Pipe profile cutting optimization method considering beveling

Abstract

A pipe profile cutting optimization method considering beveling comprises the following steps that S1, Map data of line spacing of all beveling parts is generated, and data of subsequent layout can be rapidly obtained; s2, generating all feasible cutting columns of the pipe profile raw material with the maximum length, and ensuring a universal feasible solution; s3, screening feasible cutting columns for length compression under the condition of beveling, and performing feasibility judgment after prolonging the length of the raw material according to the beveling condition; s4, solving a one-dimensional blanking problem for the obtained ranks based on a Cplex integer programming model; and S5, further optimizing large-scale problems in the actual production process by adopting a solution space search scheme based on group iterative optimization, and solving the problems of different scales by adopting different strategies to call the integer programming model. According to the scheme, the pipe fittings are grouped through a certain optimization strategy to obtain the basic solutions for combination, the solving space of single integer programming is reduced, and therefore the whole problem solving time is greatly shortened.

Description

Technical Field

[0001] The invention relates to a pipe cutting strategy, in particular to a pipe profile cutting optimization method considering beveling. Background Art

[0002] The one-dimensional cutting stock problem (1D CSP) is a classic combinatorial optimization problem in production and operations management, and is widely used in manufacturing, wood processing, metal cutting, and textile industries. The goal of this problem is to arrange items of various required lengths under a given raw material length so that the amount of raw materials used is minimized or the utilization rate is maximized. In actual production, factories often need to cut parts of different lengths from raw materials. For example, in the parts manufacturing industry, tube profiles of various lengths are cut from tube blanks. However, direct cutting may generate a large amount of waste, which not only wastes resources but also increases production costs. Therefore, how to reasonably arrange the cutting sequence and plan to minimize the amount of waste is an important issue for enterprises to optimize production.

[0003] For traditional pipe fittings manufacturers, the production demand for pipe fittings is generally dominated by straight cutting. The optimization method for straight cutting of pipe profiles is relatively complete and mature, and classic one-dimensional cutting optimization algorithms such as greedy algorithm, dynamic programming, and column generation are often used. However, as industry needs gradually diversify, for different uses of pipe profiles and personalized needs of users, traditional pipe profile straight cutting layout algorithms do not consider the optimization of cross-sections at different angles, and cannot meet customers' diverse usage and installation needs. The existing technology performs poorly in terms of pipe profile utilization, resulting in a large amount of material being wasted. In addition, a single pipe profile is cut many times and a large amount of beveled excess material cannot be used for secondary nesting, further affecting production efficiency and pipe profile utilization.

[0004] However, if all possible permutations and combinations are combined, it may be feasible for a small number of pipe parts. However, if the demand for pipe parts is large and the specifications of the pipe profiles are complex, exhaustively listing all the arrangement schemes will make the number of arrangement schemes extremely large. The one-dimensional arrangement optimization problem is an NP-hard problem. If integer programming is directly used to solve the arrangement problem when the search and solution space is huge, the calculation will take a long time. This calculation method will not be able to ensure the convergence speed of the solution process, and ultimately the entire solution process will become very long. Summary of the invention

[0005] To address the above defects, a solution space search scheme based on group optimization is proposed. A large number of layout schemes are generated as the search space through a greedy strategy, and the pipe parts are grouped through certain optimization strategies to obtain basic solutions for combination, which reduces the solution space of single integer programming and greatly reduces the time for solving the entire problem.

[0006] To achieve this object, the present invention adopts the following technical solutions:

[0007] A method for optimizing pipe profile cutting considering beveling, comprising the following steps:

[0008] S1. Generate Map data of all the row spacing of beveled parts to facilitate the rapid acquisition of subsequent layout data;

[0009] S2. Generate all feasible cutting sequences for the maximum length tube profile material to ensure a universal feasible solution;

[0010] S3. Screen the feasible cutting columns that consider the length compression in the case of bevel cutting, and make a feasibility judgment after extending the length of the raw material in the case of bevel cutting;

[0011] S4, solving the one-dimensional stock cutting problem based on the obtained feasible series and the integer programming model of Cplex;

[0012] S5. For large-scale problems in the actual production process, a solution space search scheme based on group iterative optimization is used for further optimization. Different strategies are used to call the integer programming model to solve problems of different scales.

