CN104281722A - Automatic discharging method - Google Patents
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- CN104281722A CN104281722A CN201310279300.3A CN201310279300A CN104281722A CN 104281722 A CN104281722 A CN 104281722A CN 201310279300 A CN201310279300 A CN 201310279300A CN 104281722 A CN104281722 A CN 104281722A
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Abstract
The invention discloses an automatic discharging method which comprises the following steps that a genetic algorithm is adopted to set up an algorithm mathematical model, a drawing of parts to be discharged is loaded, and the parts to be discharged are preprocessed; the genetic algorithm is subjected to initialization which comprises group initialization and setting of operation parameters and end conditions of the genetic algorithm; the parts to be discharged are placed on plates according to a gene sequence in the algorithm mathematical model, and the fitness values of all individuals in a group are calculated; judgment of the end conditions is carried out, if the end conditions are not met, the group is subjected to selection operation, crossover operation and mutation operation to obtain a next-generation group, and the last step is executed again; if the end conditions are met, the optimal solution is output. According to the method, automatic discharging is achieved through effective combination of the genetic algorithm and the computer graphics, the sequence and the placement angle of the parts to be discharged can be generated randomly through the genetic algorithm, the parts to be discharged are placed on the plates through an algorithm of no-fit polygon, and automatic discharging efficiency and the utilization rate of the plates are improved.
Description
Technical field
The present invention relates to a kind of auto-discharing method, specifically, relate to a kind of plate cutting auto-discharing method.
Background technology
In engineer applied, automatic discharge problem refers at one or more given material and some for nesting parts, is reasonably placed on material by for nesting part, and makes stock utilization the highest.The particular constraints condition that discharge problems mandate meets: any two for nesting part non-overlapping copies; Any one for nesting part can not exceed the scope of sheet material, must be placed in sheet material.
Discharge problem is class problem a---np complete problem with the highest computation complexity.Np complete problem refers to an insurmountable class problem within the scope of polynomial time complexity.When it has been generally acknowledged that the time complexity of algorithm exists polynomial expression circle, computing time is acceptable, if exceed polynomial expression circle, the increase along with problem scale increases by computing time rapidly (exponentially relation growth), and this algorithm will be difficult to be accepted in actual applications.
In prior art, irregular discharge has two kinds of methods: one is based on rectangular-shaped piece discharge, replaces for nesting part, irregular for nesting part is converted into rectangular-shaped piece, then solves with rectangular-shaped piece discharge algorithm with the least surrounding boxes of for nesting part; Two is based on computer graphics, with computer graphics related notion and method process for nesting part and sheet material, tries to achieve optimum discharge result.
Existing discharge algorithm, mainly with based on rectangular-shaped piece discharge, replaces for nesting part with the least surrounding boxes of for nesting part, cannot reach very high utilization factor to irregular figure; And simple with the scheme of computer graphics, calculated amount is complicated, efficiency is low, is difficult to reach very high availability ratio of the armor plate.
Summary of the invention
The object of the present invention is to provide a kind of auto-discharing method, realize automatic discharge by effective combination of genetic algorithm and computer graphics, improve the utilization factor of automatic discharge efficiency and sheet material.
To achieve these goals, the technical solution adopted in the present invention is as follows:
A kind of auto-discharing method, comprises the following steps: adopt genetic algorithm, set up algorithm mathematics model; Load for nesting part drawing, pre-service is carried out to for nesting part; Genetic algorithm initialization, comprises Population Initialization, arranges the operational factor of genetic algorithm and end condition; For nesting part is positioned on sheet material by the gene order algorithmically in mathematical model, and calculates the fitness of each individuality in colony; End condition judges, if do not meet end condition, colony, after Selecting operation, crossing operation, mutation operator, obtains colony of future generation, goes to previous step and continues to perform; If meet end condition, export optimum solution.
Further, for nesting part sequence number and for nesting part placed angle index value is comprised in genetic algorithm gene string.
Further, during initialization, a gene adopts the descending order arrangement of area, and a gene arranges by the order of user's initial input, all the other gene stochastic generation.
Further, every generation colony is with the highest gene of availability ratio of the armor plate for benchmark, and its fitness function value is 1000, and all the other genes obtain according to the difference relative to this gene the fitness function value that is less than 1000.
