Classifications

• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B27—WORKING OR PRESERVING WOOD OR SIMILAR MATERIAL; NAILING OR STAPLING MACHINES IN GENERAL
  • B27L—REMOVING BARK OR VESTIGES OF BRANCHES; SPLITTING WOOD; MANUFACTURE OF VENEER, WOODEN STICKS, WOOD SHAVINGS, WOOD FIBRES OR WOOD POWDER
  • B27L1/00—Debarking or removing vestiges of branches from trees or logs; Machines therefor
  • B27L1/04—Debarking or removing vestiges of branches from trees or logs; Machines therefor by rubbing the trunks in rotating drums
  • B27L1/045—Feeding devices therefor
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B27—WORKING OR PRESERVING WOOD OR SIMILAR MATERIAL; NAILING OR STAPLING MACHINES IN GENERAL
  • B27L—REMOVING BARK OR VESTIGES OF BRANCHES; SPLITTING WOOD; MANUFACTURE OF VENEER, WOODEN STICKS, WOOD SHAVINGS, WOOD FIBRES OR WOOD POWDER
  • B27L1/00—Debarking or removing vestiges of branches from trees or logs; Machines therefor
• • D—TEXTILES; PAPER
  • D21—PAPER-MAKING; PRODUCTION OF CELLULOSE
  • D21B—FIBROUS RAW MATERIALS OR THEIR MECHANICAL TREATMENT
  • D21B1/00—Fibrous raw materials or their mechanical treatment
  • D21B1/02—Pretreatment of the raw materials by chemical or physical means
• • D—TEXTILES; PAPER
  • D21—PAPER-MAKING; PRODUCTION OF CELLULOSE
  • D21G—CALENDERS; ACCESSORIES FOR PAPER-MAKING MACHINES
  • D21G9/00—Other accessories for paper-making machines
  • D21G9/0009—Paper-making control systems
  • D21G9/0018—Paper-making control systems controlling the stock preparation
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
  • G01N21/84—Systems specially adapted for particular applications
  • G01N21/85—Investigating moving fluids or granular solids
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
  • G01N21/84—Systems specially adapted for particular applications
  • G01N21/88—Investigating the presence of flaws or contamination
  • G01N21/89—Investigating the presence of flaws or contamination in moving material, e.g. running paper or textiles
  • G01N21/892—Investigating the presence of flaws or contamination in moving material, e.g. running paper or textiles characterised by the flaw, defect or object feature examined
  • G01N21/898—Irregularities in textured or patterned surfaces, e.g. textiles, wood
  • G01N21/8986—Wood
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
  • G01N33/46—Wood

