A veneer sheet cutting path intelligent optimization method and system

Abstract

The present application relates to the technical field of veneer cutting path optimization, and specifically discloses a veneer cutting path intelligent optimization method and system, which comprises the following steps: acquiring actual boundary contour data and internal pit defect area contour data of a veneer board, size data of all veneer pieces, and a texture demand coefficient matrix; calculating the size category of each veneer piece; constructing an optimization objective function; the optimization objective function comprises a first subterm, a second subterm, and a penalty term; the first subterm measures the texture matching degree of the veneer piece; the second subterm measures the matching degree of the size category of the veneer piece and the veneer board; the penalty term is used to punish the overlap of the veneer piece and the internal pit defect area; the genetic algorithm is used to solve the optimal cutting position and the optimal rotation angle of each veneer piece on the veneer board, and the optimal cutting path is generated. Through optimization calculation, the veneer cutting path optimization is realized.

Description

Technical Field

[0001] This invention relates to the field of wood veneer board cutting path optimization technology, specifically a method and system for intelligent optimization of wood veneer board cutting paths. Background Technology

[0002] Wood veneer panels refer to rectangular panels with natural wood grain textures on their surface, used for components such as cabinet doors, side panels, and tabletops in custom furniture. Wood veneer panels are obtained by cutting veneer slabs. Veneer slabs are thin boards formed from natural wood through rotary cutting or slicing; their boundaries are irregular, and they may contain knots, wormholes, and other defects. During cutting, the grain matching requirements between veneer panels must be considered, such as the continuity of grain between adjacent panels, and the veneer slab area must be divided according to the size of the veneer panels. Different sizes of veneer panels have different requirements for wood strength.

[0003] Traditional methods of cutting veneer panels from large veneer boards present several technical problems. First, operators must pre-cut irregular veneer boards into rectangular boards, and then perform layout cutting on these rectangular boards. Pre-cutting discards irregularly shaped edges, resulting in wood waste. For internal defects like pits, the traditional method often cuts away the entire area surrounding the defect, further exacerbating waste. Second, for panels requiring grain matching, the traditional method treats it as a rigid constraint; two panels must strictly align their grain, otherwise they cannot be placed. This rigid constraint leads to the abandonment of many usable panel combinations, reducing wood utilization. Third, with irregular veneer boards, there is a lack of reasonable optimization for the placement of panels of different sizes. Smaller panels, with lower wood strength requirements, can be placed at the edges; larger panels, with higher wood strength requirements, should be placed in the center, as the wood in the central area of ​​an irregular veneer board is more stable, while the edges may contain undetected micro-cracks or stress concentrations.

[0004] To address the aforementioned technical issues, it is necessary to propose an intelligent optimization method and system for wood veneer panel cutting paths. This method employs a genetic algorithm as the optimization computation tool, transforming the texture matching requirements between wood veneer panels from rigid constraints into a continuous coefficient matrix with values ​​ranging from 0 to 1. It also quantifies the placement preferences of wood veneer panels of different sizes in the edge and central regions, while incorporating a defect avoidance mechanism into the optimization objective function. By solving for the optimal cutting position and rotation angle, a cutting path is generated, thereby achieving optimization of texture matching and size arrangement while avoiding pre-cutting waste. Summary of the Invention

[0005] (1) Technical problems to be solved The purpose of this invention is to provide an intelligent optimization method and system for wood veneer board cutting paths, thereby optimizing the cutting paths of wood veneer boards and solving the problems in the prior art, such as waste caused by pre-cutting irregular edges, low utilization rate due to rigid constraints in texture matching, and lack of position optimization for wood veneer boards of different sizes.

[0006] (2) Technical solution To achieve the above objectives, the present invention provides an intelligent optimization method for cutting paths of wood veneer panels, the method comprising the following steps: S1, obtain the actual boundary contour data of the wood veneer board and the contour data of the internal pit and defect area; obtain the size data and texture requirement coefficient matrix of all wood veneer boards to be cut, wherein the value range of each element in the texture requirement coefficient matrix is ​​0 to 1.

[0007] S2, calculate the area of ​​each veneer panel based on the size data of each veneer panel; obtain the size category of each veneer panel based on the relative size relationship between the area of ​​each veneer panel and the preset area threshold; calculate the shortest distance value from each position point on the plane of the veneer panel to the actual boundary contour based on the actual boundary contour data of the veneer panel.

[0008] S3. Based on the texture requirement coefficient matrix, the size category of each veneer panel, the shortest distance from each point on the veneer panel plane to the actual boundary contour, the actual boundary contour data of the veneer panel, and the contour data of the internal pit and defect area, construct an optimization objective function. The optimization objective function includes a first sub-term, a second sub-term, and a penalty term. The first sub-term measures the degree of texture matching of the veneer panel. The second sub-term measures the degree of matching between the size category of the veneer panel and the veneer panel. The penalty term is used to penalize the overlap between the veneer panel and the internal pit and defect area. Construct optimization constraints.

[0009] S4. With the objective function as the maximum value and the optimization constraints as the constraints, a genetic algorithm is used to solve for the optimal cutting position and optimal rotation angle of each veneer board on the veneer board. Based on the optimal cutting position and optimal rotation angle of each veneer board on the veneer board, the optimal cutting path is generated.

