CN117113589B - Multi-objective intelligent optimization method for pre-connection process of weak-rigidity thin-wall structure - Google Patents
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Abstract
本发明涉及模拟仿真领域，具体公开了一种弱刚性薄壁结构预连接工艺多目标智能优化方法，包括如下依次执行的步骤：S1：确定预连接件数量和预连接后的连接孔数量，将预连接后的平均残余间隙的最小值和残余间隙变异系数的最小值作为优化目标，步骤S2：采用多目标遗传算法对多个预连接工艺参数进行优化，S3：采用逼近于理想解的排序法，从步骤S2最终输出的预连接工艺设计方案中，选取预连接工艺设计方案的最优解。通过上述方案能够有效避免由于装配间隙问题而返工，有助于减少返工率。
The invention relates to the field of simulation, and specifically discloses a multi-objective intelligent optimization method for the pre-connection process of weakly rigid thin-walled structures, which includes the following steps: S1: Determine the number of pre-connections and the number of connection holes after pre-connection, and Average residual gap after pre-connection The minimum value and residual gap variation coefficient of The minimum value of is taken as the optimization target. Step S2: Use a multi-objective genetic algorithm to optimize multiple pre-connection process parameters. S3: Use a ranking method that is close to the ideal solution to select from the pre-connection process design plan finally output in step S2. The optimal solution for pre-connection process design. The above solution can effectively avoid rework due to assembly gap problems and help reduce the rework rate.
Description
Technical Field
The invention belongs to the field of simulation, and particularly relates to a multi-objective intelligent optimization method for a pre-connection process of a weak-rigidity thin-wall structure.
Background
As an aviation wallboard with a typical weak-rigidity thin-wall structure, the aviation wallboard is formed by connecting a large number of thin-wall parts, and a large initial assembly gap is easy to occur during assembly on a mold frame. Excessive assembly clearance can cause chatter of the hole making tool during reaming of the fastener connecting hole, and can also cause hole making burrs to enter the clearance to scratch the surface of the part. In addition, excessive assembly clearances can lead to increased aircraft panel assembly distortion and assembly stress. Before the aeronautical panel is drilled and riveted, it is necessary to control the gap to a reasonable extent. To achieve this, pre-connectors such as through clamps, bolts, etc. are generally used to suppress the assembly gap by double-sided compression. However, the conventional pre-connection process is seriously dependent on experience and lacks scientific basis, and the suppression effect of the assembly gap is often required to be confirmed manually after the pre-connection, so that the pre-connection process has higher reworking rate and larger workload.
In view of the above, a multi-objective intelligent optimization method for a pre-connection process of a weak-rigidity thin-wall structure is provided.
Disclosure of Invention
The invention aims to provide a multi-objective intelligent optimization method for a pre-connection process of a weak-rigidity thin-wall structure, which helps to reduce the rework rate.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the intelligent multi-target optimizing method for the pre-connection process of the weak-rigidity thin-wall structure comprises the following steps of:
s1: determining the number of the pre-connection pieces and the number of the connection holes after the pre-connection, and on the basis, averaging the residual gaps after the pre-connectionMinimum and residual gap coefficient of variation +.>As optimization target, with pre-connection layoutOrder->Pretension->For design variables, with residual gap tolerance +.>For constraint, to find the optimal pre-connection process design>Build up as follows (1)) The formula shows a preconnected technological parameter multi-objective optimized mathematical model:
（1）；
wherein,serial number indicating connection hole, ">Indicate->Residual gap at the connection holes +.>The number of the connecting holes after the pre-connection is the number;
s2: based on the pre-connection process parameter multi-objective optimization mathematical model established in the step S1, optimizing a plurality of pre-connection process parameters by adopting a multi-objective genetic algorithm, wherein the specific optimization steps are as follows:
s2-1: setting the maximum iteration times of a multi-objective intelligent optimization algorithm of a pre-connection process, and setting the directional cross probability, classical cross probability, mutation probability and selection probability required by generation of offspring;
s2-2: extracting from the pre-connection process design consisting of the pre-connection layout, the sequence and the pre-tightening force based on random sampling in combination with the determined number of pre-connectionsGroup satisfaction->As an initial population for a multi-objective genetic algorithm;
