Reticulated shell structure model generation method and device and product

Abstract

The invention discloses a reticulated shell structure model generation method and device and a product, relates to the technical field of building structure optimization, and aims to solve the problems of material waste, overlarge structure weight, low construction efficiency and difficulty in manufacturing complex nodes in the prior art. The method comprises the following steps: modeling a latticed shell structure by utilizing a parameterized modeling tool, carrying out geometric form-finding optimization on a basic grid form by utilizing a geometric form-finding optimization tool, further optimizing the optimized grid form by adopting a grid optimization tool, and optimizing the node number and the material consumption of the latticed shell structure by adopting an optimization algorithm; carrying out structural simulation analysis on the optimized grid form in finite element analysis software, and evaluating stress distribution and structural deformation; topological optimization software is used for carrying out topological optimization on the form and the stress condition of the latticed shell nodes, and the optimized nodes are manufactured through the additive manufacturing technology. By means of the method, the material utilization rate can be effectively increased, structural light weight is achieved, the structural weight is reduced, and the construction efficiency is improved.

Description

Technical Field

[0001] This application relates to the technical field of building structure optimization, and particularly to a method for generating a reticulated shell structure model. Background Art

[0002] With the increasing global attention to green buildings and sustainable development, the construction industry is constantly exploring new materials and technologies to meet the requirements of environmental protection and high efficiency. Due to its light weight, high strength, excellent spatial load-bearing capacity, and flexible design possibilities, the reticulated shell structure has been widely used in large-scale construction projects. However, there are still some challenges in the application of the reticulated shell structure in the existing technology:

[0003] Material waste: In the design and construction process of traditional reticulated shell structure buildings, there is often a problem of material waste. Due to the lack of precise optimization design, the excess materials not only increase the cost but also cause an unnecessary burden on the environment.

[0004] Excessive structural weight: Although the reticulated shell structure itself has the characteristics of light weight and high strength, in actual applications, due to unreasonable design or improper material selection, the structural weight often exceeds the necessary range. This not only increases the construction difficulty but also limits its application in some projects sensitive to weight.

[0005] Low construction efficiency: The complex node and component design lead to a cumbersome construction process, requiring a large amount of manual processing and on-site assembly. This not only prolongs the construction period but also increases the labor cost and error rate.

[0006] Difficult to manufacture complex nodes: Some innovative reticulated shell structure designs require complex nodes and components, but traditional manufacturing processes are difficult to achieve these complex shapes, restricting the diversity and flexibility of the design.

[0007] These problems not only affect the economic benefits of reticulated shell structure buildings but also restrict their application in a wider range of fields. Therefore, the construction industry urgently needs a new solution that can overcome the limitations of the existing technology while meeting the requirements of environmental protection and high efficiency. Summary of the Invention

[0008] This application provides a method, device, and product for generating a reticulated shell structure model, aiming to solve the problems of material waste, excessive structural weight, low construction efficiency, and difficult to manufacture complex nodes existing in the existing technology.

[0009] In the first aspect, a method for generating a reticulated shell structure model, the method includes steps S1 - S6.

[0010] Step S1, use a parametric modeling tool to model the reticulated shell structure, and use a geometric form-finding optimization tool to optimize the geometric form-finding of the basic grid form to generate the basic grid form.

[0011] Step S2: Further optimize the optimized grid morphology using a grid optimization tool to generate an optimized grid morphology.

[0012] Step S3: Optimize the number of nodes and material usage of the reticulated shell structure using an optimization algorithm.

[0013] Step S4: Conduct a structural simulation analysis of the optimized grid morphology in finite element analysis software to evaluate the stress distribution and structural deformation.

[0014] Step S5: Use topology optimization software to perform topology optimization on the morphology and force conditions of the reticulated shell nodes to improve material utilization.

[0015] Step S6: Manufacture the optimized nodes using additive manufacturing technology and perform precision machining using selective laser melting or rapid casting technology to ensure structural accuracy and strength.

[0016] In the above solution, optionally, in Step S3, the node optimization of the reticulated shell structure includes:

[0017] Analyze the optimized grid morphology in finite element analysis software to obtain the force conditions of the nodes; use topology optimization software to perform topology optimization on the morphology and force conditions of the nodes, thereby improving the material utilization rate; perform 3D reconstruction on the optimized results based on 3D modeling software; verify the force on the nodes using finite element analysis software.

[0018] In the above solution, optionally, after Step S4 and before Step S5, it further includes: Conduct a structural verification of the optimized grid in finite element analysis software to evaluate the optimization results.

[0019] In the above solution, optionally, in Step S1, use Kangaroo dynamics software for geometric form-finding optimization.

[0020] In the above solution, optionally, in the structural simulation analysis of Step S4, use finite element structural simulation and combine the finite element analysis software Abaqus for material optimization calculation during the optimization process.

[0021] In the above solution, optionally, during the optimization calculation in Step S3, use a greedy algorithm and multi-objective optimization to jointly perform material conservation and lightweight design.

[0022] In the above solution, optionally, in Step S6, node manufacturing adopts topology optimization and additive manufacturing technology, and uses selective laser melting SLM direct metal printing or a rapid casting process combined with 3D printed sand molds to reduce manufacturing costs and improve product quality.

[0023] In a second aspect, a reticulated shell structure model generation device includes: a modeling and geometric form-finding optimization module, a mesh optimization module, a finite element analysis module, a node quantity and material consumption optimization module, a topology optimization module, and an additive manufacturing module.

[0024] The modeling and geometric form-finding optimization module is used to model the reticulated shell structure by using a parametric modeling tool, and perform geometric form-finding optimization on the basic mesh form by using a geometric form-finding optimization tool to generate a basic mesh form.