[0010] Furthermore, the method for obtaining the actual boundary contour data and the contour data of the internal pit and defect area of ​​the wood veneer board includes: A coordinate origin is pre-selected on the wood veneer board, and a two-dimensional plane coordinate system is established with the pre-defined mutually perpendicular coordinate axes on the wood veneer board plane as the X-axis and Y-axis. The actual boundary contour of the wood veneer board is obtained. Boundary sampling points are selected on the actual boundary contour of the wood veneer board at a pre-defined first sampling interval, and the coordinates of each boundary sampling point are recorded. The coordinates of all boundary sampling points are arranged in clockwise order to obtain the actual boundary contour data. The pit and defect area inside the wood veneer board is obtained. Defect sampling points are selected along the boundary of the pit and defect area inside the wood veneer board at a pre-defined second sampling interval, and the coordinates of each defect sampling point are recorded. The coordinates of all defect sampling points are arranged in clockwise order to obtain the contour data of the pit and defect area inside the wood veneer board.

[0011] Furthermore, the method for obtaining the size data and texture requirement coefficient matrix of all veneer panels to be cut, wherein the value of each element in the texture requirement coefficient matrix ranges from 0 to 1, includes: Obtain a list of wood veneer panels, wherein each wood veneer panel in the list has a rectangular planar shape; the list includes the number, length value, width value, and texture requirement coefficient matrix of each wood veneer panel; and sequentially label the wood veneer panels in the list as the first wood veneer panel to the next in numerical order. N Wood veneer panels, N This represents the total number of wood veneer panels.

[0012] Extract the first veneer panel to the second veneer panel from the list of veneer panels respectively. N The length and width values ​​of the wood veneer panels are used to obtain the first dimension data up to the second. N Dimensional data; the texture requirement coefficient matrix is ​​extracted from the wood veneer panel list; the texture requirement coefficient matrix is... N OK, N The matrix of columns, the texture requirement coefficient matrix of the first column i OK, j The elements of the column represent the first i Wood veneer panels and the first j The texture requirement coefficient between wood veneer panels represents the first... i Wood veneer panels and the first j The required degree of grain matching between wood veneer panels is denoted as... ; i and j All represent values ​​from 1 to N Integer variables between The value of is between 0 and 1, and the larger the value, the stronger the . i Wood veneer panels and the first j The higher the degree of texture matching between wood veneer panels, the better.

[0013] Furthermore, the method for determining the size category of each veneer panel based on the relative size relationship between the area of ​​each veneer panel and a preset area threshold includes: Move the first veneer panel to the... N The areas of the wood veneer panels are respectively recorded as the area of ​​the first wood veneer panel to the area of ​​the second. N Area of ​​wood veneer panels; pre-set M The area thresholds are numbered sequentially from the first area threshold to the sixth area threshold, arranged in ascending order of their numerical values. M Area threshold M It is an integer greater than or equal to 3; the first area threshold is 0, and the second... M The area threshold is set to positive infinity; the area of ​​the first veneer panel is determined up to the area of ​​the second veneer panel. N The area of ​​the wood veneer panel is equal to the first area threshold up to the [missing information]. M The relative magnitudes of the area thresholds, if the first... i The area of ​​the wood veneer panel is greater than the first r Area threshold and less than or equal to the first The area threshold will then be the first... i The size category of wood veneer panels is marked as number [number]. r Category, will r Recorded as the number i Size category number for wood veneer panels; r The value range is 1 to Integers between; traversal i Take 1 to N All integers between these ranges are used to obtain the first veneer panel to the second. N The size category numbers for wood veneer panels are respectively denoted as the first wood veneer panel size category number up to the [number missing]. N Wood veneer panel size category number.

[0014] Furthermore, the method for constructing the optimization objective function based on the texture demand coefficient matrix, the size category of each veneer panel, the shortest distance from each point on the veneer panel plane to the actual boundary contour, the actual boundary contour data of the veneer panel, and the contour data of the internal pit and defect areas includes: Construct optimization variables, wherein the optimization variables are the first wood veneer panel to the second... N The cutting positions of the veneer panels on the veneer board and the positive X-axis direction of the two-dimensional coordinate system from the first veneer panel to the second veneer panel. N The included angle of the positive direction of the long side of the wood veneer panel; the first wood veneer panel to the first N The cutting positions of the veneer panels on the veneer board are from the first veneer panel to the second. N The abscissa value of the geometric center of the wood veneer component on the wood veneer board. to and the value of the ordinate to To characterize; the positive X-axis direction of the two-dimensional plane coordinate system extends from the first veneer panel to the second... N The included angles along the positive direction of the long side of the wood veneer board are denoted as follows: to The unit is radians; the first wood veneer panel to the first N The positive direction of the long side of the wood veneer panel is preset.

[0015] Construct the first sub-item The first sub-item is represented as: ; in, The first one is calculated using plane geometry methods. i Wood veneer panels and the first j The minimum Euclidean distance of wood veneer panels in a two-dimensional plane coordinate system; The maximum Euclidean distance between any two points on the pre-obtained wood veneer board in a two-dimensional plane coordinate system; and They represent the first i The length of the wood veneer panel and the first j Length values ​​of wood veneer panels; and They represent the first i The width of the wood veneer panel and the first j Width values ​​for wood veneer panels.

[0016] Construct the second sub-item The second sub-item is represented as: ; in, For the first i Wood veneer panel size category number; For the calculated first i The minimum Euclidean distance between any point on the edge of the veneer panel and any point on the edge of the veneer board in a two-dimensional plane coordinate system.

[0017] Constructing penalty terms The penalty term is represented as follows: ; in, For the pre-set penalty coefficient, The value of is greater than 0; The number of internal pit and defect regions is calculated based on the contour data of the internal pit and defect regions; these internal pit and defect regions are then numbered. The value is 1 to Integer variables between these ranges represent the numbers of internal pit and defect areas; This represents the first intersection calculated using the planar geometric intersection calculation method. The internal pit defect area and the first i The overlapping area of ​​wood veneer panels.

[0018] Construct the optimization objective function The optimization objective function is expressed as: .

[0019] Furthermore, the optimization constraints include: First optimization constraint, second optimization constraint, third optimization constraint.

