Multi-plate layout optimization method and system for special-shaped parts with stacked templates

Abstract

The invention belongs to the field of layout optimization of special-shaped parts, and particularly discloses a template-stacked multi-plate layout optimization method and system for special-shaped parts, and the method comprises the steps: solving a template set with the optimal fitness through employing a genetic algorithm, and achieving the multi-plate layout optimization of the special-shaped parts; when the initial solution space of the genetic algorithm is obtained, the principle of large part area priority is adopted, large parts are firstly arranged, the occupation condition of a large area on a plate can be determined, when small parts are subsequently arranged, the residual irregular space between the large parts can be more accurately utilized, gaps can be filled, and therefore the waste area on the plate is reduced; meanwhile, the plate utilization rate serves as a constraint condition, all generated solutions are required to be feasible solutions meeting the utilization rate requirement, iterative optimization of the production efficiency is carried out on the basis, and the search process is effectively simplified. According to the method, the number of types of templates can be minimized while the threshold value of the material utilization rate is met, and the production efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the field of layout optimization of special-shaped parts, and more specifically, relates to a layout optimization method and system for special-shaped parts with multiple plates stacked by templates. Background Art

[0002] In many areas of modern manufacturing, such as aerospace parts manufacturing, high-end equipment manufacturing, customized furniture production, and electronic equipment casing manufacturing, large quantities of special-shaped parts are often processed. These special-shaped parts are complex and diverse in shape, differing from simple geometric shapes such as rectangles and circles. Their unique contours and dimensional requirements pose significant challenges when cutting and blanking from sheet materials. The problem of nesting is a constant throughout the cutting and manufacturing process of these sheet materials and is a key factor in determining production efficiency, raw material utilization, and production costs.

[0003] During the actual production process of sheet metal cutting and blanking, a large number of nesting templates need to be generated. This requires frequent replacement of nesting templates, repeated adjustment of cutting parameters, and even the replacement of cutting tools. This not only significantly increases labor costs but also significantly reduces production efficiency. Conversely, if the optimized design of part nesting can achieve the desired effect and generates fewer nesting templates, then by stacking and cutting multiple sheets using the same template, production time can be greatly reduced, production costs can be effectively reduced, and production efficiency can be significantly improved. Therefore, in-depth research on optimized design of part nesting and actively exploring effective methods to reduce the number of nesting templates are of extremely important practical significance and broad application value.

[0004] However, it is noteworthy that among the numerous research results on the optimization of special-shaped parts layout, the vast majority focus on improving raw material utilization, while relatively few studies aim to improve production efficiency by reducing the number of layout templates. Currently, many modern manufacturing companies with large-scale special-shaped parts processing needs are in urgent need of a special-shaped parts layout technology that can effectively improve production efficiency by reducing the number of layout templates to meet their growing production needs and promote high-quality development of enterprises. Summary of the Invention

[0005] In response to the above defects or improvement needs of the prior art, the present invention provides a template stacking multi-plate layout optimization method and system for special-shaped parts, the purpose of which is to ensure that the plate utilization rate meets production requirements while minimizing the number of layout templates, reducing production costs and improving production efficiency.

[0006] To achieve the above objectives, according to a first aspect of the present invention, a method for optimizing the layout of multiple sheets of special-shaped parts with stacked templates is proposed, which uses a genetic algorithm to iteratively solve the template set with the best fitness, thereby achieving the optimization of the layout of multiple sheets of special-shaped parts;

[0007] The acquisition of the initial solution space of the genetic algorithm includes the following steps:

[0008] S11, initialize the set of parts to be sorted, the template set is empty, the number of templates is N T 、Number of plates N S If it is 0, all the parts to be sorted will be added to the set of parts to be sorted;

[0009] S12, randomly disrupting the sequence of parts in the set of parts to be sorted;

[0010] S13. Select the first X parts from the set of parts to be sorted and calculate the total area S of the first X parts. X ;

[0011] S14, if S X ≤S0, where S0 is the plate area, then update X=X+1 and return to S13; otherwise, select parts and sort them in descending order of area;