[0025] The mesh optimization module is used to further optimize the optimized mesh form by using a mesh optimization tool to generate an optimized mesh form.

[0026] The finite element analysis module is used to perform structural simulation analysis on the optimized mesh form in finite element analysis software to evaluate the stress distribution and structural deformation.

[0027] The node quantity and material consumption optimization module is used to optimize the node quantity and material consumption of the reticulated shell structure by using an optimization algorithm.

[0028] The topology optimization module is used to perform topology optimization on the form and force condition of the reticulated shell nodes by using topology optimization software to improve the material utilization rate.

[0029] The additive manufacturing module is used to manufacture the optimized nodes by using additive manufacturing technology, and perform precision machining by using selective laser melting or rapid casting technology to ensure the structural accuracy and strength.

[0030] In a third aspect, a computer device includes a memory, a processor, and a computer program stored on the memory, and the processor executes the computer program to implement the steps of the above method.

[0031] In a fourth aspect, a computer-readable storage medium stores a computer program thereon, and the computer program, when executed by a processor, implements the steps of the above method.

[0032] In a fifth aspect, a computer program product includes a computer program / instructions, and the computer program / instructions, when executed by a processor, implement the steps of the above method.

[0033] Compared with the prior art, the present application has at least the following beneficial effects:

[0034] Based on further analysis and research of the problems in the prior art, it is recognized that there are problems such as material waste, excessive structural weight, low construction efficiency, and difficulty in manufacturing complex joints in the prior art. Through a series of advanced technologies and methods such as parametric modeling, geometry and mesh optimization, finite element analysis, optimization algorithms, topology optimization, and additive manufacturing, this application provides a reticulated shell structure optimization method that combines digital optimization and sustainable design concepts, which can effectively improve the material utilization rate, achieve structural lightweighting, meet green building standards, reduce the structural weight, improve the construction efficiency, and at the same time ensure the accuracy and strength of the structure, significantly enhancing the overall performance and economy of the reticulated shell structure. Description of the Drawings

[0035] Figure 1 It is a schematic flow chart of a method for generating a reticulated shell structure model provided by an embodiment of this application.

[0036] Figure 2 It is a flow chart for optimizing a reticulated shell structure model provided by an embodiment of this application.

[0037] Figure 3 It is a schematic flow chart of reticulated shell mesh optimization provided by an embodiment of this application.

[0038] Figure 4 It is a standard deviation diagram of the optimization result provided by an embodiment of this application.

[0039] Figure 5 It is a schematic diagram of the Pareto front solution set provided by an embodiment of this application.

[0040] Figure 6 It is the optimal solution A of the total structural weight provided by an embodiment of this application.

[0041] Figure 7 It is the optimal solution B of the number of joints provided by an embodiment of this application.

[0042] Figure 8 It is the optimal solution C of the amount of square timbers used provided by an embodiment of this application.

[0043] Figure 9 It is an equivalent stress diagram of the benchmark scheme provided by an embodiment of this application.

[0044] Figure 10 It is an equivalent stress diagram of the optimized scheme B provided by an embodiment of this application.

[0045] Figure 11 It is a displacement diagram of the benchmark scheme provided by an embodiment of this application.

[0046] Figure 12 It is a displacement diagram of the optimized scheme B provided by an embodiment of this application.

[0047] Figure 13 The system diagram of node optimization design provided for an embodiment of the present application.

[0048] Figure 14 The grid node numbers, member numbers and axial forces received provided for an embodiment of the present application.

[0049] Figure 15 The diagram of node stress conditions and optimization design areas provided for an embodiment of the present application.

[0050] Figure 16 The preliminary topology optimization result diagram of node A provided for an embodiment of the present application.

[0051] Figure 17 The node re - design process provided for an embodiment of the present application.

[0052] Figure 18 The optimization processes of each typical node provided for an embodiment of the present application.

[0053] Figure 19 The comparison diagram of von Mises stress distributions before and after node optimization provided for an embodiment of the present application.

[0054] Figure 20 The 3D printing flow chart provided for an embodiment of the present application.

[0055] Figure 21 The module architecture block diagram of the reticulated shell structure model generation device provided for an embodiment of the present application.

[0056] Figure 22 The internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0057] In order to make the objectives, technical solutions and advantages of the present application more clear and understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0058] In the description of the present application: Unless otherwise specified, expressions such as "including", "comprising", "having", etc. also mean "not limited to" (certain units, components, materials, steps, etc.).

[0059] The present application provides a method for generating a reticulated shell structure model to solve the problems of material waste, excessive structural weight, low construction efficiency and difficult manufacturing of complex nodes existing in the prior art.

[0060] In one embodiment, referring to Figure 1, a method for generating a reticulated shell structure model is provided, and this method includes steps S1 - S6.

[0061] Step S1, use a parametric modeling tool to model the reticulated shell structure, and use a geometric form - finding optimization tool to optimize the geometric form of the basic grid pattern to generate a basic grid pattern.

[0062] Step S2, use a grid optimization tool to further optimize the optimized grid pattern to generate an optimized grid pattern.

[0063] Step S3, use an optimization algorithm to optimize the number of nodes and material usage of the reticulated shell structure.

[0064] Step S4, conduct a structural simulation analysis on the optimized grid pattern in finite - element analysis software to evaluate the stress distribution and structural deformation.

[0065] Step S5, use topology optimization software to perform topology optimization on the shape and force - bearing conditions of the reticulated shell nodes to improve material utilization rate.

[0066] Step S6, use additive manufacturing technology to manufacture the optimized nodes, and use selective laser melting or rapid casting technology for precision machining to ensure structural accuracy and strength.

