A method, device and product for generating a lattice shell structure model

Abstract

The present application discloses a method, device and product for generating a lattice shell structure model, which relates to the technical field of building structure optimization, and is aimed at the problems of material waste, excessive structural weight, low construction efficiency and difficulty in manufacturing complex nodes in the existing technology. The lattice shell structure is modeled by using a parametric modeling tool, and the basic grid shape is geometrically optimized using a geometric form-finding optimization tool. The optimized grid shape is further optimized using a grid optimization tool, and the number of nodes and material usage of the lattice shell structure are optimized using an optimization algorithm; the optimized grid shape is structurally simulated and analyzed in finite element analysis software to evaluate stress distribution and structural deformation; topological optimization software is used to perform topological optimization on the shape and stress conditions of the lattice shell nodes, and the optimized nodes are manufactured using additive manufacturing technology. The method provided by the present application can effectively improve material utilization, achieve structural lightweighting, reduce structural weight, and improve construction efficiency.

Description

Technical Field

[0001] The present application relates to the technical field of building structure optimization, and in particular to a method for generating a lattice shell structure model. Background Art

[0002] With the increasing global focus on green buildings and sustainable development, the construction industry continues to explore new materials and technologies to meet the demands of environmental protection and efficiency. Gridshell structures have been widely used in large-scale construction projects due to their light weight, high strength, excellent spatial load-bearing capacity, and flexible design possibilities. However, existing technologies still face some challenges in the application of gridshell structures:

[0003] Material waste: Traditional gridshell structures often suffer from material waste during their design and construction. Due to the lack of precise optimization design, excess materials not only increase costs but also place an unnecessary burden on the environment.

[0004] Excessive structural weight: Although lattice shell structures are inherently lightweight and high-strength, in practice, they often exceed necessary weight due to improper design or inappropriate material selection. This not only increases construction difficulty but also limits their application in weight-sensitive projects.

[0005] Low construction efficiency: Complex node and component designs lead to a cumbersome construction process, requiring extensive manual processing and on-site assembly. This not only prolongs the construction period but also increases labor costs and error rates.

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

[0007] These issues not only affect the economic benefits of gridshell structures, 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 existing technologies while meeting environmental protection and high efficiency requirements. Summary of the Invention

[0008] The present application provides a method, device and product for generating a lattice shell structure model, aiming to solve the problems existing in the prior art such as material waste, excessive structural weight, low construction efficiency and difficulty in manufacturing complex nodes.

[0009] In a first aspect, a method for generating a lattice shell structure model comprises steps S1-S6.

[0010] Step S1: Modeling a lattice shell structure using a parametric modeling tool, and performing geometric form optimization on a basic grid shape using a geometric form optimization tool to generate a basic grid shape.

[0011] Step S2: using a mesh optimization tool to further optimize the optimized mesh shape to generate an optimized mesh shape.

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

[0013] Step S4: Perform structural simulation analysis on the optimized mesh shape in finite element analysis software to evaluate stress distribution and structural deformation.

[0014] Step S5: topology optimization software is used to perform topology optimization on the shape and stress conditions of the lattice shell nodes to improve material utilization.

[0015] Step S6: Use additive manufacturing technology to manufacture the optimized nodes, and use selective laser melting or rapid casting technology for precision processing to ensure structural accuracy and strength.

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

[0017] The optimized mesh shape is analyzed in finite element analysis software to obtain the stress conditions of the nodes; topology optimization software is used to perform topology optimization based on the shape and stress conditions of the nodes, thereby improving material utilization; the optimized results are reconstructed in three dimensions based on three-dimensional modeling software; and the stress conditions of the nodes are verified using finite element analysis software.

[0018] In the above solution, optionally, after step S4 and before step S5, the method further includes: performing structural verification on the optimized mesh in finite element analysis software and evaluating the optimization results.

[0019] In the above solution, optionally, in step S1, Kangaroo dynamics software is used to perform geometric form optimization.

[0020] In the above solution, optionally, in the structural simulation analysis of step S4, finite element structural simulation is adopted, and the finite element analysis software Abaqus is combined with the material optimization calculation during the optimization process.

[0021] In the above solution, optionally, in the optimization calculation process in step S3, a greedy algorithm is used in conjunction with multi-objective optimization to perform material saving and lightweight design.

[0022] In the above scheme, optionally, in step S6, node manufacturing adopts topology optimization and additive manufacturing technology, and adopts 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 the second aspect, a lattice 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 usage 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 lattice shell structure using parametric modeling tools, and to perform geometric form-finding optimization on the basic grid shape using geometric form-finding optimization tools to generate the basic grid shape.

[0025] The mesh optimization module is used to further optimize the optimized mesh shape using mesh optimization tools to generate an optimized mesh shape.

[0026] The finite element analysis module is used to perform structural simulation analysis on the optimized mesh shape in the finite element analysis software to evaluate 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 lattice shell structure using an optimization algorithm.

[0028] The topology optimization module is used to use topology optimization software to perform topology optimization on the shape and stress conditions of lattice shell nodes to improve material utilization.

