Lightweight design method of thin-walled structures based on additive manufacturing

Abstract

The present invention discloses a lightweight design method for thin-walled structures based on additive manufacturing, belonging to the field of additive manufacturing technology. The method comprises the following steps: S100, designing an initial structure based on function-performance mapping; S200, constructing an additive manufacturing process knowledge base and evaluating the adaptability of the additive manufacturing process; S300, performing finite element analysis of bidirectional coupling between process and structure; S400, collaborative optimization of process and structure driven by a multi-objective optimization algorithm; S500, verifying the manufacturing of characteristic parts and iteratively optimizing the design and process solutions; and S600, generating an integrated design and process solution containing both design parameters and manufacturing parameters. This application can address the technical problem of the lack of coordination between structural optimization objectives and manufacturing adaptability in existing technologies.

Description

Technical Field

[0001] The present invention relates to the technical field of additive manufacturing, and in particular to a lightweight design method for thin-walled structures based on additive manufacturing. Background Art

[0002] As high-end equipment sectors like aerospace and automotive manufacturing place ever-higher demands on structural performance, achieving lightweight structures, functional integration, and increased manufacturing flexibility have become key development directions. Thin-walled structures, due to their significant weight reduction and high structural efficiency, are widely used in complex load-bearing components such as engine cases, support frames, and heat exchange channels. These structures not only must meet stringent mechanical performance requirements but also face practical engineering challenges such as manufacturing difficulties and high dimensional stability requirements.

[0003] Topology optimization, a key approach for lightweight structural design, can generate optimal structural configurations that meet performance requirements by automatically controlling material distribution based on given boundary conditions and performance constraints. However, existing topology optimization methods mostly focus solely on mechanical properties, ignoring the practical constraints of the additive manufacturing process, such as minimum build dimensions, overhang angle restrictions, support removability, and the ability to clean powder within the component. These unaccounted-for process limitations often make it difficult to directly apply the optimization results to actual printing, or even make manufacturing impossible, leading to a significant disconnect between structural design and process implementation.

[0004] Furthermore, traditional design methods lack effective linkage between the structure generation phase and the manufacturing process, often sacrificing manufacturing feasibility for improved structural performance. The impact of manufacturing parameters on structural deformation, density, and micro-defects is often not factored into the design analysis. Complex, thin-walled structures are particularly susceptible to thermal stress accumulation, deformation, warping, and microcrack formation during additive manufacturing. Existing optimization approaches struggle to predict and control these manufacturing issues. Summary of the Invention

[0005] The present invention provides a lightweight design method for thin-walled structures based on additive manufacturing to solve the technical problem of lack of coordination between structural optimization objectives and manufacturing adaptability in the existing technology.

[0006] According to one aspect of the present invention, a method for lightweight design of thin-walled structures based on additive manufacturing is provided, comprising the following steps:

[0007] S100: Design the initial structure based on the mapping of function and performance. Obtain the mechanical performance indicators according to the functional requirements of the thin-walled structure. Based on the mechanical performance indicators, use the topology optimization method to perform the initial structural design and obtain the initial lightweight structural model that meets the performance constraints.

[0008] S200, additive manufacturing process knowledge base construction and additive manufacturing process adaptability assessment, constructing an additive manufacturing process knowledge base, performing process adaptability assessment on the initial lightweight structure model based on the additive manufacturing process knowledge base, and identifying areas and features that do not meet the additive manufacturing process requirements;

[0009] S300, process and structure bidirectional coupling finite element analysis, establishes a bidirectional coupling finite element simulation model between the structural model and the manufacturing process parameters, and analyzes the impact of process parameters on structural performance and manufacturing quality;

[0010] S400, a multi-objective optimization algorithm-driven collaborative optimization of process and structure, uses a multi-objective optimization algorithm to collaboratively optimize structural and process parameters with the goals of structural quality, performance stability, and manufacturing efficiency;

[0011] S500: Feature Part Manufacturing Verification and Iterative Optimization of Design and Process Solutions: Select representative feature structures for physical manufacturing verification to assess manufacturing defects, deformation levels, and performance to see if they meet design goals. Based on the verification results, iteratively optimize design and process parameters until the structural performance and manufacturing process requirements are met.

[0012] S600: Generate a design and process integration plan, which includes design parameters and manufacturing parameters.

[0013] Optionally, the topology optimization method in step S100 includes the following steps:

[0014] S110, Digital representation of additive manufacturing process constraints;

[0015] S120, Multi-objective topology optimization with weak coupling of force and heat;

[0016] S130, structural reinforcement and grille layout optimization design.

[0017] Optionally, step S110 includes the following steps:

[0018] S111, Analyze and identify process constraints that affect the forming success rate and structural quality during additive manufacturing, including self-supporting forming constraints and preparation process constraints;

[0019] Self-supporting forming constraints include overhang angle constraints and printing direction constraints; preparation process constraints include manufacturability constraints, machinability constraints, and connectivity constraints;

[0020] S112, digitize all constraints.

[0021] Optionally, step S120 includes the following steps:

[0022] S121; Determine two basic constraints: volume constraint and external interface constraint;

[0023] S122; constructing an objective function and normalizing it, converting multiple design objectives into a unified optimization objective function through dimensionless normalization processing;

[0024] S123; selecting and applying an optimization algorithm to obtain an optimal topology structure and transmission path, the optimization algorithm including at least one of a return matrix method, an adjoint method, and an MMA algorithm.

[0025] Optionally, in step S200, the additive manufacturing process knowledge base includes: a process classification module, a typical feature structure module, a material matching information module, an additive manufacturing process parameter module, and a post-processing and inspection module.

