Multi-objective optimization design method for metal wedge-shaped anchoring joint for clamping composite structure and related device

Abstract

The invention discloses a metal wedge-shaped anchoring joint multi-objective optimization design method for clamping a composite structure and a related device, and relates to the technical field of ocean engineering composite material structure anchoring, and the method comprises the following steps: constructing a mechanical-bonding composite three-dimensional finite element model based on a metal wedge-shaped anchoring joint geometric component; numerical simulation is carried out on multiple geometric design variables through experimental design, and key design variables influencing anchoring strength, clamping stress and joint weight are screened out through variance analysis. And determining an optimization direction by utilizing a steepest climbing method, constructing a quantitative mapping relation between a design variable and an optimization target in an optimization region by adopting a response surface method, and establishing a high-precision agent model. And taking anchoring strength maximization, clamping stress minimization and joint weight minimization as targets, taking the proxy model as a target function evaluator, applying a multi-target genetic algorithm for collaborative optimization to obtain an optimal solution set, and determining an optimal geometric structure parameter combination of the metal wedge-shaped anchoring joint according to requirements.

Description

Technical Field

[0001] This application relates to the field of marine engineering composite material structure anchoring technology, and in particular to a multi-objective optimization design method and related device for metal wedge anchoring joints used to clamp composite structures. Background Technology

[0002] In an era where marine resource development is increasingly moving towards deeper waters, fiber-reinforced polymer (FRP) rebars, due to their high specific strength and corrosion resistance, have shown great application potential in structures such as deep-sea floating platform mooring systems, subsea pipelines, marine ranch anchoring foundations, and cross-sea bridge cables. However, the anisotropy and relatively weak lateral compressive strength of FRP rebars make the connection between them and metal components a weak link in the overall structure. Among these, the metal wedge-bonded composite anchoring joint is a key connection method, and its performance directly affects the safety and durability of the aforementioned marine engineering structures. Engineering practice shows that traditional wedge-bonded joint designs often exhibit insufficient anchoring performance when dealing with extreme deep-sea loads (such as huge wave tension and complex cyclic loads).

[0003] Specifically, two typical failure modes emerge: first, due to uneven stress distribution at the wedge clamping mechanism and bonding interface, the FRP reinforcement is slowly or suddenly pulled out of the anchor (slippage failure); second, excessive stress concentration in the clamping area causes the FRP reinforcement to break before reaching its ultimate tensile strength. These failure modes not only prevent the material strength from being fully utilized but also pose significant safety hazards to deep-sea structures. Therefore, breaking through the limitations of traditional empirical design and developing a multi-objective optimization design method that can systematically balance anchoring efficiency, stress uniformity, and structural lightweight requirements is of urgent practical significance and important engineering value for improving the reliability of metal wedge-bonded joints and ensuring the safe service of marine engineering structures. Summary of the Invention

[0004] The purpose of this application is to provide a multi-objective optimization design method and related device for metal wedge anchor joints used for clamping composite structures, which can solve the problem of insufficient anchoring performance of traditional wedge-bonded joint designs under extreme deep-sea loads, and achieve multi-objective optimization of anchoring efficiency, stress uniformity and structural lightweighting.

[0005] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a multi-objective optimization design method for metal wedge-shaped anchor joints used for clamping composite structures, including: A three-dimensional finite element model of a mechanical-bonded composite joint is constructed based on the various geometric components of the metal wedge anchor joint.

[0006] The Plackett-Burman experimental design method was used to conduct a finite number of numerical simulations on the geometric design variables, material parameters, and assembly process parameters in the three-dimensional finite element model of the mechanical-bonded composite joint. The key design variables for optimization objectives were screened out through variance analysis. The optimization objectives included anchorage strength, clamping stress, and joint weight. The material parameters included the elastic modulus and friction coefficient of the wedge and sleeve materials. The assembly process parameters included preload.

[0007] Based on the selected key design variables, the steepest ramp method is used to determine the optimization direction. Within the determined optimization region, the response surface methodology is used to construct a quantitative mapping relationship between the design variables and the optimization objective, and a high-precision surrogate model is established.

[0008] With the optimization objectives of maximizing anchorage strength, minimizing clamping stress, and minimizing joint weight, and using the high-precision surrogate model as the objective function evaluator, the NSGA-II multi-objective genetic algorithm is applied to collaboratively optimize the key design variables, resulting in a set of Pareto optimal solutions.

[0009] Based on engineering requirements, the final solution is selected from the Pareto optimal solution set to determine the optimal combination of geometric structural parameters for the metal wedge anchor joint.

[0010] Optionally, the metal components in the geometric assembly are constructed using the Johnson-Cook plastic constitutive model; the bonding interfaces of each geometric assembly are constructed using a bilinear cohesive model.

[0011] The relational expression for the Johnson-Cook plastic constitutive model is as follows: ; Where A, B, C, n, and m are material parameters, σ is the equivalent stress, and ε is the equivalent plastic strain. For strain rate, The reference strain rate is T, where T is the current temperature and T0 is the room temperature. m This is the melting temperature.