Further, adopt roulette algorithm as selection opertor.
Further, when carrying out pre-service to for nesting part, can carry out discretize by the curve in the row's for the treatment of parts pattern, be simple polygon by for nesting part graphics; Or ask for the polygonal convex closure of for nesting part; Or for nesting part is carried out row, townhouse.
Further, critical rupture stress algorithm is adopted to place for nesting part.
Further, when solving critical rupture stress, define that the first polygon is anticlockwise vector polygon, the second polygon is clockwise vector polygon, first polygon, the second polygonal vector are moved to initial point place respectively, vector order is constant separately for first polygon, the second polygon, by any vector, by counterclockwise rotating, the first polygon and the second polygonal vector are joined end to end.
Further, first take out the first polygon in genetic algorithm formation sequence, by placed angle, the first polygon is placed on sheet material, adopts the most left minimum principle (BL, Bottom-Left) the first polygon to be placed on the lower left corner of sheet material; Secondly the second polygon in fetch squence, calculates the second polygon for the first polygonal critical rupture stress; Then critical rupture stress judged and assess, determining the second polygonal optimal placed location; Then the first polygon, the second polygonal composite polygon are designated as the 3rd polygon, do the first polygon calculated next time; Finally judge whether all for nesting parts complete placement all.
Further, when determining the second polygonal placement location, first choose reference point, allow it move along critical rupture stress, search for each point not in critical rupture stress, and allow the second polygon move together; Then to each the second polygonal position, the area of composite polygon is calculated; Choose the minimum position of composite polygon area again as the second polygonal placement location.
Compared with prior art, the present invention realizes automatic discharge by effective combination of genetic algorithm and computer graphics, can stochastic generation for nesting part sequence and placed angle by genetic algorithm, utilize critical rupture stress algorithm to be positioned on sheet material by for nesting part, improve the utilization factor of automatic discharge efficiency and sheet material.
Accompanying drawing explanation
Fig. 1 is the implementation schematic diagram of genetic algorithm of the present invention;
Fig. 2 is the forming process schematic diagram of critical rupture stress of the present invention;
Fig. 3 is the schematic diagram of vector polygon first polygon A of the present invention, the second polygon B;
Fig. 4 is the vector precedence diagram of the first polygon A of the present invention, the second polygon B;
Fig. 5 is the critical rupture stress schematic diagram of the second polygon B of the present invention relative to the first polygon A;
Fig. 6 is the Placement implementation schematic diagram based on critical rupture stress of the present invention.
Embodiment
Below in conjunction with the drawings and specific embodiments, auto-discharing method of the present invention is described further.
Genetic algorithm (Genetic Algorithm) is that the evolution laws (survival of the fittest, survival of the fittest genetic mechanism) of a class reference organic sphere develops and next randomization searching method.Genetic algorithm is that a colony is then made up of the individuality (individual) of the some of encoding through gene (gene) from representing a colony (population) of the potential disaggregation of problem possibility.Each individuality is actually chromosome (chromosome) and is with characteristic entity.After just producing for colony, according to the principle of the survival of the fittest and the survival of the fittest, develop by generation (generation) and produce the approximate solution of becoming better and better, in every generation, select (selection) individual according to fitness (fitness) size individual in Problem Areas, and carry out combination intersection (crossover) and variation (mutation) by means of the genetic operator (genetic operators) of natural genetics, produce the colony representing new disaggregation.The rear Sheng Dai colony causing colony as natural evolution is adapted to environment than former generation by this process more, and the optimum individual in last reign of a dynasty colony, can as problem approximate optimal solution through decoding (decoding).
The fundamental operation process of genetic algorithm is as follows: a) initialization: arrange evolutionary generation counter t=0, arranges maximum evolutionary generation T, and stochastic generation M individual as initial population P (0).B) individual evaluation: the fitness calculating each individuality in colony P (t).C) Selecting operation: selection opertor is acted on colony.The object selected the individuality optimized is genetic directly to the next generation or produces new individuality by pairing intersection be genetic to the next generation again.Selection operation is based upon on the Fitness analysis basis of individual in population.D) crossing operation: crossover operator is acted on colony.So-called intersection refers to is replaced restructuring the part-structure of two parent individualities and is generated new individual operation.What play the role of a nucleus in genetic algorithm is exactly crossover operator.E) mutation operator: mutation operator is acted on colony.Namely be that the genic value on some locus of the individuality string in colony is changed.Colony P (t) obtains colony P (t1) of future generation after selection, intersection, mutation operator.F) end condition judges: if t=T, then the maximum adaptation degree individuality that has obtained in evolutionary process exports as optimum solution, stops calculating.
