Topological optimization-based lightweight design method for box-type beam structure

Abstract

The invention relates to the technical field of structure optimization design, and particularly discloses a box-type beam structure lightweight design method based on topological optimization, which comprises the following steps: constructing a multi-physics field coupled three-dimensional parameterized model, and performing cross-scale analysis on integrated structure rigidity, thermal deformation, vibration mode and fatigue life to obtain a three-dimensional parameterized model; a variable density method and level set method mixed topological optimization strategy is adopted, and an initial material distribution scheme meeting the structural function integrity is generated by modeling the relationship between density and stress strain; starting a quantum genetic algorithm and deep reinforcement learning fused intelligent optimization engine; according to the method, through the multi-physics field coupled three-dimensional parameterized model, a variable density method and level set method mixed topological optimization strategy is combined, the relation between the structural lightweight target and the additive manufacturing process is effectively balanced, multi-physics field analysis of thermal response, vibration, fatigue and the like is introduced, and the structural lightweight target is obtained. And it is guaranteed that the box-type beam meets the mechanical property and other functional requirements in actual application.

Description

Technical Field

[0001] The present invention belongs to the technical field of structural optimization design, and in particular relates to a lightweight design method for a box beam structure based on topology optimization. Background Art

[0002] With the continuous advancement of engineering technology, especially the widespread application of additive manufacturing, traditional structural design methods are struggling to cope with the complex mechanical properties and production process requirements. Box beams, as a common structural form, are widely used in fields such as construction and transportation. Their design requirements must not only meet basic mechanical properties such as strength and stiffness, but also consider the multi-physics effects that the structure may encounter in actual use, including thermal response, vibration, fatigue, and other issues.

[0003] In traditional design methods, designers often focus on the optimization of a single physical field, such as only considering mechanical properties or thermal responses, and ignore the interactions between multiple physical fields, which may lead to the design results failing to achieve the expected comprehensive performance in practical applications. In addition, with the maturity of additive manufacturing technology, it has unique advantages over traditional manufacturing processes, such as the ability to freely design complex structures and reduce material waste. However, additive manufacturing processes also bring some new challenges, especially in the structural design stage. Unlike traditional manufacturing processes, additive manufacturing needs to consider special process constraints such as material deposition sequence, temperature distribution, minimum feature size, and support structure. These constraints make it difficult for traditional design methods to meet the special requirements required by additive manufacturing, resulting in the design results possibly not being smoothly converted into actual producible models.

[0004] Therefore, it is necessary to propose a lightweight design method for box beam structures based on topology optimization to solve the problem of how to balance the relationship between the lightweight goal of the structure and the manufacturing process in the existing technology.

[0005] The above information disclosed in this background technology is only for enhancing understanding of the background technology of the present invention and therefore it may contain information that does not constitute the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the present invention is to provide a lightweight design method for a box beam structure based on topology optimization to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A lightweight design method for a box beam structure based on topology optimization, comprising:

[0009] Construct a three-dimensional parametric model that couples multiple physical fields. By integrating cross-scale analysis of structural stiffness, thermal deformation, vibration modes, and fatigue life, and adopting a hybrid topology optimization strategy of variable density method and level set method, the relationship between density and stress and strain is modeled to generate an initial material distribution scheme that meets the functional integrity of the structure.

[0010]

[0011] Where ρ is the material density distribution, J(ρ) is the objective function, σ(ρ) is the material stress tensor, ε(ρ) is the material strain tensor, and Ω is the design domain;

[0012] An intelligent optimization engine that integrates quantum genetic algorithm and deep reinforcement learning is activated to dynamically optimize the initial material distribution scheme. By adjusting the optimization target weight coefficient and combining it with the manufacturing process constraints, an optimal topological configuration that meets the requirements of the additive manufacturing process is generated.

