CN110728011B - Optimized generation method of composite material layering warehouse - Google Patents
Optimized generation method of composite material layering warehouse Download PDFInfo
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Abstract
The invention relates to the technical field of computer simulation, in particular to a method for optimizing and generating a composite material layering library, which comprises the steps of optimizing layering sequence, optimizing layering sequence under the maximum layering number through an immune genetic algorithm, and carrying out layer loss according to an optimization result to obtain layering sequences under other layering numbers; and optimizing the lost layer position, determining the specific lost layer position through dynamic programming, and ensuring the optimal maximum objective function under the lost layer position. The optimization generation method of the composite material layering library provided by the invention comprises the steps of optimizing layering sequence and generating the layering library meeting the requirements by layer loss and layer supplement, thereby ensuring that the design reaches the target optimum. According to the invention, the layering sequence optimization and the layering warehouse are combined together, so that the trouble of manually designing the layering warehouse is avoided, and the integration of the composite material design flow is realized, and the method is rapid, convenient and accurate.
Description
Technical Field
The invention relates to the technical field of computer simulation, in particular to an optimized generation method of a composite material layering warehouse, which is suitable for the design of the composite material layering warehouse for aviation.
Background
The use of high performance composites has prompted the development of composite structural design programs. Ply-laying design is a new design content specific to composite material structural design, which is distinguished from metallic material structural design. The designability of the material is realized through the ply design, and the performances such as strength, rigidity, stability, damage resistance, damage tolerance and the like are designed.
For the research of composite material layering design, the laying angle is usually composed of standard angles, so that the problem of combination optimization of discrete variables is optimized, and a great deal of researches are carried out on the composite material layering plate by students at home and abroad, such as integer programming and branch definition method proposed by Haftka; soremekun optimally designs the layering sequence by using a genetic algorithm; the improved particle swarm algorithm proposed by Chang optimally designs the layering sequence of the laminated plates. However, these are all studies on a single layup number, and there are few studies on the optimal design of a layup library for a series of consecutive layup sequences.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide an optimized generation method of a composite material layering library, which comprises the steps of optimizing layering sequence and generating a layering library meeting requirements by layer loss and layer supplement, so that the design is ensured to reach the target optimum.
The embodiment of the invention provides a method for generating a composite material layering library in an optimized mode, which comprises the following steps:
optimizing the layering sequence, optimizing the layering sequence under the maximum layering number through an immune genetic algorithm, and carrying out layering loss according to the optimizing result to obtain the layering sequence under other layering numbers;
and optimizing the lost layer position, determining the specific lost layer position through dynamic programming, and ensuring the optimal maximum objective function under the lost layer position.
Further, in the above method, the layering sequence optimization procedure is shown in fig. 1;
optimizing each layering angle proportioning scheme through an immune genetic algorithm;
and comparing the optimization results, and selecting the angle proportion and the layering sequence of each layering of the objective function.
Further, in the above method, the optimization flow of the immune genetic algorithm is as shown in fig. 3:
the generation of iterative population individuals is completed through gene coding, and the individuals are ensured to meet constraint requirements;
calculating the fitness of individuals in the population;
according to the fitness, the population is evolved through genetic operation and serves as the initial population of the next generation population until the termination condition is met.
Further, in the above method, the layer loss is performed according to the optimization result to obtain the layer sequence under other layer numbers, which is realized by the layer loss meeting the layer loss rule.
Further, in the above method, the layer loss rule includes, but is not limited to, one or more of the following:
the layer loss at the same position is not more than two layers;
gradually throwing layers from the largest layer to the smallest layer, wherein the later layer sequence is a layer which is thrown on the basis of the former layer sequence;
after layer loss, the layer ratio needs to be met;
after layer loss, design constraint needs to be met;
after 45 DEG paired layer loss, layer compensation is carried out at the position of 45 DEG closest to the middle surface.
Further, in the method, the layer loss is performed by adopting dynamic planning, layer-by-layer loss is performed, and the layer is traced back to the upper layer once the layer is lost to a certain position and cannot be continuously lost.