[0067] This application provides a reticulated shell structure optimization method that combines digital optimization and sustainable design concepts, which can effectively improve material utilization rate, achieve structural lightweighting, meet green building standards, reduce structural weight, improve construction efficiency, and at the same time ensure the accuracy and strength of the structure, significantly enhancing the overall performance and economy of the reticulated shell structure.

[0068] In one embodiment, the material of the reticulated shell structure model can be wood. The reticulated shell wood structure building is an innovative building method that combines modern wood structure technology and reticulated shell structure form. With the increasing global attention to green buildings and sustainable development, wood, as an environmentally friendly and renewable building material, has gradually returned to the vision of the construction industry. The reticulated shell structure, due to its light weight, high strength, excellent spatial load - bearing capacity, and flexible design possibilities, has been widely used in large - scale construction projects. Combining the two not only solves the limitations of the use of wood in traditional buildings but also provides a new building mode with higher space utilization efficiency, structural stability, and environmental protection.

[0069] In recent years, the rise of digital modeling, topology optimization, and additive manufacturing technologies has provided new methods for the efficient design and manufacturing of wooden shell structures. Combining parametric modeling, structural simulation, multi-objective optimization, and 3D printing can improve wood utilization rate, reduce structural self-weight, and optimize construction costs while meeting the building function requirements. This embodiment proposes a design method for reticulated shell wooden structures based on digital procedures: modeling, simulation, optimization design, and additive manufacturing to realize the generation of a reticulated shell wooden structure model.

[0070] The generation process of the reticulated shell structure model is described in detail below in a "whole - local" manner. Refer to Figure 2 , in this embodiment, wood and metal nodes are used as the materials of the reticulated shell structure.

[0071] (1) The generation and optimization of the overall reticulated shell structure are as follows.

[0072] ① Perform parametric modeling of the reticulated shell wooden structure, and use the geometric form-finding optimization tool Kangaroo for geometric form-finding optimization to generate the basic grid form.

[0073] ② Use the grid optimization tool Tri Remesh to optimize the grid of the reticulated shell structure to generate the optimized grid form.

[0074] ③ Conduct structural simulation analysis on the optimized grid in the finite element analysis software Abaqus to evaluate the stress distribution and structural deformation.

[0075] ④ Use the greedy algorithm and multi-objective optimization (English name Multi Objective Optimization, abbreviated as MOO) to optimize the number of nodes, wood consumption, and material waste of the reticulated shell structure.

[0076] ⑤ Conduct structural verification on the optimized grid in the Abaqus software to evaluate the optimization results.

[0077] (2) The following is the optimization process of the "local" metal nodes.

[0078] ① Analyze the optimized grid model in the Abaqus software to obtain the force conditions of the nodes.

[0079] ② Use the topology optimization software Inspire to perform topology optimization on the form and force conditions of the metal nodes of the reticulated shell to improve the material utilization rate.

[0080] ③ Based on the SUBD tool of the 3D modeling software Rhino, perform 3D reconstruction on the optimized results.

[0081] ④ Use the Abaqus software to verify the force of the nodes.

[0082] ⑤ Manufacture the optimized joints using additive manufacturing (3D printing) technology, and perform precision machining using selective laser melting (abbreviated as SLM in English) or rapid casting technology to improve construction efficiency while ensuring structural accuracy and strength.

[0083] Through a series of advanced technologies and methods such as parametric modeling, geometry and mesh optimization, finite element analysis, optimization algorithms, topology optimization, and additive manufacturing, this application has achieved the full-process optimization from design to manufacturing for the problems existing in the existing latticed shell structures, such as material waste, excessive structural weight, low construction efficiency, and difficulty in manufacturing complex joints. Although only a model has been produced currently, this method is not limited to model production. The optimized latticed shell structure design and manufacturing process have high generality and scalability, can be flexibly applied to various scenarios and physical buildings, effectively improve material utilization rate, reduce structural weight, enhance construction efficiency, while ensuring structural accuracy and strength, significantly improving the overall performance and economy of the latticed shell structure, and providing an efficient, flexible, and reliable solution for the construction industry.

[0084] In addition, although the wooden structure is taken as an example for detailed description in this embodiment, its core method and optimization process are not limited to the specific material of wood. The technical means such as parametric modeling, geometry and mesh optimization, finite element analysis, optimization algorithms, topology optimization, and additive manufacturing adopted have high generality and adaptability, and can be widely applied to the design and manufacturing of latticed shell structures of various materials, including but not limited to metals, concrete, composite materials, etc. These technologies can be flexibly adjusted and optimized according to the mechanical properties and processing characteristics of different materials, so as to achieve the efficient design and precise manufacturing of latticed shell structures of different materials, effectively solve the problems such as material waste, excessive structural weight, low construction efficiency, and difficulty in manufacturing complex joints commonly existing in the prior art, significantly improve the overall performance and economy of the latticed shell structure, and provide strong support for the diverse material applications in the construction and other related fields.

[0085] The following embodiments describe the solution of this application from another perspective.

[0086] This embodiment takes wood as the material of the latticed shell structure and describes the generation process of the material of the latticed shell structure of this application, including the optimization process of the wooden latticed shell structure. This application proposes a systematic "overall - local" two - level optimization method, referring to Figure 2 to achieve the efficient lightweight design of the wooden latticed shell structure.

[0087] In the overall optimization stage, first establish a parametric model of the basic form, and then perform morphological optimization and reconstruction, setting the mesh density and mesh uniformity as design variables. Use the genetic algorithm (GA) to optimize three key indicators simultaneously: the total structural weight ( ), the total number of nodes ( ), and the usage amount of wooden square timbers ( ). The mechanical properties of each scheme are evaluated through finite element analysis, and the Pareto Front Analysis (abbreviated as PFA in English) is used to determine the optimal solution set. The optimal reticulated shell form output in this stage provides boundary conditions for subsequent node design.