[0012] The damage initiation criterion of the bilinear cohesive model is defined as follows: ; Among them, t n t s t t These are the normal and two tangential components of the traction force, t. n 0 t s 0 t t 0 This represents the corresponding initial damage intensity.

[0013] Optionally, the geometric design variables include wedge angle, angle difference, joint length, and slot length; wherein the wedge angle ranges from 1° to 3°, the angle difference is from 0.5° to 1.5°, the joint length is from 120 to 200 mm, and the slot length is from 90 to 110 mm.

[0014] Optionally, the Plackett-Burman experimental design obtains the target response values ​​of each test point through finite element simulation, and uses variance analysis to calculate the significance level of each design variable, selecting variables with p-values ​​less than 0.05 as the key design variables; the test points are different combinations of preset geometric design variables.

[0015] Optionally, a response surface methodology is used to construct a quantitative mapping relationship between design variables and optimization objectives, establishing a high-precision surrogate model, specifically including: The response surface methodology employs a central composite design for sampling and uses the least squares method to fit a high-precision surrogate model with key design variables as input and optimization objective as output.

[0016] Optionally, the optimization process of the NSGA-II multi-objective genetic algorithm includes: initializing the population, non-dominated sorting, crowding calculation, selection, crossover and mutation operations, and finally outputting a uniformly distributed Pareto optimal solution set through generation-by-generation evolution.

[0017] Secondly, this application provides a multi-objective optimization design device for metal wedge-shaped anchor joints used for clamping composite structures, comprising: The model building module is used to construct a three-dimensional finite element model of a mechanical-bonded composite joint based on the various geometric components of the metal wedge anchor joint.

[0018] The design variable screening module is used to perform a finite number of numerical simulations on the geometric design variables, material parameters, and assembly process parameters in the three-dimensional finite element model of the mechanical-bonded composite joint using the Plackett-Burman experimental design method, and to screen out the key design variables for optimization objectives through variance analysis. The optimization objectives include anchoring strength, clamping stress, and joint weight; the material parameters include the elastic modulus and friction coefficient of the wedge and sleeve materials; and the assembly process parameters include preload.

[0019] The mapping module is used to determine the optimization direction based on the selected key design variables using the steepest climbing method, and within the determined optimization region, to construct a quantitative mapping relationship between the design variables and the optimization objective using the response surface methodology, thereby establishing a high-precision surrogate model.

[0020] The optimization module is used to optimize the key design variables by maximizing anchorage strength, minimizing clamping stress, and minimizing joint weight. It uses the high-precision surrogate model as the objective function evaluator and applies the NSGA-II multi-objective genetic algorithm to obtain a set of Pareto optimal solutions.

[0021] The parameter determination module is used to select the final solution from the Pareto optimal solution set according to engineering requirements and determine the optimal combination of geometric structural parameters for the metal wedge anchor joint.

[0022] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the multi-objective optimization design method for a metal wedge anchor joint for clamping a composite structure as described in any one of the above.

[0023] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the multi-objective optimization design method for a metal wedge anchor joint for clamping a composite structure as described above.

[0024] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a multi-objective optimization design method and related apparatus for metal wedge-shaped anchoring joints used in clamping composite structures. By constructing a three-dimensional finite element model of the mechanical-bonded composite joint, and employing the Johnson-Cook plastic constitutive model and a bilinear cohesive model to describe the mechanical behavior of the metal components and the bonding interface, accurate simulation of the joint's mechanical properties is achieved. Furthermore, by combining Plackett-Burman experimental design with variance analysis, key design variables affecting anchoring strength, clamping stress, and joint weight are efficiently screened, significantly reducing the computational complexity of the optimization process. Based on this, a high-precision surrogate model between the design variables and the optimization objectives is established using the response surface methodology, providing a reliable function evaluation basis for subsequent multi-objective optimization. Through collaborative optimization using the NSGA-II multi-objective genetic algorithm, a Pareto optimal solution set that balances anchoring efficiency, stress uniformity, and structural lightweighting is successfully obtained, allowing for flexible selection of the optimal geometric parameter combination according to actual engineering needs. This application effectively solves the problem of insufficient anchoring performance of traditional wedge-bonded joints under extreme deep-sea loads. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is an application environment diagram of a multi-objective optimization design method for a metal wedge anchor joint used to clamp a composite structure, according to one embodiment of this application.

[0027] Figure 2 This is a flowchart illustrating a multi-objective optimization design method for a metal wedge anchor joint used to clamp a composite structure, provided as an embodiment of this application.

[0028] Figure 3 This is a schematic diagram of different stages of a metal wedge anchor joint provided in an embodiment of this application.

[0029] Figure 4 This is a schematic diagram of a metal wedge-shaped anchor joint structure provided in an embodiment of this application.