[0013] Preferably, in step S1, for a large number of parts containing bevel data, it is necessary to obtain the distance between any two parts when placed in any order, so as to search for the optimal layout scheme. The data of the compressible distance matrix is ​​obtained by performing collision detection after unfolding the parts. The standard lengths of a group of parts containing bevels are L1 and L2, the compressible distance is ΔL, and the combined length is L1+L2-ΔL.

[0014] The data of all beveled parts are processed and summarized into a part spacing data map.

[0015] Preferably, for the generation of the initial feasible solution of the tube profile cutting plan, a method is proposed to recursively and quickly generate all feasible cutting methods using the greedy idea, that is, first sort by the cutting length, within the range of the raw material length L, first arrange the long material blanks to generate the first cutting method, then take down a long material in the first cutting method, arrange a medium-long material, and then place the remaining material with shorter cuttings to form a new cutting method; each cutting method is formed on the basis of the latest generated cutting method, and only needs to change the number of cuttings to generate another new cutting method, which reduces a lot of unnecessary calculations and can quickly generate all feasible methods; in each cutting method, the number A of the nth type of parts n Should not be greater than the demand for the part b n , where the blanks are arranged from large to small in length from l1 to l n, the current remaining length of the i-th cutting material in the process of generating the cutting method is S i (i=1,2,3,…,n) represents.

[0016] Preferably, in step S3, since the pipe-shaped parts to be arranged may contain one or two beveled edges, if the pipe-shaped parts are arranged according to the maximum axial distance of the pipe-shaped parts, the length will inevitably be shortened when the single pipe-shaped parts are subsequently arranged. Therefore, when the pipe-shaped parts containing beveled edges are equivalently replaced with straight-cut pipe-shaped parts, it is necessary to increase the length of the pipe-shaped material by a certain length L′, thereby expanding the solution space range;

[0017] Feasibility judgment of oblique cutting layout: The series of layout solutions obtained by the above greedy strategy recursive method may include the optimal layout solution of the original problem, or may not meet the raw material length no matter how they are sorted. It is necessary to judge the feasibility of the solution to screen out the layout solution that meets the constraints of the original problem. Therefore, a reasonable method is needed to evaluate the feasibility of each layout optimization solution;

[0018] The actual length of the raw material tube profile is used as the evaluation index, that is, the raw material utilization rate of each scheme is calculated through a greedy algorithm, and the scheme that meets the length constraint and has the highest utilization rate is selected from them, aiming to maximize the screening and reduce the cost of single raw material layout, so that the total cost of cutting and blanking is minimized. The process of judging the feasibility of beveling for the initial layout scheme is as follows:

[0019] Step 1: Initialize the plan information in the nesting set, select a nesting plan, and obtain the type and quantity of the pipe parts in the plan;

[0020] Step 2: Randomly select a pipe with a length of l0 from the plan for initial placement;

[0021] Step 3: Obtain the tube-shaped part l with the largest compressible distance on the left side of the tube-shaped part l0 in the solution from the map a And the tube-shaped part l with the largest compressible distance on the right side of the tube-shaped part b , directly obtain the total compressible distance Δl through map; for the actual oblique cutting length l m Perform calculations;

[0022] l m =l0+l a +l b -Δl(3.1)

[0023] When l m >L, jump to step 2 and reselect the pipe parts for initial placement;

[0024] When l m <=L, jump to step 4;

[0025] Step 4: Update the length of l0 so that the arrangement of the tubular parts combination is considered as a whole for subsequent tubular parts addition;

[0026] Step 5: Determine whether all the tubes in the scheme have been arranged. If not, jump to step 4. If they have been arranged, retain the best scheme, that is, the scheme with the shortest total arrangement length.

[0027] Step 6: Determine whether the solution set has been sorted. If not, jump to step 2 and select a sorting solution. If it has been sorted, the algorithm ends.

