CN112308283B - Multi-specification and multi-target one-dimensional blanking method - Google Patents
Multi-specification and multi-target one-dimensional blanking method Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 55
- 238000005457 optimization Methods 0.000 claims abstract description 62
- 238000005520 cutting process Methods 0.000 claims abstract description 45
- 239000011159 matrix material Substances 0.000 claims description 37
- 239000000463 material Substances 0.000 claims description 31
- 101100269449 Arabidopsis thaliana AHK4 gene Proteins 0.000 claims description 7
- 101000997281 Rattus norvegicus Potassium voltage-gated channel subfamily C member 1 Proteins 0.000 claims description 6
- 239000002994 raw material Substances 0.000 abstract description 15
- 238000007599 discharging Methods 0.000 abstract description 4
- 238000004519 manufacturing process Methods 0.000 description 11
- 238000004364 calculation method Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 7
- 229910000831 Steel Inorganic materials 0.000 description 2
- 239000013072 incoming material Substances 0.000 description 2
- 239000010959 steel Substances 0.000 description 2
- 229910001294 Reinforcing steel Inorganic materials 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 239000011664 nicotinic acid Substances 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
- 238000002922 simulated annealing Methods 0.000 description 1
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q50/04—Manufacturing
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The invention relates to a multi-specification and multi-target one-dimensional blanking method, which is characterized in that a multi-specification and multi-target one-dimensional blanking method is established on the basis of an integer linear programming and exhaustion method, raw material specifications can be obtained through optimization according to a specified range, the number of raw material specifications can be specified, a convenient and quick blanking algorithm is adopted, and a required specification number, a blanking cutting table, the utilization rate of each specification and a complete discharging table are obtained through primary optimization, secondary optimization and consideration of the influence of a cutting seam.
Description
Technical Field
The invention relates to a multi-specification and multi-target one-dimensional blanking method, which is characterized in that on the basis of conventional integer linear programming, first optimization is performed, and if the number of the obtained material specifications exceeds the number of expected specifications, second optimization is performed to obtain an optimal blanking scheme of the number of expected specifications.
Background
The one-dimensional blanking refers to a blanking method for cutting a raw material (such as steel pipes, reinforcing steel bars and various types of steel) in the length direction to form a plurality of sections of parts (or short materials). In actual production processes, there are blanking orders containing a large number of parts of different sizes, all of which require simultaneous layout designs (including layout order, number of times of layout, cut length) on existing raw materials. The layout design is determined according to the types of the parts in the order and the length of each part, a blanking scheme meeting the requirement of the parts is often formed by combining a plurality of layout designs, and the combination with the highest utilization rate is selected in all possible combination conditions.
The one-dimensional blanking problem is a problem frequently encountered in the production process, and as the blanking scale and the blanking quantity are increased, the more complex the layout design is, the more the layout design has an outstanding effect on the material utilization rate. At present, the optimal design of a one-dimensional blanking scheme mainly improves the utilization rate of raw materials as much as possible through the optimization of a layout combination, and reduces the cost of the raw materials. The common optimization methods are as follows:
(1) The conventional integer linear programming method has the advantages of being capable of accurately obtaining the optimal solution position, and being more in specification after optimization, slower in calculation speed and unfavorable for direct guidance of production.
(2) Heuristic algorithm, which includes ant colony algorithm, simulated annealing algorithm, neural network algorithm, genetic algorithm and other bionic algorithms, has the advantages of adjusting key parameters to make the solving speed relatively fast, and has the disadvantages that the key parameter selection has no quantitative method and the optimal solution position cannot be determined accurately.
Disclosure of Invention
The design purpose is as follows: the one-dimensional blanking problem in the production process is common, and the one-dimensional blanking problem is that the material specification is large, the part specification is large and the number is huge; secondly, the requirements of raw material manufacturers on specifications and quantity are met, and the specifications are not excessive; thirdly, the material utilization rate requirement is high, and the statistics and reasonable utilization of the residual materials are realized; and fourthly, the product needs to be clearly numbered and identified, the production needs can be directly met, a multi-specification and multi-target one-dimensional blanking method can be designed, on the basis of conventional integer linear programming, primary optimization is performed, and if the number of the obtained material specifications exceeds the number of expected specifications, secondary re-optimization is performed, so that the optimal blanking scheme with the number of the expected specifications is obtained.
