Honeycomb sandwich structure repair scheme optimization design method

Abstract

The invention provides an optimization design method for a honeycomb sandwich structure repair scheme. The optimization design method comprises the steps that S1, a honeycomb sandwich intact structure model and a cementing patching repair model under different damage working conditions are established; s2, based on an Abaqus secondary development technology, compiling a Python script program, and building a parameterized modeling platform; s3, randomly extracting a plurality of sample points based on a Latin hypercube method, and performing numerical simulation by using sample point data; s4, establishing a quadratic polynomial response surface agent model based on a response surface method; and S5, performing iterative optimization on the quadratic polynomial response surface proxy model based on a genetic algorithm by taking the side pressure limit load of the patching structure as a target. According to the method, the Abaqus secondary development, the agent model and the genetic algorithm are combined, all cementing, patching and repairing working conditions of the honeycomb sandwich structure are included, and the time for formulating an optimal repairing scheme is greatly saved.

Description

Technical Field

[0001] The invention belongs to the technical field of composite material repair, and in particular relates to an optimization design method for a honeycomb sandwich structure repair scheme. Background Art

[0002] The proportion of composite materials used in the aviation field is gradually increasing. Honeycomb sandwich structures are widely used in radar antenna cover components of civil and military aircraft. They are likely to suffer from ground equipment collisions, ground sand and gravel collisions, hail collisions, bird collisions, and cracks, debonding, pits, and perforations during service. In order to ensure their overall aerodynamic integrity, adhesive patching is often used for repair. When there is no corresponding repair plan in the repair manual and a repair plan needs to be formulated, it is very important to quickly complete the formulation of the best repair plan. At present, the optimization design of the repair plan is usually to simulate and optimize the specific repair conditions of a specific structure, without fully parameterizing the structure and its working conditions; at the same time, when iteratively optimizing its repair plan, each generation of data is obtained through simulation calculations using finite element software, which consumes a lot of time. Summary of the invention

[0003] In view of this, the present invention aims to overcome the shortcomings of the above-mentioned problems in the prior art, and proposes an optimization design method for repair schemes of honeycomb sandwich structures. A parametric modeling platform is built through Python language, and parameter random sampling is performed based on the Latin hypercube method. An agent model is established according to the sampling results, and finally a genetic algorithm is used to quickly iterate and optimize the agent model to complete the optimization design of the repair scheme of the honeycomb sandwich structure.

[0004] To achieve the above object, the technical solution of the present invention is achieved as follows:

[0005] A first aspect of the present invention provides a method for optimizing the design of a honeycomb sandwich structure repair solution, comprising the following steps:

[0006] S1: Establish the intact model of honeycomb sandwich structure and the corresponding adhesive patching repair models under different damage conditions;

[0007] S2: Use Python language to write built-in Kernel programs and Abaqus built-in functions to build users and complete the construction of the parametric modeling platform;

[0008] S3: Select some repair parameters, set them to a practical value range, program them in MATLAB software based on the Latin hypercube method, randomly select multiple sample points, and use the parametric modeling platform to quickly perform numerical simulation;

[0009] S4: Taking the patch's miter rate, reinforcement sheet extension length and adhesive layer thickness as independent variables and the lateral pressure limit load of the cut-and-patch structure as the dependent variable, a quadratic polynomial response surface proxy model was established based on the response surface methodology;

[0010] S5: Based on the genetic algorithm, the agent model is iteratively optimized, and the optimal repair plan is determined by comprehensively considering the value range and calculation cycle of the patching parameters and the range of the results.

[0011] Specifically, in step 1, establishing the corresponding adhesive joint patching repair model under different damage conditions includes:

[0012] The honeycomb sandwich structure models under single-side panel repair, single-side panel and honeycomb repair, and double-side panel and honeycomb repair were established to simulate and calculate various types of damage to the honeycomb sandwich structure.