[0013] Deploy a closed-loop verification system driven by digital twins. By synchronizing stress-strain data between the physical prototype and the virtual model in real time, it triggers the iterative correction mechanism of the optimization algorithm to perform bidirectional adaptive compensation of design parameters and manufacturing errors.

[0014] The blockchain evidence storage module is called to perform tamper-proof distributed storage of key parameters, simulation results, and version iteration records during the optimization process.

[0015] Preferably, the multi-physics field coupling model is constructed in the following manner:

[0016] Use isogeometric analysis technology to perform bidirectional mapping of geometric parameters between CAD models and CAE models;

[0017] A thermal-mechanical coupled finite element format is introduced to handle the nonlinear problem of contact interface through the substructure condensation method;

[0018] Thermal-mechanical coupled finite element formula:

[0019] [K(χ)+K thermal (χ)]{u}={F}

[0020] Where K(χ) is the structural stiffness matrix, K thermal (χ) is the thermal-mechanical coupling stiffness matrix, u is the node displacement vector, and F is the external load;

[0021] Construct a fatigue life prediction network based on transfer learning and use small sample experimental data to correct the deviation of simulation results.

[0022] Preferably, the intelligent optimization engine achieves dynamic collaboration through the following mechanisms:

[0023] Initialize the quantum genetic algorithm, use quantum bits to encode topological variables, and perform global search through quantum rotation gate operations;

[0024] Quantum rotation gate operation formula:

[0025] |Ψ new >=U(θ)|Ψ old >

[0026] Where, |Ψ new > and |Ψ old > is the quantum state vector, U(θ) is the quantum rotation gate material matrix, and θ is the angle controlling the rotation;

[0027] Configure a deep reinforcement learning agent based on the Actor-Critic framework, using structural performance indicators as reward functions to dynamically adjust the optimization path;

[0028] R(s,a)=ω1·P(s,a)-ω2·MC(s,a)

[0029] Where P(s,a) is the structural performance index, MC(s,a) is the manufacturing cost, ω1 and ω2 are weight coefficients;

[0030] Manufacturing process constraints are embedded and the fitness evaluation is corrected through a penalty function mechanism, including minimum feature size, draft angle, and overhang angle control.

[0031] Preferably, the operation process of the closed-loop verification system includes:

[0032] Deploy a physical prototype embedded with a fiber Bragg grating sensor network to collect strain-temperature data at key measurement points in real time;

[0033] Build a virtual sensor network based on digital twins and predict the response status of unmonitored areas through LSTM neural networks;

[0034] The improved Kalman filter algorithm is used to fuse virtual and real data, and the optimization engine parameters are readjusted when the error threshold is triggered;

[0035] x′ k|k =x′ k|k-1 +K k (z k -H k x′ k|k-1 )

[0036] Where x′ k|k is the updated state estimate, K k is the Kalman gain, z k is the measured value, H k is the measurement matrix.

[0037] Preferably, the blockchain evidence storage module is implemented through a smart contract:

[0038] Automatically trigger hash value calculation when the design version changes, and store the result in the blockchain;

[0039] Control access rights to key design parameters through asymmetric encryption mechanisms;

[0040] The stored evidence data complies with the ISO 19770-1 standard, enabling design responsibility tracing and intellectual property evidence.

[0041] Preferably, the topology configuration generation process includes:

[0042] Construct a topological morphological innovation design module based on generative adversarial networks, and generate unconventional topological configurations through style transfer technology;

[0043] The loss function of the generative adversarial network is:

[0044] L GAN =E[log(D(r))]+E[log(1-D(G(z)))]

[0045] Where D(r) is the discriminator, G(z) is the generator, z is random noise, and L GAN is the loss of the generative adversarial network;

[0046] Adopt multi-fidelity model fusion technology, combining coarse-grained proxy models with fine finite element analysis to balance computational efficiency and solution accuracy;

[0047] Introducing manufacturing defect sensitivity analysis, and quantifying the effects of porosity and interlayer bonding strength on structural performance through Monte Carlo simulation;

[0048]

[0049] Where Sy is the sensitivity, P i is the ith simulation result, α i is the manufacturing defect parameter.