The design scheme is as follows: in order to achieve the above design objective. The invention designs a multi-specification and multi-target one-dimensional blanking method, which is optimized once on the basis of conventional integer linear programming, and then optimized again for the second time if the number of the obtained material specifications exceeds the number of expected specifications, so as to obtain the optimal blanking scheme of the number of expected specifications. The method not only can complete operation faster and greatly improve the overall requirement of the material utilization rate, but also considers the specific requirements in the production process of cutting seam influence, product identification number, residue calculation and the like.
The invention provides a multi-specification and multi-target one-dimensional blanking method, which is characterized in that: 1. the raw material specification can be automatically brushed according to a given range, and the number of raw materials can be specified; 2. the raw material utilization rate can be optimized through primary optimization and secondary optimization, optimization after the specification number of the specified raw materials can be realized, the length of the cutting seam is counted, the discharge sequence number is introduced, and the required blanking cutting diagram, the utilization rate of each specification and the complete discharge diagram are obtained.
In order to realize the scheme, the invention adopts the following technical scheme:
step1: importing a part length table comprising a process size, an identification number and a nominal size; counting a part length matrix L in a part length table and a corresponding quantity matrix beq;
step2: inputting optimized starting value HH, ending value HH, step length ；
Step3: calculating the length of each step from HH to HH, and under each length, combining the part length matrix L obtained in the step1, and obtaining all possible blanking schemes by using an exhaustion method;
step 4: combining a cutting matrix and a remainder matrix according to the Step3 result, performing primary optimization by using integer linear programming, and outputting primary optimization results, wherein the primary optimization results comprise specification, quantity and utilization rate;
step 5: according to the result of the one-time optimization,for one optimization, the number of material specifications, k2 is the number of desired material specifications, if +.>Performing secondary optimization, and performing full arrangement on the primary optimization result to obtain the firstUtilization of seed combination->And find the maximum value among them:
wherein the method comprises the steps ofThe method comprises the steps of carrying out a first treatment on the surface of the And get the utilization rate of +.>At the same time, the corresponding material specification: />。
Step6: obtaining a cutting matrix C, a remainder matrix f and a utilization rate matrix according to the secondary optimization result;
step7: inputting the width of the cutting seam according to the cutting matrix C, and calculating the length of the cutting seam;
step 8: in the step1, according to the length table of the imported blanking part, data in a process length and an identification number are respectively stored into RAW1 and RAW2, and the data in the process length and the identification number do not participate in the optimization process;
step 9: and outputting data in the process length and the identification number from the databases RAW1 and RAW2 according to the secondarily optimized cutting moment C, and compiling the data into a final blanking table together with information such as cutting seams, excess materials and the like.
Compared with the background technology, 1. When a program starts, inputting the length range of the specification of the material, automatically optimizing according to the set range to obtain the optimal material length, and ensuring that the material utilization rate is highest under the condition that the material can be transported (HH meets the transportation length limit); 2. meanwhile, when the program starts, after the part length table is imported, the length and the number of the parts can be counted, the length and the number of the parts are diversified, and the use requirement of a production order is met; 3. calculating each possible combination by using an exhaustion method, and carrying out primary optimization and secondary optimization by using an integer linear programming method to obtain the most suitable blanking scheme; 4. after the two times of optimization and the one time of optimization, the specification of the material is comparatively more, so that the optimal blanking scheme can be obtained under the condition of the set specification number through the two times of optimization, thereby meeting the limit of the production on the specification number of the raw materials; 5. considering the width and the number of the cutting seams, avoiding the situation that the total length of the rear cutting section is insufficient due to excessive cutting and excessive loss length of the cutting seams, and simultaneously aiming at different blanking cutting machines, the width of the cutting seams can be set so as to meet the actual situation in the production cutting process; 6. solving the utilization rate and the total utilization rate of each specification, solving the residual materials of each specification according to the secondarily optimized cutting matrix, further solving the utilization rate of each specification, and simultaneously managing and counting the residual materials; 7. the identification number is added to the product, and the customization requirement is met on the basis of mass production. Because the process lengths are different, but the differences are smaller, and the identification is convenient, a corresponding unique mark is marked on each cut segment; 8. and generating a complete discharging diagram, wherein the discharging diagram comprises the length, the sequence, the length of the residual materials and the identification number corresponding to each section of the discharging parts, is simple and easy to understand, and can directly guide production.
Drawings
FIG. 1 is a flow chart of a multi-specification, multi-target one-dimensional blanking method.
