An artificial neural network for real-time hardwood lumber grading

Computerized grading of hardwood lumber according to NHLA rules would permit fast assessment of sawn lumber and the evaluation of potential edging and trimming operations to improve lumber value. More importantly, to enable optimization of the hardwood lumber sawing process, a fast means of evaluating the potential value of boards before they are sawn is necessary. As log and lumber scanning systems become prevalent and common, these needs become more pressing. From an automation perspective, the NHLA lumber grades are difficult to implement efficiently in a computer program. Exhaustive approaches that examine every potential cutting size and combination to determine the grade give accurate grading solutions, at the cost of computation time. Other approaches have examined heuristic methods that implement key parts of the grading rules, or used artificial neural network methods, both with the loss of accuracy. Here, a different approach to computerized grading is examined that takes a hybridized approach using projected yield from cut-up simulation and neural network methods. This new hybrid approach has the advantage of both accuracy and high-processing speed. Such an approach lends itself to log sawing optimization with respect to NHLA grades and market values when internal log defect information is known

Wood Properties and Processing

Wood-based materials are CO2-neutral, renewable, and considered to be environmentally friendly. The huge variety of wood species and wood-based composites allows a wide scope of creative and esthetic alternatives to materials with higher environmental impacts during production, use and disposal. Quality of wood is influenced by the genetic and environmental factors. One of the emerging uses of wood are building and construction applications. Modern building and construction practices would not be possible without use of wood or wood-based composites. The use of composites enables using wood of lower quality for the production of materials with engineered properties for specific target applications. Even more, the utilization of such reinforcing particles as carbon nanotubes and nanocellulose enables development of a new generation of composites with even better properties. The positive aspect of decomposability of waste wood can turn into the opposite when wood or wood-based materials are exposed to weathering, moisture oscillations, different discolorations, and degrading organisms. Protective measures are therefore unavoidable for many outdoor applications. Resistance of wood against different aging factors is always a combined effect of toxic or inhibiting ingredients on the one hand, and of structural, anatomical, or chemical ways of excluding moisture on the other

Harvest utilization rates and strategies for enhanced value recovery during primary processing in the central appalachian region

West Virginia has an abundant forest resource base that has underpinned portions of its economy for decades. These forests support a diverse primary and secondary forest products industry. A common thread that ensures the continued success of the forest industry, from the private landowners to the manufactures of fine furniture, is the strength of the logging and primary processing components of this supply chain. The purpose of this research is to evaluate the use of raw materials on logging operations, to understand the financial and operational aspects related to pulpwood merchandising, and to investigate new strategies for the development of standard hardwood log grades.;Whole-tree and landing-based utilization rates were determined from data collected on 30 active logging jobs in West Virginia during 2008. Utilization is defined as the amount of volume that is delivered to market over the total volume harvested for individual tree stems. On average, 3.6 markets were used by the loggers sampled. The average whole-tree utilization rate in the woods was 88% and was over 97% on the landings sampled. No relationship between the number of markets and utilization rate was determined. Results from this study provide estimates on the efficiency of forest resource use as well as the characteristics and quantity of material being left after harvest.;Roundwood merchandising operations have been seen as a potential way to supply raw materials to bioenergy facilities at competitive costs. The feasibility of developing such a facility in the central Appalachians was investigated. The purchase and subsequent merchandising of over 188 tons of hardwood pulpwood resulted in negative net revenues for all species studied. Handling cost was one of the largest contributing factors. As markets and pricing change, the metrics developed in this analysis can be used to produce new estimates of merchandising profitability.;Hardwood sawlog yield information is an important component that helps define hardwood sawmill profitability. This research investigated the use of cluster analyses and ordinal logistic regression in segregating red oak sawlogs based on potential revenue. A combination of statistical classification and an applied approach was found to be the best method for developing log grades. The final log grading table presented is not without problem; however it is the first comprehensive investigation of log grades in over 45 years and will help to begin the dialogue towards the development of a standardized hardwood log grading system

Development of a 3D log processing optimization system for small-scale sawmills to maximize profits and yields from central appalachian hardwoods