Definitions

• the invention relates to monitoring the quality of woodchips, and especially to determination of the bark content in chips.
• the content of bark in the chips can be viewed as one optimizable measure of quality: on one hand a too high bark content in the chips will reduce the quality of pulp produced from the chips, and on the other hand complete removal of bark from the chips also increases the wastage of wood material. Thus, it may be desirable to monitor the content of bark in the chips and, if needed, alter it with high accuracy.
• Chips carried on a conveyor can be imaged using a camera and, by analyzing image information produced by the camera, the share of bark content in the chips can be estimated.
• One challenge in analyses based on such image information is, however, that there are lots of variable parameters associated with chips carried on a conveyor, such as variation in size, shape, wood quality, color, or position of the woodchips.
• part of the chips remains unidenti- fied/uncategorized or will often be wrongly categorized, whereby an accurate value of the total amount of bark in the chips is often difficult to produce.
• chips carried on a conveyor are imaged from both sides (from the top side and later from the bottom side, when falling of the conveyor) to determine the bark content in the chips.
• the measurement accuracy is improved when the chips are imaged by two cameras, because the material contained in both sides thereof is detected from the chip pieces.
• the measurement accuracy can further be improved by combining the image information captured by the cameras such that, in the method, the opposite sides of the chip piece imaged can be identified to belong to the same chip piece.
• a model is formed, which contains the information needed for identification of bark with respect to both sides of the chip piece in question. Based on such chip-piece-specific information it is possible to calculate a very accurate value.
• Figures la - 1c show schematic drawings of example chip pieces
• FIGS. 2a and 2b show an embodiment of a system implementing a method in accordance with this description. Detailed description
• the amount of bark is examined in a chip-piece-specific manner, meaning that every chip piece can be separately examined, and a value can be provided for the amount of bark in each chip piece.
• the amount can be represented by, for example, a value representing area, volume, mass, or another measurand.
• theamount can be represented by a value chosen from two alternatives (where the alternatives may represent the meanings "contains bark” and "does not contain bark”).
• FIGS la - lc shows simplified isometric example images of chip pieces.
• a chip piece 10 three dimensions that are perpendicular with respect to one another are shown for a chip piece 10: length L, width H/and thickness T.
• k chip piece normally has two surfaces opposite each other, extending in a plane defined by the width direction W and the length direction L. Such surfaces define for the chip piece two normally rectangular, planar "sides".
• a chip piece thus has two opposite sides in its thickness direction T. Due to the planar shape of a chip piece, it will in practice always be positioned on the conveyor such that one of these sides is facing the conveying plane of the conveyor and the other is facing outwards form the conveying plane.
• side of a chip piece (or simply “side) is within this description referring to a surface according to the above definition.
• one of these sides are visible and is shown by reference number 10a.
• Figure la is shown one of two opposite ends 10b of a chip piece and one of two opposite flanks 10c of a chip piece.
• the chip piece 10 of Figure la is entirely of wood. On the sides of the chip piece that are made of wood, a parallel grain pattern 12 caused by fibers aligned with the longitudinal growth of the tree is typically observable.
• the grain pattern 12 is in the direction of the length L of the chip piece. In some embodiments of the method, the grain pattern may be utilized to determine the longitudinal direction L.
• the chip piece 10 of Figure la is constituted entirely of wood and the grain pattern 12 is visible on both sides of the chip piece 10.
• the longitudinal ends 10b of the chip piece are typically forming latitudinal, planar surfaces extending in the widthwise direction.
• the planes of the ends 10b are typically at a right angle with respect to the planes of the flanks 10c but are slanted such that the flanks 10c are in the shape of oblique parallelograms.
• Imaged directly in the direction of the thickness, the contour (i.e., silhouette) of a chip piece defined by the ends 10b and the flanks 10c is typically rectangular.
• Figure la shows a chip piece having a length L that is greater than the width W
• the shape of the chip pieces is not limited to such pieces.
• the width W'of a chip piece can also be greater than the length L.
• the thickness Tis typically smaller than both the length L and the width I ⁇
• FIG lb is shown a chip piece with a dimensionality corresponding to the description of Figure la but of which a part is formed of bark.
• bark is meant a protective layer surrounding the wood material of a tree, such as the cork.
• the top side of the chip piece is wood 14, while the bottom side on the opposite side thereof is bark material 16.
• the top side and the bottom side are just relative names for the opposite sides of the chip piece to make it easier to understand the figures.
• the top side refers in Figure lb to the part that can be seen on top and the bottom part to the part below the top side. When being on a conveyor, the top side of a chip piece can be seen.
• a method according to this disclosure and an apparatus implementing the method may be used, in which the chip pieces are imaged from both sides thereof.
• the method comprises the steps wherein chip pieces of the woodchips are spread onto a conveyor such that the chip pieces are positioned essentially in one layer. In other words, the chip pieces are not on top of each other but separated from each other. Individual chip pieces are thus distinguishable from each other.
• the top sides of the chip pieces carried on a conveyor are imaged from above with a first camera to detect bark on the top sides and from below with a second camera to detect bark on the bottom sides of the chip pieces.
• the conveyor may be in the form of a conveying apparatus that may be a part of a longer conveying arrangement, for example.
• the conveying apparatus can be, for example, a conveyor belt, on which one side (the top side) of the chip pieces carried are imaged with the first camera.
