Fabric typesetting optimization method and device

Abstract

The invention provides a fabric typesetting optimization method and device. The method comprises the following steps: acquiring to-be-cut fabric data, to-be-typeset paper pattern data and preset typesetting constraint conditions; according to the obtained information, randomly generating a certain number of initial typesetting schemes to form an initial population; a fitness function value corresponding to each initial typesetting scheme is calculated according to the fitness function, a plurality of initial typesetting schemes are selected from the initial population according to the fitness function values to serve as parent individuals, crossover operation is conducted on the parent individuals, and new child individuals are generated; performing mutation operation on the offspring individuals, continuously iterating and updating a mutation population, calculating fitness function values of the individuals in the mutation population until the improvement rate of the fitness function values is smaller than a preset value, outputting an iteration result, taking the iteration result as an intermediate typesetting scheme, evaluating the intermediate typesetting scheme, and outputting an evaluation result; and adjusting and optimizing the intermediate typesetting scheme according to an evaluation result and a preset evaluation criterion to obtain a final typesetting scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of garment processing, in particular to a fabric layout optimization method and device. Background Art

[0002] CNC cutting machines are widely used in the textile and apparel industry, particularly in garment and luggage manufacturing. With the continuous advancement of technology and market expansion, the performance and functionality of CNC cutting machines are constantly improving and refining. Currently, several strong CNC cutting machine manufacturers have emerged in China, and their products are highly competitive in the international market.

[0003] In existing technologies, the layout kernel algorithm for CNC cutting machines often uses the minimum enveloping rectangle algorithm. This algorithm solves the minimum enveloping rectangle of each pattern (or piece) and optimizes the arrangement of these rectangles on the fabric to minimize waste. Although this algorithm is fast, it may not achieve optimal layout results when processing certain special shapes or irregular fabrics, resulting in layout evacuation and, in turn, fabric waste during the actual cutting process. Therefore, to better adapt to complex pattern characteristics and improve fabric utilization, designing a more advanced layout method has become an important research direction in layout optimization. Summary of the Invention

[0004] The present invention provides a fabric layout optimization method and device to solve at least one of the above problems.

[0005] The present invention provides a fabric layout optimization method, which includes:

[0006] Obtain the data of the fabric to be cut, the data of the pattern to be laid out, and the pre-set layout constraints;

[0007] According to the data of the fabric to be cut and the data of the paper pattern to be laid out and the pre-set layout constraints, a certain number of initial layout plans are randomly generated to form an initial population;

[0008] Calculating the fitness function value corresponding to each of the initial layout schemes according to the fitness function, selecting several initial layout schemes from the initial population as parent individuals according to the fitness function values, performing a crossover operation on the parent individuals, and generating new child individuals;

[0009] Performing mutation operations on the offspring individuals, continuously iteratively updating the mutant population, and calculating the fitness function values ​​of the individuals in the mutant population until the fitness function value improvement rate is less than a preset value, outputting the iteration result, and using the iteration result as the intermediate typesetting scheme;

[0010] Evaluate the intermediate typesetting scheme and output the evaluation result;

[0011] According to the evaluation results and the preset evaluation benchmark, the intermediate typesetting scheme is adjusted and optimized to obtain a final typesetting scheme.

[0012] Furthermore, the data of the fabric to be cut includes the fabric type, size, area, and coordinates of the cuttable area and defective area in a two-dimensional coordinate system;

[0013] The defect area coordinates are the vertex coordinates of the defect area marked by the polygon;

[0014] The data of the paper pattern to be typeset includes the number of paper patterns to be typeset, the serial number of the paper pattern to be typeset, the coordinates of the vertices of the polygon of the paper pattern to be typeset, the side length of the paper pattern to be typeset and the area of ​​the paper pattern to be typeset.

[0015] Furthermore, before randomly generating a certain number of initial layout schemes based on the data of the fabric to be cut, the data of the paper pattern to be typeset, and the pre-set layout constraint data to form an initial population, a unified coordinate system is first established to convert the coordinates of all the polygon vertices of the paper pattern to be typeset into absolute coordinates relative to the lower left corner (0,0) of the fabric to be cut, and normalization is performed.