[0029] The additive manufacturing module is 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.

[0030] In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory, wherein 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, which implements the steps of the above method when executed by a processor.

[0032] In a fifth aspect, a computer program product comprises a computer program / instruction, which implements the steps of the above method when executed by a processor.

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

[0034] This application is based on further analysis and research on existing technical problems, and recognizes the problems of material waste, excessive structural weight, low construction efficiency and difficulty in manufacturing complex nodes in the existing technology. Through a series of advanced technologies and methods such as parametric modeling, geometry and mesh optimization, finite element analysis, optimization algorithm, topology optimization and additive manufacturing, this application provides a grid shell structure optimization method that combines digital optimization and sustainable design concepts, which can effectively improve material utilization, achieve lightweight structure, comply with green building standards, reduce structural weight, improve construction efficiency, and at the same time ensure the accuracy and strength of the structure, significantly improving the overall performance and economy of the grid shell structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A schematic flow chart of a method for generating a lattice shell structure model provided in one embodiment of the present application.

[0036] Figure 2 A flowchart of a lattice shell structure model optimization provided in one embodiment of the present application.

[0037] Figure 3 A schematic diagram of the process of optimizing the lattice shell grid provided in one embodiment of the present application.

[0038] Figure 4 A standard deviation diagram of the optimization results provided for one embodiment of the present application.

[0039] Figure 5 A schematic diagram of the Pareto front solution set provided for one embodiment of the present application.

[0040] Figure 6 This is an optimal solution A for the total weight of the structure provided by one embodiment of the present application.

[0041] Figure 7 This is an optimal solution B for the number of nodes provided in one embodiment of the present application.

[0042] Figure 8 This is an optimal solution C for wood usage provided by one embodiment of the present application.

[0043] Figure 9 This is an equivalent stress diagram of a benchmark solution provided for one embodiment of the present application.

[0044] Figure 10 This is an equivalent stress diagram of optimization solution B provided in one embodiment of the present application.

[0045] Figure 11 A displacement map of a benchmark solution provided for one embodiment of the present application.

[0046] Figure 12 This is a displacement diagram of optimization solution B provided in one embodiment of the present application.

[0047] Figure 13 A diagram of a node optimization design system provided for one embodiment of the present application.

[0048] Figure 14 This is a diagram showing the mesh node numbers, member numbers, and axial forces provided for one embodiment of the present application.

[0049] Figure 15 A diagram of the node stress conditions and optimized design area provided for one embodiment of the present application.

[0050] Figure 16 This is a diagram of the preliminary topology optimization results of node A provided in one embodiment of the present application.

[0051] Figure 17 A node redesign process is provided for one embodiment of the present application.

[0052] Figure 18 This is an optimization process for each typical node provided in one embodiment of the present application.

[0053] Figure 19 A comparison diagram of von Mises stress distribution before and after node optimization provided in one embodiment of the present application.

[0054] Figure 20 A 3D printing flowchart provided for one embodiment of the present application.

[0055] Figure 21 A block diagram of the module architecture of a lattice shell structure model generation device provided in one embodiment of the present application.

[0056] Figure 22 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

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

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

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

[0060] In one embodiment, reference Figure 1, provides a method for generating a lattice shell structure model, which includes steps S1 to S6.

[0061] Step S1: Modeling a lattice shell structure using a parametric modeling tool, and performing geometric form optimization on a basic grid shape using a geometric form optimization tool to generate a basic grid shape.

[0062] Step S2: using a mesh optimization tool to further optimize the optimized mesh shape to generate an optimized mesh shape.

[0063] Step S3: Optimizing the number of nodes and material usage of the lattice shell structure using an optimization algorithm.

[0064] Step S4: Perform structural simulation analysis on the optimized mesh shape in finite element analysis software to evaluate stress distribution and structural deformation.

[0065] Step S5: topology optimization software is used to perform topology optimization on the shape and stress conditions of the lattice shell nodes to improve material utilization.

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

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

[0068] In one embodiment, the material of the gridshell structure model can be wood. Gridshell wood structure construction is an innovative construction method that combines modern wood structure technology and gridshell structure. 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 attention of the construction industry. Gridshell structures, due to their light weight, high strength, excellent spatial bearing capacity and flexible design possibilities, have 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 construction model 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 approaches for the efficient design and fabrication of timber shell structures. Combining parametric modeling, structural simulation, multi-objective optimization, and 3D printing can improve timber utilization, reduce structural deadweight, and optimize construction costs while meeting building functional requirements. This example proposes a digital program-based design method for lattice shell timber structures: modeling, simulation, optimized design, and additive manufacturing, to generate a lattice shell timber structure model.

[0070] The following is a detailed description of the generation process of the lattice shell structure model in a "whole-part" manner. Figure 2 ,This embodiment uses wood and metal nodes as the materials of the lattice shell structure.

[0071] (1) The following is the generation and optimization of the overall lattice shell structure.

[0072] ① Perform parametric modeling of the lattice shell timber structure and use the geometric form-finding optimization tool Kangaroo to perform geometric form-finding optimization and generate the basic grid shape.