[0012] S15, removing the last part from the selected parts, thereby updating the selected parts, and placing the removed last part back into the set of parts to be sorted;

[0013] S16. Arrange the selected parts on a single plate to determine whether all the selected parts can be placed in the plate; if so, obtain the arrangement template and calculate the plate utilization rate U X ; Otherwise return to S15;

[0014] S17, if U X If the utilization rate is not less than the preset threshold, the current nesting template is added to the template set and the number of templates N is updated. T =N T +1, number of plates N S =N S +N stack , N stack The maximum number of stacking layers currently allowed; otherwise, return to S12;

[0015] S18, update the set of parts to be arranged. If the set of parts to be arranged is empty, output the current template set; otherwise, return to S12;

[0016] S19, repeat steps S11 to S18 N p times, get N p template sets; each template set corresponds to a solution, forming the initial solution space.

[0017] As a further preferred method, a genetic algorithm is used to iteratively solve the template set with the best fitness, which includes the following steps:

[0018] S1. Get the initial solution space, the population size is N p ;

[0019] S2, sort the solution space in descending order according to the fitness, and eliminate the N p * 1 / 2 of the inferior solutions, keep the top N p *1 / 2 of the better solutions are subjected to genetic operations;

[0020] S3. Calculate the fitness value of each solution after the genetic operation, and determine the change in the fitness value of each solution after the genetic operation. If the fitness value is better than the solution before the genetic operation, then select the solution after the genetic operation to complete the solution space; otherwise, select the solution before the genetic operation to complete the solution space.

[0021] S4. Repeat steps S2 and S3 until the iteration termination condition is reached, obtain the final solution space, and select the solution with the best fitness value as the final solution.

[0022] As a further preferred method, the fitness calculation formula is:

[0023] fitness=αN T +βN S

[0024] Where fitness represents fitness, α and β represent the weight coefficients of the number of templates and the number of plates, respectively, and α+β=1.

[0025] As a further preference, α=0.9 and β=0.1 are set.

[0026] As a further preferred embodiment, the plate utilization rate U X The calculation formula is:

[0027]

[0028] Among them, X represents the number of parts in the plate, S i represents the area of the i-th part, H represents the length of the minimum enveloping rectangle in which all parts have been arranged, and W represents the width of the rectangular plate.

[0029] As a further preferred embodiment, the maximum number of stacking layers N currently allowed is stack The calculation formula is:

[0030]

[0031] Among them, n type Indicates the number of parts types contained in the current template, TotalNum jIndicates the total number of the j-th part in the set of parts to be sorted, TempNum j Indicates the number of the j-th part in the current template, Indicates the maximum number of layers that can be stacked for the jth type of part in the current template.

[0032] As a further preferred embodiment, in step S12, after randomly disrupting the part sequence in the set of parts to be sorted, a part sequence duplicate detection mechanism is introduced to ensure that the part sequences before and after the disruption are inconsistent.

[0033] As a further preferred embodiment, step S16, arranging the selected parts on the plate, includes the following steps:

[0034] S161, initialize the list of parts to be sorted and the list of sorted parts to be empty, and add all selected parts to the list of parts to be sorted;

[0035] S162, sequentially selecting parts from the list of parts to be sorted, calculating the NFP of the current part and all the already sorted parts, and finding the union of all the NFPs;

[0036] S163, calculate the inner critical polygon IFP of the current part and the plate, and update the critical polygon NFP where the part can be finally placed = IFP-NFP;

[0037] S164, based on the BL lower left placement strategy, select the local optimal reference point to place the current part;

[0038] S165. Add the current part to the list of arranged parts and remove it from the list of parts to be arranged. If the list of parts to be arranged is empty, the iteration ends and the arrangement result is output. Otherwise, return to S162.

[0039] According to a second aspect of the present invention, a system for optimizing the layout of multiple sheets of special-shaped parts with template stacking is provided, comprising a processor for executing the above-mentioned method for optimizing the layout of multiple sheets of special-shaped parts with template stacking.