[0026] Optionally, step S400 includes the following steps:

[0027] S410, performing geometric restoration processing on the topology optimization results to obtain a structural solid model with engineering identifiability;

[0028] S420, constructing an analytical proxy model for multi-physics performance prediction based on the structural entity model;

[0029] S430: Based on the constructed analysis model, the external boundary and geometric size parameters of the structure are further jointly optimized.

[0030] Optionally, step S410 includes the following steps:

[0031] S411, uses non-uniform B-spline basis functions to perform continuous surface reconstruction on the topological structure boundary, achieving high smoothness modeling of complex topological contours;

[0032] S412, based on the point cloud density distribution of the topological result, an automatic clipping and boundary extraction algorithm is executed to identify and separate the effective load path and redundant areas of the structure;

[0033] S413, constructing the processed boundary data into a high-precision three-dimensional solid model to provide basic input for subsequent parametric expression and analytical modeling.

[0034] Optionally, step S420 includes the following steps:

[0035] Step S421, using sampling technology to effectively cover the design variable space;

[0036] Step S422, introducing an optimization algorithm and an adaptive agent modeling method to establish an approximate expression of the relationship between variables and responses;

[0037] Step S423 , parametric modeling is performed on key features of the structure, expressed in the form of variables and imported into the optimization framework;

[0038] In step S424, based on the parameter model, innovative structural feature templates of typical stator components are integrated to improve the functional adaptability and manufacturing rationality of the structure in specific application scenarios.

[0039] Optionally, step S430 includes the following steps:

[0040] S431, uses a compromise programming method to synergistically balance multiple optimization objectives of the structure;

[0041] S432, applying the Pointer intelligent optimization method, performs multiple rounds of rapid iteration on design variables to improve optimization efficiency and convergence stability;

[0042] S433, input multiple optimization constraints, including: multi-point loading criteria, shape boundary control rules, and size parameter restrictions.

[0043] Optionally, in step S500, the iterative optimization of the process plan includes the following steps:

[0044] S510: Clarify the requirements for printed parts, including mechanical performance requirements, geometric accuracy requirements, and functional integration. These requirements serve as the starting point of the process and drive the subsequent design and manufacturing strategy formulation;

[0045] S520, topology optimization, minimizes material usage while meeting structural performance targets and preliminarily determines material distribution and component layout;

[0046] S530, component layout, considers how parts are placed on the build platform, affecting residual stress, cooling paths, and powder usage;

[0047] S540, support structure design, including type, layout, and removability analysis;

[0048] S550, setting the core parameters of the manufacturing process, including controlling the energy input, printing path, printing spacing and layer thickness.

[0049] In summary, this application includes at least one of the following beneficial technical effects:

[0050] Starting in S100, this invention incorporates the concept of "function-performance mapping," translating the functional requirements of the structure into quantifiable mechanical performance indicators. During the topology optimization process, the additive manufacturing process knowledge base constructed in S200 is combined to assess process adaptability, eliminating non-manufacturable features from the initial design stage to ensure the structure is printable. Subsequently, process-structure bidirectional coupling finite element analysis is introduced in S300 to clarify the impact of manufacturing process parameters on structural deformation, residual stress, and density, enabling the coordinated prediction of manufacturing paths and structural performance. Through the multi-objective collaborative optimization algorithm implemented in S400, structural and manufacturing parameters are co-evolved within the same optimization framework, comprehensively balancing the trade-offs between structural quality, performance stability, and manufacturing efficiency, breaking the traditional single-objective optimization path. Furthermore, the manufacturing verification mechanism for representative feature parts in S500 generates feedback signals on manufacturing defects, deformation patterns, and material responses through physical verification, implementing a closed-loop iterative adjustment mechanism of optimization, verification, and re-optimization. Finally, in S600, an integrated solution encompassing structural design and manufacturing process parameters is generated, enhancing the ability to implement the virtual structure into physical components. The entire process not only makes the structural design results practically manufacturable, but also improves optimization accuracy and engineering reliability through data-driven continuous iteration, substantially solving the problems of insufficient coordination and path fragmentation between existing structural design and additive manufacturing processes, and realizing the transition from performance-oriented optimization to collaborative optimization driven by manufacturing constraints.

[0051] In addition to the above-described objects, features and advantages, the present invention has other objects, features and advantages. The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0053] Figure 1 This is a flow chart of the lightweight design method for thin-walled structures based on additive manufacturing of the present invention;

[0054] Figure 2 A schematic diagram of the initial structure designed based on the mapping of function and performance of the present invention;

[0055] Figure 3 Schematic diagram of the additive manufacturing process knowledge base of the present invention;

[0056] Figure 4 Schematic diagram of process and structure collaborative optimization driven by the multi-objective optimization algorithm of the present invention;

[0057] Figure 5 Schematic diagram of iterative optimization of the process scheme of the present invention. DETAILED DESCRIPTION

[0058] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, the present invention can be implemented in many different ways as defined and covered below.

[0059] The following is combined with Figure 1-5 This application is described in further detail.

[0060] The embodiments of the present application disclose a lightweight design method for thin-walled structures based on additive manufacturing.

[0061] Reference Figure 1 The lightweight design method for thin-walled structures based on additive manufacturing provided by this invention focuses on the synergistic integration of structural optimization design goals and manufacturing feasibility control paths, establishing a complete closed-loop process from structural design input, process adaptability analysis, process-structure coupling simulation, collaborative optimization, verification iteration, to solution output. Specifically, it includes the following steps:

[0062] S100: Initial design input and function-performance mapping modeling. The initial structure is designed based on the function-performance mapping. Mechanical performance indicators are obtained based on the functional requirements of the thin-walled structure. Based on these mechanical performance indicators, topology optimization methods are used for initial structural design to obtain an initial lightweight structural model that meets performance constraints.