[0020] The first optimization constraint is: according to to , to , to The first wood veneer panel obtained by plane geometric calculation method to the second N The rectangular outline of the wood veneer component on the wood veneer board is located inside the area enclosed by the actual boundary outline of the wood veneer board.

[0021] The second optimization constraint is: from the first veneer panel to the... N The rectangular outlines of the wood veneer panels on the wood veneer board do not overlap.

[0022] The third optimization constraint is: to The value range is 0 to .

[0023] Furthermore, the method of using a genetic algorithm to solve for the optimal cutting position and optimal rotation angle of each veneer board on the veneer board, with the objective function as the target and the optimization constraints as the constraints, and generating the optimal cutting path based on the optimal cutting position and optimal rotation angle of each veneer board on the veneer board, includes: With the objective function being maximized and the optimization constraints being applied, a genetic algorithm is used to iteratively solve the problem. to , to , to The optimal value; according to to , to , to The optimal values ​​and dimensional data of each veneer board are used to generate the coordinates of the four vertices of each veneer board using planar geometry calculation methods. The vertex coordinates of all veneer boards are converted into cutting path codes according to the instruction format of the cutting equipment. The cutting path codes sequentially contain the straight line cutting instructions for the outer contour of each veneer board. The cutting path codes of all veneer boards are combined in sequence to obtain the optimal cutting path file. The optimal cutting path file is used to directly drive the CNC cutting equipment to complete the cutting of the large veneer board.

[0024] Based on the same inventive concept, this invention also provides an intelligent optimization system for wood veneer board cutting paths, the system comprising, in sequence: a data reading module, a classification module, an optimization calculation module, and a cutting module.

[0025] The data reading module is used to acquire the actual boundary contour data of the wood veneer board and the contour data of the internal pit and defect area; acquire the size data and texture requirement coefficient matrix of all wood veneer boards to be cut, wherein the value of each element in the texture requirement coefficient matrix ranges from 0 to 1.

[0026] The classification module is used to calculate the area of ​​each veneer panel based on the size data of each veneer panel; to obtain the size category of each veneer panel based on the relative size relationship between the area of ​​each veneer panel and a preset area threshold; and to calculate the shortest distance from each position point on the plane of the veneer panel to the actual boundary contour based on the actual boundary contour data of the veneer panel.

[0027] The optimization calculation module is used to construct an optimization objective function based on the texture requirement coefficient matrix, the size category of each veneer panel, the shortest distance from each point on the veneer panel plane to the actual boundary contour, the actual boundary contour data of the veneer panel, and the contour data of the internal pit and defect area. The optimization objective function includes a first sub-term, a second sub-term, and a penalty term. The first sub-term measures the degree of texture matching of the veneer panel. The second sub-term measures the degree of matching between the size category of the veneer panel and the veneer panel. The penalty term is used to penalize the overlap between the veneer panel and the internal pit and defect area. Optimization constraints are also constructed.

[0028] The cutting module is used to maximize the value of the optimization objective function and to use the optimization constraints as constraints. It employs a genetic algorithm to solve for the optimal cutting position and optimal rotation angle of each veneer piece on the veneer board, and generates an optimal cutting path based on the optimal cutting position and optimal rotation angle of each veneer piece on the veneer board.

[0029] (3) Beneficial effects Compared with the prior art, the beneficial effects of the present invention are: A genetic algorithm is used to solve the optimization objective function, which consists of factors such as the degree of texture matching of wood veneer panels, the adaptability of size category and edge distance, and the penalty for defect overlap, so that the cutting path of wood veneer panels under the conditions of irregular boundaries and internal pit defects can be optimized. Attached Figure Description

[0030] Figure 1 This is a flowchart of an intelligent optimization method for cutting paths of wood veneer panels according to Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the module composition of an intelligent optimization system for wood veneer board cutting paths according to Embodiment 2 of the present invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] Before giving examples, it is necessary to describe the application scenario of the present invention. The present invention is applied to cutting large wood veneer boards with pit defects and irregular boundaries to obtain rectangular wood veneer boards.

[0033] Example 1: As Figure 1 As shown in the figure, this embodiment provides an intelligent optimization method for wood veneer board cutting paths, the method including the following steps: S1, obtain the actual boundary contour data of the wood veneer board and the contour data of the internal pit and defect area; obtain the size data and texture requirement coefficient matrix of all wood veneer boards to be cut, wherein the value range of each element in the texture requirement coefficient matrix is ​​0 to 1.

[0034] S2, calculate the area of ​​each veneer panel based on the size data of each veneer panel; obtain the size category of each veneer panel based on the relative size relationship between the area of ​​each veneer panel and the preset area threshold; calculate the shortest distance value from each position point on the plane of the veneer panel to the actual boundary contour based on the actual boundary contour data of the veneer panel.

[0035] S3. Based on the texture requirement coefficient matrix, the size category of each veneer panel, the shortest distance from each point on the veneer panel plane to the actual boundary contour, the actual boundary contour data of the veneer panel, and the contour data of the internal pit and defect area, construct an optimization objective function. The optimization objective function includes a first sub-term, a second sub-term, and a penalty term. The first sub-term measures the degree of texture matching of the veneer panel. The second sub-term measures the degree of matching between the size category of the veneer panel and the veneer panel. The penalty term is used to penalize the overlap between the veneer panel and the internal pit and defect area. Construct optimization constraints.

[0036] S4. With the objective function as the maximum value and the optimization constraints as the constraints, a genetic algorithm is used to solve for the optimal cutting position and optimal rotation angle of each veneer board on the veneer board. Based on the optimal cutting position and optimal rotation angle of each veneer board on the veneer board, the optimal cutting path is generated.