[0028] Preferably, in step S4: given a single-specification long strip raw material tube profile, the length of which is L, there are n types of oblique cylindrical tube parts of different specifications to be cut, and the length of each workpiece is l i , the number is d i , i∈[1,n], requiring that all types of tube parts be arranged on the tube profile without overlapping, so that the amount of tube profile consumed in cutting is minimal;

[0029] Assume that the set of feasible layout methods P = [p1, p2, ..., p g ] contains g kinds of arrangement methods, and the number of times the jth arrangement method is used is x j The number of tube-shaped parts of the i-th type included in this arrangement is a ij (i=1,2,…,n);

[0030] The integer programming model with the minimum number of pipe parts as the optimization goal is as follows:

[0031] The solution goal is:

[0032] The constraints are:

[0033] For this type of integer programming problem, this paper uses the Cplex solver to quickly find the optimal solution to the target value.

[0034] Further, in step S5: since the number of solutions generated by the recursive algorithm of the above greedy strategy cannot be determined, in integer programming, one variable in the objective function corresponds to one packing solution and a feasible column in the constraint condition. For a solution space with a small column size, integer programming can be directly used for solving to ensure the quality and speed of the solution. However, for a problem with a large column size and hundreds of thousands or even millions of variables in the objective function, directly using integer programming will result in an extremely long solution time and the quality of the solution cannot be guaranteed. Therefore, it is necessary to quickly and accurately find the base column in the huge solution space and eliminate useless packing solutions. The solution space search scheme based on grouping optimization in this method is as follows:

[0035] First, the size n of the layout scheme generated by the problem is judged. If the size n is less than or equal to 10,000, the pipe profile layout integer programming is directly used for solving the problem. The total number of raw materials used and the number of times each scheme is used can be quickly obtained.

[0036] If the solution size is greater than n>10000, the group optimization search method is used to divide the solutions into m groups with equal spacing. The solution S of the i-th group i The number of feasible columns in is s i (i=1,2,...,m), satisfying The large-scale solution space is divided into multiple small-scale solution spaces for solution, but for each solution space, the integer

[0037] The solution obtained by planning is only a local optimal solution. If you want to solve the global optimal solution, you need to combine the basis sequences of each solution space;

[0038] For S1, we can solve its basis solution P1 by integer programming, and the corresponding basis column is R1. Then, for S2, we need to expand the column size and set S2 = {S2, R1} to ensure that the solution of S2 fully considers the optimal solution of S1. Similarly, for S i Perform integer programming and let S i ={S i ,R1,R2,...,R i-1}, we can solve for the base sequence R i , if i = n, the solution of the basic solution P n That is the global optimal solution, Gilead R n This is the global optimal solution.

[0039] One of the above technical solutions includes the following beneficial effects:. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a schematic diagram of an example of a beveled tube introduced in the background of the present invention;

[0041] Figure 2 This is the overall algorithm implementation idea of ​​an embodiment of the present invention;

[0042] Figure 3 is a schematic diagram of the unfolding of parts of an embodiment of the present invention, from which it can be seen how the compressible distance ΔL is generated;

[0043] Figure 4 is a flowchart of greedy recursive feasible column generation according to an embodiment of the present invention;

[0044] Figure 5 is a flow chart of the feasibility determination of bevel cutting according to one embodiment of the present invention;

[0045] Figure 6 This is a diagram of a pipe cutting optimization system - straight cutting case according to an embodiment of the present invention;

[0046] Figure 7 This is a pipe cutting optimization system - bevel cutting case diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0047] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0048] like Figure 2 As shown, a tube profile cutting optimization method considering beveling includes the following steps:

[0049] S1. Generate Map data of all the row spacing of beveled parts to facilitate the rapid acquisition of subsequent layout data;

[0050] S2. Generate all feasible cutting sequences for the maximum length tube profile material to ensure a universal feasible solution;

[0051] S3. Screen the feasible cutting columns that consider the length compression in the case of bevel cutting, and make a feasibility judgment after extending the length of the raw material in the case of bevel cutting;

[0052] S4, solving the one-dimensional stock cutting problem based on the obtained feasible series and the integer programming model of Cplex;

[0053] S5. For large-scale problems in the actual production process, a solution space search scheme based on group iterative optimization is used for further optimization. Different strategies are used to call the integer programming model to solve problems of different scales.