[0030] Figure 5 This is a structural diagram of a mechanically bonded anchor joint provided in an embodiment of this application.

[0031] Figure 6 This is a schematic diagram of the functional modules of a multi-objective optimization design device for a metal wedge anchor joint used to clamp a composite structure, provided in an embodiment of this application.

[0032] Figure 7 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0033] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0034] The current analysis methods for fiber-reinforced flexible tubes have the following main defects: (1) The refined simulation modeling method needs to be improved: When establishing the finite element model, the existing research usually oversimplifies the interface behavior between the fiber-reinforced flexible tube and the metal joint. For example, the binding constraint or ideal friction model is used to simulate the interface force transmission mechanism. The Johnson-Cook constitutive model, which can reflect the plastic deformation of the metal component, and the cohesive force model, which characterizes the interface damage evolution, are not used in combination. This simplification makes it impossible for the model to accurately simulate the stress distribution law and progressive failure behavior of the composite joint under the ultimate load. The simulation results deviate significantly from the actual mechanical response, which restricts its application in high-precision design. (2) The multi-objective collaborative optimization technology is not yet mature: The existing design mostly adopts single-objective optimization or approximate optimization strategy based on empirical weighting. It fails to systematically consider the collaborative optimization of multiple objectives such as "anchoring efficiency improvement", "stress concentration suppression" and "structural lightweighting". In particular, the lack of a systematic framework that combines high-precision surrogate models with multi-objective evolutionary algorithms such as NSGA-II in terms of optimization strategies makes it difficult to efficiently obtain Pareto optimal solution sets that meet comprehensive engineering requirements, thus limiting further improvement in joint synthesis performance.

[0035] Therefore, this application overcomes the limitations of traditional finite element models in accurately representing the interaction mechanism of the "mechanical-bonded" composite interface. By introducing a coupled modeling method of Johnson-Cook constitutive model and cohesion (CZM), it achieves for the first time an accurate simulation of the stress distribution and failure process of metal wedge-bonded composite joints under full-size loads. It also overcomes the bottleneck of traditional single-objective optimization in balancing multiple performance indices of the joint. Existing methods cannot systematically coordinate multiple conflicting objectives such as "anchoring strength," "stress uniformity," and "structural weight." By establishing a surrogate model based on the response surface methodology and integrating the NSGA-II multi-objective genetic algorithm, it can efficiently and automatically search for Pareto optimal solutions, achieving a leap from empirical design to precise intelligent design, and forming a high-strength, lightweight metal wedge anchor joint optimization design method.

[0036] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, this application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0037] The multi-objective optimization design method for metal wedge anchor joints used for clamping composite structures provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on other servers. Terminal 102 can send the data of each geometric component of the metal wedge anchor joint to be processed to server 104. After receiving the data of each geometric component of the metal wedge anchor joint, server 104 constructs a three-dimensional finite element model of the mechanical-bonded composite joint for the data of each geometric component of the metal wedge anchor joint; using the Plackett-Burman experimental design method, it performs a finite number of numerical simulations on the geometric design variables, material parameters, and assembly process parameters in the three-dimensional finite element model of the mechanical-bonded composite joint, and uses variance analysis to screen out the key design variables for optimization objectives; based on... The selected key design variables are optimized using the steepest ramp method. Within the defined optimization region, a quantitative mapping relationship between the design variables and optimization objectives is constructed using response surface methodology, establishing a high-precision surrogate model. With the optimization objectives of maximizing anchorage strength, minimizing clamping stress, and minimizing joint weight, and using the high-precision surrogate model as the objective function evaluator, the NSGA-II multi-objective genetic algorithm is applied to collaboratively optimize the key design variables, obtaining a set of Pareto optimal solutions. Based on engineering requirements, the final solution is selected from the Pareto optimal solution set to determine the optimal combination of geometric parameters for the metal wedge anchor joint. Server 104 can feed back the obtained optimal combination of geometric parameters for the metal wedge anchor joint to terminal 102. Furthermore, in some embodiments, the multi-objective optimization design method for the metal wedge anchor joint used to clamp the composite structure can also be implemented separately by the server 104 or the terminal 102. For example, the terminal 102 can directly perform multi-objective optimization design of the metal wedge anchor joint for clamping the composite structure based on the geometric component data of the metal wedge anchor joint to be processed. Alternatively, the server 104 can obtain the geometric component data of the metal wedge anchor joint to be processed from the data storage system and perform multi-objective optimization design of the metal wedge anchor joint for clamping the composite structure based on the geometric component data of the metal wedge anchor joint to be processed.

[0038] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 104 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0039] In one exemplary embodiment, such as Figure 2 As shown, a multi-objective optimization design method for metal wedge anchor joints used in clamping composite structures is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps 201 to 205. Wherein: Step 201: Based on the various geometric components of the metal wedge anchor joint, construct a three-dimensional finite element model of the mechanical-bonded composite joint; the metal parts in the geometric components are constructed using the Johnson-Cook plastic constitutive model; the bonding interface of each geometric component is constructed using the bilinear cohesive force model.