s2-3: establishing a pre-connection process finite element simulation model according to the pre-connection process design scheme corresponding to each individual in the initial population, and calculating the residual gap of each connection holeFor calculating the average residual gap corresponding to each group of pre-connection process design schemes>And residual gap coefficient of variation->Obtaining the fitness vector +.f of the pre-connection process multi-objective intelligent optimization algorithm shown in the following formula (2)>：
（2）；
S2-4: comprehensively applying directional crossing, classical crossing, mutation and selection operation to evolve the pre-connection process design scheme population to generate a next generation pre-connection process design scheme population;
s2-5: adopting elite strategy, reserving elite pre-connection process design schemes, introducing Pareto domination and Pareto solution sets, putting Pareto solution sets of child populations of each pre-connection process design scheme into elite solution sets, comparing the Pareto solution sets with the elite solutions of the pre-connection process design schemes, deleting repeated and dominant Pareto solutions to obtain solution sets of all the pre-connection process design schemes, mapping the solution sets to an average residual gap and residual gap variation coefficient two-dimensional target space, and forming a Pareto front solution curve;
s2-6: repeating the step S2-3 and the step S2-4 until the maximum iteration number of the set multi-objective intelligent optimization algorithm of the pre-connection process is reached, and outputting a final Pareto solution set of the design scheme of the pre-connection process;
s3: and (3) selecting an optimal solution of the pre-connection process design scheme from the Pareto solution set of the pre-connection process design scheme finally output in the step (S2) by adopting a sequencing method approaching to the ideal solution.
Preferably, the specific step of selecting the optimal solution of the pre-connection process design scheme in step S3 is as follows:
the specific steps for selecting the optimal solution of the pre-connection process design scheme in the step S3 are as follows:
s3-1: gathering the pre-connection process design scheme Pareto finally output in step S2The Pareto front solution is used as an evaluation object, each evaluation object comprises two evaluation indexes of average residual gaps and residual gap variation coefficients, and each evaluation index of each evaluation object is subjected to standardization processing and weighting to obtain a feature matrix of a Pareto solution set of a pre-connection process design scheme shown in the following formula (3):
（3）；
wherein,is->No. 4 of the evaluation object>Evaluation index value->Is->Corresponding weight coefficient；/>；/>And->Is when->Time->And->；/>For the total number of Pareto front solutions, +.>For evaluating the number of indexes>；
S3-2: as shown in the following equation (4), positive and negative ideal solutions of the pre-connection process scheme Pareto solution set are determined,
（4） ；
wherein, is ideal to understandVector composed of minimum values of elements in each column in feature matrix of Pareto solution set for pre-connection process design scheme, negative ideal solution +.>A vector formed by the maximum values of each column of elements in a feature matrix of a Pareto solution set for a pre-connection process design scheme;
s3-3: feature matrix of Pareto solution set of pre-connection process design schemeLine as->Design scheme of individual alternative pre-connection processFeature vector +.>Calculating the feature vector +.>And positive ideal solution vector->European distance->And the feature vector +.>Euclidean distance from negative ideal solution vector +.>Thereby obtaining +.>Proximity index of individual pre-connection process design scheme and ideal solution，
；
Each pre-connection process design scheme in the Pareto solution set of the pre-connection process design schemes is according to the proximity degree index of the pre-connection process design schemes to the ideal solutionAnd sequencing, wherein the approach degree index with the ideal solution is the optimal pre-connection process design scheme with the maximum.
By adopting the design scheme, the invention has the beneficial effects that: the invention can realize the collaborative optimization of the pre-connection process parameters such as the pre-connection layout, the sequence, the pre-tightening force and the like, can provide theoretical basis and technical support for the intelligent optimization of the panel pre-connection assembly process in the intelligent manufacture of aviation industry, improves the pre-connection assembly quality of aviation panels, can effectively avoid reworking due to the assembly clearance problem, and is beneficial to reducing the reworking rate.
Drawings
FIG. 1 is a schematic illustration of the aircraft panel assembly gap suppression of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. 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.
As shown in fig. 1, wherein an initial assembly gap exists between the skin and stringers prior to the pre-attachment; after the pre-connection, the gap at the pre-connection hole is zero, and the gap at the connection hole is a residual gap.