[0054] The present invention uses a pipe bevel optimization algorithm based on equivalent substitution, and regards the pipe-shaped parts containing bevel edges as "compressible" pipe-shaped parts. The length of such pipe-shaped parts is not a fixed value, and its length will vary with its arrangement position on the pipe-shaped parts, but will not exceed its axial maximum value, that is, arranging specific pipe-shaped parts in a certain order may "compress" a part of the length, and the compressed length will not exceed the sum of the axial distances of all its bevel edges. Therefore, a certain extension length can be added to the length of the original pipe-shaped parts as compressible space, and a single bevel layout algorithm is called to screen the layout scheme that meets the compression conditions, and the bevel problem is converted into a straight cutting problem for integer programming solution, thereby reducing the complexity of the algorithm, improving the optimization efficiency of the algorithm, and ensuring the quality of the solution.

[0055] like Figure 3 As shown, in step S1, for a large number of parts containing bevel data, it is necessary to obtain the distance between any two parts when placed in any order, so as to search for the optimal layout solution. The data of the compressible distance matrix is ​​obtained by performing collision detection after unfolding the parts. The standard lengths of a group of parts containing bevels are L1 and L2, the compressible distance is ΔL, and the combined length is L1+L2-ΔL.

[0056] The data of all beveled parts are processed and summarized into a part spacing data map.

[0057] In this way, the bevel features of the parts are converted into compressible specific length values ​​and corresponded through the distance matrix. The data query is concise and intuitive, which greatly simplifies the distance calculation of subsequent layout.

[0058] like Figure 4 As shown in the figure, for the generation of the initial feasible solution of the tube profile cutting plan, a method is proposed to recursively and quickly generate all feasible cutting methods using the greedy idea, that is, first sort by the cutting length, within the range of the raw material length L, first arrange the long material blanks to generate the first cutting method, then take down a long material in the first cutting method, arrange a medium-long material, and then put the remaining material with shorter cuttings to form a new cutting method; each cutting method is formed on the basis of the latest generated cutting method, and only needs to change the number of cuttings to generate another new cutting method, which reduces a lot of unnecessary calculations and can quickly generate all feasible methods; in each cutting method, the number A of the nth type of parts n Should not be greater than the demand for the part b n , where the blanks are arranged from large to small in length from l1 to l n , the current remaining length of the i-th cutting material in the process of generating the cutting method is S i (i=1,2,3,…,n) represents.

[0059] A series of initial solutions are quickly generated through a greedy strategy, which reduces the complexity of algorithm design while ensuring the quality of the generated solutions.

[0060] like Figure 5 As shown, in step S3, since the pipe-shaped parts to be arranged may contain one or two beveled edges, if the pipe-shaped parts are arranged according to the maximum axial distance of the pipe-shaped parts, the length will inevitably be shortened when the single pipe-shaped parts are subsequently sorted. Therefore, when the pipe-shaped parts containing beveled edges are equivalently replaced with straight-cut pipe-shaped parts, it is necessary to increase the length of the pipe-shaped material by a certain length L′, thereby expanding the solution space range;

[0061] Feasibility judgment of oblique cutting layout: The series of layout solutions obtained by the above greedy strategy recursive method may include the optimal layout solution of the original problem, or may not meet the raw material length no matter how they are sorted. It is necessary to judge the feasibility of the solution to screen out the layout solution that meets the constraints of the original problem. Therefore, a reasonable method is needed to evaluate the feasibility of each layout optimization solution;

[0062] The actual length of the raw material tube profile is used as the evaluation index, that is, the raw material utilization rate of each scheme is calculated through a greedy algorithm, and the scheme that meets the length constraint and has the highest utilization rate is selected from them, aiming to maximize the screening and reduce the cost of single raw material layout, so that the total cost of cutting and blanking is minimized. The process of judging the feasibility of beveling for the initial layout scheme is as follows:

[0063] Step 1: Initialize the plan information in the nesting set, select a nesting plan, and obtain the type and quantity of the pipe parts in the plan;

[0064] Step 2: Randomly select a pipe with a length of l0 from the plan for initial placement;

[0065] Step 3: Obtain the tube-shaped part l with the largest compressible distance on the left side of the tube-shaped part l0 in the solution from the map a And the tube-shaped part l with the largest compressible distance on the right side of the tube-shaped part b , directly obtain the total compressible distance Δl through map; for the actual oblique cutting length l m Perform calculations;

[0066] l m =l0+l a +l b -Δl(3.1)

[0067] When l m >L, jump to step 2 and reselect the pipe parts for initial placement;

[0068] When l m <=L, jump to step 4;

[0069] Step 4: Update the length of l0 so that the arrangement of the tubular parts combination is considered as a whole for subsequent tubular parts addition;

[0070] Step 5: Determine whether all the tubes in the scheme have been arranged. If not, jump to step 4. If they have been arranged, retain the best scheme, that is, the scheme with the shortest total arrangement length.

[0071] Step 6: Determine whether the solution set has been sorted. If not, jump to step 2 and select a sorting solution. If it has been sorted, the algorithm ends.

[0072] The legitimacy of the nesting scheme is verified by a heuristic algorithm. By finding the parts with the largest compressible distance in the scheme from both ends, it is possible to quickly verify whether the generated scheme meets the length constraint of the bevel nesting, thus ensuring the feasibility of the scheme nesting.

[0073] In addition, in step S4: given a single-specification long strip raw material tube profile, the length of which is L, there are n types of oblique cylindrical tube parts of different specifications that need to be cut, and the length of each workpiece is l i , the number is d i , i∈[1,n], requiring that all types of tube parts be arranged on the tube profile without overlapping, so that the amount of tube profile consumed in cutting is minimal;

[0074] Assume that the set of feasible layout methods P = [p1, p2, ..., p g ] contains g kinds of arrangement methods, and the number of times the jth arrangement method is used is x j The number of tube-shaped parts of the i-th type included in this arrangement is a ij (i=1,2,…,n);

[0075] The integer programming model with the minimum number of pipe parts as the optimization goal is as follows:

[0076] The solution goal is:

[0077] The constraints are:

[0078] For this type of integer programming problem, this paper uses the Cplex solver to quickly find the optimal solution to the target value.

[0079] The solver is used to accurately solve the integer programming problem, ensuring the quality of the solution.

[0080] In addition, in step S5: since the number of solutions generated by the recursive algorithm of the above greedy strategy cannot be determined, in integer programming, one variable in the objective function corresponds to one packing scheme and a feasible column in the constraint condition. For a solution space with a small column size, integer programming can be directly used for solving to ensure the quality and speed of the solution. However, for a problem with a large column size and hundreds of thousands or even millions of variables in the objective function, directly using integer programming will result in an extremely long solution time and the quality of the solution cannot be guaranteed. Therefore, it is necessary to quickly and accurately find the base column in the huge solution space and eliminate useless packing schemes. The solution space search scheme based on grouping optimization in this method is as follows:

[0081] First, the size n of the layout scheme (feasible series) generated by the problem is judged. If the size n of the scheme is less than or equal to 10,000, the pipe profile layout integer programming is directly used for solving the problem. The total number of raw materials used and the number of times each scheme is used can be quickly obtained.

[0082] If the solution size is greater than n>10000, the group optimization search method is used to divide the solutions into m groups with equal spacing. The solution S of the i-th group i The number of feasible columns in is s i (i=1,2,...,m), satisfying The large-scale solution space is divided into multiple small-scale solution spaces for solution, but for each solution space, the integer

[0083] The solution obtained by planning is only a local optimal solution. If you want to solve the global optimal solution, you need to combine the basis sequences of each solution space;

[0084] For S1, we can solve its basis solution P1 by integer programming, and the corresponding basis column is R1. Then, for S2, we need to expand the column size and set S2 = {S2, R1} to ensure that the solution of S2 fully considers the optimal solution of S1. Similarly, for S i Perform integer programming and let S i ={S i ,R1,R2,...,R i-1}, we can solve for the base sequence R i , if i = n, the solution of the basic solution P n That is the global optimal solution, Gilead R n This is the global optimal solution.