[0063] S200: Constructing an additive manufacturing process knowledge base and evaluating additive manufacturing process adaptability. Constructing an additive manufacturing process knowledge base, performing a process adaptability evaluation on the initial lightweight structure model based on the additive manufacturing process knowledge base, and identifying areas and features that do not meet additive manufacturing process requirements.

[0064] S300, process and structure bidirectional coupling finite element analysis. This function establishes a bidirectional coupling finite element simulation model between the structural model and the manufacturing process parameters to analyze the impact of process parameters on structural performance and manufacturing quality.

[0065] S400, a multi-objective optimization algorithm-driven collaborative optimization of process and structure. Based on the multi-objective optimization algorithm, the structural and process parameters are collaboratively optimized with the goals of structural quality, performance stability, and manufacturing efficiency.

[0066] S500: Feature Part Manufacturing Verification and Iterative Optimization of Design and Process Solutions. Representative feature structures are selected for physical manufacturing verification to assess manufacturing defects, deformation, and performance to see if they meet the design objectives. Based on the verification results, design and process parameters are iteratively optimized until the structural performance and manufacturing process requirements are met.

[0067] S600: Generate a design and process integration plan. The integration plan includes design parameters and manufacturing parameters.

[0068] By introducing a collaborative control mechanism for structural design and manufacturing processes, a full-process system covering design input, process adaptability assessment, structure-process coupling simulation, multi-objective collaborative optimization, manufacturing verification, and closed-loop solution output has been established, demonstrating significant technical advantages. On the one hand, this method incorporates an additive manufacturing process knowledge base and manufacturability assessment at the initial stage of structural optimization, effectively avoiding manufacturing obstacles such as unprintable structures and irremovable supports found in traditional topology optimization solutions, thereby improving the engineering feasibility of the design solution. On the other hand, by establishing a bidirectionally coupled finite element model between process parameters and structural performance, a feedforward prediction of the effects of process variables such as laser power, scanning path, and layer thickness on structural stress, deformation, and defects is achieved, significantly enhancing the adaptive control capability of the structural design over the manufacturing process. Furthermore, this method utilizes a multi-objective optimization algorithm to achieve joint optimization of structural and process parameters. Through physical verification and feedback adjustment of representative feature parts, a closed-loop iterative mechanism for performance-manufacturing-verification is established, ultimately outputting an integrated manufacturing solution that unifies design and manufacturing parameters. This method not only achieves the coordinated optimization of thin-walled structures in terms of performance indicators such as lightweight, stiffness, and density, but also significantly improves the printing success rate, product consistency, and manufacturing efficiency in the additive manufacturing process.

[0069] The topology optimization method in step S100 includes the following steps: S110, digital representation of additive manufacturing process constraints; S120, force / heat weak coupling multi-objective topology optimization; S130, structural reinforcement and grid layout optimization design.

[0070] In step S100, after the initial design model is established based on the function and performance mapping, the structural topology optimization is further carried out in combination with the additive manufacturing characteristics. In order to ensure that the optimization results have actual manufacturing feasibility, the digital representation of the additive manufacturing process constraints is first introduced in the optimization modeling. This step extracts typical manufacturing constraints, including self-supporting forming constraints and preparation process constraints, and parameterizes and computates them. Among them, the self-supporting constraint stipulates that the angle between the surface and the printing direction in the structure shall not exceed the established limit value, so as to reduce the dependence on the support structure and improve the printing quality. The preparation constraints include minimum wall thickness, minimum pore size, structural connectivity and support removability requirements. Geometric modeling is performed through surface normal analysis, volume constraint mask, path accessibility analysis and other methods, and integrated into the topology optimization constraint items.

[0071] Subsequently, in step S120, a multi-objective topology optimization with weak force / heat coupling is performed. First, the basic constraints of the optimization problem are determined, including the volume constraints for controlling the overall material usage and the unchangeable area settings for the key installation interfaces. Next, a multi-objective function system is constructed, and the objectives such as maximizing the structural stiffness and minimizing the thermal deformation are dimensionlessly normalized and uniformly constructed as an optimization objective function. The sensitivity information of the design variables to the objective function is obtained by the return matrix method or the adjoint method, and the moving asymptote method is used as the iterative solution strategy to perform global optimization updates on the design variables, and finally obtain the optimal distribution path of the material in the design space. This optimization process can effectively identify the main load-bearing path and the key heat conduction path, and form a coordinated distributed structural topology initial model with reasonable structural continuity and efficient force / thermal performance distribution.

[0072] After obtaining the preliminary topological results, step S130 is entered to refine the design of reinforcement and grid layout of key structures. For the high stress concentration areas and large thin-walled areas generated in the topological optimization, the local stability is improved and the overall stiffness is enhanced by introducing directional reinforcement structures and lattice filling units. The reinforcement design determines the direction and position of the reinforcement based on the stress distribution diagram of the topological results, and the grid layout adopts a periodic or graded density control strategy to make the structure lightweight while maintaining good structural integrity and manufacturing continuity. In the above structural refinement process, the minimum forming size, smooth transition of the connection and the connectivity of the powder discharge channel are simultaneously considered to ensure that the optimization results can meet the process requirements of the additive manufacturing process, and provide a feasible geometric basis for the subsequent process adaptability evaluation and collaborative optimization.