Compared with the prior art, the method for generating the composite material layering library in an optimized mode comprises the steps of layering sequence optimization, layering sequence under the maximum layering number is optimized through an immune genetic algorithm, and layering is carried out according to an optimization result to obtain layering sequences under other layering numbers; and optimizing the lost layer position, determining the specific lost layer position through dynamic programming, and ensuring the optimal maximum objective function under the lost layer position. The optimization generation method of the composite material layering library provided by the invention comprises the steps of optimizing layering sequence and generating the layering library meeting the requirements by layer loss and layer supplement, thereby ensuring that the design reaches the target optimum. According to the invention, the layering sequence optimization and the layering warehouse are combined together, so that the trouble of manually designing the layering warehouse is avoided, and the integration of the composite material design flow is realized, and the method is rapid, convenient and accurate.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it will be apparent that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for optimizing and generating a composite material layering library;
FIG. 2 is a schematic diagram of a layering sequence optimization procedure provided by the present invention;
FIG. 3 is a schematic diagram of an optimization flow of the immune genetic algorithm provided by the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiments of the invention are described in further detail below with reference to the drawings.
As shown in fig. 1, the embodiment of the invention discloses a method for generating a composite material layering library in an optimized manner, which comprises the following steps:
optimizing the layering sequence, optimizing the layering sequence under the maximum layering number through an immune genetic algorithm, and carrying out layering loss according to the optimizing result to obtain the layering sequence under other layering numbers;
and optimizing the position of the lost position, determining the specific lost position through dynamic programming comparison, and ensuring the optimal maximum of the objective function under the lost position.
In implementation, the embodiment of the invention optimizes the layering sequence under the maximum layering number through the immune genetic algorithm, and performs layering loss according to the optimization result to obtain layering sequences under other layering numbers. The overall layering library optimization flow is shown in fig. 1. The goal of each immune genetic optimization in FIG. 1 is the user's design goal, the first part is layering order optimization; the second part is the location optimization of the lost layer.
The optimization generation method of the composite material layering library provided by the invention comprises the steps of optimizing layering sequence and generating the layering library meeting the requirements by layer loss and layer supplement, thereby ensuring that the design reaches the target optimum. According to the invention, the layering sequence optimization and the layering warehouse are combined together, so that the trouble of manually designing the layering warehouse is avoided, and the integration of the composite material design flow is realized, and the method is rapid, convenient and accurate.
Further, as shown in fig. 2, the layering sequence optimization procedure is as follows:
optimizing each layering angle proportioning scheme through an immune genetic algorithm;
and comparing the optimization results, and selecting the angle proportion and the layering sequence of each layering of the objective function.
According to the embodiment of the invention, the layering sequence is optimized, the layering sequence is used as a variable to solve, each layer has different layering angles, and in an aviation structure, four layering layers of +/-45 degrees, 0 degrees and 90 degrees are generally adopted for laminated wallboards, so that the optimization problem is a discrete nonlinear programming problem. Meanwhile, due to the constraint of the layering ratio, the layering number proportion of each angle is limited, so that the layering number proportion scheme is optimized for each layering number proportion scheme, and finally, the optimization result is compared with the layering angle proportion and layering sequence of each layering angle proportion of the objective function.
Further, as shown in fig. 3, the optimization flow of the immune genetic algorithm is as follows:
the generation of iterative population individuals is completed through gene coding, and the individuals are ensured to meet constraint requirements; specifically, by setting basic parameters, population size, crossover probability, variation probability, gene string length, convergence criterion and concentration judgment threshold, an initial population is randomly generated, and the gene string is regulated according to constraint. By combining the coding characteristics of the genetic algorithm, the whole layering sequence is used as a variable to be converted into a group of chromosome strings, each gene in the chromosome strings represents the layering angle of each layer, and the layering angles are selected from the fixed values, so that the position sequence of the chromosome strings represents the layering sequence from outside to inside.
Calculating the fitness of individuals in the population; and (3) carrying out fitness analysis according to the optimization target and the constraint equation, judging whether the population is converged, if so, ending the flow, otherwise, calculating the antibody antigen affinity and the antibody concentration in the step (3), and the antibody selection probability.