[0016] Furthermore, the evaluation of the intermediate typesetting scheme includes:

[0017] Calculate fabric utilization and cutting costs;

[0018]

[0019] Cutting cost = cutting cost per unit length × total cutting length + unit tool change cost × number of tool changes;

[0020] The total cutting length is the total side length of the paper pattern to be typeset.

[0021] Furthermore, the fitness function value of the layout scheme is calculated according to the fitness function, and several initial layout schemes are selected as parents according to the fitness function value, specifically including:

[0022] The calculated fitness is sorted from high to low, and the top 5% of the initial layout solutions are selected as parents.

[0023] Furthermore, the preset typesetting constraint condition is set based on historical typesetting data;

[0024] The pre-set layout constraints include the layout direction, spacing, fabric utilization and rotation angle of the pattern.

[0025] Furthermore, the fitness function is specifically:

[0026] Fit(x) = α × (total area of ​​pattern to be typed / area of ​​fabric to be cut) - β × cutting cost + γ × constraint penalty;

[0027] Where x represents an initial layout plan, α is the weight coefficient of fabric utilization; β is the weight coefficient of cutting cost, and γ is the weight coefficient of constraint penalty term;

[0028]

[0029] Among them, Fit(x1) is the fitness function value of the tth generation, and Fit(x2) is the fitness function value of the t+1th generation.

[0030] Furthermore, performing a crossover operation on the parent individuals to generate new offspring individuals specifically includes:

[0031] Randomly select two parents, exchange the paper pattern position information in the two parents, and finally obtain multiple cross-transformed offspring individuals.

[0032] Furthermore, the mutation operation is performed on the offspring individuals to continuously iterate and update the mutant population, specifically including:

[0033] The paper samples in the offspring individuals are fine-tuned in position and rotated in angle to form a new mutant population.

[0034] On the other hand, the present invention also provides a fabric layout optimization device, comprising:

[0035] The information acquisition module is used to obtain the data of the fabric to be cut, the data of the pattern to be laid out and the pre-set layout constraints;

[0036] A control module is used to randomly generate a certain number of initial layout plans according to the data of the fabric to be cut and the data of the paper pattern to be laid out and pre-set layout constraints to form an initial population;

[0037] The control module is further configured to calculate a fitness function value corresponding to each of the initial layout schemes according to the fitness function, select a number of initial layout schemes from the initial population as parent individuals according to the fitness function values, perform a crossover operation on the parent individuals, and generate new child individuals;

[0038] The control module is further configured to perform mutation operations on the offspring individuals, continuously iteratively update the mutant population, and calculate the fitness function values ​​of the individuals in the mutant population until the fitness function value improvement rate is less than a preset value, output the iteration result, and use the iteration result as the intermediate typesetting scheme;

[0039] The control module is further used to evaluate the intermediate typesetting scheme and output the evaluation result;

[0040] The control module is further configured to adjust and optimize the intermediate typesetting scheme according to the evaluation result and a preset evaluation benchmark to obtain a final typesetting scheme.

[0041] Compared with the prior art, the advantages of the present invention are:

[0042] The present invention provides a fabric layout optimization method and device, which can obtain data of the fabric to be cut, data of the paper pattern to be laid out, and preset layout constraints; randomly generate a certain number of initial layout plans based on the data of the fabric to be cut, the data of the paper pattern to be laid out, and the preset layout constraints to form an initial population; calculate the fitness function value of the layout plan according to the fitness function, select several initial layout plans as parents according to the fitness function value, perform a crossover operation on the parent individuals to generate new child individuals; perform a mutation operation on the child individuals, continuously iteratively update the mutation population until the fitness function value improvement rate is less than a preset value, output an iterative result, and record it as an intermediate layout plan; evaluate the intermediate layout plan and output an evaluation result; based on the evaluation result and a preset evaluation benchmark, make necessary adjustments and optimizations to the intermediate layout plan to obtain a final layout plan; and convert the final layout plan into a CAD file. The method of the present invention not only improves fabric utilization, but also reduces cutting costs, greatly shortens layout time, and improves production efficiency. In addition, due to the generalization ability of genetic algorithms, this method has good adaptability to different types of fabrics and paper patterns.