[0037] For example, this embodiment aims to cut eight veneer panels from a single veneer slab. The veneer slab is a natural veneer slab obtained from logs through a rotary cutting process, with irregular boundaries and internal pitting defects. An image of the veneer slab is acquired using a scanning device and input into image processing software. In the image processing software, a two-dimensional planar coordinate system is established based on the planar area of ​​the veneer slab. The image of the veneer slab is digitized, and the actual boundary contour data and the contour data of the internal pitting defects are sampled and identified. Five internal pitting defects are identified in the veneer slab. A list of veneer panels to be cut is read from the production order file. The veneer panel list is provided in advance by the customer and includes detailed data for eight veneer panels. All veneer panels are rectangular. The veneer panel list includes the number, length, width, and texture requirement coefficient matrix for each veneer panel. The texture requirement coefficient matrix is ​​an 8x8 matrix, representing the texture matching requirements between each pair of the eight veneer panels.

[0038] The area of ​​eight veneer panels is calculated based on their length and width values. Seven area thresholds are pre-defined: 0 square meters, 0.05 square meters, 0.15 square meters, 0.25 square meters, 0.35 square meters, 0.45 square meters, and positive infinity, thus dividing the veneer panel areas into six ranges from smallest to largest. Each veneer panel is assigned a size category number based on its relative size to the seven area thresholds; a smaller size category number indicates a smaller area. The shortest distance from each point on the veneer panel's plane to the actual boundary contour is calculated based on the panel's actual boundary contour data and stored in a distance matrix.

[0039] An optimization objective function is constructed, consisting of three parts: a first sub-term, a second sub-term, and a penalty term. The first sub-term measures the degree of texture matching between the veneer panels and the main veneer board; the second sub-term measures the degree of matching between the size category of the veneer panels and the main veneer board; and the penalty term penalizes the overlap between the veneer panels and internal pit / defect areas. The optimization variables are the cutting positions of the eight veneer panels on the main veneer board and the angles between the positive X-axis of the two-dimensional coordinate system and the positive long sides of the eight veneer panels. Using the information of the cutting positions of the eight veneer panels on the main veneer board and the angles between the positive X-axis of the two-dimensional coordinate system and the positive long sides of the eight veneer panels, a unique cutting scheme can be determined.

[0040] Using the maximization of the objective function as the optimization goal, a genetic algorithm is used to solve for the optimal cutting position and optimal rotation angle of the eight veneer panels on the veneer board. Based on the optimal cutting position and optimal rotation angle of each veneer panel on the veneer board, the optimal cutting path is generated.

[0041] Furthermore, the method for obtaining the actual boundary contour data and the contour data of the internal pit and defect area of ​​the wood veneer board includes: A coordinate origin is pre-selected on the wood veneer board, and a two-dimensional plane coordinate system is established with the pre-defined mutually perpendicular coordinate axes on the wood veneer board plane as the X-axis and Y-axis. The actual boundary contour of the wood veneer board is obtained. Boundary sampling points are selected on the actual boundary contour of the wood veneer board at a pre-defined first sampling interval, and the coordinates of each boundary sampling point are recorded. The coordinates of all boundary sampling points are arranged in clockwise order to obtain the actual boundary contour data. The pit and defect area inside the wood veneer board is obtained. Defect sampling points are selected along the boundary of the pit and defect area inside the wood veneer board at a pre-defined second sampling interval, and the coordinates of each defect sampling point are recorded. The coordinates of all defect sampling points are arranged in clockwise order to obtain the contour data of the pit and defect area inside the wood veneer board.

[0042] For example, an image of a wood veneer slab is acquired using a scanning device and input into image processing software. In the image processing software, a pre-selected coordinate origin is located, and a two-dimensional plane coordinate system is established using pre-defined mutually perpendicular coordinate axes on the wood veneer slab plane as the X-axis and Y-axis, thereby establishing a two-dimensional plane coordinate system based on the planar region of the wood veneer slab. The image of the wood veneer slab is digitized in the image processing software, and an edge detection algorithm based on the Canny operator is used to identify the actual boundary contour and internal pit defect areas of the wood veneer slab in the image. Boundary sampling points are selected on the actual boundary contour of the wood veneer slab at a first sampling interval of 10 mm, and the coordinates of each boundary sampling point are recorded. The coordinates of all boundary sampling points are arranged in clockwise order to obtain the actual boundary contour data. Defect sampling points are selected along the boundary of the internal pit defect area of ​​the wood veneer slab at a second sampling interval of 5 mm, and the coordinates of each defect sampling point are recorded. The coordinates of all defect sampling points are arranged in clockwise order to obtain the contour data of the internal pit defect area.

[0043] Furthermore, the method for obtaining the size data and texture requirement coefficient matrix of all veneer panels to be cut, wherein the value of each element in the texture requirement coefficient matrix ranges from 0 to 1, includes: Obtain a list of wood veneer panels, wherein each wood veneer panel in the list has a rectangular planar shape; the list includes the number, length value, width value, and texture requirement coefficient matrix of each wood veneer panel; and sequentially label the wood veneer panels in the list as the first wood veneer panel to the next in numerical order. N Wood veneer panels, N This represents the total number of wood veneer panels.

[0044] Extract the first veneer panel to the second veneer panel from the list of veneer panels respectively. N The length and width values ​​of the wood veneer panels are used to obtain the first dimension data up to the second. N Dimensional data; the texture requirement coefficient matrix is ​​extracted from the wood veneer panel list; the texture requirement coefficient matrix is... N OK, N The matrix of columns, the texture requirement coefficient matrix of the first column i OK, j The elements of the column represent the first i Wood veneer panels and the first j The texture requirement coefficient between wood veneer panels represents the first... i Wood veneer panels and the first j The required degree of grain matching between wood veneer panels is denoted as... ; i and j All represent values ​​from 1 to N Integer variables between The value of is between 0 and 1, and the larger the value, the stronger the . i Wood veneer panels and the first j The higher the degree of texture matching between wood veneer panels, the better.