According to the fitness, the population is evolved through genetic operation and serves as the initial population of the next generation population until the termination condition is met. In the immune genetic algorithm, the problem to be solved is used as an antigen of an immune system, the independent variable of optimal design is used as an antibody, the objective function of the invention is the antigen, and the layering sequence is the antibody. According to the promotion and inhibition effect between the antibody and the antigen, the population selection probability is determined, and then the selection, crossing and mutation operations are carried out.
In the implementation, the basic genetic algorithm flow comprises the steps of coding to generate an initial population, decoding to calculate fitness, and generating a new population according to fitness selection, crossing and mutation until a termination rule is met. The immune genetic algorithm adopted by the invention is to add the antibody antigen affinity and the antibody concentration calculation to control the population selection on the basis of the fitness selection. The invention adopts the immune genetic algorithm to optimize the layering sequence, overcomes the problems of premature phenomenon and deception of the basic genetic algorithm, and improves the searching efficiency.
Since the genetic algorithm mainly deals with the problem of unconstrained optimization, the need for corresponding processing for objective function constraint becomes an important link. In the solution to this, the invention adopts a variable operator to always generate legal offspring from legal parents meeting constraint conditions, so that the search is always legal. The advantage of this approach is that there are no excessive additional requirements on individual coding, genetic operations, etc. And it is expected that genetic searches can be conducted from the effective space on both sides of feasible and infeasible solutions that eventually approach the optimal solution, which can be handled as a general optimization problem.
The immune genetic algorithm of the embodiment of the invention can be mainly divided into gene coding, fitness analysis and genetic operation. The generation of iterative population individuals is completed through gene coding, and the individuals are ensured to meet constraint requirements; then calculating the fitness of individuals in the population; finally, according to the fitness, the population is evolved through genetic operation and is used as an initial population of the next generation population; until the termination condition is satisfied.
Further, in the above method, the layer loss is performed according to the optimization result to obtain the layer sequence under other layer numbers, which is realized by the layer loss meeting the layer loss rule.
In the implementation, the design of the number of the layers in the layer warehouse from the maximum number of the layers to the minimum number of the layers is realized by layer loss meeting a certain layer loss rule.
Preferably, in the above method, the layer loss rule includes, but is not limited to, one or more of the following:
the layer loss at the same position is not more than two layers;
gradually throwing layers from the largest layer to the smallest layer, wherein the later layer sequence is a layer which is thrown on the basis of the former layer sequence;
after layer loss, the layer ratio needs to be met;
after layer loss, design constraint needs to be met;
after 45 DEG paired layer loss, layer compensation is carried out at the position of 45 DEG closest to the middle surface.
The rules of the embodiment of the invention can be selected by the user, and the layer loss is performed on the basis, wherein each layer loss ensures that the layer loss position is positioned so as to optimize the objective function after the layer loss, such as uniform symmetry rules, 90 DEG group avoidance and the like.
Preferably, as shown in fig. 1, the layer loss is performed by dynamic planning. According to the embodiment of the invention, as the layering sequence is required to be more, the design constraint is complex, each layer loss is carried out on the basis of the last layering sequence, and each layer loss is influenced back and forth, the layering library layer loss and supplement design is carried out by adopting the common dynamic programming to solve the optimization problem, and the layering library is further generated.
In summary, the invention provides a layering warehouse integrated optimization generation method suitable for an aviation composite material structure, which is used for rapidly generating a layering warehouse meeting the requirements of users according to the actual design targets and design constraint rules of the users. The embodiment of the invention adopts the immune genetic algorithm to optimize the layering sequence, overcomes the problems of premature phenomenon and deception of the basic genetic algorithm, and improves the searching efficiency. And because the layering sequence is required to be more and the design constraint is complex, the invention adopts a dynamic programming method to carry out the layer missing and layer supplementing design of the layering warehouse, thereby generating the layering warehouse. The invention selects the most valuable engineering method and efficient workflow, and meets the actual engineering requirements. According to the invention, the layering sequence optimization and the layering warehouse are combined together, so that the trouble of manually designing the layering warehouse is avoided, and the integration of the composite material design flow is realized, and the method is rapid, convenient and accurate.
It should be noted that the present invention may be optimized for various usage requirements of users, including the lightest quality, the optimal strength, the optimal stability, the optimal reliability, and the like.