The general algorithm of genetic algorithm: build original state, initial population Stochastic choice from solution out, likens these solutions to chromosome or gene, and this population is called as the first generation, this is different with the situation of symbol artificial intelligence system, and the original state of problem is given there.Assessment fitness, separates to each the value that (chromosome) specifies a fitness, specifies (approaching the answer of Solve problems) according to the actual degree of closeness of problem solving.Breeding (comprising filial generation sudden change), those chromosomes with higher fitness value more may produce offspring's (offspring also will undergo mutation after producing).Offspring is the product of father and mother, and they are combined into by the gene from father and mother, and this process is called as " hybridization ".The next generation, if a new generation comprises a solution, can produce one fully close to or equal to expect the output of answer, so problem just solves.If situation is really not so, the seed procedure that they father and mother of repetition carry out by a new generation, a generation generation develops down, until reach the solution of expectation.
Refer to Fig. 1, the present invention is based on effective combination of genetic algorithm and computer graphics, disclose a kind of auto-discharing method, comprise the following steps:
The first step: adopt genetic algorithm, set up the algorithm mathematics model of Optimal Layout.
First gene code is carried out to for nesting part.Genetic algorithm directly can not process the parameter of problem space, they must be converted to the chromosome be made up of by a fixed structure gene or the individuality in hereditary space.This conversion operations is just called coding, may also be referred to as (problem) expression (representation).Gene (Gene) is the element in string, and gene is for representing individual feature.Such as have one to go here and there S=1011, then 1,0,1,1 these 4 elements are wherein called gene.In the present embodiment, in genetic algorithm gene string, comprise for nesting part sequence number and for nesting part placed angle index value, as shown in table 1, for nesting part sequence is encoded.
The gene string list of table 1 for nesting part shows
Secondly Population Initialization.During initialization, a gene adopts the descending order arrangement of area, and a gene arranges by the order of user's initial input, all the other gene stochastic generation.Thus, one group of candidate solution of discharge problem is formed.
Then fitness function is set up.In the present embodiment, the fitness function of genetic algorithm is that its fitness function value is 1000, and all the other genes obtain according to the difference relative to this gene the fitness function value that is less than 1000 with the highest gene of the availability ratio of the armor plate of every generation colony for benchmark.Certainly, the present invention is not limited to this, and the fitness function of genetic algorithm also can otherwise build, and fitness function value also can be other values.
Then the selection opertor of genetic algorithm mathematical model, crossover operator, mutation operator is determined.
The Selecting operation of genetic algorithm is that selection opertor is acted on colony.The object selected the individuality optimized is genetic directly to the next generation or produces new individuality by pairing intersection be genetic to the next generation again.Selection operation is based upon on the Fitness analysis basis of individual in population.Adopt roulette algorithm to do selection opertor in the present invention, concrete operations are as follows: calculate the fitness function of each individuality in current group and sort; Assumed group number of individuals is n, and the individuality that before selecting, m (m<n) individual fitness function value is high is genetic directly to the next generation; Individual to remaining n-m, use the individual inheritance of roulette algorithms selection to of future generation.
The crossing operation of genetic algorithm, acts on colony by crossover operator.So-called intersection refers to is replaced restructuring the part-structure of two parent individualities and is generated new individual operation.What play the role of a nucleus in genetic algorithm is exactly crossover operator.
The concrete crossover process of crossover operator in the present invention is as shown in table 2, and its point of crossing, between the 3rd gene and the 4th gene, namely retains first three gene swapping gene below.Require in Layout System that for nesting part is unique, the gene therefore occurred in crossover process, with the gene substitution do not occurred.