[0013] Specifically, the step 2 specifically includes:

[0014] The simulation model is fully parameterized, including the size parameters, material parameters, ply parameters, and mesh parameters of the panel and patch under the model condition in step 1, the size parameters, material parameters, and mesh parameters of the honeycomb core, and the size parameters, material parameters, and mesh parameters of the adhesive layer. The built-in Kernel program is written in Python, and the user interface is built using the built-in functions of Abaqus. The xlrd library is introduced, and complex material parameters and ply parameters are imported through Excel to complete the construction of the parametric modeling platform.

[0015] Specifically, the step 3 specifically includes:

[0016] The damage condition that requires a honeycomb sandwich structure is selected, and then three parameters with high weights for adhesive patching are selected: the patch beveling rate K, the reinforcement sheet extension length L and the adhesive layer thickness T. The actual value ranges are set for them, and programming is performed in MATLAB software based on the Latin hypercube method. Multiple sample points are randomly selected, and numerical simulation is quickly performed using the parametric modeling platform to obtain the ultimate lateral pressure load F of the honeycomb sandwich structure under the corresponding repair scheme.

[0017] Specifically, in step 4, a quadratic polynomial response surface proxy model is established based on the response surface methodology, and its form is:

[0018] F=a1K+b1L+c1T+a2K 2 +b2L 2 +c2T 2 +a3KL+b3KT+c3LT+C

[0019] where a i ,b i ,ci (i=1,2,3) are the weight coefficients of the respective variables, and C is a constant;

[0020] And introduce the multiple correlation coefficient R 2 , the R of the regression equation 2 It indicates the deviation between the experimental value and the predicted value, R 2 The value range is [0, 1]. When the value of the complex correlation coefficient is larger, the deviation between the regression value and the simulation value is smaller.

[0021] Specifically, in step 5, the quadratic polynomial response surface proxy model established in step 4 is iteratively optimized based on a genetic algorithm, and the value range and calculation cycle of the patching parameters and the range included in the results are comprehensively considered. Programming is performed based on MATLAB, and decision quantities such as patching parameters are binary coded. The corresponding parameters such as the total number of initialized groups, the number of elites in each generation, the crossover probability, and the offspring mutation ratio are set to quickly iterate and obtain the best repair plan.

[0022] A second aspect of the present invention provides a honeycomb sandwich structure repair scheme optimization design device, comprising:

[0023] Model building module, used to establish the intact model of honeycomb sandwich structure and the corresponding adhesive patching repair models under different damage conditions;

[0024] The modeling platform building module is used to use Python language to write built-in Kernel programs and Abaqus built-in functions to build users and complete the construction of the parametric modeling platform;

[0025] The simulation module is used to select some repair parameters, set them to a practical value range, and program them in MATLAB software based on the Latin hypercube method, randomly select multiple sample points, and use the parametric modeling platform to quickly perform numerical simulation;

[0026] The proxy model building module is used to establish a quadratic polynomial response surface proxy model based on the response surface method, taking the patch's miter rate, reinforcement sheet extension length and adhesive layer thickness as independent variables and the lateral pressure limit load of the cut-and-patch structure as the dependent variable;

[0027] The model optimization module is used to iteratively optimize the agent model based on the genetic algorithm, comprehensively consider the value range and calculation cycle of the patching parameters and the range covered by the results, and determine the best repair plan.

[0028] A third aspect of the present invention provides an electronic device, comprising a processor and a memory that is communicatively connected to the processor and is used to store executable instructions for the processor, wherein the processor is used to execute the above-mentioned method for optimizing the design of a repair solution for a honeycomb sandwich structure.

[0029] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned method for optimizing the design of a repair solution for a honeycomb sandwich structure.