[0073] ② Use the mesh optimization tool Tri Remesh to optimize the mesh of the lattice shell structure and generate the optimized mesh shape.

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

[0075] ④ Greedy algorithm and multi-objective optimization (MOO) are used to optimize the number of nodes, wood usage and material waste of the lattice shell structure.

[0076] ⑤Perform structural verification on the optimized mesh in Abaqus software and evaluate the optimization results.

[0077] (2) The following is the optimization process of the “local” metal node.

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

[0079] ② Use the topology optimization software Inspire to perform topology optimization on the shape and stress conditions of the metal nodes of the lattice shell to improve material utilization.

[0080] ③ The SUBD tool based on the 3D modeling software Rhino is used to perform 3D reconstruction on the optimized results.

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

[0082] ⑤ Use additive manufacturing (3D printing) technology to manufacture optimized nodes, and use selective laser melting (SLM) or rapid casting technology for precision processing to ensure structural accuracy and strength while improving construction efficiency.

[0083] This application uses 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 to address the problems of existing grid shell structures, such as material waste, excessive structural weight, low construction efficiency, and difficulty in manufacturing complex nodes, thereby achieving full process optimization from design to manufacturing. Although a model is currently produced, this method is not limited to model production. The optimized grid shell structure design and manufacturing process is highly versatile and scalable, and can be flexibly applied to various scenarios and physical buildings, effectively improving material utilization, reducing structural weight, and improving construction efficiency. At the same time, it ensures the accuracy and strength of the structure, significantly improving the overall performance and economy of the grid shell structure, and providing an efficient, flexible and reliable solution for the construction industry.

[0084] In addition, although the wooden structure is used 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 adopted by it, such as parametric modeling, geometry and mesh optimization, finite element analysis, optimization algorithm, topology optimization and additive manufacturing, are highly versatile and adaptable, and can be widely used in the design and manufacture of grid shell structures of various materials, including but not limited to metal, concrete, composite materials, etc. These technologies can be flexibly adjusted and optimized according to the mechanical properties and processing characteristics of different materials, thereby realizing efficient design and precise manufacturing of grid shell structures of different materials, effectively solving the problems of material waste, excessive structural weight, low construction efficiency and difficulty in manufacturing complex nodes that are common in the prior art, significantly improving the overall performance and economy of the grid shell structure, and providing strong support for the application of diversified materials in construction and other related fields.

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

[0086] This embodiment uses wood as the lattice shell structure material to describe the generation process of the lattice shell structure material of this application, which includes the optimization process of the lattice shell wood structure. This application proposes a systematic "whole-local" two-level optimization method. Figure 2 , in order to achieve efficient and lightweight design of wooden lattice shell structures.

[0087] In the overall optimization stage, a parametric model of the basic form is first established, followed by form optimization reconstruction, with mesh density and mesh uniformity set as design variables. A genetic algorithm (GA) is used to simultaneously optimize three key indicators: total weight of the structure ( ), total number of nodes ( ) and wood usage ( Finite element analysis was used to evaluate the mechanical properties of each solution, and Pareto Front Analysis (PFA) was used to determine the optimal solution set. The optimal lattice shell shape output from this stage provided the boundary conditions for subsequent node design.

[0088] During the local optimization phase, topology optimization was performed using the Solid Isotropic Material with Penalization (SIMP) method within the variable density approach for key nodes identified during the overall optimization. Based on the stress analysis results from Abaqus, topology optimization was performed in Inspire with the optimization objective set to maximize stiffness. Von Mises stress analysis was then performed on the optimized nodes, taking into account 3D printing constraints (such as minimum wall thickness and overhang angle). The final output was a model file ready for 3D printing.

[0089] The innovations of this application are as follows: (1) The load conditions output by the overall optimization guide the design of local nodes, ensuring the mechanical consistency of the two-level optimization. (2) A closed loop is achieved from digital optimization to digital construction, and the optimization results can directly guide 3D printing construction. This systematic approach significantly improves the design efficiency of timber lattice shell structures and provides new ideas for the lightweight optimization design of timber lattice shell structures.

[0090] The overall design and optimization stages of the timber lattice shell are described in detail below.

[0091] Step A1: Parametric modeling and design variables. A parametric timber lattice shell model was established based on the Rhino-Grasshopper platform, and two design variables, geometric parameters and material parameters, were set.

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

[0093] Average grid size (2m): controls the overall grid density based on the reference length.

[0094] Mesh Iterations (0-50): Adjusts the positions of mesh vertices in each iteration to optimize the quality and uniformity of the mesh. As the number of iterations increases, the mesh becomes more uniform.

[0095] 2. Material parameters, including standard timber length, timber cross-sectional dimensions, and timber length range.

[0096] Standard timber length (4.0m): Consider transportation and processing restrictions.

[0097] Wooden square cross-sectional dimensions (40mm, 60mm, 80mm, 100mm).