[0043] It can be seen that compared with the prior art, the present invention has outstanding substantial features and significant progress, and the beneficial effects of its implementation are also obvious. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 The figure is a flow chart of a fabric layout optimization method of the present invention.

[0045] Figure 2 This is a schematic diagram of the layout area in one embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the technical solutions and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0047] Figure 1 The figure is a flow chart of a fabric layout optimization method of the present invention.

[0048] like Figure 1 As shown, the present invention provides a fabric layout optimization method, the method comprising:

[0049] S1. Obtaining the data of the fabric to be cut, the data of the pattern to be laid out, and pre-set layout constraints.

[0050] S2. Randomly generate a certain number of initial layout plans based on the data of the fabric to be cut, the data of the paper pattern to be laid out, and pre-set layout constraints to form an initial population.

[0051] S3. Calculate the fitness function value corresponding to each of the initial layout schemes according to the fitness function, select several initial layout schemes from the initial population as parent individuals according to the fitness function value, perform a crossover operation on the parent individuals, and generate new child individuals.

[0052] S4. Perform mutation operations on the offspring individuals, continuously iterate and update the mutant population, and calculate the fitness function values ​​of the individuals in the mutant population until the fitness function value improvement rate is less than a preset value, output the iterative result, and use the iterative result as the intermediate typesetting scheme.

[0053] S5. Evaluate the intermediate typesetting scheme and output the evaluation result.

[0054] S6. Adjust and optimize the intermediate layout plan according to the evaluation results and the preset evaluation benchmark to obtain a final layout plan.

[0055] The method of the present invention not only improves fabric utilization, but also reduces cutting costs, greatly shortens typesetting time, and improves production efficiency. In addition, due to the generalization ability of genetic algorithms, the method has good adaptability to different types of fabrics and paper patterns.

[0056] In one embodiment of the present invention,

[0057] S1. Obtaining the data of the fabric to be cut, the data of the pattern to be laid out, and pre-set layout constraints.

[0058] The data of the fabric to be cut includes the fabric type, size, area, and coordinates of the cuttable area and the defective area in a two-dimensional coordinate system.

[0059] The defect area coordinates are vertex coordinates of the defect area marked by a polygon.

[0060] The data of the paper pattern to be typeset includes the number of paper patterns to be typeset, the serial number of the paper pattern to be typeset, the coordinates of the vertices of the polygon of the paper pattern to be typeset, the side length of the paper pattern to be typeset and the area of ​​the paper pattern to be typeset.

[0061] The preset typesetting constraint conditions are set according to historical typesetting data.

[0062] The pre-set layout constraints include the layout direction, spacing, fabric utilization and rotation angle of the pattern.

[0063] First, a unified coordinate system is established, and the coordinates of all the polygon vertices of the pattern to be typeset are converted into absolute coordinates relative to the lower left corner (0,0) of the fabric to be cut, and normalized.

[0064] S2. Randomly generate a certain number of initial layout plans based on the data of the fabric to be cut, the data of the paper pattern to be laid out, and pre-set layout constraints to form an initial population.

[0065] S3. Calculate the fitness function value corresponding to each of the initial layout schemes based on the fitness function, select several initial layout schemes from the initial population as parent individuals based on the fitness function values, and perform a crossover operation on the parent individuals to generate new offspring individuals. The crossover operation can simulate the process of genetic recombination and increase the diversity of the population.

[0066] Preferably, the fitness function is specifically:

[0067] Fit(x) = α × (total area of ​​paper patterns to be typed / area of ​​fabric to be cut) - β × cutting cost + γ × constraint penalty term.

[0068] Among them, x represents an initial layout plan, α is the weight coefficient of fabric utilization; β is the weight coefficient of cutting cost, and γ is the weight coefficient of constraint penalty term.

[0069] α, β, and γ are used to adjust the relative importance of fabric utilization, cutting cost, and constraint penalty in the fitness function. The constraint penalty is a penalty value set according to the layout constraints. If the constraints are not met, the penalty is a large negative number, otherwise it is 0.