[0045] For example, the eight veneer panels are sequentially designated as the first veneer panel to the eighth veneer panel. The extracted texture requirement coefficient matrix is ​​as follows: .

[0046] The texture requirement coefficient matrix is ​​a symmetric matrix. All elements on the downward diagonal of the texture requirement coefficient matrix are 1.0, indicating that the texture requirement coefficient between the same veneer panel and itself is always 1.0. The elements in the first row, second column and the second row, first column of the texture requirement coefficient matrix are 0.9, indicating that the texture requirement coefficient between the first and second veneer panels is 0.9. This is because, according to the customer's pre-specified requirements, the texture continuity between the first and second veneer panels is highly demanding; that is, during the cutting process, the first and second veneer panels should be kept as adjacent as possible. Considering the margin for practical operation and mathematical calculation, this texture requirement coefficient is set to 0.9. The elements in the first row, third column and the third row, first column of the texture requirement coefficient matrix are 0.0, indicating that the texture requirement coefficient between the first and third veneer panels is 0.0. This is because, according to the customer's pre-specified requirements, there is no texture matching requirement between the first and third veneer panels. The element in the third row and seventh column, and the element in the seventh row and third column of the texture requirement coefficient matrix are both 0.6. This indicates that the texture requirement coefficient between the third veneer panel and the seventh veneer panel is 0.6. This is because, according to the customer's pre-defined requirements, there is a texture matching requirement between the third and seventh veneer panels. However, considering the intended use and operational difficulty of the third and seventh veneer panels, a certain degree of texture discontinuity between them is tolerable. The texture requirement coefficient matrix is ​​extracted from the veneer panel list and is pre-defined by the customer, reflecting the customer's preference for texture matching and the strictness of their texture consistency requirements.

[0047] Furthermore, the method for determining the size category of each veneer panel based on the relative size relationship between the area of ​​each veneer panel and a preset area threshold includes: Move the first veneer panel to the... N The areas of the wood veneer panels are respectively recorded as the area of ​​the first wood veneer panel to the area of ​​the second. N Area of ​​wood veneer panels; pre-set M The area thresholds are numbered sequentially from the first area threshold to the sixth area threshold, arranged in ascending order of their numerical values. M Area threshold MIt is an integer greater than or equal to 3; the first area threshold is 0, and the second... M The area threshold is set to positive infinity; the area of ​​the first veneer panel is determined up to the area of ​​the second veneer panel. N The area of ​​the wood veneer panel is equal to the first area threshold up to the [missing information]. M The relative magnitudes of the area thresholds, if the first... i The area of ​​the wood veneer panel is greater than the first r Area threshold and less than or equal to the first The area threshold will then be the first... i The size category of wood veneer panels is marked as number [number]. r Category, will r Recorded as the number i Size category number for wood veneer panels; r The value range is 1 to Integers between; traversal i Take 1 to N All integers between these ranges are used to obtain the first veneer panel to the second. N The size category numbers for wood veneer panels are respectively denoted as the first wood veneer panel size category number up to the [number missing]. N Wood veneer panel size category number.

[0048] For example, the areas of the first to eighth veneer panels are respectively denoted as the area of ​​the first to eighth veneer panels. Since the areas of the fourth and fifth veneer panels are within the range of 0 square meters to 0.05 square meters, the size category of the fourth and fifth veneer panels is marked as the first category, i.e., the size category number of the fourth veneer panel is 1, and the size category number of the fifth veneer panel is 1. Similarly, the size category numbers of the first, second, and eighth veneer panels are 5. The size category numbers of the sixth, third, and seventh veneer panels are 3.

[0049] Furthermore, the method for constructing the optimization objective function based on the texture demand coefficient matrix, the size category of each veneer panel, the shortest distance from each point on the veneer panel plane to the actual boundary contour, the actual boundary contour data of the veneer panel, and the contour data of the internal pit and defect areas includes: Construct optimization variables, wherein the optimization variables are the first wood veneer panel to the second... N The cutting positions of the veneer panels on the veneer board and the positive X-axis direction of the two-dimensional coordinate system from the first veneer panel to the second veneer panel. N The included angle of the positive direction of the long side of the wood veneer panel; the first wood veneer panel to the first N The cutting positions of the veneer panels on the veneer board are from the first veneer panel to the second. NThe abscissa value of the geometric center of the wood veneer component on the wood veneer board. to and the value of the ordinate to To characterize; the positive X-axis direction of the two-dimensional plane coordinate system extends from the first veneer panel to the second... N The included angles along the positive direction of the long side of the wood veneer board are denoted as follows: to The unit is radians; the first wood veneer panel to the first N The positive direction of the long side of the wood veneer panel is preset.

[0050] Construct the first sub-item The first sub-item is represented as: ; in, The first one is calculated using plane geometry methods. i Wood veneer panels and the first j The minimum Euclidean distance of wood veneer panels in a two-dimensional plane coordinate system; The maximum Euclidean distance between any two points on the pre-obtained wood veneer board in a two-dimensional plane coordinate system; and They represent the first i The length of the wood veneer panel and the first j Length values ​​of wood veneer panels; and They represent the first i The width of the wood veneer panel and the first j Width values ​​for wood veneer panels.

[0051] Construct the second sub-item The second sub-item is represented as: ; in, For the first i Wood veneer panel size category number; For the calculated first i The minimum Euclidean distance between any point on the edge of the veneer panel and any point on the edge of the veneer board in a two-dimensional plane coordinate system.