[0030] Compared with the prior art, the honeycomb sandwich structure repair scheme optimization design method described in the present invention has the following advantages:

[0031] The present invention uses Abaqus secondary development technology to comprehensively parameterize the intact working condition of the honeycomb sandwich structure, the single-side panel patching working condition, the single-side panel plus core patching working condition, and the double-side panel plus core patching working condition, and can quickly model and analyze any working condition. Based on the Latin hypercube method and the response surface method, a proxy model of the finite element model is established. Under the premise of ensuring the accuracy, the iteration speed of the genetic algorithm can be greatly improved, the optimal repair parameters can be quickly obtained, the optimization design of the honeycomb sandwich structure repair plan can be completed, and the time cost can be greatly saved. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0033] Figure 1 is a flow chart of the method of the present invention;

[0034] Figure 2 Provides a user interface for the parametric modeling platform;

[0035] Figure 3 A scatter plot diagram of Latin hypercube sampling. DETAILED DESCRIPTION

[0036] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0037] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and the like are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", and the like may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0038] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood by specific circumstances.

[0039] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0040] Embodiment 1:

[0041] Since there are many sample data, only one group of corresponding sample data is selected here to demonstrate the embodiment, and the other sample data selected by the present invention are similar in rules.

[0042] like Figure 1 As shown, the present invention provides a method for optimizing the design of a repair scheme for a honeycomb sandwich structure of a radar antenna cover, wherein the panel material is an epoxy resin composite material reinforced with a 7781 type glass fiber woven cloth; the honeycomb core is a Nomex paper honeycomb of model HRH-10-1 / 8-3-0; and the adhesive film is a BMS5-101-II type 10 grade adhesive film.

[0043] The methods specifically include:

[0044] Step 1: Establish a honeycomb sandwich structure model;

[0045] The intact model of honeycomb sandwich structure, the single-side panel patch model, the single-side panel plus core patch model, and the double-side panel plus core patch model are established. The panel is modeled with orthotropic material, and the three-dimensional eight-node reduced integration unit (C3D8R) is selected, and one unit is set on the thickness of the single-layer plate; the honeycomb core is also modeled with a three-dimensional eight-node reduced integration unit (C3D8R), and its microstructure is equivalent to an orthotropic material, and two units are arranged in the thickness direction; at the repair interface, including the interface between the patch and the honeycomb replacement core, the honeycomb core interface, and the patch and the motherboard interface, a zero-thickness three-dimensional cohesive unit (COH3D8) is inserted to simulate the glue layer. The glue layer unit is connected to the panel unit and the honeycomb unit using Tie binding, and the Vumat subroutine is written to simulate the stiffness degradation of the material. The simulation results are compared with the literature to verify the correctness of the model.

[0046] Step 2: Build a parametric modeling platform;

[0047] The simulation model is fully parameterized, including the size parameters, material parameters, layer parameters, and mesh parameters of the panel and patch under four working conditions, the size parameters, material parameters, and mesh parameters of the honeycomb core, and the size parameters, material parameters, and mesh parameters of the glue layer. The built-in Kernel program is written in Python, and the user interface is built using the built-in functions of Abaqus. The xlrd library is introduced, and complex material parameters and layer parameters are imported through Excel to complete the construction of the parametric modeling platform, saving a lot of modeling time. Figure 2 It is the platform interface.

[0048] Step 3: Acquisition of proxy model parameter samples;

[0049] The damage condition that requires a honeycomb sandwich structure is selected, and then three parameters with higher weights for adhesive patching repair are selected: the bevel rate K of the patch, the extension length L of the reinforcement sheet and the thickness T of the adhesive layer. The actual value ranges are set for them. The value range of the bevel rate K of the patch is 20-50, the value range of the extension length L of the reinforcement sheet is 5-15mm, and the value range of the adhesive layer thickness T is 0.08-0.20mm. Programming is performed in MATLAB software based on the Latin hypercube method, 24 sample points are randomly selected, and numerical simulation is quickly performed using the parametric modeling platform to obtain the ultimate lateral pressure load F of the honeycomb sandwich structure under the corresponding repair scheme. Figure 3 A scatter plot diagram of Latin hypercube sampling.