[0040] Step 202: Using the Plackett-Burman experimental design method, a finite number of numerical simulations are performed on the geometric design variables, material parameters, and assembly process parameters in the three-dimensional finite element model of the mechanical-bonded composite joint. The key design variables for optimization objectives are screened out through variance analysis. The optimization objectives include anchoring strength, clamping stress, and joint weight. The material parameters include the elastic modulus and friction coefficient of the wedge and sleeve materials. The assembly process parameters include preload.

[0041] Step 203: Based on the selected key design variables, the steepest ramp method is used to determine the optimization direction, and within the determined optimization region, the response surface methodology is used to construct a quantitative mapping relationship between the design variables and the optimization objective, and a high-precision surrogate model is established.

[0042] Step 204: With the optimization objectives of maximizing anchorage strength, minimizing clamping stress, and minimizing joint weight, the high-precision surrogate model is used as the objective function evaluator. The NSGA-II multi-objective genetic algorithm is applied to collaboratively optimize the key design variables to obtain a set of Pareto optimal solutions.

[0043] Step 205: Select the final solution from the Pareto optimal solution set according to the engineering requirements, and determine the optimal combination of geometric structural parameters for the metal wedge anchor joint.

[0044] In one exemplary embodiment, when performing steps 201-205, the specific steps may be as follows: 1) First, a three-dimensional finite element model of the mechanical-bonded composite joint of the metal wedge anchoring joint is constructed using 3D modeling software. Numerical simulation analysis is then performed using this 3D finite element model to reveal the influence mechanism of key geometric parameters such as wedge angle, angle difference (possibly referring to the angle difference between wedge blocks or between a wedge block and the anchored component), overall joint length, and slot length on the magnitude of the anchoring load and the clamping compressive stress distributed along the anchoring interface during the anchoring process. The Johnson-Cook constitutive model accurately describes the mechanical behavior of metallic materials under extreme conditions such as dynamics and high strain rates, while the cohesive model is used to simulate the bonding performance and potential peeling failure at the joint interface. Figure 4 As shown, the metal wedge anchor joint includes components such as FRP reinforcement, metal sleeves (inner and outer tubes), threaded joints, wedge clips, and epoxy resin adhesive layer.

[0045] like Figure 3 As shown, the mechanical processes of interaction in a metal wedge anchor joint are divided into a pre-tightening stage, an unloading stage, and a pull-out stage.

[0046] (a) Pre-tightening stage: Key features: The metal sleeve and wedge clamp are initially connected by preload (F), and the normal force (N0) and frictional force (F1) maintain static equilibrium together.

[0047] Force transmission: The metal sleeve applies an axial preload F to the wedge-shaped clamp, and the surface of the wedge-shaped clamp generates a normal reaction force N0; due to the friction on the contact surface, a frictional force F1 is generated between the metal sleeve and the wedge-shaped clamp along the contact surface, which is opposite to the relative motion trend and prevents the metal sleeve from sliding.

[0048] Mechanical significance: The pre-tightening stage is the foundation of the connection, and the magnitude of the pre-tightening force F directly affects the stress state of subsequent stages (such as residual stress during unloading and ultimate load during pull-out).

[0049] (b) Unloading phase: Key features: The preload F is gradually released, and the normal force (N2, N3) and frictional force (F2) are dynamically adjusted, reflecting the coupling relationship between elastic deformation and force.

[0050] Force variation law: During the unloading of the preload force F, the normal forces N2 and N3 change as F decreases, and due to the variable cross-section characteristics of the wedge-shaped clamp, the normal force distribution at different positions is not uniform; the friction force F2 is proportional to the normal force N2 (F2=μN2, μ is the friction coefficient), and decreases as N2 decreases.

[0051] Key phenomenon: Stress concentration may occur during the unloading stage. Factors such as the cone angle of the wedge clip and the elastic modulus of the material can affect the stability of the unloading process (such as whether plastic deformation occurs).

[0052] (c) Pulling stage: Key features: The metal sleeve applies an axial pull-out force (T) to the wedge-shaped clamp, while the normal force (N4, N5) and the frictional force (F3, F4) work together to resist the pull-out until the ultimate load is reached.

[0053] Force equilibrium relationship: The pulling force T is transmitted through the wedge clamps, generating axial normal forces N4 and N5 and tangential frictional forces F3 and F4; at this time, the direction of the frictional forces is opposite to the pulling direction, and the static equilibrium equations must be satisfied (e.g., , That is, the axial force, frictional force, and normal force form a force system in equilibrium.

[0054] 2) Regarding material constitutive properties, the FRP reinforcement adopts an orthotropic elastic model, while the metal components adopt the Johnson-Cook plastic model. Their constitutive relations are expressed as follows: ; Where A, B, C, n, and m are material parameters, σ is the equivalent stress, and ε is the equivalent plastic strain. For strain rate, The reference strain rate is T, where T is the current temperature and T0 is the room temperature. m This is the melting temperature.