[0052] Constructing penalty terms The penalty term is represented as follows: ; in, For the pre-set penalty coefficient, The value of is greater than 0; The number of internal pit and defect regions is calculated based on the contour data of the internal pit and defect regions; these internal pit and defect regions are then numbered. The value is 1 to Integer variables between these ranges represent the numbers of internal pit and defect areas; This represents the first intersection calculated using the planar geometric intersection calculation method. The internal pit defect area and the first i The overlapping area of ​​wood veneer panels.

[0053] Construct the optimization objective function The optimization objective function is expressed as: .

[0054] For example, in the optimization objective function, all length, width, and distance values ​​are in millimeters, all areas are in square millimeters, all angles are in radians, and the penalty coefficient is 10, with the penalty coefficient being the reciprocal of square millimeters. It is worth noting that the shortest distance from each point on the veneer board plane to the actual boundary contour is calculated and stored as discrete values ​​through sampling. This allows the shortest distance to be used when constructing the optimization objective function. i The minimum Euclidean distance between any point on the edge of the veneer panel and any point on the edge of the veneer board in a two-dimensional plane coordinate system is selected from these, and then... .

[0055] Furthermore, the optimization constraints include: First optimization constraint, second optimization constraint, third optimization constraint.

[0056] The first optimization constraint is: according to to , to , to The first wood veneer panel obtained by plane geometric calculation method to the second N The rectangular outline of the wood veneer component on the wood veneer board is located inside the area enclosed by the actual boundary outline of the wood veneer board.

[0057] The second optimization constraint is: from the first veneer panel to the... N The rectangular outlines of the wood veneer panels on the wood veneer board do not overlap.

[0058] The third optimization constraint is: to The value range is 0 to .

[0059] Furthermore, the method of using a genetic algorithm to solve for the optimal cutting position and optimal rotation angle of each veneer board on the veneer board, with the objective function as the target and the optimization constraints as the constraints, and generating the optimal cutting path based on the optimal cutting position and optimal rotation angle of each veneer board on the veneer board, includes: With the objective function being maximized and the optimization constraints being applied, a genetic algorithm is used to iteratively solve the problem. to , to , to The optimal value; according to to , to , to The optimal values ​​and dimensional data of each veneer board are used to generate the coordinates of the four vertices of each veneer board using planar geometry calculation methods. The vertex coordinates of all veneer boards are converted into cutting path codes according to the instruction format of the cutting equipment. The cutting path codes sequentially contain the straight line cutting instructions for the outer contour of each veneer board. The cutting path codes of all veneer boards are combined in sequence to obtain the optimal cutting path file. The optimal cutting path file is used to directly drive the CNC cutting equipment to complete the cutting of the large veneer board.

[0060] For example, by substituting each parameter into the optimization objective function and optimization constraints, with the objective function being maximized and the optimization constraints being applied, a genetic algorithm is used to iteratively solve the problem to obtain the desired result. to , to , to The optimal value. to , to , to The optimal value of can determine a unique cutting scheme for the large veneer board. For example, the calculated value of the first veneer board piece is... , The optimal values ​​are 191 mm and 247 mm, respectively. The optimal value is 1.5 radians. Therefore, the geometric center coordinates of the first veneer panel are 191 mm on the X-axis and 247 mm on the Y-axis, with an angle of 1.5 radians between the positive X-axis direction and the positive direction of the long side of the first veneer panel. Based on these parameters and the dimensional data of the first veneer panel, a planar geometric calculation method is used to generate the coordinates of the four vertices of the first veneer panel, thereby determining the unique cutting scheme for the first veneer panel. Similarly, the cutting schemes for the second to eighth veneer panels can be obtained. The vertex coordinates of the first to eighth veneer panels are converted into cutting path codes according to the instruction format of the cutting equipment. The cutting path codes sequentially contain the straight-line cutting instructions for the outer contour of each veneer panel. The cutting path codes of all veneer panels are combined in sequence to obtain the optimal cutting path file. The optimal cutting path file is used to directly drive the CNC cutting equipment to complete the cutting of the large veneer panel.

[0061] Example 2: Based on the same inventive concept, such as Figure 2 As shown in the figure, this embodiment also provides an intelligent optimization system for wood veneer board cutting paths. The system includes, in sequence, a data reading module, a classification module, an optimization calculation module, and a cutting module.

[0062] The data reading module is used to acquire the actual boundary contour data of the wood veneer board and the contour data of the internal pit and defect area; acquire the size data and texture requirement coefficient matrix of all wood veneer boards to be cut, wherein the value of each element in the texture requirement coefficient matrix ranges from 0 to 1.

[0063] The classification module is used to calculate the area of ​​each veneer panel based on the size data of each veneer panel; to obtain the size category of each veneer panel based on the relative size relationship between the area of ​​each veneer panel and a preset area threshold; and to calculate the shortest distance from each position point on the plane of the veneer panel to the actual boundary contour based on the actual boundary contour data of the veneer panel.

[0064] The optimization calculation module is used to construct an optimization objective function based on the texture requirement coefficient matrix, the size category of each veneer panel, the shortest distance from each point on the veneer panel plane to the actual boundary contour, the actual boundary contour data of the veneer panel, and the contour data of the internal pit and defect area. The optimization objective function includes a first sub-term, a second sub-term, and a penalty term. The first sub-term measures the degree of texture matching of the veneer panel. The second sub-term measures the degree of matching between the size category of the veneer panel and the veneer panel. The penalty term is used to penalize the overlap between the veneer panel and the internal pit and defect area. Optimization constraints are also constructed.

[0065] The cutting module is used to maximize the value of the optimization objective function and to use the optimization constraints as constraints. It employs a genetic algorithm to solve for the optimal cutting position and optimal rotation angle of each veneer piece on the veneer board, and generates an optimal cutting path based on the optimal cutting position and optimal rotation angle of each veneer piece on the veneer board.