• the conveying apparatus can be arranged to end such that the chip pieces fall from the end thereof into a receiving container or onto a subsequent conveyor.
• the second camera can be adapted to, with respect to the side imaged with the first camera, image the opposite side of the chip pieces (the bottom side) when the chip pieces fall of the end of the conveyor apparatus.
• Other kinds of conveyors may, however, be used, as long as the chip pieces can be spread thereon such that the chip pieces can be imaged from both sides thereof and are distinguishable from each other in the image information.
• Figures 2a and 2b shows schematic drawings of an embodiment of the arrangement implementing the method.
• the arrangement comprises a conveying apparatus 20 in the form of a conveyor belt, having a conveying plane onto which woodchips can be spread as loose chip pieces 21.
• the arrangement comprises a first camera 22 arranged to image the top sides of chip pieces carried on the conveyor and a second camera 23 arranged to image the bottom sides of chip pieces 21 falling off the end of the conveyor 20.
• the top and bottom sides of the chip pieces are illuminated in Figure 2a with lights 24 and 25.
• a chip piece like the one in Figure lb is shown, in which the top side is wood 14 and the bottom side is bark 16.
• Figure 2b shows, in a view directly from above, a simplified schematic drawing of chip pieces 21 to be carried on the conveyor 20 separated from each other.
• the cameras 22 and 23 or lights 24 and 25 are not shown in Figure 2b.
• the grain patterns of the wood parts of the chip pieces are also not shown.
• Identification of bark in chip pieces based on image information may be carried out in several ways.
• image information produced by the cameras is analyzed to find individual chip pieces and to distinguish them from each other.
• one goal of the analysis is to distinguish the chip pieces from their background.
• a contour is defined for each chip piece (i.e., silhouette).
• the distinguishment of a chip piece and the background may also be based on, for example, a color and/or contrast difference between the chip piece and the background.
• the belt of the conveyor in Figure 2a and 2b belt 20
• the material on the sides of the chip pieces is analyzed based on image information produced by the cameras.
• the image recognition algorithm (or algorithms) used can, for example, simply be adapted to categorize an imaged surface of the chip piece to either one of two predefined categories: bark or wood.
• the categorization defined by the identification information may be more multi-leveled (entirely wood, partly bark, entirely bark) and/or other parameters may be related to the categorization, such that numerical values related to the dimensions or quality of the chips.
• already known image identification algorithms may be used in the detection of bark. The identification may be performed, for example, with distinguishment based on color and/or contrast differences between bark and wood.
• the cameras that are imaging chip pieces from both sides can be, for example, line or matrix cameras which take color or black-and-white images.
• the cameras can image chip pieces within the range of visible light and/or outside the range of visible light (such that within the ultraviolet range or infrared range).
• the lighting (lights 24 and 25 in the example of Figure 2a) is preferably of high intensity to obtain great contrast.
• the wavelengths of the light produced by the lighting can be chosen such that the chip pieces are distinguished from their background (such as from the conveying level of the conveyor) from above and, on the other hand, the bark or wood parts of the chip pieces are distinguished from each other by their color and/or contrast.
• the chip pieces can be illuminated using, for example, Xenon, LED, halogen tai UV lights.
• the conveying plane of the conveyor (such as a conveying belt) can, for example, be chosen such that it is blue by color, whereby the chip pieces are well distinguished from the conveying level, especially if they are illuminated with, for example, blue or yellow light.
• Figure 2a shows individual, spot like light sources for illuminating the chip pieces
• the chip pieces can also be illuminated by planar light sources, such as light matrixes. In this way disturbances caused by shadows and reflections may be minimized.
• the general color shade or contrast of wood or bark parts can be close to each other, and distinguishment based solely on color and/or contrast may not be enough.
• the wood may, for example, be darkened due to moisture damage. Distinguishment of wood and bark parts may in such situations be more challenging.
• the wood parts comprise a structure formed of wood fibres in the direction of the longitudinal growth of the tree, which shows as a parallel grain pattern. There is no such grain patter in the bark parts, whereby detection of grain pattern may be utilized for distinguishing wood and bark parts in the method.
• a machine vision algorithm which is thought to recognize patterns formed of parallel lines like them in grain patterns may be used, for example.
• distinguishment based on color and/or contrast difference and distinguishment based on detection of grain pattern can be used together or separately to identify the surface material of a chip piece. Especially good results may be obtained by combining the above-mentioned ways of distinguishment, i.e. distinguishment based on color/contrast information and distinguishment based on grain pattern.
• the bark content of the chips may be determined.
• the chip pieces with visible bark on both sides would be counted in the result value of both cameras.
• such chip pieces would be counted in the final result twice, which would cause an error in the final result value.
• the image information produced by the first and the second camera is in the method combined to chip-piece-specific image information.
• the combining may be based, for example, on position information of the detected chip pieces.
• contour information of the chip pieces may be used in the combining.
• bark potentially contained in both sides i.e., top and bottom side) of chip pieces may be detected in a chip-piece-specific manner.
• identification information may be seen as a digital model of a chip piece, which contains parameter values needed for the identification of measurable features.
• the identification information presents a model of each chip piece based on at least image information of each side of the chip piece.
• this model can be enriched with other information.
• the number of chip pieces containing bark can be accurately counted. Because the image information provided by two cameras is combined together in a chip-piece-specific manner, information about the material on each side of a chip piece can be obtained for each chip piece by means of the identification information.