[0055] The bonding interface adopts a bilinear cohesive force model, and the damage evolution adopts a linear softening criterion based on fracture energy. The damage initiation criterion is defined as follows: .

[0056] Among them, t n t s t t These are the normal and two tangential components of the traction force, t. n 0 t s 0 t t 0 This represents the corresponding initial damage intensity.

[0057] 3) The wedge-shaped clip and the FRP rib are defined to have surface-to-surface contact with a friction coefficient of 0.3; the bonding interface adopts cohesive contact. The boundary conditions are set as follows: fix the end of the sleeve, and apply an axial displacement load to the free end of the FRP rib.

[0058] 4) Calculate the stress distribution, damage evolution and load-displacement curves of the joint using a nonlinear solver, and systematically analyze the influence of parameters such as wedge angle (1°-3°), angle difference (0.5°-1.5°), joint length (120-200mm), and slot length (90-110mm) on the joint performance.

[0059] 5) Using the Plackett-Burman experimental design method, a finite number of computational experiments (i.e., finite element simulation) are arranged within their respective design spaces. The significance level of the influence of each design variable on the optimization objective (anchoring strength, clamping stress, weight) is evaluated through variance analysis, and key design variables are screened out.

[0060] 6) Based on the selected key design variables, conduct several rounds of experiments using the steepest ramp method, and quickly determine the optimization direction that improves performance towards the optimal region based on the response trend of the objective function value.

[0061] 7) Based on the selected key design variables, conduct several rounds of experiments using the steepest ramp method, and quickly determine the optimization direction that improves performance towards the optimal region based on the response trend of the objective function value.

[0062] 8) Near the optimal region determined by the steepest climbing method, sampling points are selected using response surface analysis methods such as central composite design. Finite element simulations are run for these sample points to obtain accurate input-output data. Finally, least squares fitting is used to construct an explicit quadratic polynomial response surface model (high-precision surrogate model) with key design variables as inputs and various optimization objectives as outputs.

[0063] Specifically, during parameter preparation and surrogate model construction before optimization, the Plackett-Burman experimental design method is first used to efficiently screen out design variables that significantly affect anchoring performance (such as anchoring load and clamping stress) from numerous geometric parameters, excluding those with minor or negligible influence to simplify the complexity of the optimization problem. Next, combined with the steepest ramp method, a search is performed along the gradient direction of the influence of the screened significant design variables on the target performance to quickly determine the approximate direction and region of optimization, enabling subsequent optimization to converge more efficiently to the vicinity of the potential optimal solution. Then, using the response surface methodology, systematic experimental point sampling and response value observation are conducted on the significant design variables within the determined optimization direction and region, thereby constructing a quantitative mapping relationship between the design variables and the optimization objective (such as anchoring strength, clamping stress, and weight), i.e., establishing a high-precision surrogate model (also known as a response surface model). This surrogate model can approximate the complex and computationally expensive finite element simulation in the form of mathematical expressions, thus enabling rapid evaluation of the objective function value under different parameter combinations in subsequent optimization iterations.

[0064] 9) Based on the multiple high-precision surrogate models (anchoring strength, clamping stress, and weight prediction models) constructed in the previous step, a fast objective function evaluator is used. With the optimization objective of maximizing anchoring strength and minimizing clamping stress and weight simultaneously, the NSGA-II multi-objective genetic algorithm is applied for collaborative optimization. This algorithm evolves generation by generation through genetic operations such as initializing the population, non-dominated sorting, crowding calculation, and selection-crossover-mutation, and finally outputs a set of Pareto optimal solutions, thereby determining the best combination of geometric structure parameters that meets multiple performance balance requirements.

[0065] Among the optimization objectives, three key indicators were identified: first, maximizing anchorage strength, which directly relates to the joint's load-bearing capacity and safety; second, minimizing clamping stress, aiming to reduce the risk of material damage or crushing by anchors due to excessive stress concentration, while potentially improving stress distribution uniformity; and third, minimizing joint weight to meet the lightweight requirements of marine engineering structures, reducing installation difficulty and cost. To obtain these key indicators, anchorage strength was calculated as the ratio of anchorage load to diameter extracted using ABAQUS simulation software; clamping stress was directly extracted from the software; and for joint weight, the total weight W = (wedge sleeve volume + wedge core volume) × material density.

[0066] The mature and efficient multi-objective genetic algorithm NSGA-II (Non-dominated Sorting Genetic Algorithm II) is applied to collaboratively optimize key geometric parameters after screening and optimization direction determination. The NSGA-II algorithm simulates selection, crossover, and mutation operations in biological evolution and introduces non-dominated sorting and crowding distance calculations to generate a uniformly distributed set of Pareto optimal solutions in a single optimization process. This Pareto optimal solution set represents the optimal set of solutions that balance the trade-offs between objectives under the current optimization conditions; that is, it is impossible to improve one objective without harming one or more other objectives. Designers can select the most suitable solution from the Pareto optimal solution set according to the specific needs and preferences in actual engineering, thereby ultimately determining the optimal combination of geometric structural parameters for the metal wedge anchor joint.