[0066] It should be noted that the specific methods by which each module performs operations in the system described in the above embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0067] Finally, it should be noted that although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent optimization of cutting paths for wood veneer panels, characterized in that, The method Includes the following steps: S1, obtain the actual boundary contour data of the wood veneer board and the contour data of the internal pit and defect area; obtain the size data and texture requirement coefficient matrix of all wood veneer boards to be cut, wherein the value of each element in the texture requirement coefficient matrix is ​​in the range of 0 to 1; S2, calculate the area of ​​each veneer panel based on its size data; and determine the size category of each veneer panel based on the relative size relationship between its area and a preset area threshold. Based on the actual boundary contour data of the wood veneer board, calculate the shortest distance from each point on the plane of the wood veneer board to the actual boundary contour. S3. Based on the texture requirement coefficient matrix, the size category of each veneer panel, the shortest distance from each point on the veneer panel plane to the actual boundary contour, the actual boundary contour data of the veneer panel, and the contour data of the internal pit and defect area, an optimization objective function is constructed. The optimization objective function includes a first sub-term, a second sub-term, and a penalty term. The first sub-term measures the degree of texture matching of the veneer panel. The second sub-term measures the degree of matching between the size category of the veneer panel and the veneer panel. The penalty term is used to penalize the overlap between the veneer panel and the internal pit and defect area. Construct optimization constraints; S4. With the objective function as the maximum value and the optimization constraints as the constraints, a genetic algorithm is used to solve for the optimal cutting position and optimal rotation angle of each veneer board on the veneer board. Based on the optimal cutting position and optimal rotation angle of each veneer board on the veneer board, the optimal cutting path is generated.

2. The intelligent optimization method for cutting paths of wood veneer panels as described in claim 1, characterized in that, The method for obtaining the actual boundary contour data and the contour data of the internal pit and defect area of ​​the wood veneer board includes: A coordinate origin is pre-selected on the wood veneer board, and a two-dimensional plane coordinate system is established with the pre-defined mutually perpendicular coordinate axes on the wood veneer board plane as the X-axis and Y-axis. The actual boundary contour of the wood veneer board is obtained. Boundary sampling points are selected on the actual boundary contour of the wood veneer board at a pre-defined first sampling interval, and the coordinates of each boundary sampling point are recorded. The coordinates of all boundary sampling points are arranged in clockwise order to obtain the actual boundary contour data. The pit and defect area inside the wood veneer board is obtained. Defect sampling points are selected along the boundary of the pit and defect area inside the wood veneer board at a pre-defined second sampling interval, and the coordinates of each defect sampling point are recorded. The coordinates of all defect sampling points are arranged in clockwise order to obtain the contour data of the pit and defect area inside the wood veneer board.

3. The intelligent optimization method for cutting paths of wood veneer panels as described in claim 2, characterized in that, The method for obtaining the size data and texture requirement coefficient matrix of all veneer panels to be cut, wherein the value of each element in the texture requirement coefficient matrix ranges from 0 to 1, includes: Obtain a list of wood veneer panels, wherein each wood veneer panel in the list has a rectangular planar shape; the list includes the number, length value, width value, and texture requirement coefficient matrix of each wood veneer panel; and sequentially label the wood veneer panels in the list as the first wood veneer panel to the next in numerical order. N Wood veneer panels, N This represents the total number of wood veneer panels. Extract the first veneer panel to the second veneer panel from the list of veneer panels respectively. N The length and width values ​​of the wood veneer panels are used to obtain the first dimension data up to the second... N Dimensional data; texture requirement coefficient matrix extracted from the wood veneer panel list; the texture requirement coefficient matrix is... N OK, N The matrix of columns, the texture requirement coefficient matrix of the first column i OK, j The elements of the column represent the first i Wood veneer panels and the first j The texture requirement coefficient between wood veneer panels represents the first... i Wood veneer panels and the first j The required degree of grain matching between wood veneer panels is denoted as... ; i and j All represent values ​​from 1 to N Integer variables between The value of is between 0 and 1; the larger the value, the stronger the . i Wood veneer panels and the first j The higher the degree of texture matching between wood veneer panels, the better.

4. The intelligent optimization method for cutting paths of wood veneer panels as described in claim 3, characterized in that, The method for determining the size category of each veneer panel based on the relative size relationship between the area of ​​each veneer panel and a preset area threshold includes: Move the first veneer panel to the... N The areas of the wood veneer panels are respectively recorded as the area of ​​the first wood veneer panel to the area of ​​the second. N Area of ​​wood veneer panels; pre-set M The area thresholds are numbered sequentially from the first area threshold to the sixth area threshold, arranged in ascending order of their numerical values. M Area threshold M It is an integer greater than or equal to 3; the first area threshold is 0, and the second... M The area threshold is set to positive infinity; the area of ​​the first veneer panel is determined up to the area of ​​the second veneer panel. N The area of ​​the wood veneer panel is equal to the first area threshold up to the [missing information]. M The relative magnitudes of the area thresholds, if the first... i The area of ​​the wood veneer panel is greater than the first r Area threshold and less than or equal to the first The area threshold will then be the first... i The size category of wood veneer panels is marked as number [number]. r Category, will r Recorded as the number i Size category number for wood veneer panels; r The value range is 1 to Integers between; traversal i Take 1 to N All integers between these ranges are used to obtain the first veneer panel to the second. N The size category numbers for wood veneer panels are respectively denoted as the first wood veneer panel size category number up to the [number missing]. N Wood veneer panel size category number.

