Development of A 3D Log Sawing Optimization System for Small Sawmills in Central Appalachia, US

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 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

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

Time Domain Ultrasonic Signal Characterization for Defects in Thin Unsurfaced Hardwood Lumber

One of the major users of thin, unsurfaced hardwood lumber is the pallet manufacturing industry. Almost all manufactured products spend part of their life cycle on a pallet during transportation. This makes pallets a critical component of both the transportation and manufacturing sectors of the economy. Many newly constructed wooden pallets, however, are not currently manufactured to deliver the best performance (strength, durability, and safety)—despite interest by pallet users and pallet manufacturers—because manual grading and sorting of parts is impractical due to processing speeds and volume, labor costs, and laborer skill. This paper describes initial work aiming to create an automated grading/sorting system for hardwood pallet parts using ultrasonic. Experiments were conducted on yellow-poplar (Liriodendron tulipifera, L.) and red oak (Quercus rubra, L.) deckboards using pressure-contact, rolling transducers in a pitch-catch arrangement. Sound and unsound knots, cross grain, bark pockets, holes, splits, and decay were characterized using six ultrasound variables calculated from the received waveforms. Our scanning system shows good data-collection repeatability, and scanning rate has little effect on the calculated variables. For each defect type, at least one ultrasonic variable demonstrated significant capability to discriminate between that defect and clear wood. Energy loss variables exhibited the greatest sensitivity to many defect types. Based on the empirical relationships identified in this study, we are now developing models to classify defects using ultrasonic signal characteristics. Scanning properties of the prototype apparatus suggest that it can readily be translated into a commercial product

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

Assessment of glued timber integrity by limited-angle microfocus X-ray computed tomography

Glued timber products have an extensive range of applications in construction. In this work a Microfocus X-ray Computed Tomography method was developed to inspect gluing defects in timber samples and was applied successfully on experimental data. The bonding plane was segmented into glued and non-glued regions and imaged with 5mm resolution. Moreover, the gap topology between timber lamellas was precisely characterised. Alimited-angle reconstruction with anisotropic frame binning together with a specific glue line readout method efficiently filters out undesired wood structure highlighting the information of the adhesive joint. This method imposes limitations on the size of the specimen in only one dimension. The presence and absence of glue could be detected for glue line thicknesses over 50μm and air gaps larger than 150μm could be characterised. Several information reduction approaches were combined in the reconstruction process to implement the assessment of a 100×100mm2 bonding plane in less than 40

Detection of Knots in the Logs Using Finite Element Analysis

The detection of internal log defects has been shown to have a potential for increasing the lumber value. As an alternative to other available expensive log scanning devices, a method using radio frequency waves has been used to detect the knots. The main focus of the current research is to investigate the effectiveness of using radio frequency waves to detect the knots. Electrostatic finite element analysis is performed to predict the defects in logs. A script has been written using the commercial finite element ANSYS software to predict defects in log sections. The results are then compared with the experimental data measured on actual log sections. Analysis proved that it is possible to detect presence of knots in the log sections

Identificación del cilindro nudoso en imágenes Tc de trozas de Pinus radiata: Estudio comparativo