[0070] Preferably, the fitness function value of the layout scheme is calculated according to the fitness function, and several initial layout schemes are selected as parents according to the fitness function value, specifically including:

[0071] The calculated fitness is sorted from high to low, and the top 5% of the initial layout solutions are selected as parents.

[0072] Preferably, performing a crossover operation on the parent individuals to generate new offspring individuals specifically includes:

[0073] Randomly select two parents, exchange the paper pattern position information in the two parents, and finally obtain multiple cross-transformed offspring individuals.

[0074] S4. Perform mutation operations on the offspring individuals, continuously iterate and update the mutant population, and calculate the fitness function values ​​of the individuals in the mutant population until the fitness function value improvement rate is less than a preset value, output the iterative result, and use the iterative result as the intermediate typesetting scheme.

[0075] Preferably, the step of performing mutation operations on the offspring individuals and continuously iteratively updating the mutant population specifically includes:

[0076] The paper samples in the offspring individuals are fine-tuned in position and rotated in angle to form a new mutant population.

[0077] Performing mutations on offspring individuals can change their genes with a certain probability, thereby introducing new areas of the solution space. Mutation operations help prevent the algorithm from falling into local optimal solutions.

[0078] Preferably,

[0079] Among them, Fit(x1) is the fitness function value of the tth generation, and Fit(x2) is the fitness function value of the t+1th generation.

[0080] S5. Evaluate the intermediate typesetting scheme and output the evaluation result.

[0081] The assessment includes calculating fabric utilization and cutting costs.

[0082]

[0083] Cutting cost = cutting cost per unit length × total cutting length + unit tool change cost × number of tool changes;

[0084] The total cutting length is the total side length of the paper pattern to be typeset.

[0085] The unit length cutting cost, unit tool change cost and number of tool changes are obtained based on historical typesetting data.

[0086] S6. According to the evaluation results and the preset evaluation benchmark, the intermediate layout plan is adjusted and optimized as necessary to obtain the final layout plan, such as Figure 2 As shown, for subsequent cutting and production.

[0087] On the other hand, the present invention also provides a fabric layout optimization device, comprising:

[0088] The information acquisition module is used to obtain the data of the fabric to be cut, the data of the pattern to be laid out and the pre-set layout constraints;

[0089] A control module is used to randomly generate a certain number of initial layout plans according to the data of the fabric to be cut and the data of the paper pattern to be laid out and pre-set layout constraints to form an initial population;

[0090] The control module is further configured to calculate a fitness function value corresponding to each of the initial layout schemes according to the fitness function, select a number of initial layout schemes from the initial population as parent individuals according to the fitness function values, perform a crossover operation on the parent individuals, and generate new child individuals;

[0091] The control module is further configured to perform mutation operations on the offspring individuals, continuously iteratively update the mutant population, and calculate the fitness function values ​​of the individuals in the mutant population until the fitness function value improvement rate is less than a preset value, output the iteration result, and use the iteration result as the intermediate typesetting scheme;

[0092] The control module is further used to evaluate the intermediate typesetting scheme and output the evaluation result;

[0093] The control module is further configured to adjust and optimize the intermediate typesetting scheme according to the evaluation result and a preset evaluation benchmark to obtain a final typesetting scheme.

[0094] The same and similar parts between the various embodiments in this specification can be referenced to each other.

[0095] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A fabric layout optimization method, characterized in that: Methods include: Obtain the data of the fabric to be cut, the data of the pattern to be laid out, and the pre-set layout constraints; According to the data of the fabric to be cut and the data of the paper pattern to be laid out and the pre-set layout constraints, a certain number of initial layout plans are randomly generated to form an initial population; Calculating the fitness function value corresponding to each of the initial layout schemes according to the fitness function, selecting several initial layout schemes from the initial population as parent individuals according to the fitness function values, performing a crossover operation on the parent individuals, and generating new child individuals; Performing mutation operations on the offspring individuals, continuously iteratively updating the mutant population, and calculating the fitness function values ​​of the individuals in the mutant population until the fitness function value improvement rate is less than a preset value, outputting the iteration result, and using the iteration result as the intermediate typesetting scheme; Evaluate the intermediate typesetting scheme and output the evaluation result; According to the evaluation results and the preset evaluation benchmark, the intermediate typesetting scheme is adjusted and optimized to obtain a final typesetting scheme.