[0088] In the local optimization stage, for the key nodes determined by the overall optimization, the Solid Isotropic Material with Penalization (abbreviated as SIMP) in the variable density method is used for topology optimization. Based on the stress analysis results of Abaqus, the optimization goal is set as maximizing stiffness in Inspire for topology optimization. The von Mises Stress Analysis is carried out on the optimized nodes, and the process constraints of 3D printing (such as the minimum wall thickness, overhang angle, etc.) are considered. Finally, the model file that can be directly used for 3D printing is output.

[0089] The innovation of this application is reflected in: (1) guiding the local node design through the load conditions output by the overall optimization to ensure the mechanical coherence of the two-level optimization. (2) realizing the closed-loop from digital optimization to digital construction, and the optimization results can directly guide the 3D printing construction. This systematic method significantly improves the design efficiency of the wooden reticulated shell structure and provides a new idea for the lightweight optimization design of the wooden reticulated shell structure.

[0090] The overall design and optimization stage of the wooden reticulated shell is described in detail below.

[0091] Step A1, parametric modeling and design variables. A parametric wooden reticulated shell model is established based on the Rhino-Grasshopper platform, and two design variables, namely geometric parameters and material parameters, are set.

[0092] 1. Geometric parameters, including the average mesh size and the number of mesh iterations.

[0093] Average mesh size (2m): Control the overall density of the mesh according to the reference length.

[0094] Number of mesh iterations (0 - 50): Adjust the positions of the mesh vertices in each iteration to optimize the quality and uniformity of the mesh. As the number of iterations increases, the mesh will gradually tend to be uniform.

[0095] 2. Material parameters, including the standard length of wooden square timbers, the cross-sectional size of wooden square timbers, and the wood length interval.

[0096] Standard wooden square length (4.0 m): Considering transportation and processing limitations.

[0097] Cross-sectional dimensions of the wooden square (40 mm, 60 mm, 80 mm, 100 mm).

[0098] Length range of the wood: According to the requirements of relevant design specifications, in actual engineering applications, for common reticulated shell structure forms and wood types, there is usually a certain empirical slenderness ratio value range. For components in general timber reticulated shells, the slenderness ratio is often controlled between 100 and 150. To improve the structural safety, in the subsequent analysis of this application, a more conservative slenderness ratio of 100 is selected as the calculation basis. Combining with the actual length of the component, the required minimum cross-sectional size S is determined, referring to the following formula to meet the requirements of structural stability.

[0099]

[0100] Step A2, simulation and optimization. To achieve the lightweight of the structure and the efficient allocation of material resources, this application constructs an optimization strategy combining the greedy algorithm (English name Greedy Algorithm) and multi-objective optimization. The overall process is shown in Figure 3. This method first determines the basic geometric input parameters, generates the structural mesh through the Tri Remesh tool, then introduces the greedy algorithm to improve the material utilization efficiency, and finally realizes the coordination and balance among multiple objectives under the multi-objective optimization framework.

[0101] Step A21, greedy algorithm and multi-objective optimization. The greedy algorithm is a heuristic algorithm based on local optimal selection. Its core idea is to select the optimal solution under the current conditions at each step, thus expecting to obtain the global optimal solution. This method has good computational efficiency and simplicity of implementation in solving resource allocation and combinatorial optimization problems.

[0102] In this study, the greedy algorithm is used to match the mesh line segments with the standard wooden square sizes, so as to maximize the material utilization efficiency. Let: (1) List : The standard wooden square lengths (4.0 m) corresponding to different cross-sectional dimensions.

[0103] (2) List : The lengths of the mesh line segments generated after Tri Remesh optimization.

[0104] (3) In each iteration, select a from list B, and judge whether it satisfies , where .

[0105] (4) If it is satisfied, then remove from list B, and continue to select and accumulate the values in list B.

[0106] (5) If not satisfied, increase the wood usage by 1 and update the wooden square materials.

[0107] This process is repeated until list B is empty. Finally, the usage of each standard wooden square is output, and the total usage of wooden squares is calculated accordingly and the corresponding total mass of wooden squares . The total mass of structural nodes is denoted as , then the calculation formula for the total structural weight (optimization objective FO1) is as follows.

[0108]

[0109] Where: represents the total structural weight; represents the total mass of nodes, which is obtained by accumulating the masses of each node according to the quantity; represents the total weight of the standard wooden squares used.

[0110] After the greedy algorithm is completed to obtain the preliminary result, a multi-objective optimization algorithm is further introduced to achieve collaborative optimization. The following two key variables are set as optimization variables in this process.

[0111] (1) Number of iterations of the Tri Remesh tool: .

[0112] (2) Average length range of target grid line segments: .

[0113] At the same time, three optimization objective functions are constructed: minimizing the total structural weight, minimizing the number of nodes, and minimizing the usage of wooden squares.

[0114] Minimizing the total structural weight (optimization objective 1): FO1: .

[0115] Minimizing the number of nodes (optimization objective 2): FO2: . Where, represents the total number of nodes obtained after generating the grid, which directly reflects the complexity of the structure.

[0116] Minimizing the usage of wooden squares (optimization objective 3): FO3: .

[0117] The multi-objective optimization problem is comprehensively expressed as the following formula.

[0118]

[0119] In summary, the material matching mechanism based on the greedy algorithm proposed in this application can quickly realize the optimization of resource allocation at the structural component level; while the multi-objective evolutionary optimization method based on the genetic algorithm can effectively address the coupling and conflict problems between multi-objective functions and achieve the evolutionary process from local optimum to global optimum. This multi-algorithm collaborative mechanism not only improves the systematicness and stability of structural performance optimization, but also provides effective support for the material conservation and sustainability goals in architectural design.

