volume recovery (Faaland and Briggs 1984; mills used the grade sawing method to produce Geerts 1984; Funk et al 1993; Todoroki and lumber. A total of 230 logs of two species, Ro¨nnqvist 1997, 1999; Bhandarkar et al 2002, red oak (Quercus rubra) and yellow-poplar 2008), although Occen˜a et al (1997) and (Liriodendron tulipifera), were
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