Abstract: Near InfraRed NIR technology, including NIR cameras and detectors, is used to accurately determine the strength of the green veneer sheet or other wood product. Based on the determined strength for a given green veneer sheet or other wood product, one or more actions are taken with respect to green veneer sheet or other wood product to ensure the green veneer sheet or other wood product is put the most efficient, effective, and valuable use.

Abstract: Near InfraRed NIR technology, including NIR cameras and detectors, and one or more visual cameras are used to generate superimposed image data representing a visual/NIR composite image of wood product and correlating moisture levels with physical features of the surfaces of the wood product. By analyzing the visual/NIR composite image represented by superimposed image data, moisture pockets near an open physical feature that, absent the presence open physical feature would be a problem, can be identified and ignored. Based on the identified moisture levels at various locations in a given wood product, and the proximity of physical features to the identified moisture locations, one or more actions are taken with respect to wood product to ensure the wood product is put to the most efficient, effective, and valuable use.

Abstract: Near InfraRed NIR technology, including NIR cameras and detectors, and machine learning methods and systems, including one or more Machine Learning (ML) based moisture level detection models, are used to accurately identify moisture content and the specific locations of the moisture on an entire surface of a veneer sheet or other wood product and provide moisture level prediction data for the veneer sheet or other wood product. Based on the moisture level prediction data for a given wood product, one or more actions are taken with respect to wood product to ensure the wood product is put to the most efficient, effective, and valuable use.

Abstract: Near InfraRed NIR technology, including NIR cameras and detectors, is used to accurately identify moisture content, the specific locations of the moisture on an entire surface of a dry veneer sheet or other wood product, and the strength of the dry veneer sheet or other wood product. Based on the identified moisture content and/or predicted strength for a dry veneer sheet or other wood product, one or more actions are taken with respect to dry veneer sheet or other wood product to ensure the dry veneer sheet or other wood product is put the most efficient, effective, and valuable use.

Abstract: A method and system for a veneer grading and stacking uses one or more vision systems to generate images of the individual full or partial sheets of veneer and accurately determine the dimensions of each individual full or partial sheet of veneer. The one or more vision systems are also used to analyze the surface of each individual full or partial sheet of veneer quickly and automatically and assign a grade to each individual full or partial sheet of veneer. One or more veneer selection and stacking robots are then used to move each individual full or partial sheet of veneer from a veneer analysis and selection conveyor system to an appropriate veneer stack based on the grade assigned to the individual full or partial sheet by the one or more vision systems.

Abstract: Irregularities on the surfaces of veneer, such as full veneer sheets and/or veneer core material are detected using Near InfraRed (NIR) technology, including Near InfraRed/Short Wave InfraRed (NIR/SWIR) cameras and detectors. A grade is then assigned to the veneer based, at least in part, on the detected irregularities. The graded veneer is then stacked based, at least in part, on the grade assigned to the veneer. The graded veneer stacks are then provided to local robotic panel assembly and pressing systems that include one or more local robotic panel assembly cells for processing the veneer into layered wood product panels.

Abstract: Irregularities on the surfaces of veneer, such as full veneer sheets and/or veneer core material are detected using Near InfraRed (NIR) technology, including Near InfraRed/Short Wave InfraRed (NIR/SWIR) cameras and detectors. A grade is then assigned to the veneer based, at least in part, on the detected irregularities. The graded veneer is then stacked based, at least in part, on the grade assigned to the veneer. The graded veneer stacks are then provided to local robotic panel assembly and pressing systems that include one or more local robotic panel assembly cells for processing the veneer into layered wood product panels.

Abstract: A method and system for production of layered wood products employs local and independently operating robotic panel assembly cells including a vison system for inspecting and grading veneer sheets, one or more veneer handling robots, one or more core handling robots, and one or more glue application robots to produce stacks of layered wood product panels locally near the pressing stations. Consequently, the stacks of layered wood product panels are independently built at, or near, the location of the pressing stations using inspected and graded veneer sheets. This eliminates the need for traditional panel conveyors, traditional layered wood product panel assembly layup lines, and stack press delivery lines. This, in turn, eliminates thousands of moving parts and dozens of people from the layered wood product production process.

Abstract: Near InfraRed NIR technology, including NIR cameras and detectors, is used to accurately identify surface irregularities on a veneer surface. A grade is then assigned to the veneer based, at least in part, on the detected irregularities. In one embodiment, the veneer is then provided to an improved veneer stacking system that produces more consistently graded veneer stacks and safer veneer stacks, is less expensive to operate, and is far safer than currently available methods and systems for full veneer sheet, veneer strip, and partial veneer sheet stacking.

Abstract: Near InfraRed NIR technology, including NIR cameras and detectors, is used to accurately identify surface irregularities on a veneer surface. A grade is then assigned to the veneer based, at least in part, on the detected irregularities. In one embodiment, the veneer is then provided to an improved veneer stacking system that produces more consistently graded veneer stacks and safer veneer stacks, is less expensive to operate, and is far safer than currently available methods and systems for full veneer sheet, veneer strip, and partial veneer sheet stacking.

Abstract: Near InfraRed NIR technology, including NIR cameras and detectors, is used to accurately identify surface irregularities on a surface of a veneer sheet or other wood product. Based on the identified surface irregularities for a given wood product, one or more actions are taken with respect to wood product or production process to ensure the wood product is put to the most efficient, effective, and valuable use.

Abstract: Near InfraRed NIR technology, including NIR cameras and detectors, are used to detect irregularity in the surface of a wood product. Based on the detected irregularities at various locations in a given wood product, one or more actions are taken with respect to a production process used to produce the wood product to ensure the wood product is put to the most efficient, effective, and valuable use.

Abstract: Near InfraRed NIR technology, including NIR cameras and detectors, and machine learning methods and systems, including one or more Machine Learning (ML) based surface irregularity prediction models, are used to accurately identify surface irregularities on a surface of a wood product, such as a veneer sheet or ribbon, and provide irregularity prediction data for the wood product. Based on the irregularity prediction data for a given wood product, one or more actions are taken with respect to wood product or the production process to ensure the wood product is put to the most efficient, effective, and valuable use.

Abstract: A black and white image of a wood product, such as a veneer sheet, is captured with a first camera and a color image of the wood product is captured with a second camera. Computer processing of the black and white image is performed to determine dimensions of the wood product, the existence of voids within the wood product, and the presence of debris on the wood product. Computer processing of the color image is performed to determine whether colored defects are present in the wood product. A grade is assigned to the wood product based on this computer processing. The wood product can then be sorted based on the grade.

Abstract: A seating structure includes a plurality of boss structures arranged in a pattern and a plurality of web structures joining adjacent boss structures within the pattern. At least some of the web structures are spaced apart such that they define openings therebetween. Adjacent rows of said web structures are spaced or staggered.

Abstract: A seating structure includes a plurality of boss structures arranged in a pattern and a plurality of web structures joining adjacent boss structures within the pattern. At least some of the web structures are non-planar and at least some adjacent web structures are spaced apart such that they define openings therebetween. In another aspect, the seating structure includes a plurality of boss structures arranged in a pattern and defining a support surface and a plurality of web structures joining adjacent boss structures within the pattern. At least some adjacent web structures are spaced apart and shaped such that they define substantially non-circular openings therebetween when viewed in a direction substantially perpendicular to the support surface. A reinforced frame is also provided.