[0073] Step S110 includes the following steps: S111, analyzing and identifying process constraints that affect the forming success rate and structural quality in the additive manufacturing process, including self-supporting forming constraints and preparation process constraints; self-supporting forming constraints include overhang angle constraints and printing direction constraints; preparation process constraints include manufacturability constraints, machinability constraints and connectivity constraints; S112, digitizing all constraint conditions.

[0074] In this embodiment, step S110 involves the digital characterization of additive manufacturing process constraints. This involves analyzing and identifying the primary process constraints that affect the AM process's build success rate and structural quality, and incorporating these constraints into the topology optimization model in a quantifiable and modelable manner. This effectively links these constraints with manufacturing feasibility early in the structural optimization process. Specifically, in step S111, the process constraints are systematically identified, combining the characteristics of a typical metal laser powder bed fusion (SLM) additive manufacturing process, and are divided into two dimensions: self-supporting forming constraints and preparation process constraints.

[0075] Self-supporting forming constraints mainly include overhang angle constraints and printing direction constraints. The overhang angle constraint is used to prevent the surface of the structure from collapsing or sintering failure during the printing process due to excessive overhang angles. The maximum unsupported forming angle is usually set to 45°. By analyzing the angle between the normal of each surface of the structure and the printing direction and establishing a surface vector field, all structural areas that do not meet the angle requirements can be identified and eliminated. The printing direction constraint is directly related to the scanning path, heat conduction direction and support settings. It needs to be determined in combination with the functional zoning and thermal flow path of the design structure, and embedded as an input parameter in the topology optimization solution framework.

[0076] Preparation process constraints include manufacturability constraints, machinability constraints, and connectivity constraints. Manufacturability constraints are used to prevent the appearance of detailed structures that are smaller than the minimum forming capability of the additive manufacturing process, ensuring that the structural boundaries, channels, and thin-walled areas are within the controllable accuracy range of the equipment. Machinability constraints are aimed at post-processing needs, such as heat treatment deformation repair, support removal, and functional surface finishing, requiring the structure to have the necessary clamping surfaces, accessible areas, and processing paths. Connectivity constraints are mainly used to ensure that the powder can flow and be cleaned during the forming process, especially for complex internal cavity structures. Closed dead spaces or closed spaces without vias should be avoided to improve the printing success rate and consistency of the finished product.

[0077] In step S112, the process constraints identified above are modeled and expressed digitally so that they can be directly embedded in the topology optimization calculation process. Specific methods include establishing a spatial geometric restriction mask, defining a minimum entity unit filter function, constructing an angle restriction field based on the printing direction, and building a structural accessibility map based on the feasibility of support removal. These parameters are integrated into the objective function and constraint function of topology optimization through Boolean operations, shape penalty functions, sensitivity weighting factors, etc., to achieve dynamic screening of unmanufacturable features and guide control of the structural optimization path, thereby maintaining real-time consistency between the structural design and the additive manufacturing process capabilities during the topology evolution process.

[0078] Step S120 includes the following steps: S121; determining two basic constraints: volume constraint and external interface constraint; S122; constructing an objective function and normalizing it, converting multiple design objectives into a unified optimization objective function through dimensionless normalization processing; S123; selecting and applying an optimization algorithm to obtain the optimal topology structure and transfer path, the optimization algorithm including at least one of the return matrix method, adjoint method and MMA algorithm.

[0079] In this embodiment, step S120 involves multi-objective topology optimization with weak coupling of force / heat, which aims to obtain an initial structural topology model with advantages of structural lightweight, stiffness and thermal performance through multi-physical field performance coupling optimization under the premise of considering the feasibility of additive manufacturing process. This step first sets the basic constraints of the optimization problem in S121, including two types of volume constraints and external interface constraints. Among them, the volume constraint is used to limit the maximum usage ratio of structural materials, for example, controlling the material usage rate to 30%~50% of the total volume of the design space to ensure the achievement of the lightweight goal; the external interface constraint is for functional areas that cannot be changed, such as mounting holes, flange surfaces, interface channels, etc., which are set as non-design domains, and fixed boundary properties are maintained during the topology optimization iteration process to ensure the assembly compatibility and functional continuity of the structure and other components of the system.

[0080] Subsequently, in S122, an optimization objective function is constructed and normalized to unify the dimensions of different performance indicators, facilitating collaborative processing in a multi-objective optimization framework. In view of the typical requirements of thin-walled structures in actual service, the present invention simultaneously considers multiple design objectives such as maximizing stiffness, minimizing thermal stress, and controlling thermal deformation. Dimensionless normalization is used to standardize each objective function, construct a unified objective expression, and set the weighting coefficients between the various performance indicators according to actual needs to achieve trade-off optimization between multiple objectives. Dynamic weighting or segmented penalty strategies can be introduced into the normalization function to enhance the optimization guidance capability for key performance objectives.

[0081] In S123, a suitable topology optimization algorithm is selected and applied to solve the above model to obtain the optimal material distribution path and structural configuration. During the optimization process, one or more combinations of mainstream topology optimization methods such as the return matrix method, the adjoint method, and the MMA method can be selected. The return matrix method is suitable for small-scale problems with rapid convergence, the adjoint method can be used for sensitivity analysis and gradient-guided optimization, and the MMA algorithm is preferably used in the present invention due to its high convergence efficiency for nonlinear objective functions and its advantages in solving large-scale variables. The SIMP material interpolation method is combined with the optimization algorithm, and the unit density is used as a continuous variable to control the distribution state of the material in the design space. During the iterative process, the penalty coefficient is adjusted to strengthen the differentiation ability of the "0-1" structure, and finally a three-dimensional structural prototype that takes into account mechanical stiffness, thermal conductivity and manufacturing processability is obtained. The topology optimization result generated in this step has a clear force / heat load transfer path, efficient material utilization efficiency and good structural continuity, which can effectively support subsequent geometric refinement design, manufacturing adaptability evaluation and process parameter collaborative optimization.