Table 2 crossing operation process
The mutation operator of genetic algorithm, acts on colony by mutation operator.Namely be that the genic value on some locus of the individuality string in colony is changed.Mutation operator is divided into two kinds: one to be choose its angle parameter of gene transformation; Two is selection two their genes of place-exchange.In the present embodiment, mutation operator adopts the second mutation operation, and its implementation is as shown in table 3, the gene swapping of the Stochastic choice second place and the 5th position.
2, table 3 variation implementation
Second step: load for nesting part drawing, pre-service is carried out to for nesting part.
The pre-service of for nesting part figure, is mainly convenient to the follow-up process to parts pattern, makes parts pattern be suitable for automatic-discharging and numerical control programming process, to meet the needs that stock layout calculates.
When carrying out pre-service to for nesting part, can carry out discretize by the curve in the row's for the treatment of parts pattern, be simple polygon by for nesting part graphics.The core of for nesting part figure polygonization is the discrete of arc section, and band circular arc part is converted into the approximate polygon close with former part shape.In departure process, its circumscribed broken line is asked to convex arc, concave arc is asked in it and connects broken line.After processing like this, all parts patterns all can use For Polygons Representation, and the area of part also can be approximately this area of a polygon.Therefore, enormously simplify a series of graph transformation carried out and the process intersecting judgement below, avoid secondary graphics process.
When pre-service is carried out to for nesting part, also can ask for the polygonal convex closure of for nesting part.The most frequently used algorithm of convex hull of convex closure is Graham scanning method and Jarvis step-by-step method.Convex closure (Convex Hull) is the concept in a computational geometry (graphics).With not rigorous words, the point set on given two dimensional surface, convex closure is exactly outermost point is coupled together the convex polygon formed, and it can comprise a little concentrated all points.When pre-service is carried out to for nesting part, also can to for nesting part carry out to row (two same shape polygonal parts, wherein one be rotated counterclockwise 180 degree after to row), townhouse.Automatically can be completed by system the pre-service of figure, can also be completed by human-computer interaction function.
3rd step: genetic algorithm initialization, comprises Population Initialization, arranges the operational factor of genetic algorithm and end condition.Evolutionary generation counter is set, maximum evolutionary generation is set, stochastic generation initial population.During initialization, a gene adopts the descending order arrangement of area, and a gene arranges by the order of user's initial input, and all the other gene stochastic generation thus, thus, form one group of candidate solution of discharge problem.When the fitness of optimum individual reaches given threshold value, or when the fitness of optimum individual and colony's fitness no longer rise, or when iterations reaches default algebraically, algorithm stops.In the present embodiment, the fitness function of genetic algorithm, be that its fitness function value is 1000 with the highest gene of the availability ratio of the armor plate of every generation colony for benchmark, the algebraically preset was set to for 1000 generations.
4th step: adopt critical rupture stress algorithm, for nesting part is positioned on sheet material by the gene order algorithmically in mathematical model, calculates the fitness of each individuality in the candidate solution of discharge problem.Retain the candidate solution of qualified discharge problem according to fitness, abandon the candidate solution that other are ineligible.
Critical rupture stress (NFP) determines two polygonal relative positions, and gives two polygons and contact with each other but nonoverlapping a series of position.Refer to Fig. 2, given first polygon A and the second polygon B, the second polygon B are as follows relative to the forming process of the critical rupture stress of the first polygon A: fix the first polygon A; Under the second polygon B contacts nonoverlapping prerequisite with the first polygon A, the second polygon B does non-rotary rigid motion and rotates a circle around the first polygon A; Second polygon B chooses any reference point, and its track is the critical rupture stress of the second polygon B relative to the first polygon A, is designated as NFP
aB.
Refer to Fig. 3, Fig. 4 and Fig. 5, the solution procedure of critical rupture stress is as follows: the first polygon A is anticlockwise vector polygon, and the second polygon B is clockwise vector polygon; The vector of the first polygon A, the second polygon B is moved to initial point (0,0) place respectively; Vector order is constant separately for first polygon A, the second polygon B, by any vector, by counterclockwise rotating, is joined end to end by the vector of the first polygon A and the second polygon B.
Refer to Fig. 6, adopt critical rupture stress algorithm to place for nesting part in the present invention, concrete implementation step is as follows:
(1) first polygon first polygon A in genetic algorithm formation sequence is first taken out, by placed angle, the first polygon A is placed on sheet material, adopt " the most left minimum principle " (BL, Bottom-Left, for nesting part is positioned at the most left lowest part of effective sheet material as far as possible, an emission effect arranged will certainly be formed in discharge process, thus be also called to discharge by row mode) the first polygon A is placed on the lower left corner of sheet material.