[0050] Preferably, the method further comprises:

[0051] Construct a material-structure collaborative optimization module driven by a material gene library, and screen composite materials systems that meet multi-objective constraints through high-throughput calculations;

[0052] Develop a WebGL-based 3D visualization interactive interface for designers to perform real-time editing of topological configurations and pre-evaluate performance;

[0053] Deploy edge computing nodes to perform distributed parallel processing of the design-analysis-optimization process, shortening the design cycle.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] The present invention effectively balances the relationship between the goal of lightweight structure and the additive manufacturing process through a three-dimensional parametric model coupled with multiple physical fields, combined with a hybrid topology optimization strategy of the variable density method and the level set method. Multi-physical field analysis such as thermal response, vibration, and fatigue is introduced into the design process to ensure that the box beam meets the mechanical properties and other functional requirements in practical applications; and an intelligent optimization engine based on quantum genetic algorithm and deep reinforcement learning is used to dynamically adjust the weight coefficients during the optimization process and combine the manufacturing process constraints to ensure that the optimization results meet the special needs of additive manufacturing, thereby avoiding the limitations of traditional design methods. In addition, through a closed-loop verification system driven by digital twins, the stress-strain data of the physical prototype and the virtual model are synchronized in real time, and two-way adaptive compensation of design parameters and manufacturing errors is achieved, thereby improving the accuracy and reliability of the design. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a flow chart of the lightweight design method of box beam structure based on topology optimization of the present invention. DETAILED DESCRIPTION

[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0058] Example 1:

[0059] See also Figure 1 As shown in FIG, a lightweight design method for a box beam structure based on topology optimization includes:

[0060] Construct a three-dimensional parametric model that couples multiple physical fields. By integrating cross-scale analysis of structural stiffness, thermal deformation, vibration modes, and fatigue life, and adopting a hybrid topology optimization strategy of variable density method and level set method, the relationship between density and stress and strain is modeled to generate an initial material distribution scheme that meets the functional integrity of the structure.

[0061] The multiphysics coupled model is constructed in the following way:

[0062] Use isogeometric analysis technology to perform bidirectional mapping of geometric parameters between CAD models and CAE models;

[0063] A thermal-mechanical coupled finite element format is introduced to handle the nonlinear problem of contact interface through the substructure condensation method;

[0064] Construct a fatigue life prediction network based on transfer learning and use small sample experimental data to correct the deviation of simulation results.

[0065] Furthermore, through multi-physics coupling and cross-scale analysis, the overall performance of the structure can be more comprehensively evaluated and optimized, improving the accuracy and reliability of the design. A hybrid topology optimization strategy can effectively allocate materials, improve the functional integrity of the structure, and reduce redundant materials. By introducing thermal-mechanical coupled finite element analysis and transfer learning-based fatigue life prediction, complex nonlinear problems can be solved, resulting in more efficient and stable design solutions.

[0066] An intelligent optimization engine that integrates quantum genetic algorithms and deep reinforcement learning is launched to dynamically optimize the initial material distribution plan. By adjusting the optimization target weight coefficient and combining it with the manufacturing process constraints, the optimal topological configuration that meets the requirements of the additive manufacturing process is generated.

[0067] The intelligent optimization engine achieves dynamic collaboration through the following mechanisms:

[0068] Initialize the quantum genetic algorithm, use quantum bits to encode topological variables, and perform global search through quantum rotation gate operations;

[0069] Configure a deep reinforcement learning agent based on the Actor-Critic framework, using structural performance indicators as reward functions to dynamically adjust the optimization path;

[0070] Manufacturing process constraints are embedded and the fitness evaluation is corrected through a penalty function mechanism, including minimum feature size, draft angle, and overhang angle control.