[0085] Problems of different scales are discussed in different categories, and different solving strategies are used to accelerate the algorithm. The quality of solutions is guaranteed for small-scale problems, while fast solutions are guaranteed for large-scale problems, which meets the actual production needs of enterprises.

[0086] Computational experiments and analysis

[0087] 1) Computational experiments

[0088] In order to verify the feasibility and effectiveness of the equivalent substitution-based tube bevel optimization algorithm and the group optimization solution space search scheme, actual production order data was obtained from a tube cutting customization enterprise for iterative optimization and testing. The iterative optimization algorithm was implemented in Java and compiled by Eclipse 3.7. All experiments were performed on an Intel i7 CPU with a main frequency of 3.4 GHz (quad-core) and a Windows 7 SP1 operating system with 8 GB RAM.

[0089] 2) Example Analysis In order to verify the feasibility and effectiveness of the proposed pipe profile layout optimization algorithm, the algorithm reported in the relevant literature and the test examples provided by the actual production enterprise were tested. At the same time, in order to realize and promote the industrial application of the proposed algorithm, a corresponding software system was developed. In order to verify the optimization effect and feasibility of the algorithm in different application scenarios of straight cutting and bevel cutting, this paper designed two groups of experiments as follows:

[0090] Experiment 1: The test is for 10 groups of straight cutting test data of different lengths of tube profiles provided by tube profile manufacturer A. The parts are 25 sets of square tubes of the same specifications, and the length range of the tube profiles is 5000 to 12500.

[0091] Experiment 2: The test is based on some actual production data provided by tube profile manufacturer A in August 2024, which is divided into 10 groups in total. The tube profile lengths and part specifications may be different. The tube profile types include square tubes, rectangular tubes and angle steels.

[0092] 3) Experiment 1 Calculation and Analysis

[0093] Experiment 1 uses 10 sets of tube profile data provided by tube profile enterprise A for straight cutting test, and compares the calculation results with the current mainstream commercial software for tube profile cutting at home and abroad, as shown in Table 2. From the comparison results of the arrangement in the table, it can be seen that the total experimental test results of this algorithm are significantly better than the two mainstream commercial software. The number of tube profiles used in the data sets No. 2, 3, and 4 is 1 less than that of the commercial software, and the algorithm running time is 2.831s, 5.025s, and 20.357s respectively. The other data sets are the same as the optimization results of the commercial software. The total number of tube profiles reduced by 3 in the 10 data case results, and the algorithm running time is less than 21s. The experimental results of the algorithm in this chapter are significantly better than the two current commercial software, which can save more raw materials and have higher cutting efficiency.

[0094] Matching tube profile cutting system such as Figure 6 As shown, the cutting arrangement of parts of different specifications and lengths is visualized, including different cutting methods and cutting times. For each cutting method, the dark part is the sampled parts and the white part is the cutting residue.

[0095] Table 2 Comparison of straight cutting test data

[0096]

[0097] 3) Experiment 2 Calculation and Analysis

[0098] Experiment 2 uses 10 sets of pipe profile data provided by pipe profile company A for bevel cutting test. The types of pipe profiles include square pipes, rectangular pipes and angle steels. The calculation results are compared with the current mainstream commercial software for pipe profile cutting at home and abroad, as shown in Table 3. From the sample arrangement results in the table, it can be seen that the total experimental test results of this algorithm are only 1 root different from the comprehensive optimal results of the two mainstream commercial software, and the algorithm running time is less than 21s. Among them, the result of sequence number 8 is 1 less than the optimal result of the commercial software, and the algorithm running time is 3.66s, while sequence numbers 5 and 10 are 1 more than the comprehensive results of the commercial software, and the algorithm running time is 2.85s and 1.19s respectively. The experimental results of the algorithm in this chapter are significantly better than the optimization results of commercial software 1, and the overall difference is 1 root compared with commercial software 2.