[0082] In this embodiment, step S200 involves constructing an AM process knowledge base and evaluating process suitability, aiming to provide systematic, accessible knowledge support for subsequent topological structure manufacturing feasibility analysis and optimization. The process knowledge base described in this step includes multiple functional modules, each used to store and manage core information resources related to AM processes, forming the foundational database for structure-process collaborative design.

[0083] The process knowledge base first includes a process classification module, which systematically categorizes available additive manufacturing processes according to their technical paths. These include, but are not limited to, laser selective melting, electron beam melting, directed energy deposition, and binder jetting. Each process path corresponds to different forming accuracy, material suitability, heat input characteristics, and typical defect modes. This module categorizes the process windows, forming mechanisms, and equipment constraints of each process, and is used to preliminarily determine the forming technology path suitable for the target structure.

[0084] The Typical Feature Structures module documents the morphological characteristics and manufacturing challenges associated with common structural elements used in additive manufacturing, including overhangs, ribs, thin-walled channels, bridges, and internally enclosed cavities. Each structural feature is accompanied by printability evaluation metrics, support strategies, print orientation sensitivity, post-processing methods, and typical failure modes. These can be used to evaluate local process adaptability after structural modeling or topology optimization.

[0085] The material matching information module stores information on commonly used materials for different process routes, including material grades, powder characteristics, thermophysical properties, printability levels, and typical defect susceptibility. By matching the functional zoning of the target structure with material suitability, collaborative process and material selection is achieved, avoiding forming failures, element segregation, or internal defects caused by improper material selection.

[0086] The additive manufacturing process parameter module contains recommended parameter ranges and empirical parameter models for various process paths, involving key variables such as laser power, scanning speed, layer thickness, scanning spacing, preheating temperature, platform temperature, and atmosphere control. This module can provide default parameter templates, or output a recommended process window based on process simulation and actual verification data for subsequent process-structure coupling modeling and collaborative optimization calculations. The post-processing and inspection module is used to manage the process chain information after the structure is printed, including the applicability judgment and parameter recommendations of post-processing processes such as hot isostatic pressing, stress relief annealing, support removal, surface polishing, and finishing. It also includes applicable standards for quality inspection methods, equipment requirements, and inspection node layout logic. This module provides a full-process technical basis for structural verification, process closed-loop control, and quality assurance.

[0087] By constructing the above-mentioned process knowledge base module and embedding it into the structural design process, a systematic process adaptability analysis of the model can be performed after the structural optimization is completed, potential manufacturing difficulty areas can be identified, and warnings can be issued for local structural features that do not meet the process window. This can also provide data support for subsequent coupled simulation, process optimization, and manufacturing solution generation, ensuring the manufacturing feasibility, cost rationality, and quality stability of the structural solution, and providing a knowledge base for additive manufacturing-oriented high-performance thin-walled structure design.

[0088] In this embodiment, step S300 aims to establish a bidirectional coupling analysis mechanism between process parameters and structural performance. Finite element simulations are used to quantify the impact of the manufacturing process on structural response and provide coupled data support for subsequent optimization. Unlike traditional structural optimization methods that rely solely on geometric models as input, this method incorporates key process variables from the additive manufacturing process, such as heat input, melt pool evolution, scanning path, and cooling rate, into the analysis model, achieving physical coupling between structure and process.

[0089] First, a finite element structural mesh is constructed based on a high-precision 3D solid model, and thermo-mechanical properties consistent with the material are assigned, including elastic modulus, thermal expansion coefficient, thermal conductivity, and specific heat capacity. The process parameter modeling component uses typical additive manufacturing equipment parameters as a foundation, setting input variables such as laser power, scanning speed, layer thickness, scanning strategy, and platform preheating temperature. Within the simulation environment, the process path generation module defines the layer-by-layer scanning sequence and laser trajectory, and heat source modeling is used to simulate the laser energy input behavior.

[0090] During the thermal analysis phase, the transient, unsteady-state heat conduction equation is used to solve the point-by-point temperature evolution during the printing process, recording thermal history information at key moments, such as peak temperature, temperature rise rate, and cooling gradient. Subsequently, in the mechanical analysis module, the thermal stress distribution, residual deformation, displacement accumulation, and overall component warping trends caused by the temperature field are solved based on thermo-elastic or thermo-plastic-elastic coupled constitutive relationships. This simulation can identify local stress concentrations, high deformation areas, and stress release paths caused by the manufacturing process, helping to mitigate the risk of performance degradation caused by thermal manufacturing processes during the structural design phase.

[0091] Furthermore, to embody a bidirectional feedback mechanism between process and structure, this invention supports incorporating structural responses into process path design and scanning strategy adjustments. For example, if simulations reveal excessive thermal stress in a specific area due to uneven cooling, local optimization adjustments can be made to the scanning sequence, laser power, or support structure settings in that area. If overall warping trends are present, feedforward compensation can be implemented using platform preheating strategies or scanning direction distribution.

[0092] Furthermore, this bidirectional coupling model supports defect prediction and process robustness analysis. By combining typical defect formation thresholds from the material database, it can predict defect-prone areas based on simulated temperature fields and adjust process parameters to avoid high-risk thermal history areas. Ultimately, this coupled analysis model not only provides structural response prediction capabilities but also establishes a quantitative "manufacturing-performance" mapping, providing the core data foundation for objective function construction, constraint setting, and parameter optimization in multi-objective collaborative optimization.