[0071] Furthermore, by integrating a quantum genetic algorithm with deep reinforcement learning, dynamic optimization of the initial material distribution scheme is achieved, improving the accuracy and efficiency of topological design. Combining the global search capabilities of the quantum genetic algorithm with the adaptive optimization capabilities of deep reinforcement learning effectively improves structural performance while adjusting the optimization path based on manufacturing process constraints to ensure that the design meets the requirements of additive manufacturing. By embedding manufacturing process constraints, fitness evaluations are corrected in real time during the optimization process, avoiding designs that do not meet actual production requirements and ultimately generating a more efficient, optimal topological structure that meets manufacturing specifications.

[0072] The topology generation process includes:

[0073] Construct a topological morphological innovation design module based on generative adversarial networks, and generate unconventional topological configurations through style transfer technology;

[0074] Adopt multi-fidelity model fusion technology, combining coarse-grained proxy models with fine finite element analysis to balance computational efficiency and solution accuracy;

[0075] Manufacturing defect sensitivity analysis is introduced, and the effects of porosity and interlayer bonding strength on structural performance are quantified through Monte Carlo simulation.

[0076] Furthermore, through generative adversarial networks and style transfer techniques, innovative unconventional topologies were designed, enhancing design diversity and performance optimization potential. Multi-fidelity model fusion technology optimized the balance between computational efficiency and accuracy, ensuring fast and accurate design analysis. Furthermore, manufacturing defect sensitivity analysis combined with Monte Carlo simulation quantified the impact of porosity and interlayer bond strength on structural performance, thereby enhancing the reliability and stability of the design in actual manufacturing.

[0077] Deploy a closed-loop verification system driven by digital twins. By synchronizing stress-strain data between the physical prototype and the virtual model in real time, it triggers the iterative correction mechanism of the optimization algorithm to perform bidirectional adaptive compensation of design parameters and manufacturing errors.

[0078] The operating procedures of the closed-loop verification system include:

[0079] Deploy a physical prototype embedded with a fiber Bragg grating sensor network to collect strain-temperature data at key measurement points in real time;

[0080] Build a virtual sensor network based on digital twins and predict the response status of unmonitored areas through LSTM neural networks;

[0081] The improved Kalman filter algorithm is used to fuse virtual and real data, and the optimization engine parameters are readjusted when the error threshold is triggered.

[0082] Furthermore, the closed-loop verification system driven by digital twins synchronizes data from physical prototypes and virtual models in real time, accurately monitoring design parameters and manufacturing errors and dynamically adjusting optimization algorithms to achieve adaptive compensation between design and manufacturing. The combination of an embedded fiber Bragg grating sensor network and a virtual sensor network enhances real-time monitoring of key measurement points. The application of an LSTM neural network and an improved Kalman filter algorithm enhances data fusion accuracy, enabling rapid response and optimization adjustments when errors exceed thresholds, ensuring the accuracy and reliability of product design.

[0083] The blockchain evidence storage module is called to perform tamper-proof distributed storage of key parameters, simulation results, and version iteration records during the optimization process.

[0084] The blockchain evidence storage module is implemented through smart contracts:

[0085] Automatically trigger hash value calculation when the design version changes, and store the result in the blockchain;

[0086] Control access rights to key design parameters through asymmetric encryption mechanisms;

[0087] The stored evidence data complies with the ISO 19770-1 standard, enabling design responsibility tracing and intellectual property evidence.

[0088] Construct a material-structure collaborative optimization module driven by a material gene library, and screen composite materials systems that meet multi-objective constraints through high-throughput calculations;

[0089] Develop a WebGL-based 3D visualization interactive interface for designers to perform real-time editing of topological configurations and pre-evaluate performance;

[0090] Deploy edge computing nodes to perform distributed parallel processing of the design-analysis-optimization process, shortening the design cycle.