[0120] Step A22, optimize the solution strategy. Use the genetic algorithm for solution. Parameter settings: the population is set to 50 generations, with 50 individuals in each generation, the crossover probability is 0.9, the mutation probability is 0.01, the crossover distribution index is 20, the mutation distribution index is 20, and the random seed is 1. After 2500 generations of iterative calculation, the Pareto optimal solution set is obtained. The comparison of typical optimization results is as Figure 4 shown.

[0121] Step A3, analyze the optimization results. After 2500 generations of iteration, the Pareto solution set is obtained. As Figure 5 shown, it is the grid form of a total of 15 frontier solutions, and Table 1 shows the optimization target results corresponding to each optimization solution.

[0122]

[0123] Optimal solution for the total structural weight: Figure 6 The optimization solution A under the total structural weight is shown. It is characterized by a relatively large number of nodes, a relatively high grid density, and a relatively regular state. However, the length of the wooden members is relatively short, indicating that when optimizing the total weight, although a denser grid will cause an increase in the number of nodes and the mass of the nodes. However, in the calculation of the total weight, the influence degree of the total mass of the wood is higher. Therefore, during the program optimization process, it is more inclined to use shorter wooden members with smaller cross-sectional dimensions to reduce the total mass of the wooden members and thus achieve the goal of a lower total structural weight.

[0124] Optimal solution for the number of nodes: Figure 7 The optimization solution B with the least number of nodes is shown. It is manifested in a significant reduction in the number of nodes, a relatively low total usage of wooden squares, a relatively small grid density, and a higher degree of grid uniformity. However, due to the relatively long length of the grid lines in this state, the program will allocate wooden squares with larger cross-sectional dimensions according to the slenderness ratio. As a result, the total mass of the wood is relatively large, and the reduction degree of the total structural mass compared to the original form is relatively low.

[0125] Optimal solution for the usage of wooden squares: Figure 8The least amount of lumber solution C is shown. It is manifested as a relatively uniform but denser grid. It can be seen that under the condition of less wood consumption, the grid tends to use shorter grid lines so that the same raw material can accommodate more lumber. This indicates an obvious trade-off between wood consumption and grid density, that is, the number of nodes. Future research can further explore optimization strategies to control the growth of the number of nodes while maintaining the efficient use of materials.

[0126] By analyzing the optimal solutions under three optimization objectives, the optimal solution of the number of nodes is finally selected as the final optimization result. This solution is the most balanced among all the solution sets, achieving multi-objective collaborative optimization among the total structural weight, the total number of nodes, and the amount of lumber used, and also having the highest grid uniformity.

[0127] Compared with the original structure, the total structural weight is reduced from 676.34 kg to 569.55 kg, achieving a reduction of about 16%. The number of nodes is reduced from 49 to 30, achieving a reduction of about 39%. The total amount of lumber used is reduced from 53 to 46, achieving a reduction of about 13%.

[0128] Step A4, verification of the optimization results based on Abaqus. To verify the performance of the optimization scheme under actual working conditions, Abaqus was used to conduct a static analysis of typical optimization results. The boundary conditions and load conditions were set to be consistent with those in the Grasshopper simulation, and simulations were carried out for two groups of optimization schemes (Scheme B and the benchmark scheme) respectively to analyze their stress distributions and maximum displacements. Refer to Figures 9 - 12 , where Figure 9 represents the equivalent stress analysis diagram of the benchmark scheme, Figure 10 represents the equivalent stress analysis diagram of the optimized scheme B, Figure 11 represents the displacement diagram of the benchmark scheme, Figure 12 represents the displacement diagram of the optimized scheme B.

[0129] The simulation results show that the maximum equivalent stress of the optimized scheme B is 0.4 MPa, far lower than the ultimate strength of wood along the grain (compressive strength is about 10 MPa, tensile strength is about 8 MPa), and the stress concentration in the node area is significantly alleviated; the maximum displacement of the optimized scheme is 1.332 mm, and the maximum displacement of the optimized scheme is 1.002 mm. The maximum displacement after optimization is reduced by 25% compared with that before optimization. The maximum displacement of the structure meets the deformation limit specified in the code.

[0130] In addition, a comparative analysis was carried out on the cross-section utilization rate, the stress direction of node connections, the stability boundary, etc. The optimized structure is significantly superior to the unoptimized scheme in terms of comprehensive performance, verifying the feasibility and efficiency of the collaborative optimization design strategy.

[0131] The following details the "local" node topology optimization design and 3D printing manufacturing of the wooden reticulated shell.

[0132] Step B1, "Local" node topology optimization design. On the basis of completing the overall structure optimization, further conduct topology optimization design on the key connection nodes in the wooden reticulated shell structure to further reduce the material consumption, improve the structural efficiency, and optimize the cost. During the optimization process, with "maximizing the structural stiffness" as the objective function, the lightweight of the node structure form is realized. For the optimized nodes, metal additive manufacturing technology is adopted to quickly produce the optimized nodes. This shortens the conversion cycle between design and production, and significantly reduces the cost and time investment in links such as modeling, mold modification, and mold manufacturing in the traditional design path.

[0133] Step B11, SIMP density interpolation method. According to the different research objects, topology optimization problems are usually divided into two categories: discrete body structures and continuum structures. Since this step focuses on the node part, the topology optimization method of continuum structures is mainly discussed. In such optimization problems, common mathematical modeling methods include the homogenization method, the variable density method, the Independent Continuous Mapping (ICM for short), and the Evolutionary Structural Optimization (ESO for short), etc. Among them, the variable density method has become the most widely used modeling means due to its strong practicality. Its core idea is to use the interpolation function of continuous variables with values between 0 and 1 to establish the relationship between the element density and the corresponding material elastic modulus, assuming that the material stiffness is proportional to the element density. In the variable density method, the Solid Isotropic Material with Penalization (SIMP) is one of the most commonly used specific forms, and its interpolation expression is as follows.