The invention discloses a multi-objective intelligent optimization method for a pre-connection process of a weak-rigidity thin-wall structure, which comprises the following steps of:
s1: determining the number of pre-connectorsAnd the number of connecting holes after pre-connection +.>On the basis of this, the average residual gap after the pre-connection is +.>Minimum and residual gap coefficient of variation +.>Is used as an optimization target with a pre-connection layout +.>Order->Pretension->For design variables, with residual gap tolerance +.>For constraint conditions, find the optimal pre-connection process design scheme +.>Establishing a pre-connection process parameter multi-objective optimization mathematical model shown in the following formula (1):
（1）；
wherein,serial number indicating connection hole, ">Indicate->Residual gap at the connection holes +.>The number of the connecting holes after the pre-connection is the number;
s2: optimizing a plurality of pre-connection process parameters by adopting a multi-objective genetic algorithm such as MOGA-II based on the pre-connection process parameter multi-objective optimized mathematical model established in the step S1, wherein a secondary development technology is adopted to establish a pre-connection process finite element simulation model, and the residual gaps at all connection holes are outputSo as to calculate the average residual gap +.f. of the pre-connection for each group of pre-connection process designs>And residual gapCoefficient of variation->；
The specific optimization step of step S2 is as follows:
s2-1: setting the maximum iteration times of a multi-objective intelligent optimization algorithm of a pre-connection process, and setting the directional cross probability, classical cross probability, mutation probability and selection probability required by generation of offspring;
s2-2: combining the determined number of pre-connectorsBased on random sampling, extracting +.>Group satisfaction->As an initial population of a multi-objective genetic algorithm to promote the convergence efficiency of the genetic algorithm;
s2-3: according to the design scheme of the pre-connection process corresponding to each individual in the initial population, a finite element simulation model of the pre-connection process is established, and the residual gap of each connection hole is calculatedFor calculating the average residual gap corresponding to each group of pre-connection process design schemes>And residual gap coefficient of variation->Obtaining the fitness vector +.f of the pre-connection process multi-objective intelligent optimization algorithm shown in the following formula (2)>；
（2）；
S2-4: comprehensively applying directional crossing, classical crossing, mutation and selection operation to evolve the pre-connection process design scheme population to generate a next generation pre-connection process design scheme population;
s2-5: adopting elite strategy to reserve elite pre-connection process design scheme, improving convergence performance of multi-objective optimization algorithm of pre-connection process, and ensuring adaptability of offspring of pre-connection process design scheme to be larger than adaptability of father. Considering that the adaptability of the multi-objective optimization of the pre-connection process is represented by a fitness vector comprising two fitness elements, introducing Pareto dominance and Pareto solution sets, putting the Pareto solution sets of the sub population of each pre-connection process design scheme into elite solution sets, comparing the Pareto solution sets with the elite solution of the pre-connection process design scheme, deleting repeated and dominant Pareto solutions to obtain solution sets of all the pre-connection process design schemes, mapping the solution sets to an average residual gap and residual gap variation coefficient two-dimensional target space, and forming a Pareto front solution curve;
s2-6: repeating the step S2-3 and the step S2-4 until the maximum iteration number of the set multi-objective intelligent optimization algorithm of the pre-connection process is reached, and outputting a final Pareto solution set of the design scheme of the pre-connection process;
s3: and selecting an optimal solution of the pre-connection process design scheme from the Pareto solution set of the pre-connection process design scheme finally output in the step S2 by adopting a sequencing method approaching to an ideal solution, such as TOPSIS.