[0091] Furthermore, by constructing a material-structure collaborative optimization module driven by a material gene library, it is possible to efficiently screen composite materials systems that meet multi-objective constraints, thereby improving the accuracy of material selection and design efficiency. A WebGL-based 3D visualization interactive interface enables designers to edit topological configurations and perform performance pre-evaluations in real time, enhancing flexibility and operability during the design process. Deploying edge computing nodes for distributed parallel processing significantly shortens the design cycle, improves the computational efficiency of the design-analysis-optimization process, and accelerates the implementation of innovative results.

[0092] Example 2:

[0093] Application example: Research on lightweight box beam structure based on topology optimization

[0094] In the manufacturing of high-end equipment such as aerospace and large bridges, box girders, as critical load-bearing structures, face the dual challenges of achieving lightweight structures to reduce overall weight and improve energy efficiency, while also ensuring the feasibility and affordability of manufacturing processes. Traditional design methods often focus on optimizing a single performance metric, making it difficult to find the optimal balance between lightweighting and manufacturing process efficiency. Therefore, this project aims to effectively address this technical challenge through a lightweight design method for box girder structures based on topology optimization.

[0095] 1. Implementation of technical solutions

[0096] (1) Construction of multi-physics field coupling model to lay the foundation for lightweight

[0097] By using isogeometric analysis technology, high-precision mapping between the box girder CAD model and the CAE model is achieved, ensuring that the structural form can be fully considered in the early stages of design.

[0098] Thermal-mechanical coupling finite element analysis is introduced, combined with the substructure condensation method to deal with complex contact interface problems, and a multi-physics field coupling model is constructed to provide a comprehensive performance evaluation basis for lightweight design.

[0099] A hybrid topology optimization strategy combining the variable density method and the level set method is adopted, integrating structural stiffness, thermal deformation, vibration mode, and fatigue life analysis to generate an initial material distribution scheme that meets basic functional requirements, laying the foundation for subsequent lightweight design.

[0100] (2) Intelligent optimization engine drive, precise balance between lightweight and manufacturing process

[0101] Initialize the quantum genetic algorithm, use quantum bits to encode topological variables, and combine quantum rotation gate operations to perform efficient global search and quickly explore the possible space of lightweight design.

[0102] A deep reinforcement learning agent is configured. Based on the Actor-Critic framework, it uses the comprehensive indicators of structural performance and manufacturing cost as the reward function to dynamically adjust the optimization path to ensure that while pursuing lightweighting, the feasibility and economy of the manufacturing process are not neglected.

[0103] Manufacturing process constraints such as minimum feature size, draft angle, overhang angle, etc. are embedded, and the fitness evaluation is corrected through a penalty function mechanism to ensure that the optimization results meet the requirements of advanced manufacturing processes such as additive manufacturing, achieving a precise balance between lightweighting and manufacturing processes.

[0104] (3) Closed-loop verification system to ensure the practical feasibility of lightweight design

[0105] Deploy a physical prototype embedded with a fiber Bragg grating sensing network to collect strain-temperature data at key measurement points in real time, providing direct feedback on the actual effect of the lightweight design.

[0106] Build a virtual sensor network based on digital twins, use LSTM neural network to predict the response status of unmonitored areas, and achieve deep fusion of virtual and real data.

[0107] Through the improved Kalman filter algorithm, the difference between virtual and real data is analyzed in real time. When the error exceeds the preset threshold, the parameter readjustment mechanism of the optimization engine is automatically triggered to ensure the feasibility and accuracy of lightweight design in the actual manufacturing process.

[0108] (4) Blockchain evidence storage to protect the intellectual property rights of lightweight design

[0109] When the design version changes, the hash value calculation is automatically triggered and the result is stored in the blockchain to ensure the transparency and traceability of the lightweight design process.

[0110] Through asymmetric encryption mechanism, access rights to key design parameters are strictly controlled to protect the core intellectual property rights of lightweight design.