5. The intelligent optimization method for cutting paths of wood veneer panels as described in claim 4, characterized in that, The method for constructing an optimization objective function based on the texture demand coefficient matrix, the size category of each veneer panel, the shortest distance from each point on the veneer panel plane to the actual boundary contour, the actual boundary contour data of the veneer panel, and the contour data of the internal pit and defect areas includes: Construct optimization variables, wherein the optimization variables are the first wood veneer panel to the second... N The cutting positions of the veneer panels on the veneer board and the positive X-axis direction of the two-dimensional coordinate system from the first veneer panel to the second veneer panel. N The included angle of the positive direction of the long side of the wood veneer panel; the first wood veneer panel to the first N The cutting positions of the veneer panels on the veneer board are from the first veneer panel to the second. N The abscissa value of the geometric center of the wood veneer component on the wood veneer board. to and the value of the ordinate to To characterize; the positive X-axis direction of the two-dimensional plane coordinate system extends from the first veneer panel to the second... N The included angles along the positive direction of the long side of the wood veneer board are denoted as follows: to The unit is radians; the first wood veneer panel to the first N The positive direction of the long side of the wood veneer panel is predetermined; Construct the first sub-item The first sub-item is represented as: ； in, The first one is calculated using plane geometry methods. i Wood veneer panels and the first j The minimum Euclidean distance of wood veneer panels in a two-dimensional plane coordinate system; The maximum Euclidean distance between any two points on the pre-obtained wood veneer board in a two-dimensional plane coordinate system; and They represent the first i The length of the wood veneer panel and the first j Length values ​​of wood veneer panels; and They represent the first i The width of the wood veneer panel and the first j Width values ​​for wood veneer panels; Construct the second sub-item The second sub-item is represented as: ； in, For the first i Wood veneer panel size category number; For the calculated first i The minimum Euclidean distance between any point on the edge of the veneer panel and any point on the edge of the veneer board in a two-dimensional plane coordinate system; Constructing penalty terms The penalty term is represented as follows: ； in, For the pre-set penalty coefficient, The value of is greater than 0; The number of internal pit and defect regions is calculated based on the contour data of the internal pit and defect regions; these internal pit and defect regions are then numbered. The value is 1 to Integer variables between these ranges represent the numbers of internal pit and defect areas; This represents the first intersection calculated using the planar geometric intersection calculation method. The internal pit defect area and the first i The overlapping area of ​​wood veneer panels; Construct the optimization objective function The optimization objective function is expressed as: 。 6. The intelligent optimization method for cutting paths of wood veneer panels as described in claim 5, characterized in that, The optimization constraints include: First optimization constraint, second optimization constraint, third optimization constraint; The first optimization constraint is: according to to , to , to The first wood veneer panel obtained by plane geometric calculation method to the second N The rectangular outline of the wood veneer panel on the wood veneer board is located inside the area enclosed by the actual boundary outline of the wood veneer board; The second optimization constraint is: from the first veneer panel to the... N The rectangular outlines of the wood veneer panels on the wood veneer board do not overlap; The third optimization constraint is: to The value range is 0 to .

7. The intelligent optimization method for cutting paths of wood veneer panels as described in claim 6, characterized in that, The method of maximizing the value of the objective function and using the optimization constraints to solve for the optimal cutting position and optimal rotation angle of each veneer piece on the veneer board using a genetic algorithm, and generating the optimal cutting path based on the optimal cutting position and optimal rotation angle of each veneer piece on the veneer board, includes: With the objective function being maximized and the optimization constraints being applied, a genetic algorithm is used to iteratively solve the problem. to , to , to The optimal value; according to to , to , to The optimal values ​​and dimensional data of each veneer board are used to generate the coordinates of the four vertices of each veneer board using planar geometry calculation methods. The vertex coordinates of all veneer boards are converted into cutting path codes according to the instruction format of the cutting equipment. The cutting path codes sequentially contain the straight line cutting instructions for the outer contour of each veneer board. The cutting path codes of all veneer boards are combined in sequence to obtain the optimal cutting path file. The optimal cutting path file is used to directly drive the CNC cutting equipment to complete the cutting of the large veneer board.

8. A smart optimization system for cutting paths of wood veneer panels, used to execute the method described in any one of claims 1-7, characterized in that, The system comprises, in sequence, a data reading module, a classification module, an optimization calculation module, and a cutting module; The data reading module is used to acquire the actual boundary contour data of the wood veneer board and the contour data of the internal pit and defect area; acquire the size data and texture requirement coefficient matrix of all wood veneer boards to be cut, wherein the value of each element in the texture requirement coefficient matrix ranges from 0 to 1; The classification module is used to calculate the area of ​​each veneer panel based on the size data of each veneer panel; and to obtain the size category of each veneer panel based on the relative size relationship between the area of ​​each veneer panel and a preset area threshold. Based on the actual boundary contour data of the wood veneer board, calculate the shortest distance from each point on the plane of the wood veneer board to the actual boundary contour. The optimization calculation module is used to construct an optimization objective function based on the texture requirement coefficient matrix, the size category of each veneer panel, the shortest distance from each point on the veneer panel plane to the actual boundary contour, the actual boundary contour data of the veneer panel, and the contour data of the internal pit and defect areas. The optimization objective function includes a first sub-term, a second sub-term, and a penalty term. The first sub-term measures the degree of texture matching of the veneer panel. The second sub-term measures the degree of matching between the size category of the veneer panel and the veneer panel. The penalty term is used to penalize the overlap between the veneer panel and the internal pit and defect areas. Construct optimization constraints; The cutting module is used to maximize the value of the optimization objective function and to use the optimization constraints as constraints. It employs a genetic algorithm to solve for the optimal cutting position and optimal rotation angle of each veneer piece on the veneer board, and generates an optimal cutting path based on the optimal cutting position and optimal rotation angle of each veneer piece on the veneer board.

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