The specific steps for selecting the optimal solution of the pre-connection process design scheme in the step S3 are as follows:
s3-1: gathering the pre-connection process design scheme Pareto finally output in step S2The Pareto front solution is used as an evaluation object, each evaluation object comprises two evaluation indexes of average residual gap and residual gap variation coefficient, and each evaluation index of each evaluation object is subjected to standardization processing and weighting to obtainThe feature matrix of the Pareto solution set of the pre-connection process design scheme is shown in the following formula (3):
（3）；
wherein,is->No. 4 of the evaluation object>Evaluation index value->Is->Corresponding weight coefficient；/>；/>And->Is when->Time->And->；/>For the total number of Pareto front solutions, +.>For the number of evaluation indexes (since each evaluation object here contains two evaluation indexes of average residual gap and residual gap variation coefficient, therefore +.>），/>；
S3-2: as shown in the following equation (4), positive and negative ideal solutions of the pre-connection process scheme Pareto solution set are determined,
（4）；
wherein, is ideal to understandVector composed of minimum values of elements in each column in feature matrix of Pareto solution set for pre-connection process design scheme, negative ideal solution +.>A vector formed by the maximum values of each column of elements in a feature matrix of a Pareto solution set for a pre-connection process design scheme;
s3-3: feature matrix of Pareto solution set of pre-connection process design schemeLine as->Feature vector of the individual alternative pre-connection process design +.>Calculating the feature vector +.>And positive ideal solution vector->European distance->And the feature vector +.>Euclidean distance from negative ideal solution vector +.>Thereby obtaining +.>Proximity index of individual pre-connection process design scheme and ideal solution，
；
Each pre-connection process design scheme in the Pareto solution set of the pre-connection process design schemes is according to the proximity degree index of the pre-connection process design schemes to the ideal solutionAnd sequencing, wherein the approach degree index with the ideal solution is the optimal pre-connection process design scheme with the maximum.
In summary, the invention can realize the collaborative optimization of the pre-connection process parameters such as the pre-connection layout, the sequence, the pre-tightening force and the like, can provide theoretical basis and technical support for the intelligent optimization of the panel pre-connection assembly process in the intelligent manufacture of aviation industry, improves the pre-connection assembly quality of aviation panels, can effectively avoid reworking due to the problem of assembly gaps, and is beneficial to reducing the reworking rate.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (2)
1. A multi-objective intelligent optimization method for a pre-connection process of a weak-rigidity thin-wall structure is characterized by comprising the following steps of: the method comprises the following steps of:
s1: determining the number of the pre-connection pieces and the number of the connection holes after the pre-connection, and on the basis, averaging the residual gaps after the pre-connectionMinimum and residual gap coefficient of variation +.>As optimization target, with pre-connection layoutOrder->Pretension->For design variables, with residual gap tolerance +.>For constraint, to find the optimal pre-connection process design>Establishing a pre-connection process parameter multi-objective optimization mathematical model shown in the following formula (1):
（1） ；
wherein,serial number indicating connection hole, ">Indicate->Residual gap at the connection holes +.>The number of the connecting holes after the pre-connection is the number;
s2: based on the pre-connection process parameter multi-objective optimization mathematical model established in the step S1, optimizing a plurality of pre-connection process parameters by adopting a multi-objective genetic algorithm, wherein the specific optimization steps are as follows:
s2-1: setting the maximum iteration times of a multi-objective intelligent optimization algorithm of a pre-connection process, and setting the directional cross probability, classical cross probability, mutation probability and selection probability required by generation of offspring;
s2-2: extracting from the pre-connection process design consisting of the pre-connection layout, the sequence and the pre-tightening force based on random sampling in combination with the determined number of pre-connectionsGroup satisfaction->As an initial population for a multi-objective genetic algorithm;
s2-3: establishing a pre-connection process finite element simulation model according to the pre-connection process design scheme corresponding to each individual in the initial population, and calculating the residual gap of each connection holeFor calculating the average residual gap corresponding to each group of pre-connection process design schemes>And residual gap coefficient of variation->Obtaining the fitness vector +.f of the pre-connection process multi-objective intelligent optimization algorithm shown in the following formula (2)>；
（2） ；
S2-4: comprehensively applying directional crossing, classical crossing, mutation and selection operation to evolve the pre-connection process design scheme population to generate a next generation pre-connection process design scheme population;
s2-5: adopting elite strategy, reserving elite pre-connection process design schemes, introducing Pareto domination and Pareto solution sets, putting Pareto solutions of child populations of each pre-connection process design scheme into the elite solution sets, comparing the Pareto solutions with the elite solutions of the pre-connection process design schemes, deleting repeated and dominant Pareto solutions to obtain solution sets of all the pre-connection process design schemes, mapping the solution sets to an average residual gap and residual gap variation coefficient two-dimensional target space, and forming a Pareto front solution curve;
s2-6: repeating the step S2-3 and the step S2-4 until the maximum iteration number of the set multi-objective intelligent optimization algorithm of the pre-connection process is reached, and outputting a final Pareto solution set of the design scheme of the pre-connection process;
s3: and (3) selecting an optimal solution of the pre-connection process design scheme from the Pareto solution set of the pre-connection process design scheme finally output in the step (S2) by adopting a sequencing method approaching to the ideal solution.