[0111] 2. Topological configuration generation and evaluation to optimize lightweighting effects

[0112] The topological morphological innovation design module of the generative adversarial network is launched to generate diverse unconventional topological configurations through style transfer technology, providing more possibilities for lightweight design.

[0113] By adopting multi-fidelity model fusion technology, combining coarse-grained proxy models with fine finite element analysis, we can efficiently evaluate the lightweighting effect and manufacturing feasibility of different topological configurations, ensuring that the final selected topological configuration achieves the best balance between lightweighting and manufacturing process.

[0114] 3. Material-Structure Collaborative Optimization to Improve the Overall Performance of Lightweight Design

[0115] Build a material-structure collaborative optimization module driven by a material gene library, and use high-throughput calculations to screen out composite material systems that meet lightweight requirements and adapt to manufacturing processes, thereby achieving the best match between materials and structures.

[0116] Develop a WebGL-based 3D visualization interactive interface to support designers in real-time editing and performance pre-evaluation of topological configurations and material selections, thereby improving the flexibility and accuracy of lightweight design.

[0117] 4. Shorten the design cycle and parallelize processing to accelerate the implementation of lightweight design

[0118] Deploy edge computing nodes to achieve distributed parallel processing of the design-analysis-optimization process, significantly shorten the lightweight design cycle, and accelerate the transformation of innovative results into actual products.

[0119] V. Conclusion

[0120] By implementing this topology-optimized lightweight box-girder design method, this project successfully found the optimal balance between lightweighting goals and manufacturing processes, resulting in a box-girder structure that meets multi-physics performance requirements while complying with advanced manufacturing processes. This method not only improves design efficiency and accuracy, but also ensures the practical feasibility and intellectual property protection of lightweight design, providing strong technical support for the field of high-end equipment manufacturing.

[0121] Example 3:

[0122] The present invention also provides a computer-readable storage medium, which stores a program for a lightweight design method for a box beam structure based on topology optimization, such as any of the above. When the program is executed by a processor, the various processes of the lightweight design method embodiment are implemented and can achieve the same technical effect. To avoid repetition, the program is not described here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0123] In the description of this specification, the reference terms "one embodiment", "some embodiments", "examples", "specific examples" or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0124] The drawings of the embodiments disclosed in the present invention only involve structures related to the embodiments disclosed in the present invention. Other structures may refer to common designs. In the absence of conflicts, the same embodiment and different embodiments of the present invention may be combined with each other.

[0125] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0126] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A lightweight design method for box beam structure based on topology optimization, characterized in that: include: Construct a three-dimensional parametric model that couples multiple physical fields. By integrating cross-scale analysis of structural stiffness, thermal deformation, vibration modes, and fatigue life, and adopting a hybrid topology optimization strategy of variable density method and level set method, the relationship between density and stress and strain is modeled to generate an initial material distribution scheme that meets the functional integrity of the structure. Where ρ is the material density distribution, J(ρ) is the objective function, σ(ρ) is the material stress tensor, ε(ρ) is the material strain tensor, and Ω is the design domain; An intelligent optimization engine that integrates quantum genetic algorithm and deep reinforcement learning is activated to dynamically optimize the initial material distribution scheme. By adjusting the optimization target weight coefficient and combining it with the manufacturing process constraints, an optimal topological configuration that meets the requirements of the additive manufacturing process is generated. Deploy a closed-loop verification system driven by digital twins. By synchronizing stress-strain data between the physical prototype and the virtual model in real time, it triggers the iterative correction mechanism of the optimization algorithm to perform bidirectional adaptive compensation of design parameters and manufacturing errors. The blockchain evidence storage module is called to perform tamper-proof distributed storage of key parameters, simulation results, and version iteration records during the optimization process.