[0040] According to a third aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned template stacking multi-sheet layout optimization method for special-shaped parts is implemented.

[0041] In general, the above technical solutions conceived by the present invention have the following technical advantages compared with the existing technology:

[0042] 1. For the layout of large quantities of special-shaped parts with multiple plates, the traditional layout method usually designs a template for each plate, only considering improving utilization and saving costs by saving materials. In fact, for the layout production of large quantities of special-shaped parts with multiple plates, frequent replacement of layout templates, repeated adjustment of cutting parameters, and even operations such as replacement of cutting tools will increase additional costs such as labor costs. Overall, it will even significantly increase production costs. The present invention takes into account both utilization and production efficiency, and will be able to use the same template for stacking and layout of parts. It minimizes the number of layout templates while meeting production utilization requirements, thereby greatly reducing production time, effectively reducing production costs, and significantly improving production efficiency.

[0043] 2. The present invention has designed a principle of giving priority to large-area parts, which not only improves the utilization rate of materials, but also simplifies the difficulty of layout. Large parts occupy more space on the plate due to their large size, and the room for adjustment after placement is relatively small. By arranging the large parts first, the occupancy of larger areas on the plate can be determined. When arranging small parts later, the remaining irregular spaces between the large parts can be more accurately utilized to fill these gaps, thereby reducing the waste area on the plate, improving the overall material utilization rate, and reducing production costs. In addition, the number of large parts is usually less than that of small parts. Arranging them first can reduce the number and complexity of parts that need to be considered later. Moreover, after the shape and position of the large parts are determined, a boundary and reference are provided for the arrangement of small parts, making the arrangement of small parts more targeted and operational, reducing the complexity and calculation amount of the entire arrangement process, and improving the arrangement efficiency.

[0044] 3. In the process of optimizing production efficiency and utilization rate, the present invention takes the plate utilization rate as a constraint condition, requiring that all generated solutions are feasible solutions that meet the utilization rate requirements. On this basis, iterative optimization of production efficiency is performed, which effectively simplifies the search process and improves the efficiency of the algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 The overall flow chart of the optimization method for arranging special-shaped parts with multiple sheets by stacking templates provided by an embodiment of the present invention;

[0046] Figure 2 A flow chart of a template stacking algorithm provided in an embodiment of the present invention;

[0047] Figure 3 A schematic diagram of part coding provided by an embodiment of the present invention;

[0048] Figure 4 A flow chart of a single plate arrangement algorithm provided in an embodiment of the present invention;

[0049] Figure 5 A schematic diagram of an algorithm testing example provided by an embodiment of the present invention;

[0050] Figure 6 (a) and (b) are schematic diagrams of the layout obtained by the traditional method and the method of the present invention, respectively. DETAILED DESCRIPTION

[0051] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0052] The embodiment of the present invention provides a method for optimizing the layout of multiple sheets of special-shaped parts with stacked templates, such as Figure 1 As shown, the following steps are included:

[0053] S1. Obtain the coordinates of each point of the special-shaped parts and encode the parts in sequence according to the type of parts and their corresponding quantity; initialize the population size N p Genetic parameters such as N are generated by template cascading algorithm p A set of templates constitutes the initial solution space.

[0054] Specifically, the total number of all parts is calculated based on the type of special-shaped parts and their corresponding quantities, and then sequentially coded. For example, if there are parts a, b, and c, and their corresponding quantities are 2, 2, and 1, respectively, these three parts can be coded as {1, 2, 3, 4, 5}.

[0055] Specifically, initialize the genetic parameters of the population, including the population size N p , crossover rate, mutation rate, number of iterations, etc., assuming N p =50, the initial solution space includes 50 template sets. Figure 2 As shown in the figure, the main function of the template stacking algorithm is to generate a template set that meets the utilization requirements and calculate the fitness value corresponding to the corresponding template set. Specifically, it includes the following steps:

[0056] S11, initialize the set of parts to be sorted, the template set to be empty, and initialize the number of templates N T and the number of plates N S If it is 0, all parts will be added to the set of parts to be sorted.