The current status of log sawing practices in small hardwood sawmills across West Virginia was investigated and the effects of log sawing practices on lumber recovery evaluated. A total of 230 logs two species, red oak (Quercus rubra) and yellow-poplar (Liriodendron tulipifera), were measured in five typical hardwood sawmills in the state. Log characteristics such as length, diameter, sweep, taper, and ellipticality were measured. Additionally, the characteristics of sawing equipment such as headrig type, headrig kerf width, and sawing thickness variation were recorded. A general linear model (GLM) was developed using Statistical Analysis System (SAS) to analyze the relationship between lumber recovery and the characteristics of logs and sawing equipment for small sawmills in West Virginia. The results showed that the factors of log grade, log diameter, species, log sweep, log length, different sawmills, the interaction between log species and grade, and the interaction between log species and log length had significant impacts on volume recovery. Log grade, log species and headrig type had significant effects on value recovery.;Hardwood lumber production includes a sequence of interrelated operations. Methods to optimize the entire lumber production process and increase lumber recovery are important issues for forest products manufacturers. Therefore, a 3D log sawing optimization system was developed to perform 3D log generation, opening face determination, headrig log sawing simulation, cant resawing, and lumber grading. External log characteristics such as length, largeend and small-end diameters, diameters at each foot, and external defects were collected from five local sawmills in central Appalachia. The positions and shapes of internal log defects were predicted using a model developed by the USDA Forest Service. 3D modeling techniques were applied to reconstruct a 3D virtual log that included internal defects. Heuristic and dynamic programming algorithms were developed to determine the opening face and grade sawing optimization. The National Hardwood Lumber Association (NHLA) grading rules were computerized and incorporated into the system to perform lumber grading. Preliminary results have shown that hardwood sawmills have the potential to increase lumber value by determining the optimal opening face and optimizing the sawing patterns. Our study showed that without flitch edging and trimming, the average lumber value recovery in the sawmills could be increased by 10.01 percent using a heuristic algorithm or 14.21 percent using a dynamic programming algorithm, respectively. An optimal 3D visualization system was developed for edging and trimming of rough lumber in central Appalachian. Exhaustive search procedures and a dynamic programming algorithm were employed to achieve the optimal edging and trimming solution, respectively.;An optimal procedure was also developed to grade hardwood lumber based on the National Hardwood Lumber Association (NHLA) grading rules. The system was validated through comparisons of the total lumber value generated by the system as compared to values obtained at six local sawmills. A total of 360 boards were measured for specific characteristics including board dimensions, defects, shapes, wane and the results of edging and trimming for each board. Results indicated that lumber value and surface measure from six sawmills could be increased on average by 19.97 percent and 6.2 percent, respectively, by comparing the optimal edging and trimming system with real sawmill operations.;A combined optimal edging and trimming algorithm was embedded as a component in the 3D log sawing optimization system. Multiple sawing methods are allowed in the combined system, including live sawing, cant sawing, grade sawing, and multi-thickness sawing. The system was tested using field data collected at local sawmills in the central Appalachian region. Results showed that significant gains in lumber value recovery can be achieved by using the 3D log sawing system as compared to current sawmill practices. By combining primary log sawing and flitch edging and trimming in a system, better solutions were obtained than when using the model that only considered primary log sawing. The resulting computer optimization system can assist hardwood sawmill managers and production personnel in efficiently utilizing raw materials and increasing their overall competitiveness in the forest products market

Automatic knot detection and measurements from X-ray CT images of wood: A review and validation of an improved algorithm on softwood samples

International audienceAn algorithm to automatically detect and measure knots in CT images of softwood beams was developed. The algorithm is based on the use of 3D con- nex components and a 3D distance transform constituting a new approach for knot diameter measurements. The present work was undertaken with the objective to automatically and non-destructively extract the distributions of knot characteristics within trees. These data are valuable for further studies related to tree development and tree architecture, and could even contribute to satisfying the current demand for automatic species identification on the basis of CT images. A review of the literature about automatic knot detection in X-ray CT images is provided. Relatively few references give quantitatively accurate results of knot measurements (i.e., not only knot localisation but knot size and incli- nation as well). The method was tested on a set of seven beams of Norway spruce and silver fir. The outputs were compared with manual measurements of knots performed on the same images. The results obtained are promising, with detection rates varying from 71 to 100%, depending on the beams, and no false alarms were reported. Particular attention was paid to the accuracy obtained for automatic measurements of knot size and inclination. Comparison with manual measurements led to a mean R2 of 0.86, 0.87, 0.59 and 0.86 for inclination, maximum diameter, length and volume, respectively

Modelling the influence of radiata pine log variables on structural lumber production

We run logit models to explain the variability of Pinus radiata structural lumber in 71 second and third unpruned logs. The response variable was the proportion of lumber with a static modulus of elasticity greater or equal than 8 GPa, pMSG8+, and the explanatory variables were log volume, branch index, largest branch, log internode index, wood basic density, and acoustic velocity. The average pMSG8+ volume was 44,30 % and 36,18 % in the second and third log respectively. Ten models were selected based on meeting statistical assumptions, their goodness of fit, and the statistical significance of their parameters. The best models (R2 - adj. > 0,75) included acoustic velocity (AV) as explanatory variable, which explained 56,25 % of the variability of pMSG8+. Models without AV presented goodness of fit ranging from 0,60 to 0,75 (R2 - adj.), and variables with the highest weight to explain the variability of pMSG8+ were volume, followed by wood basic density, branch index, and largest branch. It is possible to model pMSG8+ from log variables even when acoustic velocity is not available; however, this requires wood basic density models calibrated for the Pinus radiata growing zone