2. The lightweight design method of a box beam structure based on topology optimization according to claim 1, characterized in that: The multi-physics coupling model is constructed in the following way: Use isogeometric analysis technology to perform bidirectional mapping of geometric parameters between CAD models and CAE models; A thermal-mechanical coupled finite element format is introduced to handle the nonlinear problem of contact interface through the substructure condensation method; Thermal-mechanical coupled finite element formula: [K(x)+K thermal (x)]{u}={F} Where K(χ) is the structural stiffness matrix, K thermal (χ) is the thermal-mechanical coupling stiffness matrix, u is the node displacement vector, and F is the external load; Construct a fatigue life prediction network based on transfer learning and use small sample experimental data to correct the deviation of simulation results.

3. The lightweight design method of box beam structure based on topology optimization according to claim 2, characterized in that: The intelligent optimization engine achieves dynamic collaboration through the following mechanisms: Initialize the quantum genetic algorithm, use quantum bits to encode topological variables, and perform global search through quantum rotation gate operations; Quantum rotation gate operation formula: |P new >=U(θ)|Ψ old > Where, |Ψ new > and |Ψ old > is the quantum state vector, U(θ) is the quantum rotation gate material matrix, and θ is the angle controlling the rotation; Configure a deep reinforcement learning agent based on the Actor-Critic framework, using structural performance indicators as reward functions to dynamically adjust the optimization path; R(s,a)=ω1·P(s,a)-ω2·MC(s,a) Where P(s,a) is the structural performance index, MC(s,a) is the manufacturing cost, ω1 and ω2 are weight coefficients; Manufacturing process constraints are embedded and the fitness evaluation is corrected through a penalty function mechanism, including minimum feature size, draft angle, and overhang angle control.

4. The lightweight design method of box beam structure based on topology optimization according to claim 3 is characterized in that: The operation process of the closed-loop verification system includes: Deploy a physical prototype embedded with a fiber Bragg grating sensor network to collect strain-temperature data at key measurement points in real time; Build a virtual sensor network based on digital twins and predict the response status of unmonitored areas through LSTM neural networks; The improved Kalman filter algorithm is used to fuse virtual and real data, and the optimization engine parameters are readjusted when the error threshold is triggered; x′ k|k =x′ k|k-1 +K k (z k -H k x′ k|k-1 ) Where x′ k|k is the updated state estimate, K k is the Kalman gain, z k is the measured value, H k is the measurement matrix.

5. The lightweight design method of box beam structure based on topology optimization according to claim 4 is characterized in that: The blockchain evidence storage module is implemented through smart contracts: Automatically trigger hash value calculation when the design version changes, and store the result in the blockchain; Control access rights to key design parameters through asymmetric encryption mechanisms; The stored evidence data complies with the ISO 19770-1 standard, enabling design responsibility tracing and intellectual property evidence.

6. The lightweight design method of box beam structure based on topology optimization according to claim 5, characterized in that: The topology configuration generation process includes: Construct a topological morphological innovation design module based on generative adversarial networks, and generate unconventional topological configurations through style transfer technology; The loss function of the generative adversarial network is: L GAN N[log(D(r))]+E[log(1-D(G(z)))] Where D(r) is the discriminator, G(z) is the generator, z is random noise, and L GAN is the loss of the generative adversarial network; Adopt multi-fidelity model fusion technology, combining coarse-grained proxy models with fine finite element analysis to balance computational efficiency and solution accuracy; Introducing manufacturing defect sensitivity analysis, and quantifying the effects of porosity and interlayer bonding strength on structural performance through Monte Carlo simulation; Where Sy is the sensitivity, P i is the ith simulation result, α i is the manufacturing defect parameter.

7. The lightweight design method of box beam structure based on topology optimization according to claim 6, characterized in that: The method further comprises: Construct a material-structure collaborative optimization module driven by a material gene library, and screen composite materials systems that meet multi-objective constraints through high-throughput calculations; Develop a WebGL-based 3D visualization interactive interface for designers to perform real-time editing of topological configurations and pre-evaluate performance; Deploy edge computing nodes to perform distributed parallel processing of the design-analysis-optimization process, shortening the design cycle.

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