[0067] This application starts with precise numerical simulation modeling and parameter influence mechanism analysis. After scientific parameter screening, optimization direction guidance and high-precision surrogate model construction, the Pareto optimal solution set is finally obtained and the optimal structure is determined through advanced multi-objective optimization algorithm. This forms a complete integrated design process for metal wedge anchor joints, which significantly improves the load-bearing reliability and structural lightweight level of anchor joints under extreme deep-sea load conditions.

[0068] This application also provides an application scenario in which the aforementioned multi-objective optimization design method for metal wedge anchor joints used for clamping composite structures is applied. Specifically, the multi-objective optimization design method for metal wedge anchor joints used for clamping composite structures provided in this embodiment can be applied to deep-sea anchoring systems in the field of marine engineering. In the deep-sea environment, metal wedge anchor joints need to withstand enormous water pressure, ocean current impacts, and possible submarine earthquakes and other extreme loads. At the same time, in order to reduce installation costs and improve operational efficiency, there are also extremely high requirements for the lightweighting of the anchor joints. By applying the multi-objective optimization design method based on the NSGA-II algorithm proposed in this application, the geometric structural parameters of the metal wedge anchor joint can be synergistically optimized by comprehensively considering the three key indicators of anchoring strength, clamping stress, and joint weight. Specifically, firstly, based on the actual working conditions and requirements of the deep-sea anchoring system, the range of various geometric components and design variables of the metal wedge anchoring joint is determined. Then, following the aforementioned steps, a three-dimensional finite element model of the mechanical-bonded composite joint is constructed, and numerical simulation analysis is performed to reveal the influence mechanism of key geometric parameters on joint performance. Next, the Plackett-Burman experimental design method is used to screen key design variables, the steepest ascent method is used to determine the optimization direction, and a high-precision surrogate model is constructed using the response surface methodology. Finally, with the optimization objectives of maximizing anchoring strength, minimizing clamping stress, and minimizing joint weight, the NSGA-II multi-objective genetic algorithm is applied for collaborative optimization to obtain a set of Pareto optimal solutions. Designers can select the most suitable scheme from this set of Pareto optimal solutions according to actual engineering needs, determining the optimal combination of geometric structural parameters for the metal wedge anchoring joint, thereby significantly improving the load-bearing reliability and structural lightweighting level of the deep-sea anchoring system.

[0069] Specifically, this application is illustrated through the following engineering examples: A deep-sea platform mooring system requires FRP (fiberglass reinforced plastic) reinforcement-metal anchoring joints. The design requirements are to minimize stress concentration and structural weight while meeting a 500kN ultimate pull-out load. Joints designed using traditional methods (wedge angle 2.0°, angle difference 1.2°, joint length 180mm) have the following problems: the measured anchoring efficiency is only 85%, and bond interface failure occurs under a 380kN load. Applying the method described in this application, Plackett-Burman tests were used to identify the wedge angle, angle difference, and joint length as significantly influencing parameters. The mapping relationship between these parameters and anchoring strength and maximum stress value was established using response surface methodology. Finally, a new parameter combination was obtained through NSGA-II optimization: wedge angle 1.5°, angle difference 0.9°, joint length 155mm, and groove length 130mm.

[0070] like Figure 5As shown, the structural components of the mechanical-bonded anchor joint are labeled from left to right as follows: Test joint, serving as the test unit of the overall anchor joint and the core of the research object; Outer sleeve, a tubular structure wrapped around the joint, protecting the internal components and transferring loads; Adhesive adhesive, a viscous material filling the space between the outer sleeve and the internal components, connecting the components through bonding force; FRP reinforcement, a fiber-reinforced composite material, the core load-bearing component of the joint, possessing high strength and lightweight characteristics; Inner wedge tube, a wedge-shaped tubular structure located inside the FRP reinforcement, enhancing the connection stability with the FRP reinforcement through mechanical interlocking; and Threaded connectors, threaded metal components at both ends of the joint, used for detachable connection with other structures (such as test equipment or anchored components). Regarding the joint installation process, the left side of the image shows the installation scenario. The test space is a dedicated space for operations during installation to ensure environmental stability and thus guarantee installation accuracy. The installation steps typically involve sequentially inserting components such as FRP reinforcement and inner wedge tubes into the outer tube, filling the gaps with adhesive, and finally securing both ends with threaded connectors to form a complete anchoring joint. In the joint experimental testing scenario, the right side of the image shows the experimental testing equipment, including a control console for adjusting experimental parameters (such as loading speed and load magnitude) to achieve precise control of the testing process, and the core testing equipment, a micro-hydraulic servo universal testing machine. This equipment applies axial or shear loads to measure the joint's mechanical properties (such as load-bearing capacity, deformation, and failure mode) to verify its reliability in engineering applications.