[0050] Step 4: Establish agent model;

[0051] Taking the patch miter rate K, reinforcement sheet extension length L and adhesive layer thickness T of the S3 sample point as independent variables and the ultimate lateral pressure load F of the patch structure as the dependent variable, a quadratic polynomial response surface proxy model was established in the Design-Expert software based on the response surface methodology, and its form is:

[0052] F=a1K+b1L+c1T+a2K 2 +b2L 2 +c2T 2 +a3KL+b3KT+c3LT+C

[0053] where a i ,b i ,c i (i=1,2,3) are the weight coefficients of each variable, and C is a constant.

[0054] And introduce the multiple correlation coefficient R 2 , the R of the regression equation 2 It indicates the deviation between the experimental value and the predicted value, R 2 The value range is [0, 1]. When the value of the complex correlation coefficient is larger, the deviation between the regression value and the simulation value is smaller.

[0055] Step 5: Use genetic algorithm to iteratively optimize the agent model;

[0056] The quadratic polynomial response surface surrogate model was solved based on the genetic algorithm. The range of values ​​of the patching parameters, the calculation cycle, and the range of the results were comprehensively considered. The programming was based on MATLAB. The decision quantities such as the patching parameters were encoded in binary. The corresponding initialization total population number was set to 100, the number of elites in each generation was 10, the crossover probability was 0.80, the offspring mutation ratio and other parameters were set to 0.01, the fitness function was the response surface surrogate model, and the fitness function deviation was 10 －5 The number of genetic generations is 70. In the genetic process, the mutation method is based on single-point crossover. Compared with directly iterating the finite element model, this method can quickly iterate to obtain the best repair solution.

[0057] Embodiment 2:

[0058] A honeycomb sandwich structure repair scheme optimization design device, comprising:

[0059] Model building module, used to establish the intact model of honeycomb sandwich structure and the corresponding adhesive patching repair models under different damage conditions;

[0060] The modeling platform building module is used to use Python language to write built-in Kernel programs and Abaqus built-in functions to build users and complete the construction of the parametric modeling platform;

[0061] The simulation module is used to select some repair parameters, set them to a practical value range, and program them in MATLAB software based on the Latin hypercube method, randomly select multiple sample points, and use the parametric modeling platform to quickly perform numerical simulation;

[0062] The proxy model building module is used to establish a quadratic polynomial response surface proxy model based on the response surface method, taking the patch's miter rate, reinforcement sheet extension length and adhesive layer thickness as independent variables and the lateral pressure limit load of the cut-and-patch structure as the dependent variable;

[0063] The model optimization module is used to iteratively optimize the agent model based on the genetic algorithm, comprehensively consider the value range and calculation cycle of the patching parameters and the range covered by the results, and determine the best repair plan.

[0064] Embodiment three:

[0065] An electronic device includes a processor and a memory connected to the processor for storing instructions executable by the processor, wherein the processor is used to execute the above-mentioned method for optimizing the design of a repair solution for a honeycomb sandwich structure.

[0066] Embodiment 4:

[0067] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned method for optimizing the design of a repair solution for a honeycomb sandwich structure.

[0068] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A honeycomb sandwich structure repair scheme optimization design method, characterized by: The steps include: S1: Establish the intact model of honeycomb sandwich structure and the corresponding adhesive patching repair models under different damage conditions; S2: Use Python language to write built-in Kernel programs and Abaqus built-in functions to build users and complete the construction of the parametric modeling platform; S3: Select some repair parameters, set them to a practical value range, program them in MATLAB software based on the Latin hypercube method, randomly select multiple sample points, and use the parametric modeling platform to quickly perform numerical simulation; S4: Taking the patch's miter rate, reinforcement sheet extension length and adhesive layer thickness as independent variables and the lateral pressure limit load of the cut-and-patch structure as the dependent variable, a quadratic polynomial response surface proxy model was established based on the response surface methodology; S5: Based on the genetic algorithm, the agent model is iteratively optimized, and the optimal repair plan is determined by comprehensively considering the value range and calculation cycle of the patching parameters and the range of the results.

2. The method for optimizing the design of a honeycomb sandwich structure repair solution according to claim 1, characterized in that: In step 1, establishing corresponding adhesive joint patching repair models under different damage conditions includes: The honeycomb sandwich structure models under single-side panel repair, single-side panel and honeycomb repair, and double-side panel and honeycomb repair were established to simulate and calculate various types of damage to the honeycomb sandwich structure.