Fig. 2 is a schematic view of a cut seam arrangement.
Fig. 3 is a part length representation intent.
FIG. 4 is a cut representation of the intent of the present invention as generated using the method of the present invention.
FIG. 5 is a schematic diagram of a secondary optimization result generated by the method of the present invention.
Fig. 6 is a diagram showing the final blanking information generated by the method of the present invention.
Detailed Description
(1) Description of the flow chart
As shown in fig. 1, the operation flow of the above-described program is as follows:
(1) importing a part length table, as shown in fig. 3, counting the specification and the number of the parts according to the nominal incoming material length, and storing the identification number and the process length in the table into a database according to the specification and the number;
(2) inputting a starting point, a finishing point and an optimized step length of optimized material specification, and establishing all possible blanking conditions by applying an exhaustion method;
(3) taking the exhaustion result and the statistics result as the input of primary optimization, performing primary optimization, and outputting the primary optimization result;
(4) after optimization, judging whether the target specification number meets the requirement, if so, skipping secondary optimization, and directly outputting a result; if the number of the input data does not accord with the required number specification, performing secondary optimization;
(5) obtaining a blanking matrix, residual materials, the utilization rate of each specification and the total utilization rate, and outputting a secondary optimization result and a cutting graph;
(6) and (3) further adding the calculated cutting seam, extracting the identification number and the process length in the database in the step (1), and outputting a final blanking table.
(2) Description of one-dimensional blanking model
The length table of the parts is imported, as shown in fig. 3, and the table contains the process size, the identification number and the nominal incoming length.
The part length table is completed in excel, and the process sizeAnd the identification number is a parameter of the part itself, nominal incoming material length +.>By process dimension->Determining, namely determining a method: />， />Is an allowed normalization range value. After the normalization->Specification is relative->The number of parts required is reduced to m, here exemplified by m=100;
counting n kinds of parts to obtainLength matrix L, of->Beq of the number matrix of (b), whereinLength of seed material->The corresponding number is +.>A number matrix can be used: />There is->。
Simultaneously inputting a length range of one-time optimization specification: from HH to HH, the step size is set to。
The minimum value of the length of the HH raw material, HH is at least greater than the maximum value in L, HH is the maximum value of the length of the raw material, and the step lengthControllable calculation speed and optimization accuracy, +.>The larger the calculation speed is, the faster the optimization accuracy is reduced, +.>The smaller the calculation speed is, the slower the calculation speed is, and the optimization accuracy is improved.
(3) Use of the exhaustive method in the invention
Length ranges from HH to HH (step size)) In total->Number, all lengthsFor the number of e (e=1, … N), the corresponding value is +.>The length of the raw material is determined to be +.>When get +.>Respectively for->Length of seed material->Maximum number of blanking Max (i) and minimum remainder Min (i); combining according to the calculation result, and obtaining the specification length of +.>A number matrix and a margin matrix.
Quantity matrix:；
margin matrix:；
similarly, all lengths are sequentially calculatedCorresponding quantity matrix->Margin matrix。
(4) Application of integer linear programming method in the present invention
The invention adopts matlab programming, primary optimization and secondary optimization both adopt integer linear programming method, and programming refers to matlab function library: the lingprog function (integer linear programming function) needs to determine constraint conditions and optimization targets in advance when the function is referenced. Other programming software may be used, and if other programming software (e.g., c++, C, etc.) is used, the integer solution may be determined by a branch-and-bound method after programming according to a linear program.
(5) One-time optimization
When optimizing once, all the results obtained by the exhaustion method are combined as follows:as a reference lingprog function input, linear programming constraints:
the optimization targets are as follows:；
solving a primary linear programming result: optimizing specificationsThe method comprises the steps of carrying out a first treatment on the surface of the Wherein k1 is the optimized material specification number; if k1 is greater than the number of desired specifications k2, further reduction of the specifications is required for secondary optimization.
(6) Secondary optimization
And (3) secondarily optimizing linear programming constraint conditions: inputting the number k2 of the secondary optimization expected length specification, and fully arranging the primary optimization results to be summed upAfter the seed arrangement, respectively carrying out optimization solution to obtain the +.> />Utilization of seed combination->And the following values for the combination with the highest total utilization rate among the z combinations were obtained:
total utilization rate:；
the corresponding material specification: ；
blanking and cutting the matrix:；
matrix of remainder:；
utilization of each specification:。
the above parameters are used to generate a blanking cutting table, which is shown in fig. 4 (k2=3 types here), and three specifications after secondary optimization in fig. 4 are:corresponding number: />The method comprises the steps of carrying out a first treatment on the surface of the And (3) the following steps: is obtained by multiplying a blanking cutting matrix C and a length matrix L.