The invention improves the design efficiency of the composite material layer, greatly shortens the design period and provides the optimal structural layer design for designers.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (7)
1. The method for optimally generating the composite material layering warehouse is characterized by comprising the following steps of:
optimizing the layering sequence, optimizing the layering sequence under the maximum layering number through an immune genetic algorithm, and carrying out layering loss according to the optimizing result to obtain the layering sequence under other layering numbers;
and optimizing the lost layer position, determining the specific lost layer position through dynamic programming, and ensuring the optimal maximum objective function under the lost layer position.
The layering sequence optimizing flow is as follows:
optimizing each layering angle proportioning scheme through an immune genetic algorithm;
comparing the optimized results, selecting the angle proportion of each layer of the objective function and the layer sequence,
the layer loss is carried out according to the optimization result to obtain the layer sequence under other layer numbers, which is realized by the layer loss meeting the layer loss rule,
the optimization flow of the immune genetic algorithm is as follows:
the generation of iterative population individuals is completed through gene coding, and the individuals are ensured to meet constraint requirements;
calculating the fitness of individuals in the population;
according to the fitness, the population is evolved through genetic operation and is used as the initial population of the next generation population until the termination condition is met,
the design of the number of layers in the layer library from the maximum number of layers to the minimum number of layers is realized by layer loss meeting the layer loss rule, wherein the layer loss rule comprises one or more of the following:
the layer loss at the same position is not more than two layers;
gradually throwing layers from the largest layer to the smallest layer, wherein the later layer sequence is a layer which is thrown on the basis of the former layer sequence;
after layer loss, the layer ratio needs to be met;
after layer loss, design constraint needs to be met;
after 45 DEG paired layer loss, layer compensation is carried out at the position of 45 DEG closest to the middle surface.
2. The method of claim 1, wherein the goal of each immunogenecity optimization is a user's design goal, the first part being layering sequence optimization; the second part is the location optimization of the lost layer.
3. The method of claim 1, wherein the layer loss uses dynamic programming for layer loss compensation design of the layer library to generate the layer library.
4. A method according to claim 3, wherein the generation of individuals of the iterative population is accomplished by genetic coding and the individuals are guaranteed to meet constraint requirements by setting basic parameters, population size, crossover probability, mutation probability, cluster length, convergence criteria and concentration judgment threshold, randomly generating an initial population, and adjusting the cluster according to the constraint.
5. A method according to claim 3, wherein the fitness of the individuals in the population is calculated, and for fitness analysis based on the optimization objective and constraint equation, it is determined whether the population is converging, if so, the process is ended, otherwise the antibody antigen affinity and antibody concentration, and the antibody selection probability are calculated.
6. The method of claim 5, wherein the population is evolved by genetic manipulation and is an initial population for a next generation population until a termination condition is met, based on fitness.
7. The method of claim 5, wherein in the immune genetic algorithm, the problem to be solved is used as an antigen of an immune system, the independent variable of optimal design is used as an antibody, the objective function is the antigen, the layering sequence is the antibody, the population selection probability is determined according to the promotion and inhibition between the antibody and the antigen, and then the selection, crossing and mutation operations are performed.
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| CN106874573A (en) * | 2017-01-20 | 2017-06-20 | 西北工业大学 | A kind of method for designing of subregion Varying-thickness composite laminated plate |
| CN107844664A (en) * | 2017-11-23 | 2018-03-27 | 江苏理工学院 | A kind of Optimization Design applied to lamina laying angle |
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| US10322792B2 (en) * | 2016-02-08 | 2019-06-18 | The Boeing Company | Ply optimization feasibility analysis for multi-layer composite parts |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN106874573A (en) * | 2017-01-20 | 2017-06-20 | 西北工业大学 | A kind of method for designing of subregion Varying-thickness composite laminated plate |
| CN107844664A (en) * | 2017-11-23 | 2018-03-27 | 江苏理工学院 | A kind of Optimization Design applied to lamina laying angle |
Non-Patent Citations (2)
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| 陈景昊.基于共享铺层融合技术复合材料层压板铺层顺序优化设计.固体火箭技术.2012,第35卷(第6期),第803-806页. * |
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