[0057] S12. Randomly disrupt the sequence of parts in the set of parts to be sorted, and ensure that the actual sequence before and after the disruption is inconsistent.

[0058] In this embodiment, Figure 3As shown, there are two parts of part a, corresponding to the codes {1, 2}; there are two parts of part b, corresponding to the codes {3, 4}; and there is only one part of part c, corresponding to the code {5}. Therefore, the part sequence codes {1, 2, 3, 4, 5} and {2, 1, 4, 3, 5} are actually the same part sequence. To address this, the present invention incorporates a duplicate detection mechanism to avoid this meaningless perturbation and improve algorithm efficiency.

[0059] S13. Select the first X parts in the set of parts to be sorted and calculate their total area S X .

[0060] S14, if S X ≤ plate area S0, then update X=X+1 and return to S13 to ensure that the template can achieve a high utilization rate; otherwise, sort the selected parts in descending order and enter S15 to give priority to parts with large areas to maximize utilization.

[0061] S15. Remove the last part from the selected parts in descending order to ensure that the remaining selected parts have a chance to be completely arranged into the plate, and put the last part back into the set of parts to be arranged.

[0062] S16. Arrange the remaining selected parts using the single plate arrangement algorithm to determine whether all parts can be placed in the plate. If so, obtain the current template and calculate the plate utilization rate U. X , go to S17; otherwise jump to S15.

[0063] like Figure 4 As shown in the figure, the main function of the single plate nesting algorithm is to determine whether all the currently selected parts can be completely placed in the plate and calculate the plate utilization rate. The algorithm includes the following sub-steps:

[0064] S161, initializing the list of parts to be arranged and the list of parts already arranged;

[0065] S162, calculating the outer critical polygons NFP of the selected part and all the arranged parts, and finding the union of all NFPs;

[0066] S163, calculate the inner critical polygon IFP of the current part and the plate, and update the critical polygon NFP where the part can be finally placed = IFP-NFP;

[0067] S164, based on the BL lower left placement strategy, select the local optimal reference point to place the part;

[0068] S165. The current part is added to the list of arranged parts, and the list of parts to be arranged is updated. If the list of parts to be arranged is empty, the process ends and the optimal result is output. Otherwise, jump to S162.

[0069] S17, if the plate utilization rate U X ≥ utilization threshold U0, then add the current template to the template set and calculate the maximum number of stacking layers N currently allowed stack , and update the number of templates N T =N T +1, number of plates N S =N S +N stack ; Otherwise jump to S12;

[0070] Specifically, the plate utilization rate U X , the maximum number of stacking layers currently allowed N stack The calculation formula is as follows:

[0071]

[0072] Among them, X represents the number of parts in the plate, S i represents the area of the i-th part, H represents the length of the minimum enveloping rectangle of all the parts arranged, and W represents the fixed width of the rectangular plate;

[0073]

[0074] Among them, n type Indicates the number of parts types contained in the current template, TotalNum j Indicates the total number of the j-th part in the set of parts to be sorted, TempNum j Indicates the number of the j-th part in the current template, Indicates the maximum number of layers that can be stacked for the jth part in the current template. type The minimum value of the maximum number of layers that can be stacked for this type of part, that is, the maximum number of stacking layers N allowed by the current template stack .

[0075] S18. Update the set of parts to be arranged. If the set of parts to be arranged is empty, output the template set; otherwise, jump to S12.

[0076] Specifically, the calculation formula for the fitness value of the template set is as follows:

[0077] fitness=αN T +βN S

[0078] Among them, α and β represent the weight coefficients of the number of templates and the number of plates respectively. Generally, these two coefficients are correlated, α+β=1. X≥Utilization threshold U0 is used as a constraint condition. The generated template sets are all feasible solutions that meet the utilization constraint. Therefore, the number of templates is given priority in the value of fitness. It is preferred to set α = 0.9 and β = 0.1.

[0079] S19, repeat steps S11 to S18 N p times, get N p template sets and the corresponding number of plates; each template set corresponds to a solution, forming the initial solution space.