[0099] Matching tube profile cutting system such as Figure 7 As shown, the cutting arrangement of parts of different specifications and lengths in different task batches is visualized, including different cutting methods and cutting times. For each cutting method, the dark part is the arranged parts, the white part is the cutting waste, and the overlapping part between two adjacent parts represents the bevel compression length.

[0100] Table 3 Comparison of bevel cutting test data

[0101]

[0102] The technical principle of the present invention is described above in conjunction with specific embodiments. These descriptions are only for explaining the principle of the present invention and cannot be interpreted as limiting the scope of protection of the present invention in any way. Based on the explanations herein, those skilled in the art can associate other specific implementations of the present invention without paying creative labor, and these methods will fall within the scope of protection of the present invention.

Claims

1. A method for optimizing tube profile cutting considering beveling, characterized in that: The following steps are involved: S1. Generate Map data of all the row spacing of beveled parts to facilitate the rapid acquisition of subsequent layout data; S2. Generate all feasible cutting sequences for the maximum length tube profile material to ensure a universal feasible solution; S3. Screen the feasible cutting columns that consider the length compression in the case of bevel cutting, and make a feasibility judgment after extending the length of the raw material in the case of bevel cutting; S4, solving the one-dimensional stock cutting problem based on the obtained feasible series and the integer programming model of Cplex; S5. For large-scale problems in the actual production process, a solution space search scheme based on group iterative optimization is used for further optimization. Different strategies are used to call the integer programming model to solve problems of different scales.

2. The tube profile cutting optimization method considering beveling according to claim 1, characterized in that: In step S1, for a large number of parts containing bevel data, it is necessary to obtain the distance between any two parts when placed in any order, so as to search for the optimal layout solution. The data of the compressible distance matrix is ​​obtained by performing collision detection after unfolding the parts. The standard lengths of a group of parts containing bevels are L1 and L2, the compressible distance is ΔL, and the combined length is L1+L2-ΔL; The data of all beveled parts are processed and summarized into a part spacing data map.

3. The tube profile cutting optimization method considering beveling according to claim 2, characterized in that: For the generation of the initial feasible solution of the tube profile cutting plan, a method is proposed to recursively and quickly generate all feasible cutting methods using the greedy idea, that is, first sort by the cutting length, and within the range of the raw material length L, first arrange the long material blanks to generate the first cutting method, and then take down a long material in the first cutting method, arrange a medium-long material, and then put the remaining material with shorter cuttings to form a new cutting method; each cutting method is formed on the basis of the latest generated cutting method, and only needs to change the number of cuttings to generate another new cutting method, which reduces a lot of unnecessary calculations and can quickly generate all feasible methods; in each cutting method, the number A of the nth type of parts n Should not be greater than the demand for the part b n , where the blanks are arranged from large to small in length from l1 to l n , the current remaining length of the i-th cutting material in the process of generating the cutting method is S i (i=1,2,3,…,n) represents.