[0098] Timber Length Range: Based on the requirements of relevant design specifications, in actual engineering applications, there is usually a certain empirical range of slenderness ratio values for common lattice shell structures and wood species. For components in general timber lattice shells, the slenderness ratio is usually controlled between 100 and 150. To improve structural safety, this application selects a more conservative slenderness ratio of 100 as the calculation basis in subsequent analysis. Combined with the actual length of the component, the required minimum cross-sectional dimension S is determined, referring to the following formula to meet the structural stability requirements.

[0099]

[0100] Step A2: Simulation and Optimization. To achieve lightweight structures and efficient allocation of material resources, this application develops an optimization strategy based on a combination of a greedy algorithm and multi-objective optimization. The overall process is shown in Figure 3. This method first determines the basic geometric input parameters and generates a structural mesh using the Tri Remesh tool. A greedy algorithm is then introduced to improve material utilization efficiency, ultimately achieving coordination and balance among multiple objectives within the multi-objective optimization framework.

[0101] Step A21: Greedy Algorithm and Multi-Objective Optimization. A greedy algorithm is a heuristic algorithm based on local optimal selection. Its core concept is to select the optimal solution under the current conditions at each step in the problem-solving process, hoping to obtain a global optimal solution. This method offers excellent computational efficiency and ease of implementation for solving resource allocation and combinatorial optimization problems.

[0102] In this study, a greedy algorithm is used to match grid segments with standard wood dimensions to maximize material utilization efficiency. Assume: (1) List : Standard timber length corresponding to different cross-sectional dimensions (4.0m).

[0103] (2) List : The length of the mesh segment generated after Tri Remesh optimization.

[0104] (3) In each iteration, select one from list B , to determine whether ,in .

[0105] (4) If satisfied, Remove from list B and continue to select the values in list B and accumulate them.

[0106] (5) If not satisfied, the wood usage Increase by 1 and update the wood material.

[0107] This process is repeated until list B is empty. Finally, the usage of each standard wood is output and the total usage of wood is calculated based on it. And the corresponding total mass of wood The total mass of the structural node is recorded as , then the total weight of the structure (optimization target FO1) is calculated as follows.

[0108]

[0109] in: Indicates the total weight of the structure; Represents the total mass of the node, which is obtained by adding up the masses of each node according to their quantity; Indicates the total weight of the standard timber used.

[0110] After the greedy algorithm completes and obtains preliminary results, a multi-objective optimization algorithm is further introduced to achieve collaborative optimization. In this process, the following two key variables are set as optimization variables.

[0111] (1) Tri Remesh tool iteration times: .

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

[0113] At the same time, three optimization objective functions are constructed: minimizing the total weight of the structure, minimizing the number of nodes, and minimizing the amount of wood used.

[0114] Minimize the total weight of the structure (optimization goal 1): FO1: .

[0115] Minimize the number of nodes (optimization goal 2): FO2: .in, It indicates the total number of nodes obtained after the mesh is generated, which directly reflects the complexity of the structure.

[0116] Minimize the amount of wood used (optimization goal 3): FO3: .

[0117] The comprehensive expression of the multi-objective optimization problem is as follows.

[0118]

[0119] In summary, the greedy algorithm-based material matching mechanism proposed in this application can rapidly optimize resource allocation at the structural component level. The multi-objective evolutionary optimization method based on a genetic algorithm effectively addresses the coupling and conflict issues between multiple objective functions, achieving an evolutionary process from local optimum to global optimum. This multi-algorithm collaborative mechanism not only improves the systematicity and stability of structural performance optimization but also effectively supports the goals of material conservation and sustainability in architectural design.

[0120] Step A22, optimize the solution strategy. Use the genetic algorithm to solve the problem. The parameter settings are: the population is set to 50 generations, 50 individuals per 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 calculations, the Pareto optimal solution set is obtained. Typical optimization results are as follows: Figure 4 shown.

[0121] Step A3, analysis of optimization results. After 2500 iterations, the Pareto solution set is obtained, as shown in Figure 5 The grid forms of 15 frontier solutions are shown in Table 1. The optimization target results corresponding to each optimization solution are shown in Table 1.

[0122]

[0123] Optimal solution for total structural weight: Figure 6 The figure shows the optimized solution A for the total weight of the structure. It exhibits a high number of nodes, a high mesh density, and a relatively regular appearance. However, the corresponding timber members are relatively short. This indicates that, when optimizing the total weight, although a denser mesh results in an increase in the number and mass of nodes, the total mass of the timber has a greater influence on the total weight calculation. Therefore, the program prefers to use shorter timber members with smaller cross-sectional dimensions to reduce their total mass and thus achieve a lower total weight.

[0124] Optimal solution for the number of nodes: Figure 7 The optimized solution B, with the minimum number of nodes, is shown. This solution significantly reduces the number of nodes, reduces the total amount of timber used, and results in a lower mesh density and a more uniform mesh. However, due to the longer mesh lines in this state, the program allocates timber with larger cross-sectional dimensions based on the slenderness ratio. This results in a higher total timber mass, and the reduction in total structural mass compared to the original configuration is less significant.