[0093] Step S400 includes the following steps: S410, performing geometric restoration processing on the topology optimization results to obtain a structural entity model with engineering identifiability; S420, constructing an analytical proxy model for multi-physics field performance prediction based on the structural entity model; S430, further jointly optimizing the structure's external boundary and geometric dimension parameters based on the constructed analytical model.

[0094] In this embodiment, step S400 involves the collaborative optimization of process and structure driven by a multi-objective optimization algorithm, aiming to achieve deep synergy between structural performance and manufacturing process through geometric reconstruction, proxy modeling and parametric optimization based on the topology optimization initial mold. First, in step S410, the topology optimization result is geometrically restored to obtain a three-dimensional solid structure with engineering recognizability and modeling continuity. Since the topology optimization output is usually a density distribution map or an irregular contour, it is necessary to convert it into a solid model that meets the modeling accuracy requirements through boundary extraction, smoothing and surface reconstruction technology. The present invention uses non-uniform rational B-spline (NURBS) functions to perform continuous surface fitting on the boundary area of ​​the topology result, uses point cloud density distribution to crop non-structural areas, and combines an automatic closing algorithm to generate a high-precision geometric model with a smooth contour and complete volume definition. The model has clear structural boundaries, connection surfaces and functional unit contours, and is suitable for subsequent parametric analysis and engineering calls.

[0095] After completing high-precision geometric modeling, step S420 is entered to construct an analytical proxy model for multi-physical field performance prediction based on the solid model. In order to improve the efficiency of structural optimization and avoid the computational burden of direct finite element simulation, the present invention introduces adaptive proxy modeling methods such as response surface model and Kriging model, generates training samples in the design variable space through advanced sampling techniques such as Latin hypercube sampling and Sobol sequence, and fits the mapping relationship between design variables and structural responses based on sample data. On this basis, an approximate model with rapid prediction capability is constructed to evaluate the influence of different structural morphologies and process parameter combinations on structural performance. In order to enhance the engineering applicability of the model, the template features of typical stator components are also introduced as the basic unit of structural expression in the modeling process to realize the fusion modeling of structural features and manufacturing rules.

[0096] After the construction of the analysis agent model is completed, step S430 is executed to jointly optimize the structural shape boundary and geometric size parameters to achieve a synergistic improvement in performance, quality and manufacturing efficiency. The present invention is based on a multi-objective optimization framework and introduces a compromise programming method to weigh and adjust multiple optimization objectives. For example, minimizing structural mass, maximizing stiffness, and minimizing manufacturing time are used as objective functions, and the optimal solution selection is achieved by setting a multi-objective weighted function or a Pareto frontier screening method. At the same time, a Pointer intelligent optimization method or a similar efficient algorithm based on a combination of gradient guidance and population evolution is used to perform iterative searches in a large-scale variable space to improve the optimization speed and convergence efficiency. During the optimization process, structural variables such as wall thickness distribution, aperture size, rib spacing, and process variables such as scanning speed, laser power, and powder coating thickness are all input into the optimization solver as adjustable parameters. Optimization constraints include multi-point load conditions, boundary smoothness control, structural continuity maintenance, manufacturing process window accessibility, thermal stress risk area constraints, etc.

[0097] The compromise programming method is a multi-objective optimization strategy that aims to establish a balance between multiple conflicting design objectives. By setting weights, priorities, or satisfaction functions for each optimization objective, the compromise programming method can transform the original multi-objective problem into an equivalent single-objective problem or solve the Pareto optimal solution set, thereby achieving a reasonable trade-off between different dimensions such as performance, cost, and manufacturability. It is widely used in scheme selection and prioritization in complex engineering design. The Pointer intelligent optimization method is a multi-objective topology and parameter collaborative optimization method based on the swarm intelligence evolution mechanism. This method integrates intelligent optimization strategies such as particle swarm, genetic algorithm, and agent modeling, and realizes global search and local convergence of design variables through multiple rounds of rapid iteration. It has strong adaptability and high search efficiency, and is particularly suitable for solving nonlinear, multi-peak, and multi-constraint problems. The Pointer method can effectively improve the optimization efficiency and solution stability in the structure-process coupling design process, and has significant engineering practical value.

[0098] Step S410 includes the following steps: S411, using non-uniform B-spline basis functions to perform continuous surface reconstruction on the boundary of the topological structure to achieve high-smoothness modeling of complex topological contours; S412, based on the point cloud density distribution of the topological results, executing automatic clipping and boundary extraction algorithms to identify and separate the effective load path and redundant areas of the structure; S413, constructing the processed boundary data into a high-precision three-dimensional solid model to provide basic input for subsequent parametric expression and analytical modeling.

[0099] In this embodiment, step S410 performs geometric restoration on the topology optimization results, resolving the issue of topology optimization initial models being unable to be directly used for CAD modeling, simulation analysis, and engineering manufacturing. Because topology optimization algorithms typically output a continuous density distribution or an approximate voxel grid, their boundary morphology often exhibits irregularities, jagged edges, or topological discontinuities. Therefore, geometric reconstruction is required to convert these into a high-precision 3D structural model that is recognizable, analyzable, and manufacturable.

[0100] In sub-step S411, non-uniform rational B-spline basis functions are used to reconstruct the continuous surface of the topological structure boundary. This method can achieve high-order expression of complex boundary curvature through parameter space mapping, and has strong local controllability and fitting accuracy. It is suitable for modeling and smoothing the edge transition area of ​​high-degree-of-freedom topological forms. In actual operation, the boundary point set of the topology optimization result is first sampled to extract the main path contour line and key cross-section points. Then, a control polygon mesh is constructed in the three-dimensional parameter domain, and the NURBS surface interpolation method is used to generate a smooth and closed structural shape. It ensures that the model surface has continuous first-order derivatives and local adjustability in the main path direction, providing a geometric basis for subsequent structural identification and analysis modeling.