[0134]

[0135] Among them, represents the relative density design variable of the element; represents the artificially set penalty coefficient to reduce the existence of interpolation intermediate variables; and represent the elastic moduli of the materials in the parts where the relative density of the design area is approximately 0 and 1 respectively, usually taking to avoid the singularity of the stiffness matrix.

[0136] This elastic modulus interpolation formula makes the relative elastic modulus of a large amount of material tend to 0 by introducing the penalty coefficient , so that a large number of elements in the "half-existing and half-nonexistent" intermediate state in the structure are reduced.

[0137] Step B12, an optimization model aiming at maximizing stiffness. Taking the nodes of a space structure under static loads as the research object. The most common optimization goal of the static topology optimization problem of a structure is to maximize the static stiffness of the structure (i.e., minimize the flexibility). For a topology optimization problem with the structural volume fraction as the constraint condition, it can be described by the following formula.

[0138]

[0139] Among them, the design variable represents the relative density of the element after finite element discretization; represents the geometry of the optimized design variable; represents the flexibility of the structure; , and respectively represent the global stiffness, displacement, and external load matrices of the structure; and respectively represent the function of the actual volume of the structure with respect to the variable and the constraint volume fraction value of the entire optimization problem; and respectively represent the upper and lower limit values of the design variable; i represents the number of elements; N represents the total number of elements into which the structure is discretized.

[0140] Step B13, an optimization design tool. Inspire has multiple functions such as geometric modeling, structural simulation, topology optimization, and manufacturing simulation, forming an integrated workflow from modeling, analysis to design verification, effectively improving the efficiency and accuracy of structural optimization, and is especially suitable for the structural lightweight design requirements oriented to manufacturing.

[0141] To further improve the geometric performance and visual quality of the node model, this application introduces Rhinoceros software (a three-dimensional modeling software, abbreviated as Rhino) for post-processing. Using its SUBD subdivision modeling tool to reconstruct and optimize the initial geometric model generated by Inspire, making the node model more expressive in construction and more aesthetically designed while maintaining engineering feasibility. The collaborative use of Inspire and Rhino realizes the organic integration of structural performance and formal aesthetics, expanding the design boundary of engineering nodes in the context of digital construction. Figure 13 Shows the integration process between topology optimization and modeling tools, clarifying the collaborative mechanism between the two in model generation, geometric reconstruction, and visual expression.

[0142] Step B14, node analysis and optimization. The overall static analysis of the optimized structure is carried out by using Abaqus finite element analysis software. The results show that the main force acting on each connection node of the structure is axial force, and the influence of bending moment and shear force is relatively small. Since the optimization steps of each node of the structure are the same, only 3 typical nodes on the structure and the members connected to them are selected as examples for display. As Figure 14 shown, the numbers of typical nodes on the structure, the numbers of members and the axial force distribution are marked, providing reasonable load boundary conditions for subsequent node topology optimization.

[0143] In the local optimization stage, Altair Inspire software (i.e., Inspire software) is used to carry out topology optimization design for the node structure. Due to the similarity of the force characteristics and geometric compositions of each node, node A is selected as a typical case for analysis. The original geometric model and finite element model of node A are as Figure 15 shown. This node is composed of a central cylinder and six connecting plates. The outer diameter of the cylinder is 102 mm, the height is 90 mm, and the wall thickness is 5 mm. Each connecting bolt hole of the node is a key part for force bearing and connection, and is defined as a "non-design area" in the topology optimization to retain its original form, and the rest is used as the "design area" to participate in the topology optimization. The external load acts on the center position of the bolt hole in the form of axial force.

[0144] Figure 16 The topology optimization result under the goal of maximizing stiffness, with the weight constraint set to 30% of the total design space volume, can be observed that under this volume constraint, a fully connected topology has achieved distinct structural characteristics (the dotted outer frame represents the original form of the node). Most of the central cylindrical volume of the original node is removed, while the connection areas between the connecting plates are retained and connected to each other. The degree of material removed from the connecting plates varies according to their load-bearing conditions. It should be noted that the weights of the connecting plates except L17 are significantly reduced, and the plate at the connection with the L17 member has a lower mass reduction after optimization due to greater force and shows a V-shaped support configuration. This result highlights the material distribution characteristics in the design area and reflects the underlying topology characteristics of the optimized node. However, due to the geometric discontinuity between the specified design area and non-design area, the optimization result cannot form a continuous and cohesive entity, hindering the direct extraction of the geometric model. Therefore, it is necessary to redesign a complete and reasonable node component according to the geometric topology of the optimization result.

[0145] After the initial optimization is completed, the optimized nodal geometric model is imported into the Rhinoceros platform, and the SUBD subdivision modeling tool is used to further geometrically reconstruct it. The subdivision surface modeling technology was initially widely used in the field of computer graphics (abbreviated as CG in English), mainly for modeling animated characters and virtual environments. In recent years, it has gradually demonstrated strong modeling capabilities in the fields of industrial design and mechanical manufacturing. Its application in the design of complex space structure nodes provides a new technical path for constructing high-quality structural nodes with continuous and smooth surfaces.

[0146] The reconstruction process starts with analyzing the geometric and topological features of the non-design area and the materials retained in the design area. On the premise of keeping the key mechanical characteristics of the node unchanged, seamless transition between the design area and the non-design area is achieved through subdivision modeling, effectively eliminating geometric discontinuities in the model. At the same time, the secondary redundancies generated during the optimization process are abstracted, and the structural functions are reduced to the main force-bearing and connecting units. Subsequently, the model is appropriately constructed and beautified according to manufacturing requirements, such as adding standard bolt holes and chamfer designs, to improve the assembly performance and visual quality of the node. Figure 17 Shows the fusion effect of the non-design area and the structure retained by topological optimization during the geometric reconstruction process (where the dashed outer frame represents the original shape of the node).