[0125] Optimal solution for wood usage: Figure 8Solution C, which minimizes the amount of wood used, is shown. It exhibits a relatively uniform but denser mesh. It can be seen that when less wood is used, the mesh tends to use shorter grid lines to accommodate more wood within the same raw material. This indicates a clear trade-off between wood use and mesh density, or the number of nodes. Future research can further explore optimization strategies to control the growth of nodes while maintaining efficient material utilization.

[0126] By analyzing the optimal solutions under the three optimization objectives, the solution with the optimal number of nodes was selected as the final optimization result. This solution was the most balanced among all the solutions. It achieved multi-objective coordinated optimization among the total weight of the structure, the total number of nodes, and the amount of timber used, while also achieving the highest degree of mesh uniformity.

[0127] Compared to the original structure, the total weight of the structure was reduced from 676.34 kg to 569.55 kg, a reduction of approximately 16%. The number of nodes was reduced from 49 to 30, a reduction of approximately 39%. The total number of timber beams was reduced from 53 to 46, a reduction of approximately 13%.

[0128] Step A4: Verification of optimization results based on Abaqus. To verify the performance of the optimization scheme under actual working conditions, Abaqus was used to perform static analysis on typical optimization results. The boundary conditions and load conditions were set to be consistent with the Grasshopper simulation, and two groups of optimization schemes (Scheme B and the baseline scheme) were simulated separately to analyze their stress distribution and maximum displacement. Figures 9-12 ,in, Figure 9 Represents the equivalent stress analysis diagram of the benchmark scheme, Figure 10 It represents the equivalent stress analysis diagram of the optimization scheme B. Figure 11 represents the displacement map of the benchmark solution, Figure 12 Represents the displacement map of optimization solution B.

[0129] Simulation results show that the maximum equivalent stress of optimized solution B is 0.4 MPa, far below the ultimate strength of wood along the grain (compressive strength is approximately 10 MPa, and tensile strength is approximately 8 MPa). Stress concentration in the joint area is significantly reduced. The maximum displacement of the optimized solution is 1.332 mm, while the maximum displacement of the optimized solution is 1.002 mm, a 25% reduction compared to the pre-optimization displacement. The maximum displacement of the structure meets the deformation limit specified in the specification.

[0130] In addition, a comparative analysis was conducted on cross-sectional utilization, node connection force direction, stability boundary, etc. The optimized structure was significantly better than the non-optimized scheme in comprehensive performance, verifying the feasibility and efficiency of the collaborative optimization design strategy.

[0131] The following describes in detail the topology optimization design and 3D printing manufacturing of the "local" nodes of the timber lattice shell.

[0132] Step B1: Localized node topology optimization. Building on the overall structural optimization, topology optimization is performed on key connection nodes within the timber lattice shell structure to further reduce material usage, improve structural efficiency, and optimize costs. The optimization objective is to maximize structural stiffness, achieving lightweight node structures. Metal additive manufacturing technology is employed to rapidly produce the optimized nodes. This shortens the transition period between design and production, significantly reducing the cost and time investment in modeling, mold modification, and mold manufacturing, which are typically associated with traditional design approaches.

[0133] Step B11: SIMP density interpolation method. Depending on the research object, topology optimization problems are generally divided into two categories: discrete structures and continuum structures. Since this step focuses on the nodes, we will focus on topology optimization methods for continuum structures. Common mathematical modeling methods for this type of optimization problem include homogenization, variable density methods, independent continuous mapping (ICM), and evolutionary structural optimization (ESO). The variable density method is currently the most widely used modeling approach due to its practicality. Its core concept is to use an interpolation function, a continuous variable with values between 0 and 1, to establish a relationship between element density and the corresponding material elastic modulus, assuming that the material stiffness is proportional to the element density. Among variable density methods, the solid isotropic penalty method (SIMP) is one of the most commonly used specific forms, and its interpolation expression is as follows.

[0134]

[0135] in, represents the relative density design variable of the unit; Represents the artificially set penalty coefficient to reduce the existence of interpolation intermediate variables; and They represent the elastic modulus of the material in the design area where the relative density is approximately 0 and 1, respectively. , in order to avoid the stiffness matrix singularity.

[0136] The elastic modulus interpolation formula is introduced by introducing the penalty coefficient The relative elastic modulus of a large number of materials It tends to 0, thus greatly reducing the number of units in the structure that are in an intermediate state of "half there, half there".

[0137] Step B12: Optimize the model with the goal of maximizing stiffness. The study targets nodes in a spatial structure subjected to static loads. The most common optimization objective for structural static topology optimization is to maximize the structure's static stiffness (i.e., minimize its flexibility). A topology optimization problem with the structure's volume fraction as a constraint can be described using the following equation.

[0138]

[0139] Among them, the design variables Represents the relative density of the unit after finite element discretization; The geometry representing the optimized design variables; Indicates the flexibility of the structure; 、 and They represent the overall stiffness, displacement and external load matrices of the structure respectively; and Respectively represent the actual volume of the structure with respect to the variable The function of and the constraint volume fraction value of the entire optimization problem; and They represent the upper and lower limits of the design variables respectively; i represents the number of units; and N represents the total number of units into which the structure is discretized.