[0101] In sub-step S412, an automatic cropping and boundary extraction algorithm is executed based on the point cloud density distribution of the topological results to further clean up and optimize the main contours of the structure. By setting a density threshold, low-density areas are identified and eliminated, and redundant areas in the material distribution that do not have load-bearing significance are separated from the model. A local sub-region partitioning method based on voxel grids or octrees is used to divide the topological region into blocks, and structural boundary lines are extracted using gradient recognition technology. Combined with the structural main path identification algorithm, the effective load transfer area in the structure can be further identified and automatically distinguished from the non-structural domain, achieving a simultaneous improvement in geometric modeling accuracy and structural function identification.

[0102] In sub-step S413, the processed boundary data is converted into a high-precision three-dimensional solid model. Voxel-recognizable solid geometry data is generated through three-dimensional modeling software or an integrated CAD kernel to realize the process of converting point sets and curved surfaces into closed bodies. During the modeling process, the closure of the structural surface and the continuity of the boundaries are guaranteed, and geometric errors such as surface splicing gaps, duplicate surfaces or normal anomalies are eliminated. The generated solid model not only has the main structural morphology and force flow characteristics of the topology optimization results, but also meets the geometric input requirements of finite element meshing, parametric feature extraction and process adaptability evaluation, providing a complete and high-fidelity modeling foundation for subsequent process-structure coupling analysis, multi-objective collaborative optimization and manufacturing verification stages.

[0103] Step S420 includes the following steps: Step S421, using sampling technology to effectively cover the design variable space; Step S422, introducing optimization algorithms and adaptive agent modeling methods to establish approximate expressions of the relationship between variables and responses; Step S423, parametrically modeling the key features in the structure, expressing them in the form of variables and importing them into the optimization framework; Step S424, based on the parametric model, integrating the innovative structural feature templates of typical static components to improve the functional adaptability and manufacturing rationality of the structure in specific application scenarios.

[0104] In this embodiment, step S420 is used to construct an analytical proxy model for multi-physics field performance prediction based on a high-precision structural model, aiming to significantly reduce the computational cost of full-order finite element analysis while maintaining the accuracy of performance evaluation, and provide rapid response capabilities for subsequent collaborative optimization. In step S421, in order to ensure that the design variable space is fully covered and representative sample data is obtained, advanced sampling technology is used to construct a training sample set. Specific methods include Latin hypercube sampling, Sobol sequence sampling or orthogonal experimental design, which are used to uniformly select sample points in the multidimensional design variable space to avoid uneven sample distribution or imbalanced aggregation. The sample points include structural geometric variables and process parameter variables. Each sample point needs to obtain its corresponding structural response results through simulation calculation, such as maximum stress, overall deformation, thermal gradient, displacement peak, etc.

[0105] In step S422, an optimization algorithm and an adaptive proxy modeling method are introduced based on the above sample set to construct an approximate expression between the design variables and the performance response. The proxy models used include but are not limited to response surface models, Kriging models, radial basis function networks, or support vector regression. During the model establishment process, a cross-validation method is used to evaluate the accuracy of the proxy model, dynamically adjust the sample point distribution and the complexity of the fitting function, and improve the generalization ability and predictive stability of the model. At the same time, combined with global optimization methods such as genetic algorithms and multi-objective particle swarm optimization algorithms, rapid optimization solutions are performed on the proxy model, significantly reducing the computing resources and time overhead required for traditional finite element iterations.

[0106] In step S423, to ensure that the optimized model has structural adjustability and manufacturing feasibility, key features in the topological geometric model are parametrically modeled. This process first decomposes the structural model, extracting typical design units such as circular holes, ribs, reinforcing ribs, and shell segments, and defining their variable parameters in the form of size, position, boundary contours, etc. Each characteristic variable is then embedded in the optimization framework, allowing the proxy model to achieve structural deformation, configuration adjustment, and functional adaptation by directly adjusting parameter values. This parametric modeling process ensures that the structural design has a high degree of controllability and modification flexibility, and is an important basis for achieving multiple rounds of optimization iterations and manufacturing adjustment feedback.

[0107] In step S424, innovative structural feature templates of typical stator components are further integrated based on the parametric model to improve the functional adaptability and manufacturing rationality of the structure in specific application scenarios. The templates include rib-shell integrated structures for bearing radial / axial composite loads, multi-channel variable cross-section configurations with local strengthening functions, and honeycomb-lattice hybrid filling structures that take into account both thermal protection and weight reduction. These structural templates are refined from historical design experience and process verification data, have good mechanical properties and forming stability, and are embedded in parametric features to achieve structural fusion with the current optimization model. Fusion methods include technical routes such as geometric Boolean combination, structural area substitution, and local feature migration to ensure that formability within the manufacturing window is maintained while achieving structural innovation.

[0108] Step S430 includes the following steps: S431, using the compromise programming method to coordinately balance multiple optimization objectives of the structure; S432, applying the Pointer intelligent optimization method to perform multiple rounds of rapid iterations on the design variables to improve optimization efficiency and convergence stability; S433, inputting multiple optimization constraints, including: multi-point loading criteria, shape boundary control rules, and size parameter restrictions.