• the information may be a simple classification between two alternatives (contains bark / do not contain bark), or it may be a numerical value representing the amount of bark. The numerical value may, for example, be based on a surface area of bark determined from image information.
• chip pieces constituted entirely of wood may be distinguished from chip pieces having bark on either side thereof.
• chip pieces having bark on both the top and bottom side thereof will be counted only once, whereby the measurement accuracy of the estimation of the total amount of chip pieces containing bark in the woodchips will improve.
• the total amount can be presented, for example, as a ratio in relation to all chip pieces or in relation to chip pieces entirely of wood. In applications where a numerical value is produced for the amount of bark in a single chip piece, this numerical value may be used to calculate the total amount of bark in the woodchips.
• the calculation steps of the method as described above may, for example, be performed with a calculation unit belonging to the system (not shown in Figures 2a and 2b).
• the calculation unit may be adapted to receive image information produced by the first and the second camera and to combine the image information to identification information, such that bark potentially contained in the top and bottom sides of the chip pieces is identified in a chip-piece- specific manner based on identification information.
• the thickness of chip pieces carried on the conveyor can further be measured.
• the thickness information can be combined as part of the identification information/model.
• the bark content of the piece be estimated more accurately.
• the amount of bark contained in the chip pieces can be calculated in a chip-piece-specific manner based on supplemented identification information and the total amount of bark in the chips can be calculated based on the estimated chip-piece-specific estimates of the amount of bark.
• an estimate of the amount of bark in a chip piece can be calculated in the form of, for example, the volume (or mass) of bark in a piece.
• the amount of wood contained in each chip piece can be calculated.
• a more accurate total estimate of the bark content of the woodchips can be calculated.
• the volume or mass cannot be directly calculated based on the available information, one can rely on selected basic presumptions about the structure of a chip piece. For example, if the image information produced by the cameras shows that the bark does not extend over the entire thickness of a chip piece from one side to the other, it can be presumed that a certain percentage of the piece is bark, such as 50% of the thickness of the chip piece.
• One way to measure the thickness of chip pieces is to form a pattern (e.g., a line in the lateral direction of the conveyor belt) by use of a laser installed in an oblique angle with respect to the imaging angle of the camera imaging chip pieces on the conveyor.
• the laser 26 forms such a pattern on the conveyor 20.
• a pattern 26a formed by a laser When there is no chip piece on the conveyor at the location of the laser beam, a straight line is formed on the conveyor. On the other hand, when there is a chip piece at the location of the laser, a line will be deformed in accordance with the cross-section of the chip piece. Based on this deformation, the thickness of said chip piece can be estimated.
• the first camera can be adapted to image both the color/contrast of the chip pieces as well as a line produced by a line laser measuring the thickness.
• a line laser measuring the thickness.
• the thickness can also be measured by other means, such as by use of a lidar.
• the above-described method and the apparatus implementing the same is in addition to measuring the amount of bark, suitable for other things as well.
• the method and apparatus can, for example, be used to detect irregularities in chip pieces. In this way, it is possible to obtain a lot of additional information on the chipper producing the woodchips and the condition of the chipper blades.
• the shape of the chip pieces can be analyzed as well.
• the method it is possible to, for example, identify a contour shape of wood chips that deviate from a rectangle, such as a trapezoidal contour (Ze., the ends of the wood chips are not parallel to each other).
• chip pieces having a trapezoidal shape indicate that there is temporarily too much wood in the woodchipper and that the ends of the uppermost trees are able to move/turn sideways.
• Such information can be used, for example, to control the capacity of the peeling line.
• An artificial intelligence algorithm that has been taught to identify the contours of chip pieces with a trapezoidal shape may thus be part of the method.
• Information about the shape of a chip piece can be added to the chip piece identification information / model.
• Blunt damage and dullness of the blades can also be identified based on image information produced by the cameras.
• blunt damage is meant a damage caused by compression applied to a chip piece, which appears as a distortion (curl) of the ends of the chip piece.
• dullness of the blades of the chipper producing woodchips can be assessed based on the appearance of the cutting surface of the end of a chip piece.
• the method may comprise an artificial intelligence algorithm that is thought to, from the image information produced by the cameras, detect visual signs in connection to such fault situations.

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• Life Sciences & Earth Sciences (AREA)
• Engineering & Computer Science (AREA)
• Health & Medical Sciences (AREA)
• Wood Science & Technology (AREA)
• Chemical & Material Sciences (AREA)
• Immunology (AREA)
• Pathology (AREA)
• Analytical Chemistry (AREA)
• Biochemistry (AREA)
• General Health & Medical Sciences (AREA)
• General Physics & Mathematics (AREA)
• Physics & Mathematics (AREA)
• Textile Engineering (AREA)
• Mechanical Engineering (AREA)
• Food Science & Technology (AREA)
• Medicinal Chemistry (AREA)
• Forests & Forestry (AREA)
• Length Measuring Devices By Optical Means (AREA)
• Dry Formation Of Fiberboard And The Like (AREA)

Applications Claiming Priority (2)

Publications (1)

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• 2021
  • 2021-12-20 FI FI20216300A patent/FI20216300A1/fi unknown
• 2022
  • 2022-12-16 WO PCT/FI2022/050843 patent/WO2023118651A1/en not_active Ceased
  • 2022-12-16 EP EP22910276.9A patent/EP4453546A1/de active Pending

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