(2) the next polygon second polygon B in next fetch squence, calculates the critical rupture stress NFP of the second polygon B for the first polygon A
aB.
(3) then to critical rupture stress NFP
aBcarry out judging and assessing, determine the optimal placed location of the second polygon B.
When determining the placement location of the second polygon B, first choose reference point, allow it along critical rupture stress NFP
aBmobile, search for each not at critical rupture stress NFP
aBinterior point, and allow the second polygon B move together; Then to the position of each the second polygon B, the area of composite polygon is calculated; Choose the placement location of the minimum position of composite polygon area as the second polygon B again.
(4) then the composite polygon of the first polygon A, the second polygon B is designated as the 3rd polygon, is the first polygon A calculated next time.
(5) finally judge whether all for nesting parts complete placement all.If do not completed, the next polygon continuing to take out for nesting part sequence is designated as the second polygon B, goes to (2); If completed, then exit module, all for nesting parts have been placed.
5th step: end condition judges.
End condition judges, if do not meet end condition, colony is after Selecting operation, crossing operation, mutation operator, obtain colony of future generation, namely the candidate solution of the discharge problem retained is carried out to Selecting operation, crossing operation, the mutation operator of genetic algorithm, generate one group of candidate solution of new discharge problem, go to the 4th step and continue to perform.
If meet end condition, then the maximum adaptation degree individuality that has obtained in evolutionary process exports as optimum solution, stops calculating, exports stock layout result.
In sum, first auto-discharing method of the present invention sets up the mathematical model based on genetic algorithm, one group of candidate solution of composition discharge problem; Secondly, load for nesting part drawing, pre-service is carried out to for nesting part, is suitable for automatic-discharging and numerical control programming process to make for nesting part figure; Then adopt critical rupture stress algorithm to place for nesting part by the gene order of mathematical model, the adaptability condition according to setting calculates the fitness of each candidate solution; Then retain qualified candidate solution according to fitness, abandon ineligible candidate solution; The end condition finally carrying out genetic algorithm judges, if do not meet end condition, the Selecting operation of genetic algorithm, crossing operation and mutation operator are carried out to the candidate solution retained, generate new candidate solution, again adopt critical rupture stress algorithm to place for nesting part, calculate the fitness of each candidate solution, otherwise, meet end condition, then export optimum solution, carry out stock layout.
The present invention realizes automatic discharge by effective combination of genetic algorithm and computer graphics, can stochastic generation for nesting part sequence and placed angle by genetic algorithm, utilize critical rupture stress algorithm to be positioned on sheet material by for nesting part, improve the utilization factor of automatic discharge efficiency and sheet material.
More than describe preferred embodiment of the present invention in detail, should be appreciated that those of ordinary skill in the art just design according to the present invention can make many modifications and variations without the need to creative work.Therefore, all technician in the art according to the present invention's design on prior art basis by logic analysis, reasoning or according to the available technical scheme of limited experiment, all should by among the determined protection domain of these claims.
Claims (10)
1. an auto-discharing method, is characterized in that, comprises the following steps:
Adopt genetic algorithm, set up algorithm mathematics model;
Load for nesting part drawing, pre-service is carried out to for nesting part;
Genetic algorithm initialization, comprises Population Initialization, arranges the operational factor of genetic algorithm and end condition;
For nesting part is positioned on sheet material by the gene order algorithmically in mathematical model, and calculates the fitness of each individuality in colony;
End condition judges, if do not meet end condition, colony, after Selecting operation, crossing operation, mutation operator, obtains colony of future generation, goes to previous step and continues to perform; If meet end condition, export optimum solution.
2. auto-discharing method as claimed in claim 1, is characterized in that: comprise for nesting part sequence number and for nesting part placed angle index value in genetic algorithm gene string.
3. auto-discharing method as claimed in claim 1, is characterized in that: during initialization, and a gene adopts the descending order arrangement of area, and a gene arranges by the order of user's initial input, all the other gene stochastic generation.