3. The method for optimizing the design of a honeycomb sandwich structure repair solution according to claim 1, characterized in that: The step 2 specifically includes: The simulation model is fully parameterized, including the size parameters, material parameters, ply parameters, and mesh parameters of the panel and patch under the model condition in step 1, the size parameters, material parameters, and mesh parameters of the honeycomb core, and the size parameters, material parameters, and mesh parameters of the adhesive layer. The built-in Kernel program is written in Python, and the user interface is built using the built-in functions of Abaqus. The xlrd library is introduced, and complex material parameters and ply parameters are imported through Excel to complete the construction of the parametric modeling platform.

4. The method for optimizing the design of a honeycomb sandwich structure repair solution according to claim 1, characterized in that: The step 3 specifically includes: The damage condition that requires a honeycomb sandwich structure is selected, and then three parameters with high weights for adhesive patching are selected: the patch beveling rate K, the reinforcement sheet extension length L and the adhesive layer thickness T. The actual value ranges are set for them, and programming is performed in MATLAB software based on the Latin hypercube method. Multiple sample points are randomly selected, and numerical simulation is quickly performed using the parametric modeling platform to obtain the ultimate lateral pressure load F of the honeycomb sandwich structure under the corresponding repair scheme.

5. The method for optimizing the design of a honeycomb sandwich structure repair solution according to claim 1, characterized in that: In step 4, a quadratic polynomial response surface surrogate model is established based on the response surface methodology, and its form is: F=a1K+b1L+c1T+a2K 2 +b2L 2 +c2T 2 +a3KL+b3KT+c3LT+C; where a i ,b i ,c i (i=1,2,3) are the weight coefficients of the respective variables, and C is a constant; And introduce the multiple correlation coefficient R 2 , the R of the regression equation 2 It indicates the deviation between the experimental value and the predicted value, R 2 The value range is [0, 1]. When the value of the complex correlation coefficient is larger, the deviation between the regression value and the simulation value is smaller.

6. The method for optimizing the design of a honeycomb sandwich structure repair solution according to claim 1, characterized in that: In step 5, the quadratic polynomial response surface proxy model established in step 4 is iteratively optimized based on a genetic algorithm, and the value range and calculation cycle of the patching parameters and the range included in the results are comprehensively considered. Programming is performed based on MATLAB, and decision quantities such as the patching parameters are binary coded. The corresponding total number of initialized groups, the number of elites in each generation, the crossover probability, and the offspring mutation ratio parameters are set to quickly iterate and obtain the optimal repair plan.

7. A honeycomb sandwich structure repair scheme optimization design device, characterized in that: include: Model building module, used to establish the intact model of honeycomb sandwich structure and the corresponding adhesive patching repair models under different damage conditions; The modeling platform building module is used to use Python language to write built-in Kernel programs and Abaqus built-in functions to build users and complete the construction of the parametric modeling platform; The simulation module is used to select some repair parameters, set them to a practical value range, and program them in MATLAB software based on the Latin hypercube method, randomly select multiple sample points, and use the parametric modeling platform to quickly perform numerical simulation; The proxy model building module is used to establish a quadratic polynomial response surface proxy model based on the response surface method, taking the patch's miter rate, reinforcement sheet extension length and adhesive layer thickness as independent variables and the lateral pressure limit load of the cut-and-patch structure as the dependent variable; The model optimization module is used to iteratively optimize the agent model based on the genetic algorithm, comprehensively consider the value range and calculation cycle of the patching parameters and the range covered by the results, and determine the best repair plan.

8. An electronic device, comprising a processor and a memory connected to the processor for storing instructions executable by the processor, characterized in that: The processor is used to execute the method for optimizing the design of a repair solution for a honeycomb sandwich structure as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it implements the method for optimizing the design of a repair solution for a honeycomb sandwich structure as described in any one of claims 1 to 6.

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