[0080] S2, sort the solution space in descending order according to the fitness, and eliminate the N p * 1 / 2 of the inferior solutions, keep the top N p *1 / 2 of the better solutions are subjected to genetic operations;

[0081] Specifically, the solution space is sorted in descending order according to fitness, which makes it easier to eliminate inferior solutions and retain better solutions, thereby improving the quality of solutions. The better solutions are selected for genetic operations such as crossover and mutation, which increases the diversity of solutions while improving the quality of solutions.

[0082] S3. Calculate the fitness value of each solution after the genetic operation, and determine the change in the fitness value of each solution after the genetic operation. If the fitness value is better than the solution before the genetic operation, select the solution after the genetic operation to complete the solution space; otherwise, select the solution before the genetic operation to complete the solution space.

[0083] Specifically, after selecting Np*1 / 2 optimal solutions for genetic operation, the template stacking algorithm is called to calculate the fitness value fitness corresponding to the new solution. If the fitness value fitness corresponding to the new solution is new >fitness old , the new solution is retained to complete the solution space, otherwise, the original solution that has been selected but not genetically operated is retained to complete the solution space.

[0084] S4. Determine whether the iteration termination condition is met. If the iteration termination condition is met, output the final nesting result; otherwise, return to S2.

[0085] Specifically, determine whether the number of iterations G>G max If it is satisfied, the algorithm process terminates and the final solution space is obtained. The solution with the best fitness is selected as the final solution, and the corresponding number of templates, the layout diagram of each template and its corresponding number of stacking layers are output; otherwise, return to S2 to continue.

[0086] The effectiveness of the method proposed in the present invention is verified as follows:

[0087] Six parts from the standard test cases collected by the European Special Interest Group on Nesting Problems (ESICUP) were selected as test cases, such as Figure 5 As shown. The plate length L = 50, the plate width W = 50, there are 18 parts to be arranged, the parts are allowed to rotate 90 degrees, and the plate utilization threshold U0 = 70%. The traditional nesting method that only considers the plate utilization and the nesting method proposed by the present invention that minimizes the number of templates while meeting the utilization are used to calculate the nesting results of the test case. The nesting layout diagram is shown in the figure below. Figure 6 shown.

[0088] Figure 6 (a) is the result of the traditional method, which requires 3 templates, and the number of stacking layers of each template is 1, that is, a total of 3 plates are used. The utilization rates of the 3 plates are 76.03%, 74.96%, and 71.5% respectively, and the average utilization rate is 74.16%. The fitness index is front =3. Figure 6 (b) is the layout result of the method of the present invention, which only requires one template and the number of stacking layers is 3, that is, three plates are used, the plate utilization rate is 73.74%, and the fitness index is fitness. back =1.2. Obviously, the plate utilization rates of the two layout methods are not much different, but the method of the present invention significantly optimizes the number of templates, that is, the optimization method for layout of special-shaped parts with stacked templates provided by the present invention can effectively improve production efficiency.

[0089] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for optimizing the layout of multiple sheets of special-shaped parts with stacked templates, characterized in that: Genetic algorithm is used to iteratively solve the template set with the best fitness, thus realizing the optimization of multi-sheet layout for special-shaped parts. The acquisition of the initial solution space of the genetic algorithm includes the following steps: S11, initialize the set of parts to be sorted, the template set is empty, the number of templates is N T 、Number of plates N S If it is 0, all the parts to be sorted will be added to the set of parts to be sorted; S12, randomly disrupting the sequence of parts in the set of parts to be sorted; S13. Select the first X parts from the set of parts to be sorted and calculate the total area S of the first X parts. X ; S14, if S X ≤S0, where S0 is the plate area, then update X=X+1 and return to S13; otherwise, select parts and sort them in descending order of area; S15, removing the last part from the selected parts, thereby updating the selected parts, and placing the removed last part back into the set of parts to be sorted; S16. Arrange the selected parts on a single plate to determine whether all the selected parts can be placed in the plate; if so, obtain the arrangement template and calculate the plate utilization rate U X ; Otherwise return to S15; S17, if U X If the utilization rate is not less than the preset threshold, the current nesting template is added to the template set and the number of templates N is updated. T =N T +1, number of plates N S =N S +N stack , N stack The maximum number of stacking layers currently allowed; otherwise, return to S12; S18, update the set of parts to be arranged. If the set of parts to be arranged is empty, output the current template set; otherwise, return to S12; S19, repeat steps S11 to S18 N p times, get N p template sets; each template set corresponds to a solution, forming the initial solution space.