4. auto-discharing method as claimed in claim 1, it is characterized in that: every generation colony with the highest gene of availability ratio of the armor plate for benchmark, its fitness function value is 1000, and all the other genes obtain according to the difference relative to this gene the fitness function value that is less than 1000.
5. auto-discharing method as claimed in claim 1, is characterized in that: adopt roulette algorithm as selection opertor.
6. auto-discharing method as claimed in claim 1, is characterized in that: when carrying out pre-service to for nesting part, and can carry out discretize by the curve in the row's for the treatment of parts pattern, be simple polygon by for nesting part graphics; Or ask for the polygonal convex closure of for nesting part; Or for nesting part is carried out row, townhouse.
7. the auto-discharing method as described in as arbitrary in claim 1 to 6, is characterized in that: adopt critical rupture stress algorithm to place for nesting part.
8. auto-discharing method as claimed in claim 7, it is characterized in that: when solving critical rupture stress, define that the first polygon is anticlockwise vector polygon, the second polygon is clockwise vector polygon, first polygon, the second polygonal vector are moved to initial point place respectively, vector order is constant separately for first polygon, the second polygon, by any vector, by counterclockwise rotating, the first polygon and the second polygonal vector are joined end to end.
9. auto-discharing method as claimed in claim 7 or 8, it is characterized in that: first take out the first polygon in genetic algorithm formation sequence, by placed angle, the first polygon is placed on sheet material, adopts the most left minimum principle the first polygon to be placed on the lower left corner of sheet material; Secondly the second polygon in fetch squence, calculates the second polygon for the first polygonal critical rupture stress; Then critical rupture stress judged and assess, determining the second polygonal optimal placed location; Then the first polygon, the second polygonal composite polygon are designated as the 3rd polygon, do the first polygon calculated next time; Finally judge whether all for nesting parts complete placement all.
10. auto-discharing method as claimed in claim 9, it is characterized in that: when determining the second polygonal placement location, first choose reference point, allow it move along critical rupture stress, search for each point not in critical rupture stress, and allow the second polygon move together; Then to each the second polygonal position, the area of composite polygon is calculated; Choose the minimum position of composite polygon area again as the second polygonal placement location.
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| CN106570824A (en) * | 2016-10-12 | 2017-04-19 | 网易（杭州）网络有限公司 | Splicing method and device for scattered graphs |
| CN106971242A (en) * | 2017-03-21 | 2017-07-21 | 上海大学 | A kind of clothes Automatic Optimal discharging method |
| CN109967796A (en) * | 2019-03-18 | 2019-07-05 | 广东三维家信息科技有限公司 | Cutting method and device based on board-like material |
| CN109967796B (en) * | 2019-03-18 | 2020-11-20 | 广东三维家信息科技有限公司 | Cutting method and device based on plate type material |
| CN110091133B (en) * | 2019-05-28 | 2020-05-19 | 广东三维家信息科技有限公司 | Section bar processing optimization method and device |
| CN110091133A (en) * | 2019-05-28 | 2019-08-06 | 广东三维家信息科技有限公司 | Shape extrusion optimization method and device |
| CN110457756A (en) * | 2019-07-15 | 2019-11-15 | 天津大学 | One kind being based on critical rupture stress plate automatic nesting method |
| CN110598893A (en) * | 2019-07-18 | 2019-12-20 | 山东大学 | Multi-specification part layout method and system |
| CN112862701A (en) * | 2021-01-15 | 2021-05-28 | 复旦大学 | Automatic typesetting method |
| CN112862701B (en) * | 2021-01-15 | 2022-11-18 | 复旦大学 | Automatic typesetting method |
| CN115146332A (en) * | 2022-07-25 | 2022-10-04 | 广州市圆方计算机软件工程有限公司 | Wood-made ceiling board material discharge optimization method |
| CN115146332B (en) * | 2022-07-25 | 2024-04-12 | 广州市圆方计算机软件工程有限公司 | Wood ceiling material discharging optimization method |
| CN117634710A (en) * | 2024-01-23 | 2024-03-01 | 湖南三一中诚车身有限公司 | Plate sizing method and device |
| CN117634710B (en) * | 2024-01-23 | 2024-04-26 | 湖南三一中诚车身有限公司 | Plate sizing method and device |
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