4. The tube profile cutting optimization method considering beveling according to claim 3, characterized in that: In step S3, Since the pipe parts to be arranged may contain one or two bevels, if the arrangement is carried out according to the maximum axial distance of the pipe parts, the length will inevitably be shortened when the single pipe parts are subsequently arranged. Therefore, when the pipe parts with bevels are replaced by straight-cut pipe parts, it is necessary to increase the length of the pipe material by a certain length L′, thereby expanding the solution space range; Feasibility judgment of oblique cutting layout: The series of layout solutions obtained by the above greedy strategy recursive method may include the optimal layout solution of the original problem, or may not meet the raw material length no matter how they are sorted. It is necessary to judge the feasibility of the solution to screen out the layout solution that meets the constraints of the original problem. Therefore, a reasonable method is needed to evaluate the feasibility of each layout optimization solution; The actual length of the raw material tube profile is used as the evaluation index, that is, the raw material utilization rate of each scheme is calculated through a greedy algorithm, and the scheme that meets the length constraint and has the highest utilization rate is selected from them, aiming to maximize the screening and reduce the cost of single raw material layout, so that the total cost of cutting and blanking is minimized. The process of judging the feasibility of beveling for the initial layout scheme is as follows: Step 1: Initialize the plan information in the nesting set, select a nesting plan, and obtain the type and quantity of the pipe parts in the plan; Step 2: Randomly select a pipe with a length of l0 from the plan for initial placement; Step 3: Obtain the tube-shaped part l with the largest compressible distance on the left side of the tube-shaped part l0 in the solution from the map a And the tube-shaped part l with the largest compressible distance on the right side of the tube-shaped part b , directly obtain the total compressible distance Δl through map; for the actual oblique cutting length l m Perform calculations; he m =l0+l a +l b -Δl(3.1) When l m >L, jump to step 2 and reselect the pipe parts for initial placement; When l m <=L, jump to step 4; Step 4: Update the length of l0 so that the arrangement of the tubular parts combination is considered as a whole for subsequent tubular parts addition; Step 5: Determine whether all the tubes in the scheme have been arranged. If not, jump to step 4. If they have been arranged, retain the best scheme, that is, the scheme with the shortest total arrangement length. Step 6: Determine whether the solution set has been sorted. If not, jump to step 2 and select a sorting solution. If it has been sorted, the algorithm ends.

5. The tube profile cutting optimization method considering beveling according to claim 4, characterized in that: In step S4: Given a single-size long strip raw tube profile, the length of which is L. There are n types of oblique cylindrical tube parts of different sizes that need to be cut, and the length of each workpiece is l. i , the number is d i , i∈[1,n], requiring that all types of tube parts be arranged on the tube profile without overlapping, so that the amount of tube profile consumed in cutting is minimal; Assume that the set of feasible layout methods P = [p1, p2, ..., p g ] contains g kinds of arrangement methods, and the number of times the jth arrangement method is used is x j The number of tube-shaped parts of the i-th type included in this arrangement is a ij (i=1,2,…,n); The integer programming model with the minimum number of pipe parts as the optimization goal is as follows: The solution goal is: The constraints are: For this type of integer programming problem, this paper uses the Cplex solver to quickly find the optimal solution to the target value.

6. The tube profile cutting optimization method considering beveling according to claim 5, characterized in that: In step S5: Since the number of solutions generated by the recursive algorithm of the above greedy strategy cannot be determined, in integer programming, one variable in the objective function corresponds to one packing scheme and a feasible column in the constraint condition. For a solution space with a small column size, integer programming can be used directly to solve it, ensuring the quality and speed of the solution. However, for problems with a large column size and hundreds of thousands or even millions of variables in the objective function, directly using integer programming will result in an extremely long solution time and the quality of the solution cannot be guaranteed. Therefore, it is necessary to quickly and accurately find the base column in the huge solution space and eliminate useless packing schemes. The solution space search scheme based on grouping optimization in this method is as follows: First, the size n of the layout scheme generated by the problem is judged. If the size n is less than or equal to 10,000, the pipe profile layout integer programming is directly used for solving the problem. The total number of raw materials used and the number of times each scheme is used can be quickly obtained. If the solution size is greater than n>10000, the group optimization search method is used to divide the solutions into m groups with equal spacing. The solution S of the i-th group i The number of possible sequences in is s i (i=1,2,...,m), satisfying The large-scale solution space is divided into multiple small-scale solution spaces for solution, but for each solution space, the integer The solution obtained by planning is only a local optimal solution. If you want to solve the global optimal solution, you need to combine the basis sequences of each solution space; For S1, we can solve its basis solution P1 by integer programming, and the corresponding basis column is R1. Then, for S2, we need to expand the column size and set S2 = {S2, R1} to ensure that the solution of S2 fully considers the optimal solution of S1. Similarly, for S i Perform integer programming and let S i ={S i ,R1,R2,...,R i-1 }, we can solve for the base sequence R i , if i = n, the solution of the basic solution P n That is the global optimal solution, Gilead R n This is the global optimal solution.

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