[0140] Step B13: Optimize design tools. Inspire offers multiple functions, including geometric modeling, structural simulation, topology optimization, and manufacturing simulation. This provides an integrated workflow from modeling and analysis to design verification, effectively improving the efficiency and accuracy of structural optimization. This is particularly applicable to lightweight structural design requirements for manufacturing.

[0141] To further enhance the geometric expression and visual quality of the node model, this application incorporated Rhinoceros software (3D modeling software, abbreviated as Rhino) for post-processing. Its SUBD subdivision modeling tools were used to reconstruct and optimize the initial geometric model generated by Inspire. This resulted in a node model with greater structural expressiveness and design aesthetics while maintaining engineering feasibility. The collaborative use of Inspire and Rhino achieves an organic fusion of structural performance and formal aesthetics, expanding the design boundaries of engineering nodes within the context of digital fabrication. Figure 13 The integration process between topology optimization and modeling tools is demonstrated, and the collaborative mechanism between the two in model generation, geometric reconstruction and visualization expression is clarified.

[0142] Step B14, node analysis and optimization. The optimized structure is subjected to overall static analysis using Abaqus finite element analysis software. The results show that the connection nodes of the structure are mainly subjected to axial force, with little influence from bending moment and shear force. Since the optimization steps for each node of the structure are the same, only three typical nodes on the structure and the rods connected to them are selected as examples for demonstration. Figure 14 The figure shows the numbers of typical nodes and members in the structure, as well as the axial force distribution, which provides 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 perform topology optimization design on the node structure. Since the stress characteristics and geometric composition of each node are similar, node A is selected as a typical case for analysis. The original geometric model of node A is compared with the finite element model as shown in the figure. Figure 15 As shown in Figure 1, the node consists of a central cylinder and six connecting plates. The cylinder has an outer diameter of 102 mm, a height of 90 mm, and a wall thickness of 5 mm. The bolt holes in the node are critical load-bearing and connection areas. They are defined as "non-design areas" in topology optimization to preserve their original form. The remaining areas are considered "design areas" for topology optimization. External loads are applied as axial forces at the centers of the bolt holes.

[0144] Figure 16 The topology optimization results for maximizing stiffness were obtained with a weight constraint set to 30% of the total design space volume. It can be observed that within this volume constraint, a fully connected topology exhibits distinct structural characteristics (the dashed outline represents the original node configuration). Most of the original node's central cylindrical volume has been removed, while the connection regions between the connecting plates remain connected and interconnected. The extent of material removal from the connecting plates varies depending on their load-bearing conditions. Notably, the connecting plates, except for L17, experience significant weight reduction. However, the plate connected to the L17 member, due to its higher loads, exhibits lower mass reduction and exhibits a V-braced configuration after optimization. This result highlights the material distribution characteristics within the design region and reflects the underlying topological characteristics of the optimized node. However, due to the geometric discontinuity between the designated design and non-design regions, the optimized results cannot form a continuous, cohesive entity, hindering direct extraction of the geometric model. Therefore, it is necessary to redesign the structurally complete and reasonable node components based on the geometric topology of the optimized results.

[0145] After the initial optimization, the optimized node geometry model was imported into the Rhinoceros platform for further geometric reconstruction using the SUBD subdivision modeling tool. Subdivision surface modeling technology was initially widely used in the field of computer graphics (CG), primarily for modeling animated characters and virtual environments. In recent years, it has gradually demonstrated strong modeling capabilities in industrial design and mechanical manufacturing. Its application in the design of complex spatial structure nodes provides a new technical path for constructing high-quality structural nodes with continuous and smooth surfaces.

[0146] The reconstruction process begins with analyzing the geometric and topological characteristics of the non-design areas and the retained materials within the design areas. While preserving the key mechanical characteristics of the joints, subdivision modeling achieves a seamless transition between the design and non-design areas, effectively eliminating geometric discontinuities in the model. Furthermore, minor redundancies generated during the optimization process are abstracted from structural functions into primary load-bearing and connection elements. Subsequently, the model is appropriately constructed and aesthetically enhanced based on manufacturing requirements, such as incorporating standard bolt holes and chamfers to improve the joint's assembly performance and visual quality. Figure 17 The fusion effect of the non-design area and the topology optimization preserved structure in the geometric reconstruction process is demonstrated (where the dotted box represents the original shape of the node).

[0147] The final node model was physically printed using a metal additive manufacturing process, with a material density of 7.98 g / cm³. The reconstructed node A weighed approximately 1794.20 g, a 55% reduction compared to the original weight of 3955.27 g. This significantly improved material utilization and validated the practical application of the proposed "topology optimization - subdivision reconstruction - 3D printing" workflow in the lightweight design of complex nodes.

[0148] According to the same logic, other typical nodes of the structure are optimized. Figure 18 Shown are the basic topology optimization results and the final reconstructed shape of each typical node (where the dotted box represents the original shape of the node).