2. The intelligent optimization method for the pretonnection process of the weak-rigidity thin-wall structure according to claim 1, which is characterized by comprising the following steps:
the specific steps for selecting the optimal solution of the pre-connection process design scheme in the step S3 are as follows:
s3-1: gathering the pre-connection process design scheme Pareto finally output in step S2The Pareto front solution is used as an evaluation object, each evaluation object comprises two evaluation indexes of average residual gaps and residual gap variation coefficients, and each evaluation index of each evaluation object is subjected to standardization processing and weighting to obtain a feature matrix of a Pareto solution set of a pre-connection process design scheme shown in the following formula (3):
（3）；
wherein,is->No. 4 of the evaluation object>Evaluation index value->Is->Corresponding weight coefficient；/>；/>And->Is when->Time->And->；/>For the total number of Pareto front solutions, +.>For evaluating the number of indexes>；
S3-2: as shown in the following equation (4), positive and negative ideal solutions of the pre-connection process scheme Pareto solution set are determined,
（4） ；
wherein, is ideal to understandVector composed of minimum values of elements in each column in feature matrix of Pareto solution set for pre-connection process design scheme, negative ideal solution +.>A vector formed by the maximum values of each column of elements in a feature matrix of a Pareto solution set for a pre-connection process design scheme;
s3-3: feature matrix of Pareto solution set of pre-connection process design schemeLine as->Feature vector of the individual alternative pre-connection process design +.>Calculating the feature vector +.>And positive ideal solution vectorEuropean distance->And the feature vector +.>Euclidean distance from negative ideal solution vector +.>Thereby obtaining +.>Proximity index of individual pre-connection process design and ideal solution +.>，
；
Each pre-connection process design scheme in the Pareto solution set of the pre-connection process design schemes is according to the proximity degree index of the pre-connection process design schemes to the ideal solutionAnd sequencing, wherein the approach degree index with the ideal solution is the optimal pre-connection process design scheme with the maximum.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112115579A (en) * | 2020-08-12 | 2020-12-22 | 江苏师范大学 | Multi-target optimization method for injection molding process parameters of glass fiber reinforced plastics |
| CN112380760A (en) * | 2020-10-13 | 2021-02-19 | 重庆大学 | Multi-algorithm fusion based multi-target process parameter intelligent optimization method |
| CN113111436A (en) * | 2021-04-15 | 2021-07-13 | 泉州装备制造研究所 | Airplane large component pre-connection layout and multi-constraint action sequence optimization method |
| CN113353242A (en) * | 2021-05-17 | 2021-09-07 | 南京航空航天大学 | Temporary fastener layout optimization method for composite wing box pre-connection stage |
| CN115130749A (en) * | 2022-06-29 | 2022-09-30 | 重庆大学 | A data-driven multi-objective optimization method integrating NSGA-Ⅲ and TOPSIS |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112115579A (en) * | 2020-08-12 | 2020-12-22 | 江苏师范大学 | Multi-target optimization method for injection molding process parameters of glass fiber reinforced plastics |
| CN112380760A (en) * | 2020-10-13 | 2021-02-19 | 重庆大学 | Multi-algorithm fusion based multi-target process parameter intelligent optimization method |
| CN113111436A (en) * | 2021-04-15 | 2021-07-13 | 泉州装备制造研究所 | Airplane large component pre-connection layout and multi-constraint action sequence optimization method |
| CN113353242A (en) * | 2021-05-17 | 2021-09-07 | 南京航空航天大学 | Temporary fastener layout optimization method for composite wing box pre-connection stage |
| CN115130749A (en) * | 2022-06-29 | 2022-09-30 | 重庆大学 | A data-driven multi-objective optimization method integrating NSGA-Ⅲ and TOPSIS |
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