2. The fabric layout optimization method according to claim 1, characterized in that: The data of the fabric to be cut includes the fabric type, size, area, and coordinates of the cuttable area and defective area in a two-dimensional coordinate system; The defect area coordinates are the vertex coordinates of the defect area marked by the polygon; The data of the paper pattern to be typeset includes the number of paper patterns to be typeset, the serial number of the paper pattern to be typeset, the coordinates of the vertices of the polygon of the paper pattern to be typeset, the side length of the paper pattern to be typeset and the area of ​​the paper pattern to be typeset.

3. The fabric layout optimization method according to claim 2, characterized in that: Before randomly generating a certain number of initial layout schemes based on the data of the fabric to be cut, the data of the paper pattern to be typeset, and the pre-set layout constraint data to form an initial population, a unified coordinate system is first established to convert the coordinates of the vertices of the polygons of the paper pattern to be typeset into absolute coordinates relative to the lower left corner (0,0) of the fabric to be cut, and normalize them.

4. The fabric layout optimization method according to claim 3, characterized in that: The evaluation of the intermediate typesetting scheme includes: Calculate fabric utilization and cutting costs; Cutting cost = cutting cost per unit length × total cutting length + unit tool change cost × number of tool changes; The total cutting length is the total side length of the paper pattern to be typeset.

5. The fabric layout optimization method according to claim 3, characterized in that: Calculating the fitness function value of the layout scheme according to the fitness function, and selecting several initial layout schemes as parents according to the fitness function value, specifically including: The calculated fitness is sorted from high to low, and the top 5% of the initial layout solutions are selected as parents.

6. The fabric layout optimization method according to claim 1, characterized in that: The preset typesetting constraint conditions are set based on historical typesetting data; The pre-set layout constraints include the layout direction, spacing, fabric utilization and rotation angle of the pattern.

7. The fabric layout optimization method according to claim 1, characterized in that: The fitness function is specifically: Fit(x) = α × (total area of ​​pattern to be typed / area of ​​fabric to be cut) - β × cutting cost + γ × constraint penalty; Where x represents an initial layout plan, α is the weight coefficient of fabric utilization; β is the weight coefficient of cutting cost, and γ is the weight coefficient of constraint penalty term; Among them, Fit(x1) is the fitness function value of the tth generation, and Fit(x2) is the fitness function value of the t+1th generation.

8. The fabric layout optimization method according to claim 1, characterized in that: The crossover operation is performed on the parent individuals to generate new offspring individuals, specifically including: Randomly select two parents, exchange the paper pattern position information in the two parents, and finally obtain multiple cross-transformed offspring individuals.

9. The fabric layout optimization method according to claim 1, characterized in that: The mutation operation is performed on the offspring individuals to continuously iterate and update the mutant population, specifically including: The paper samples in the offspring individuals are fine-tuned in position and rotated in angle to form a new mutant population.

10. A fabric layout optimization device, characterized in that: The device includes: The information acquisition module is used to obtain the data of the fabric to be cut, the data of the pattern to be laid out and the pre-set layout constraints; A control module is used to randomly generate a certain number of initial layout plans according to the data of the fabric to be cut and the data of the paper pattern to be laid out and pre-set layout constraints to form an initial population; The control module is further configured to calculate a fitness function value corresponding to each of the initial layout schemes according to the fitness function, select a number of initial layout schemes from the initial population as parent individuals according to the fitness function values, perform a crossover operation on the parent individuals, and generate new child individuals; The control module is further configured to perform mutation operations on the offspring individuals, continuously iteratively update the mutant population, and calculate the fitness function values ​​of the individuals in the mutant population until the fitness function value improvement rate is less than a preset value, output the iteration result, and use the iteration result as the intermediate typesetting scheme; The control module is further used to evaluate the intermediate typesetting scheme and output the evaluation result; The control module is further configured to adjust and optimize the intermediate typesetting scheme according to the evaluation result and a preset evaluation benchmark to obtain a final typesetting scheme.

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