[0149] Step B15: Analyze optimization results. Under the same loading conditions, perform finite element analysis on the initial node design and the maximum stiffness optimized design to comprehensively evaluate the mechanical properties of the optimized node. This analysis was performed using Inspire software. Using the von Mises yield criterion and related plastic flow laws, the yield strength of the metal 3D printed part was set to 480 MPa and the ultimate tensile strength to 560 MPa. Boundary conditions remained the same as those of the initial node model.

[0150] Take node A as an example, Figure 19The von Mises stress distribution at node A under destructive loading is shown. In the stress analysis of the original design, the primary deformation and stress concentration occurred at the cylindrical interface, while a large area of low stress was observed at the connecting plate. The optimized node demonstrates reduced material usage and a more uniform overall stress distribution. The maximum stress remains below the material's yield strength (480 MPa), demonstrating that topology optimization aimed at maximizing stiffness can achieve more efficient material utilization under the same loading conditions.

[0151] Step B2: 3D printing of the node. Additive manufacturing (AM), also commonly referred to as 3D printing, is a digital manufacturing process based on computer-aided design (CAD) models. 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 produce using traditional subtractive manufacturing processes.

[0152] AM technology is applicable to the manufacturing needs of a wide range of materials, including polymers, ceramics, and metals. Metal additive manufacturing, in particular, has demonstrated broad development potential and application prospects in scientific research and industry due to its promising prospects for sustainable manufacturing.

[0153] The geometric forms of topologically optimized spatial structural nodes are often extremely complex, making conventional manufacturing methods inadequate. Additive manufacturing technology can efficiently and accurately meet the manufacturing requirements of these complex components, providing practical support for the engineering practice of structural optimization design and significantly enhancing its practical application capabilities.

[0154] This study employed Selective Laser Melting (SLM), a highly mature and reliable technology in metal additive manufacturing. The equipment used was an iSLM350DN SLM 3D printer manufactured by Zhongrui Company, with a layer thickness of 0.05 mm. Figure 20 The metal 3D printing process flow chart is shown.

[0155] The printing results under the "maximum stiffness" optimization goal showed that the surface finish and flatness of the node components were excellent, and the materials used also showed a high degree of density, verifying the feasibility and superiority of SLM technology in the manufacture of high-precision metal structure nodes.

[0156] A local optimization process, based on a "topology optimization-geometry reconstruction-process adaptation" approach, was used to optimize the nodes of timber lattice shell structures. By performing topology optimization design and subdivision modeling reconstruction on typical nodes, and integrating metal additive manufacturing technology to complete physical fabrication, a closed-loop local optimization workflow was established. Results showed that the average weight reduction of the optimized nodes was approximately 1944 g, a 40% reduction. Simultaneously, the von Mises stress increased by an average of approximately 20.3 MPa, maintaining structural performance within a safe range and achieving a more uniform stress distribution. This study validated the effectiveness of the proposed process in improving material utilization efficiency and alleviating the bulkiness of traditional node structures, breaking through the technical bottlenecks of limited node morphology and low material utilization in traditional designs. This method provides a new technical reference for improving the performance and manufacturing adaptability of modern timber lattice shell structures at the node design level, demonstrating promising engineering application potential and promotional value.

[0157] This application proposes a material-saving approach to lightweight design of timber lattice shell structures, establishing a multi-level collaborative process from overall morphological optimization to local node topology optimization. By integrating parametric modeling, structural simulation, multi-objective optimization, and 3D printing, the design significantly improves structural performance and material utilization efficiency, meeting the development needs of green buildings. In the overall optimization phase, multi-objective optimization is used to achieve coordinated control of the total structural weight, number of nodes, and timber usage. The optimal solution is selected, reducing the total structural weight by 16%, the number of nodes by 39%, and the total timber usage by 13%. In the local optimization phase, finite element analysis and topology optimization are combined to lightweight the design of key nodes, which are then efficiently manufactured using 3D printing. Optimization results show an average node weight reduction of 58.5%, demonstrating that the optimization results can achieve both structural performance and structural expression. Abaqus simulations verify the stability and resource efficiency of the optimization scheme under multiple working conditions. This method demonstrates significant advantages in material conservation, node control, and manufacturing adaptability, and has great potential for engineering application.

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

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

[0160] In one embodiment, reference Figure 21 , also provides a lattice shell structure model generation device, including: modeling and geometric form-finding optimization module, mesh optimization module, finite element analysis module, node number and material usage optimization module, topology optimization module and additive manufacturing module.

[0161] The modeling and geometric form-finding optimization module is used to model the lattice shell structure using parametric modeling tools, and to perform geometric form-finding optimization on the basic grid shape using geometric form-finding optimization tools to generate the basic grid shape.

[0162] The mesh optimization module is used to further optimize the optimized mesh shape using mesh optimization tools to generate an optimized mesh shape.

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

[0164] The node quantity and material consumption optimization module is used to optimize the node quantity and material consumption of the lattice shell structure using an optimization algorithm.

[0165] The topology optimization module is used to use topology optimization software to perform topology optimization on the shape and stress conditions of lattice shell nodes to improve material utilization.

[0166] The additive manufacturing module is 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.

[0167] The specific implementation content of each module can be found in the above definition of a lattice shell structure model generation method, which will not be repeated here.