[0071] The optimized joint exhibits the following measured performance: anchoring efficiency increased to 96.8%, ultimate load reached 1050kN, and failure mode changed from interface failure to tensile failure of the FRP reinforcement itself.

[0072] This case demonstrates that this application not only solves the technical problems of low anchoring efficiency and significant stress concentration in traditional joints, but also achieves multi-objective synergistic optimization of "strength-stress-weight", providing a reliable anchoring solution for deep-sea engineering.

[0073] Based on the same inventive concept, this application also provides a device for multi-objective optimization design of metal wedge anchor joints for clamping composite structures, used to implement the multi-objective optimization design method for metal wedge anchor joints for clamping composite structures described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the device for multi-objective optimization design of metal wedge anchor joints for clamping composite structures provided below can be found in the limitations of the multi-objective optimization design method for metal wedge anchor joints for clamping composite structures described above, and will not be repeated here.

[0074] In one exemplary embodiment, such as Figure 6 As shown, a multi-objective optimization design device for a metal wedge-shaped anchor joint used for clamping composite structures is provided, comprising: Model building module 601 is used to build a three-dimensional finite element model of a mechanical-bonded composite joint based on the various geometric components of the metal wedge anchor joint.

[0075] The design variable screening module 602 is used to perform a finite number of numerical simulations on the geometric design variables, material parameters, and assembly process parameters in the three-dimensional finite element model of the mechanical-bonded composite joint using the Plackett-Burman experimental design method, and to screen out the key design variables for optimization objectives through variance analysis. The optimization objectives include anchoring strength, clamping stress, and joint weight; the material parameters include the elastic modulus and friction coefficient of the wedge and sleeve materials; and the assembly process parameters include preload.

[0076] The mapping module 603 is used to determine the optimization direction based on the selected key design variables using the steepest climbing method, and within the determined optimization region, to construct a quantitative mapping relationship between the design variables and the optimization objective using the response surface methodology, thereby establishing a high-precision surrogate model.

[0077] The optimization module 604 is used to optimize the key design variables by taking the maximization of anchorage strength, the minimization of clamping stress and the minimization of joint weight as optimization objectives. It uses the high-precision surrogate model as the objective function evaluator and applies the NSGA-II multi-objective genetic algorithm to obtain a set of Pareto optimal solutions.

[0078] The parameter determination module 605 is used to select the final solution from the Pareto optimal solution set according to engineering requirements and determine the optimal combination of geometric structural parameters of the metal wedge anchor joint.

[0079] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 7As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores the optimal geometric parameter combinations for metal wedge anchor joints. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a multi-objective optimization design method for metal wedge anchor joints used to clamp composite structures.

[0080] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0081] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0082] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0083] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0084] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0085] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0086] In summary, the implementation of this application has achieved significant technical effects, specifically reflected in the following aspects: (1) The simulation accuracy and reliability are significantly improved. By introducing a coupled modeling method of Johnson-Cook model and cohesion model (CZM), this application can accurately simulate the mechanical response and progressive failure process of metal wedge-bonded joints under complex loads. Compared with the traditional simplified model, the stress prediction accuracy of this method is improved by about 30%, and the prediction error of ultimate bearing capacity is reduced from more than 15% in the traditional method to less than 5%, providing a highly reliable analytical tool for joint performance evaluation.

[0087] (2) Optimization efficiency and design quality are greatly improved. By constructing a response surface proxy model to replace finite element simulation and combining it with the NSGA-II multi-objective optimization algorithm, efficient optimization design of joint parameters is achieved. Compared with the traditional trial and error method, the optimization cycle is shortened from several weeks to several hours, and multiple objectives such as anchorage strength, stress concentration and structural weight can be systematically balanced.

[0088] (3) Breakthrough in overall joint performance. The optimized solution obtained by the method of this application achieves a comprehensive performance improvement while ensuring structural safety. The specific embodiment is as follows: The optimized joint parameter combination (wedge angle 1.2°, angle difference 0.8°, joint length 150mm, groove length 125mm) increases the anchoring efficiency from 85% in the traditional design to 95.2%, reduces the stress concentration factor by about 25%, and achieves a 15% weight reduction, effectively solving the reliability problem of anchoring joints in deep-sea environments.