Fig. 5 is a graph showing the secondary optimization results (schematic diagram) including the utilization and total utilization of various specifications.
Checking and calculating the total number: multiplying the number in the number column by the number in the blanking pattern and summing, 1x1+41x1+10x1+1x2+4x1+4x1+14x2+2x3+2x2=100, to illustrate an missing part.
(7) Counting the number of cuts
According to the cutting matrix C, solving the number of the cutting seams;
as shown in fig. 2, is of lengthThe cutting number is +.>The end defect area needs to be cut; number of cut seams: />The method comprises the steps of carrying out a first treatment on the surface of the For example: FIG. 2 cut matrix +.>The number of cutting slits is:the cutting amount is 10mm, and the total length of the cutting seam is: 5x10 = 50mm.
(8) Outputting final blanking information table
Step1: according to the length table of the imported blanking part, the process length and the identification number are stored in a database RAW1 and a database RAW 2;
step2: according to the cutting matrix C obtained by the secondary optimization, the remainder matrix f and the cutting length (equal to the number of the cutting slits multiplied by the cutting amount), a blanking form with the cutting slits is generated;
step3: and outputting the process length and the drawing number from the databases RAW1 and RAW2, and compiling the final blanking table.
As shown in fig. 6; meanwhile, the sequence numbers of RAW1 and RAW2 are continuously checked according to the cutting matrix C, and write omission or repeated write-in is avoided.
It should be understood that: although the above embodiments describe the design concept of the present invention in more detail, these descriptions are merely descriptions of the design concept of the present invention, and not limitations on the design concept of the present invention, and any combination, addition or modification not exceeding the design concept of the present invention falls within the scope of the present invention.
Claims (1)
1. A multi-specification and multi-target one-dimensional blanking method is characterized in that: when the program starts, inputting the length range of the material specification, automatically optimizing according to the set range to obtain the optimal material length, meanwhile, when the program starts, after the length list of the parts is imported, the length and the number of the parts can be counted, the length and the number of the parts are diversified, each possible combination is calculated by an exhaustion method, and the optimal blanking scheme is obtained by performing primary optimization and secondary optimization through an integer linear programming method;
step1: importing a part length table comprising a process size, an identification number and a nominal size; counting a part length matrix L in a part length table and a corresponding quantity matrix beq;
step2: inputting an optimized starting value HH, a termination value HH and a step length l;
step3: calculating the length of each step from HH to HH, and under each length, combining the part length matrix L obtained in the step1, and obtaining all possible blanking schemes by using an exhaustion method;
step 4: combining a cutting matrix and a remainder matrix according to the result of the step3, performing primary optimization by using integer linear programming, and outputting primary optimization results including specification, quantity and utilization rate;
step 5: according to the primary optimization result, k1 is the material specification number obtained by primary optimization, k2 is the expected material specification number, if k1>k2, performing secondary optimization, performing full arrangement on the primary optimization result, and obtaining the utilization rate RO of zk (zk=1, …, z) type combination zk And find the maximum value among them:
R＝max(RO 1 ，…，RO z )，
wherein the method comprises the steps ofAnd when the utilization rate is R, the corresponding material specification is obtained: [ H ] 1 ，…，H k2 ]；
Step6: obtaining a cutting matrix C, a remainder matrix f and a utilization rate matrix according to the secondary optimization result;
step7: inputting the width of the cutting seam according to the cutting matrix C, and calculating the length of the cutting seam;
step 8: in the step1, according to the imported blanking part length table, data in a process length and an identification number are respectively stored into a database RAW1 and a database RAW2, and the data in the process length and the identification number do not participate in the optimization process;
step 9: and outputting data in the process length and the identification number from the databases RAW1 and RAW2 according to the secondarily optimized cutting moment C, and compiling the data into a final blanking table together with information such as cutting seams, excess materials and the like.
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| CN113158580B (en) * | 2021-05-10 | 2021-12-10 | 南京林业大学 | One-dimensional stock layout method for solid wood board |
| CN114985295B (en) * | 2022-06-20 | 2023-12-29 | 福建威而特旋压科技有限公司 | Automatic steel screening method based on stamping blanking width |
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