[0168] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 22 As shown. The computer device includes a processor, memory, a communication interface, and a human-computer interaction interface (e.g., a combination of a display, keyboard, and mouse, or a touch screen) connected via a system bus. 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 via wired or wireless communication. Wireless communication can be achieved via Wi-Fi, a carrier network, NFC (near-field communication), or other technologies. The computer device implements the aforementioned lattice shell structure model generation method by loading and running a computer program.

[0169] Those skilled in the art will understand that Figure 22 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0170] In one embodiment, a computer-readable storage medium is further provided, on which a computer program is stored. 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 also provided, including a computer program / instruction, which implements the steps of the above-mentioned embodiment method when executed by a processor.

[0172] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

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 the 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; Specifically, a greedy algorithm and a multi-objective optimization based on a genetic algorithm are used to coordinate material conservation and lightweight design, wherein the greedy algorithm is used to match the grid segments with the standard wood dimensions, and the multi-objective optimization based on the genetic algorithm simultaneously optimizes three key indicators: the total weight of the structure, the total number of nodes, and the amount of wood used; Step S3 specifically includes: the greedy algorithm is used to match the grid segments with the standard wood dimensions to maximize material utilization efficiency; Step S31, using the list Indicates the standard wood length corresponding to different cross-sectional dimensions; Step S32, using the list Indicates the length of the mesh segment generated after Tri Remesh optimization; Step S33, in each iteration, from the list B Choose one , to determine whether ,in ; Step S34: If satisfied, Remove from list B, and continue to select the value of list B and accumulate; Step S35: If not satisfied, then the wood usage Increase by 1 and update the wood material; Loop through steps S31 to S35 until list B is empty; eventually output the usage of each standard wood block and calculate the total wood block usage based on this. And the corresponding total mass of wood ; The total mass of the structural node is recorded as , then the total weight of the structure, recorded as the optimization target FO1, is calculated as follows: ， in, Indicates the total weight of the structure; Represents the total mass of the node, which is obtained by adding up the masses of each node according to their quantity; Indicates the total weight of the standard timber used; After the greedy algorithm completes and obtains preliminary results, a multi-objective optimization algorithm is further introduced to achieve collaborative optimization; Step S4: Perform structural simulation analysis on the optimized grid shape in finite element analysis software to evaluate stress distribution and structural deformation. The optimal grid shell shape output in this stage provides boundary conditions for subsequent node design. 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: Use additive manufacturing technology to manufacture the optimized nodes, and use 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, wherein: In step S3, the node optimization of the lattice shell structure includes: Analyze the optimized mesh shape in finite element analysis software to obtain the stress conditions of the nodes; Use topology optimization software to optimize the shape and stress conditions of the nodes to improve material utilization; Perform 3D reconstruction of the optimized results based on 3D modeling software; Finite element analysis software is used to verify the stress on the nodes.

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

7. A lattice shell structure model generation device, characterized in that: include: The modeling and geometric form-finding optimization module is used to model the lattice shell structure using parametric modeling tools, and to optimize the basic grid shape using geometric form-finding optimization tools 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; Node quantity and material usage optimization module, used to optimize the node quantity and material usage of the lattice shell structure using optimization algorithms; Specifically, a greedy algorithm and a multi-objective optimization based on a genetic algorithm are used to coordinate material conservation and lightweight design, wherein the greedy algorithm is used to match the grid segments with the standard wood dimensions, and the multi-objective optimization based on the genetic algorithm simultaneously optimizes three key indicators: the total weight of the structure, the total number of nodes, and the amount of wood used; specifically, the greedy algorithm is used to match the grid segments with the standard wood dimensions, thereby maximizing the material utilization efficiency; step S31, using the list Indicates the standard wood length corresponding to different cross-sectional dimensions; Step S32, using the list Represents the length of the mesh segment generated after Tri Remesh optimization; Step S33, in each iteration, from the list B Choose one , to determine whether ,in ; Step S34, if satisfied, then Remove from list B, continue to select the value of list B to accumulate; step S35, if it is not satisfied, then the wood usage Increase by 1 and update the wood material; loop through steps S31-S35 until list B is empty; finally output the usage of each standard wood and calculate the total wood usage based on it And the corresponding total mass of wood ; The total mass of the structural node is recorded as , then the total weight of the structure, recorded as the optimization target FO1, is calculated as follows: ;in, Indicates the total weight of the structure; Represents the total mass of the node, which is obtained by adding up the masses of each node according to their quantity; Represents the total weight of the standard wood used; after the greedy algorithm completes and obtains preliminary results, a multi-objective optimization algorithm is further introduced to achieve collaborative optimization; The finite element analysis module is used to perform structural simulation analysis on the optimized grid shape in finite element analysis software to evaluate stress distribution and structural deformation. The optimal grid shell shape output in this stage provides boundary conditions for subsequent node design. Topology optimization module, which uses topology optimization software to optimize the shape and stress conditions of lattice shell nodes to improve material utilization; The additive manufacturing module is 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.

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

9. 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.

10. 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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