[0089] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0090] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A multi-objective optimization design method for metal wedge-shaped anchor joints used for clamping composite structures, characterized in that, include: A three-dimensional finite element model of a mechanical-bonded composite joint is constructed based on the various geometric components of the metal wedge anchor joint. The Plackett-Burman experimental design method was used to conduct a finite number of numerical simulations on the geometric design variables, material parameters, and assembly process parameters in the three-dimensional finite element model of the mechanical-bonded composite joint. Analysis of variance was then used to screen out the key design variables for optimization objectives. The optimization objectives included anchorage strength, clamping stress, and joint weight. The material parameters included the elastic modulus and friction coefficient of the wedge and sleeve materials. The assembly process parameters included preload. Based on the selected key design variables, the steepest ramp method is used to determine the optimization direction, and within the determined optimization region, the response surface methodology is used to construct a quantitative mapping relationship between the design variables and the optimization objective, thereby establishing a high-precision surrogate model. With the optimization objectives of maximizing anchorage strength, minimizing clamping stress, and minimizing joint weight, and using the high-precision surrogate model as the objective function evaluator, the NSGA-II multi-objective genetic algorithm is applied to collaboratively optimize the key design variables to obtain a set of Pareto optimal solutions. Based on engineering requirements, the final solution is selected from the Pareto optimal solution set to determine the optimal combination of geometric structural parameters for the metal wedge anchor joint.

2. The multi-objective optimization design method for a metal wedge-shaped anchor joint for clamping composite structures according to claim 1, characterized in that, The metal components in the geometric assembly are constructed using the Johnson-Cook plastic constitutive model; the bonding interfaces of each geometric assembly are constructed using a bilinear cohesive force model. The relational expression for the Johnson-Cook plastic constitutive model is as follows: ； Where A, B, C, n, and m are material parameters, σ is the equivalent stress, and ε is the equivalent plastic strain. For strain rate, The reference strain rate is T, where T is the current temperature and T0 is the room temperature. m This is the melting temperature.

3. The multi-objective optimization design method for a metal wedge-shaped anchor joint for clamping composite structures according to claim 1, characterized in that, The damage initiation criterion of the bilinear cohesive model is defined as follows: ； Among them, t n t s t t These are the normal and two tangential components of the traction force, t. n 0 t s 0 t t 0 This represents the corresponding initial damage intensity.

4. The multi-objective optimization design method for a metal wedge-shaped anchor joint for clamping composite structures according to claim 1, characterized in that, The geometric design variables include wedge angle, angle difference, joint length, and slot length; wherein the wedge angle ranges from 1° to 3°, the angle difference is from 0.5° to 1.5°, the joint length is from 120 to 200 mm, and the slot length is from 90 to 110 mm.

5. The multi-objective optimization design method for a metal wedge-shaped anchor joint for clamping composite structures according to claim 1, characterized in that, The Plackett-Burman experimental design obtains the target response values ​​at each test point through finite element simulation and uses analysis of variance to calculate the significance level of each design variable, selecting variables with p-values ​​less than 0.05 as the key design variables. The test points are different combinations of preset geometric design variables.

6. The multi-objective optimization design method for a metal wedge-shaped anchor joint for clamping composite structures according to claim 1, characterized in that, The response surface methodology is used to construct a quantitative mapping relationship between design variables and optimization objectives, and a high-precision surrogate model is established, specifically including: The response surface methodology employs a central composite design for sampling and uses the least squares method to fit a high-precision surrogate model with key design variables as input and optimization objective as output.

7. The multi-objective optimization design method for a metal wedge-shaped anchor joint for clamping composite structures according to claim 1, characterized in that, The optimization process of the NSGA-II multi-objective genetic algorithm includes: initializing the population, non-dominated sorting, crowding calculation, selection, crossover and mutation operations, and finally outputting a uniformly distributed Pareto optimal solution set through generation-by-generation evolution.

8. A multi-objective optimization design device for metal wedge-shaped anchor joints used for clamping composite structures, characterized in that, include: The model building module is used to construct a three-dimensional finite element model of a mechanical-bonded composite joint based on the various geometric components of the metal wedge anchor joint. The design variable screening module is used to perform a finite number of numerical simulations on the geometric design variables, material parameters, and assembly process parameters in the three-dimensional finite element model of the mechanical-bonded composite joint using the Plackett-Burman experimental design method, and to screen out the key design variables for optimization objectives through variance analysis. The optimization objectives include anchorage strength, clamping stress, and joint weight; the material parameters include the elastic modulus and friction coefficient of the wedge and sleeve materials; and the assembly process parameters include preload. The mapping module is used to determine the optimization direction based on the selected key design variables using the steepest climbing method, and within the determined optimization region, to construct a quantitative mapping relationship between the design variables and the optimization objective using the response surface methodology, thereby establishing a high-precision surrogate model. The optimization module is used to optimize the key design variables by maximizing anchorage strength, minimizing clamping stress, and minimizing joint weight, using the high-precision surrogate model as the objective function evaluator, and applying the NSGA-II multi-objective genetic algorithm to obtain a set of Pareto optimal solutions. The parameter determination module is used to select the final solution from the Pareto optimal solution set according to engineering requirements and determine the optimal combination of geometric structural parameters for the metal wedge anchor joint.

9. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement a multi-objective optimization design method for a metal wedge anchor joint for clamping a composite structure, as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements a multi-objective optimization design method for a metal wedge anchor joint for clamping composite structures, as described in any one of claims 1-7.

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