[0147] The final nodal model is completed by solid printing using the metal additive manufacturing process, with a material density of 7.98 g / cm³. The mass of the reconstructed Node A is approximately 1794.20 g, which is a 55% reduction compared to 3955.27 g before optimization, significantly improving the material utilization efficiency and verifying the practical application value of the "topological optimization - subdivision reconstruction - 3D printing" workflow proposed in this study in the lightweight design of complex nodes.

[0148] Other typical nodes of the structure are optimized according to the same logic. Figure 18 Shows the basic topological optimization results and the final shapes after reconstruction of each typical node (where the dashed outer frame represents the original shape of the node).

[0149] Step B15, analysis of optimization results. Under the same load conditions, finite element analysis is carried out on the initial design and the maximum stiffness optimized design of the node to comprehensively evaluate the mechanical properties of the optimized node. The Inspire software is used for analysis. The von Mises yield criterion and related plastic flow laws are adopted, and the yield strength of the metal 3D printed component is set to 480 MPa, and the ultimate tensile strength is set to 560 MPa. The boundary conditions are kept the same as those of the initial model of the original node.

[0150] Taking Node A as an example. Figure 19For the von Mises stress distribution diagram of Node A under destructive loading conditions, in the stress analysis of the original design, the main deformations and stress concentrations occurred at the cylindrical interface, while a large area of low stress was observed at the connecting plate. The optimized node shows a reduction in material usage and a more uniform overall stress distribution. The maximum stress remains below the yield strength of the material (480 Mpa), indicating that topology optimization aiming at maximizing stiffness can make more effective use of materials under the same loading conditions.

[0151] Step B2, 3D printing manufacturing of the node. Additive Manufacturing (AM), also often referred to as 3D Printing, is a digital manufacturing process based on a computer-aided design (CAD) model. Its basic principle is to build the target component by stacking materials layer by layer. This unique layer-by-layer accumulation method enables the realization of many complex structures that are difficult to process in traditional subtractive manufacturing processes.

[0152] AM technology is suitable for the manufacturing requirements of a variety of materials, including polymers, ceramics, and metal materials, etc. Among them, metal additive manufacturing has shown broad development potential and application prospects in scientific research and industrial fields due to its good sustainable manufacturing prospects.

[0153] For the spatially structured nodes after topology optimization, their geometric forms are often extremely complex and difficult to be manufactured by conventional methods. With the help of additive manufacturing technology, the manufacturing requirements of these complex components can be efficiently and accurately realized, providing practical support for the engineering practice of structural optimization design and significantly enhancing its practical application ability.

[0154] This study adopted the Selective Laser Melting (SLM) technology, which has a high maturity and reliability in the field of metal additive manufacturing. The equipment used is the iSLM350DN type SLM 3D printer produced by Zhongrui Company, and its printing layer thickness is 0.05 mm. Figure 20 The shown metal 3D printing flow chart.

[0155] For the printing results under the optimization goal of "maximum stiffness", the surface finish and flatness of the node components are both excellent, and the materials used also show a high degree of densification, verifying the feasibility and superiority of SLM technology in the manufacturing of high-precision metal structure nodes.

[0156] The local optimization process is based on the method path of "topological optimization - geometric reconstruction - process adaptation", and conducts local optimization on the joints of the wooden lattice shell structure. Through the topological optimization design and subdivision modeling reconstruction of typical joints, and combined with metal additive manufacturing technology to complete the physical manufacturing, a set of locally optimized workflow that can be executed in a closed loop is constructed. The results show that: the average weight reduction of the optimized joints is about 1944 g, and the weight reduction ratio reaches 40%; at the same time, the von Mises stress is increased by about 20.3 MPa on average, the structural performance remains within the safe range, and the stress distribution is more uniform. This research verifies the effectiveness of the proposed process in improving the material utilization efficiency and alleviating the problem of heavy traditional joint structures, and breaks through the technical bottlenecks of limited joint forms and low material utilization rate in traditional designs. This method provides a new technical reference for the performance improvement and manufacturing adaptability of modern wooden lattice shell structures at the joint design level, showing good engineering application potential and promotion value.

[0157] This application proposes a lightweight design method for wooden lattice shell structures oriented towards material conservation, and constructs a multi-level collaborative process from overall form optimization to local joint topological optimization. By integrating technologies such as parametric modeling, structural simulation, multi-objective optimization, and 3D printing, the structural performance and material utilization efficiency are significantly improved, meeting the development needs of green buildings. In the overall optimization stage, the collaborative control of the total structural weight, the number of joints, and the wood consumption is achieved through multi-objective optimization; the optimal solution is selected, the total structural weight is reduced by 16%, the number of joints is reduced by 39%, and the total wood consumption is reduced by 13%; in the local optimization stage, finite element analysis and topological optimization are combined to conduct lightweight design on key joints, and 3D printing is used for efficient manufacturing. The optimization results show that the average weight reduction of the joints reaches 58.5%, proving that the optimization results can achieve both structural performance and construction expression. Abaqus simulation verifies the stability and resource efficiency of the optimization scheme under multiple working conditions. This method shows obvious advantages in material conservation, joint control, and manufacturing adaptability, and has good engineering application potential.

[0158] Although it still faces challenges such as the level of automation, material adaptability, and 3D printing cost control, its practicality and expandability can be further improved through process intelligentization and new manufacturing technologies in the future.