El objetivo de este estudio fue comparar la precisión de los algoritmos de máxima verosimilitud (MV) y otro basado en redes neuronales artificiales (RNA), en la identificación del cilindro nudoso a partir de imágenes TC (Tomografía Computarizada) de trozas podadas de pino radiata (Pinus radiata D.Don). Para este fin, treinta trozas podadas fueron seleccionadas y luego escaneadas en un escáner médico multi-slice de rayos X (Tomografía Computarizada). Del total de imágenes TC obte-nidas del escáner, 270 fueron seleccionadas para este estudio. Estas imágenes TC fueron clasificadas utilizando ambos algoritmos y los mapas temáticos obtenidos de este proceso, fueron posteriormente filtrados utilizando un filtro median de 7 x 7. Los resultados de la evaluación cuantitativa indicaron que el cilindro nudoso puede ser identificado con una precisión de 98.5 % y 96.3 % utilizando el clasificador MV y RNA, respectivamente. Aunque ambos algoritmos presentaron elevados valores de precisión para identificar el cilindro nudoso, el análisis estadístico de estos resultados arrojo dife-rencias significativas entre ambos valores de precisión; por lo tanto se concluye que el algoritmo de máxima verosimilitud presenta un mejor desempeño que el algoritmo basado en redes neuronales artificiales, en la identificación del cilindro nudoso en imágenes TC de trozas de pino radiata (Pinus radiata D.Don). AbstractThe aim of this study was to compare the accuracy of both the maximum likelihood classifier (ML) algorithm and another one based on an artificial neural networks classifier (ANN) algorithm for knotty core identification in CT images of pruned radiata pine (Pinus radiata D. Don) logs. For this purpose, thirty pruned radiata pine logs were chosen and then scanned in an X-ray multi-slice medical scanner (Computed Tomography (CT)). From the total CT images obtained, a sample of 270 CT images was selected for this study. Th is CT images were classified using both methods and the thematic map obtained aft erwards, were filtered by a 7 x 7 median filter. Quantitative assessment results showed that knotty core can be identified with 98.5 % and 96.3 % accuracy by using the ML and ANN classifiers respectively. Although both algorithms showed a high capacity level to detect knotty core statistical analysis showed significant differences among those accuracy values; this is an indication that the maximum likelihood classifier algorithm shows a better performance compared to the algorithms based on artificial neural networks for knotty core identification in CT images of radiata pine logs

Identificacion del cilindro nudoso en imagenes tc de trozas podadas de Pinus radiata utilizando el clasificador de maxima verosimilitud

El presente estudio tuvo por objetivo identifi car el cilindro nudoso en imágenes de tomografía computarizada (TC) de trozas podadas de Pinus radiata, utilizando un algoritmo de clasifi cación su-pervisada. El proceso de clasifi cación fue necesario para identifi car y separar el cilindro nudoso de la zona libre de defectos y nudos.  Diez trozas podadas de Pinus radiata fueron escaneadas en un escáner médico de rayos X, multi-slice, de marca Philips, donde las imágenes TC resultantes fueron obtenidas cada 5 mm. Un total de 270  imágenes TC fueron  clasifi cadas con el clasifi cador de máxima verosimi-litud, y los mapas temáticos resultantes, fueron fi ltrados con un fi ltro median de 7 x 7. Luego, 90 mapas temáticos fueron seleccionados y utilizados para evaluar la precisión del proceso de clasifi cación. Para ello,  la matriz de confusión e índice kappa fueron obtenidas utilizando una muestra de 70 pixeles selec-cionados aleatoriamente de cada mapa temático. Un valor de precisión de 98,5 % fue obtenido para la identifi cación del cilindro nudoso y de 92,5 % para la precisión global de la clasifi cación. El valor Ka-ppa fue de 0,730, lo cual indica que existe un fuerte grado de conformidad entre los datos de referencia y el procedimiento de clasifi cación. Estos resultados sugieren que es factible aplicar el procedimiento de clasifi cación para identifi car las características internas de trozas podadas de Pinus radiata.This study aims to identify the defective core on computed tomography images (CT) of pruned radiata pine logs, using an algorithm of supervised classifi cation. The classifi cation process was requi-red to identify and separate the defective core from the free defect part and knots. Ten pruned radiata pine logs were scanned into a medical X-ray multi-slice Philips scanner and the resulting CT images at 5 mm. were obtained. A total amount of 270 CT images were classifi ed under with the maximum likelihood classifi er and the resulting thematic maps were fi ltered with a median fi lter of 7 x 7. Then, 90 thematic maps were selected and used to assess the accuracy of the classifi cation process. To ac-complish this, the Confusion Matrix and Kappa statistic were obtained using a sample consisting of 70 randomly selected pixels of each thematic map. An accuracy value of 98.5% was obtained for the defective core identifi cation and 92.5 % for the overall accuracy of the classifi cation. The Kappa value was 0.730 indicating a strong agreement between the ground truth and the classifi cation procedure. These results suggest that it is feasible to implement the classifi cation procedure for identifying the internal characteristics of pruned radiata pine logs