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

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

Center for Research on Sustainable Forests 2012 Annual Report

In 2012, the Center for Research on Sustainable Forests (CRSF) completed its second year under an expanded mission to serve the needs of all forest stakeholders in Maine. Building on its rich tradition of working with industrial partners to conduct research related to commercial forestry in the state, the CRSF now strives to solve the challenges of three distinct segments of Maine’s 17 million acres of forest: Commercial Forests, Family Forests, and Conservation Lands. With a renewed focus on relevant, stakeholder-driven research, the CRSF has emerged as a key source of scientific information about all of these forest resources

Internal Defect Detection in Hardwood Logs With Fast Magnetic Resonance Imaging.

Identification of defects such as knots in logs before the cutting operation would allow lumber mills to maximize the value of lumber from each log. This dissertation presented images obtained from scanning an oak log with magnetic resonance imaging (MRI). The unique characteristics of MRI images of hardwood logs were noted and were used to derive a quick algorithm to isolate defects. Defect regions had some pixels that varied considerably in intensity from their neighborhood, providing a seed for initiating the defect region. There was an overlap between the pixel gray level of the defects and clear wood. Therefore, traditional thresholding techniques did not cleanly separate these regions. In this study, region-growing methods were used to extract the defects. The algorithm grew the defect region seed until the border-pixel gray levels approached the average level of the neighborhood. The region-growing methods obtained more accurate defect regions than thresholding methods because of the simultaneous consideration of gray level and adjacency information. Two methods of MRI imaging were considered: spin-echo and echo-planar. Spin-echo imaging provided clear, detailed images but required about 20 seconds of acquisition time, which was too slow to be used in a production environment. Echo-planar images could be acquired in about 1/2 second, which was fast enough for production, but the images were fuzzy and noisy. The dissertation presented an algorithm that found the defect regions in spin-echo images. Region-growing methods use a number of parameters and the best parameters were unique for each image. However, common image statistics could be used to predict the proper parameters. The dissertation also presented an algorithm that found most of the defect regions in echo-planar images. Enhancing the echo-planar images using common general-purpose image-enhancement techniques failed because the lack of discrimination allowed the process to smooth image structures as well as noise. By taking advantage of the structure of a tree, smoothing between MRI frames accomplished the goal of smoothing along homogeneous areas and not across image structures. This z-axis smoothing enhanced the echo-planar image visually and reduced the number of false alarm defect regions

Making Carrboro home : user alteration of company space

"This investigation considers the mill housing in Carrboro, North Carolina, and its evolution once it passed from company to private ownership. Seeking to bring together an existing body of knowledge, and apply it to a specific place and time, the study supplements the scholarship and evaluations of the built environment. Carrboro fits into a national textile history, and its mills are simultaneously consistent with and different from the industry as a whole. Like much of the Southern textile industry, the company built the workers' housing, maintained it for decades, and then sold the majority of the properties at auction in 1939. A change in the houses was inevitable, as individuals altered what were once identical structures. Using material cultural theory, industrial landscape studies and an understanding of the ways that buildings evolve, a small sample of the original mill houses reveal the cultural weathering and alterations made after the auction. These renovations demonstrate the shifting requirements and desires of the owners and how people show personal identity within the built environment. "--Abstract from author supplied metadat

Advancement of field-deployable, computer-vision wood identification technology

Globally, illegal logging poses a significant threat. This results in environmental damage as well as lost profits for legitimate wood product producers and taxes for governments. A global value of $30 to$100 billion is estimated to be associated with illegal logging and processing. Field identification of wood species is fundamental to combating species fraud and misrepresentation in global wood trade. Using computer vision wood identification (CVWID) systems, wood can be identified without the need for time-consuming and costly offsite visual inspections by trained wood anatomists. While CVWID research has received significant attention, most studies have not considered the generalization capabilities of the models by testing them on a field sample, and only report overall accuracy without considering misclassifications. The aim of this dissertation is to advance the design and development of CVWID systems by addressing three objectives: 1) to develop functional, field-deployable CVWID models for Peruvian and North American hardwoods, 2) test the ability of CVWID to solve increasingly challenging problems (e.g., larger class sizes, lower anatomical diversity, and spatial heterogeneity in the context of porosity), and 3) to evaluate the generalization capabilities by testing models on independent specimens not included in training and analyzing misclassifications. This research features four main sections: 1) an introduction summarizing each chapter, 2) a chapter (Chapter 2) developing a 24-class model for Peruvian hardwoods and testing its generalization capabilities with independent specimens not used in training, 3) a chapter (Chapter 3) on the design and implementation of a continental scale 22-class model for North American diffuse-porous hardwoods using wood anatomy-driven model performance evaluation, and 3) a chapter (Chapter 4) on the development of a 17-class models for North American ring-porous hardwoods, in particular examining the model\u27s effectiveness in dealing with the greater spatial heterogeneity of ring-porous hardwoods

A PHYSIOCRATIC SYSTEMS FRAMEWORK FOR OPEN SOURCE AGRICULTURAL RESEARCH AND DEVELOPMENT

This dissertation presents a new participatory approach to agricultural research and development. It surveys the biological, sociological, economic, and technical landscape and proposes a framework for adaptive management based on the 18th century Physiocratic school of land-based economics. Industrial specialization and heavy emphasis on deductive approaches to science have contributed to the disconnection of large portions of the population from natural systems. Conventional agriculture and agricultural research methods following this pattern have created expensive social, environmental, and economic external costs, while adaptive management and resilient agricultural systems have been hindered by the cost and complexity of quantifying environmental services. However, the convergence of low cost computing, sensors, memory, and resulting data analytic methods, combined with new collaborative tools and social media, have created an exciting open source environment with the potential to engage more people in analyzing and managing our natural environment