[0159] In summary, the "overall - local" collaborative optimization strategy proposed in this application provides a feasible path for the efficient, green, and intelligent design of wooden structure shells, and has broad application prospects.

[0160] In one embodiment, referring to Figure 21 , a reticulated shell structure model generation device is further provided, including: a modeling and geometric form-finding optimization module, a mesh optimization module, a finite element analysis module, a joint number and material consumption optimization module, a topological optimization module, and an additive manufacturing module.

[0161] The modeling and geometric form-finding optimization module is used to model the latticed shell structure using parametric modeling tools and optimize the geometric form of the basic grid morphology using geometric form-finding optimization tools to generate the basic grid morphology.

[0162] The grid optimization module is used to further optimize the optimized grid morphology using grid optimization tools to generate the optimized grid morphology.

[0163] The finite element analysis module is used to perform structural simulation analysis on the optimized grid morphology in finite element analysis software to evaluate the stress distribution and structural deformation.

[0164] The node quantity and material usage optimization module is used to optimize the node quantity and material usage of the latticed shell structure using optimization algorithms.

[0165] The topology optimization module is used to perform topology optimization on the morphology and stress conditions of the latticed shell nodes using topology optimization software to improve the material utilization rate.

[0166] The additive manufacturing module is used to manufacture the optimized nodes using additive manufacturing technology and perform precision machining using selective laser melting or rapid casting technology to ensure the structural accuracy and strength.

[0167] The specific implementation content of each module can be referred to the limitation of a method for generating a latticed shell structure model in the above text and will not be elaborated here.

[0168] In one embodiment, a computer device is provided. The computer device can be a terminal, and its internal structure diagram can be as Figure 22 shown. The computer device includes a processor, a memory, a communication interface, and a human-computer interaction interface (such as a combination of a display, a keyboard, and a mouse, or a touch screen, etc.) connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities, and the communication interface is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved through WIFI, operator networks, NFC (Near Field Communication), or other technologies. The computer device realizes the above method for generating a latticed shell structure model by loading and running a computer program.

[0169] Those skilled in the art can understand that Figure 22 the structure shown in

[0170] In one embodiment, a computer-readable storage medium is further provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method in the above embodiment are implemented.

[0171] In one embodiment, a computer program product is further provided, including computer programs / instructions, and when the computer programs / instructions are executed by a processor, the steps of the method in the above embodiment are implemented.

[0172] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, 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, it should be considered as the scope recorded in this specification.

Claims

1. A method for generating a lattice shell structure model, characterized in that: include: Step S1, using a parametric modeling tool to model a lattice shell structure, and using a geometric form optimization tool to perform geometric form optimization on a basic grid shape to generate a basic grid shape; Step S2, further optimizing the optimized mesh shape using a mesh optimization tool to generate an optimized mesh shape; Step S3, using an optimization algorithm to optimize the number of nodes and material usage of the lattice shell structure; Step S4, performing structural simulation analysis on the optimized mesh shape in finite element analysis software to evaluate stress distribution and structural deformation; Step S5, using topology optimization software to perform topology optimization on the shape and stress conditions of the lattice shell nodes to improve material utilization; Step S6, using additive manufacturing technology to manufacture the optimized nodes, using selective laser melting or rapid casting technology for precision processing to ensure structural accuracy and strength.

2. The method for generating a lattice shell structure model according to claim 1, characterized in that: In step S3, the node optimization of the lattice shell structure includes: Analyze the optimized mesh shape in the finite element analysis software to obtain the stress conditions of the nodes; Use topology optimization software to optimize the shape and stress of the nodes to improve material utilization; Perform three-dimensional reconstruction of the optimized results based on three-dimensional modeling software; Finite element analysis software is used to verify the force on the nodes.

3. The method for generating a lattice shell structure model according to claim 1, characterized in that: After step S4 and before step S5, the method further includes: The optimized mesh is structurally verified in the finite element analysis software to evaluate the optimization results.

4. The method for generating a lattice shell structure model according to claim 1, characterized in that: In step S1, Kangaroo dynamics software is used to perform geometric form optimization.

5. The method for generating a lattice shell structure model according to claim 1, characterized in that: In step S4 structural simulation analysis, finite element structural simulation is used, and Abaqus is combined with material optimization calculations during the optimization process.

6. The method for generating a lattice shell structure model according to claim 1, characterized in that: In the optimization calculation process in step S3, a greedy algorithm and multi-objective optimization are used to coordinate material saving and lightweight design.

7. The method for generating a lattice shell structure model according to claim 1, characterized in that: In step S6, node manufacturing uses topology optimization and additive manufacturing technology, using SLM direct metal printing or a rapid casting process combined with 3D printed sand molds to reduce manufacturing costs and improve product quality.

8. A lattice shell structure model generating device, characterized in that: include: The modeling and geometric form-finding optimization module is used to model the lattice shell structure using the parametric modeling tool, and to perform geometric form-finding optimization on the basic grid shape using the geometric form-finding optimization tool to generate the basic grid shape; A mesh optimization module is used to further optimize the optimized mesh shape using a mesh optimization tool to generate an optimized mesh shape; Finite element analysis module, used to perform structural simulation analysis on the optimized mesh shape in finite element analysis software, and evaluate stress distribution and structural deformation; Node quantity and material usage optimization module, used to optimize the node quantity and material usage of the lattice shell structure using optimization algorithms; Topology optimization module, which uses topology optimization software to perform topology optimization on the shape and stress conditions of lattice shell nodes to improve material utilization; Additive Manufacturing Module, used to manufacture optimized nodes using additive manufacturing technology, using selective laser melting or rapid casting technology for precision processing to ensure structural accuracy and strength.

9. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method of claim 1.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to claim 1 are implemented.

11. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to claim 1 are implemented.

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