2. The method for optimizing layout of multiple sheets of special-shaped parts with template stacking according to claim 1, characterized in that: The genetic algorithm is used to iteratively solve the template set with the best fitness, which includes the following steps: S1. Get the initial solution space, the population size is N p ; S2, sort the solution space in descending order according to the fitness, and eliminate the N p * 1 / 2 of the inferior solutions, keep the top N p *1 / 2 of the better solutions are subjected to genetic operations; S3. Calculate the fitness value of each solution after the genetic operation, and determine the change in the fitness value of each solution after the genetic operation. If the fitness value is better than the solution before the genetic operation, then select the solution after the genetic operation to complete the solution space; otherwise, select the solution before the genetic operation to complete the solution space. S4. Repeat steps S2 and S3 until the iteration termination condition is reached, obtain the final solution space, and select the solution with the best fitness value as the final solution.

3. The method for optimizing layout of multiple sheets of special-shaped parts with template stacking according to claim 1, characterized in that: The calculation formula of the fitness is: fitness=αN T +βN S Where fitness represents fitness, α and β represent the weight coefficients of the number of templates and the number of plates, respectively, and α+β=1.

4. The method for optimizing layout of multiple sheets of special-shaped parts with template stacking according to claim 3, characterized in that: Set α=0.9, β=0.

1.

5. The method for optimizing layout of multiple sheets of special-shaped parts with template stacking according to claim 1, characterized in that: The plate utilization rate U X The calculation formula is: Among them, X represents the number of parts in the plate, S i represents the area of the i-th part, H represents the length of the minimum enveloping rectangle in which all parts have been arranged, and W represents the width of the rectangular plate.

6. The method for optimizing layout of multiple sheets of special-shaped parts with template stacking according to claim 1, characterized in that: The maximum number of stacking layers N currently allowed stack The calculation formula is: Among them, n type Indicates the number of parts types contained in the current template, TotalNum j Indicates the total number of the j-th part in the set of parts to be sorted, TempNum j Indicates the number of the j-th part in the current template, Indicates the maximum number of layers that can be stacked for the jth type of part in the current template.

7. The method for optimizing layout of multiple sheets of special-shaped parts with template stacking according to claim 1, characterized in that: In step S12, after randomly disrupting the part sequence in the set of parts to be sorted, a part sequence duplicate detection mechanism is introduced to ensure that the part sequences before and after the disruption are inconsistent.

8. The method for optimizing layout of multiple sheets of special-shaped parts with template stacking according to any one of claims 1 to 7, characterized in that: Step S16, arranging the selected parts on the plate, includes the following steps: S161, initialize the list of parts to be sorted and the list of sorted parts to be empty, and add all selected parts to the list of parts to be sorted; S162, sequentially selecting parts from the list of parts to be sorted, calculating the NFP of the current part and all the already sorted parts, and finding the union of all the NFPs; S163, calculate the inner critical polygon IFP of the current part and the plate, and update the critical polygon NFP where the part can be finally placed = IFP-NFP; S164, based on the BL lower left placement strategy, select the local optimal reference point to place the current part; S165. Add the current part to the list of arranged parts and remove it from the list of parts to be arranged. If the list of parts to be arranged is empty, the iteration ends and the arrangement result is output. Otherwise, return to S162.

9. A template stacking multi-plate layout optimization system for special-shaped parts, characterized by: The method comprises a processor for executing the method for optimizing the layout of multiple sheets of special-shaped parts stacked by templates according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for optimizing layout of multiple sheets of special-shaped parts stacked by templates according to any one of claims 1 to 8 is implemented.

Images