[0109] In this embodiment, step S430 is used to further jointly optimize the outer shape boundary and geometric size parameters of the structure on the basis of analyzing the proxy model, so as to achieve a comprehensive balance between structural performance, manufacturing efficiency and process adaptability. In sub-step S431, a compromise programming method is first used to collaboratively weigh multiple optimization objectives. The optimization objectives in the present invention mainly include structural lightweight objectives, stiffness and strength stability objectives, thermal stress control objectives, manufacturing time minimization objectives, and energy consumption or powder utilization maximization objectives. By constructing a multi-objective objective function group and introducing weighted coefficients or establishing a Pareto front set, the multi-objective problem is converted into an equivalent single-objective programming problem using the weighted sum method, ε-constraint method or fuzzy satisfaction function, thereby establishing optimization priorities and adjustability between different performance indicators and obtaining a solution with the best global performance.

[0110] In sub-step S432, in order to improve the solution efficiency and convergence stability of the structure-process multivariable problem, the Pointer intelligent optimization method is introduced to perform multiple rounds of rapid iterations on the design variables. This optimization strategy combines population intelligence algorithm, local gradient-guided search and dynamic adaptive learning mechanism, and has strong global search capabilities and anti-trap convergence performance. By initializing the population of the initial distribution of the design variable space, a particle swarm, genetic algorithm or differential evolution algorithm is used for global exploration, and the high-potential area is encrypted and iterated in combination with the local gradient path. At the same time, the response surface function output by the proxy model is introduced as a fast evaluation submodule to evaluate the changing trend of the objective function and the distribution state of the solution space in each round of iteration, accelerate the convergence of the search path, and reduce computing resource consumption. During the optimization process, the search step size and mutation strategy are dynamically adjusted to achieve adaptive iterative depth control until the convergence tolerance or the maximum number of iterations is met.

[0111] In sub-step S433, multiple optimization constraints are input to regulate the feasibility region of the design solution to ensure that the final result not only has superior performance but also meets the requirements of engineering applications. The constraints include but are not limited to the following three categories: the first is the multi-point loading criterion, which requires the optimized structure to maintain stiffness and strength requirements under various working conditions, and sets upper and lower response limits for key positions; the second is the shape boundary control rule, which stipulates that the structural shape must maintain a certain geometric continuity, boundary smoothness and no drastic contour mutations to ensure machinability and aesthetics; the third is the size parameter restriction, which clarifies the size range of each key structural unit, including wall thickness, rib height, aperture, minimum connection size, etc., to prevent the appearance of detailed structures that are smaller than the resolution capability of additive manufacturing and ensure manufacturing feasibility. The above constraints are embedded in the optimization solver in mathematical form, and the design variables are screened for legitimacy and response penalties in each round of iteration to ensure that the final optimized solution is dual-compliant in terms of structural performance and manufacturing implementation path.

[0112] In step S500, the iterative optimization of the process plan includes the following steps: S510, clarifying the requirements for printing workpieces, including mechanical performance requirements, geometric accuracy requirements, and functional integration. These requirements serve as the starting point of the process and drive the subsequent design and manufacturing strategy formulation; S520, topology optimization, minimizing material usage while meeting structural performance goals, and preliminarily determining material distribution and component layout; S530, component layout, considering the placement of parts on the printing platform, affecting residual stress, cooling path and powder utilization rate; S540, support structure design, including type, layout, and removability analysis; S550, setting core parameters of the manufacturing process, including controlling energy input, printing path, printing spacing and layer thickness.

[0113] In this embodiment, step S500 implements multiple rounds of iterative optimization of the structural and process solutions through physical manufacturing and response performance testing of representative feature parts. This establishes a closed-loop control mechanism of structural design, manufacturing verification, and feedback adjustment, ensuring that the final structure not only possesses theoretically optimized performance but also practical manufacturability and service reliability. In sub-step S510, the key application requirements of the workpiece to be printed are first identified. These requirements primarily include three categories: mechanical performance requirements, such as safety factors under maximum working loads, equivalent stress limits, and fatigue life cycles; geometric accuracy requirements, such as dimensional tolerances, form and position tolerances, and surface roughness levels; and functional integration, such as the inclusion of internal cooling channels, guide holes, threaded connections, vibration damping zones, or socket-and-spigot fits. These requirements serve as the starting point for the entire design-to-manufacturing process, driving the subsequent structural modeling path and manufacturing strategy selection.

[0114] In sub-step S520, based on the aforementioned performance constraints and functional requirements, the topology optimization process is re-executed to further minimize material usage without compromising mechanical and functional performance. This optimization process combines the results of the previously analyzed proxy model with the manufacturing adaptability knowledge base to dynamically adjust volume constraint weights, load path settings, and manufacturing direction guidance functions to output a new round of optimized material distribution results. After the optimization is complete, the material retention area, cavity distribution, and rib orientation are preliminarily determined, resulting in a preliminary component layout that balances structural performance and printing rationality.

[0115] In sub-step S530, the layout design is optimized based on the spatial positioning of the components on the printing platform. This layout has a direct impact on component residual stress, cooling path distribution, and powder accumulation efficiency. This includes factors such as component orientation, tilt angle, distance from the platform, and partitioned printing strategy. By simulating and analyzing the thermal field distribution, gravity deformation trends, and support dependencies under different printing postures, the optimal printing posture is selected to control warping caused by thermal gradients or support effects. The density of component layout on the platform is also optimized to improve printing batch efficiency and powder utilization.

[0116] In sub-step S540, process design is carried out around the type and layout of the support structure. The support design not only determines the quality of component forming, but also affects the post-processing operability and process safety. Support structure types include various typical structures such as cone-column, tree-shaped, multi-point, and bridge-type structures, which need to be adapted and selected according to the local characteristics of the component. The support layout must take into account the force direction, heat dissipation path, minimum contact area, and removable path. By establishing a support removability analysis model, the geometric accessibility, contact strength, and processing residue of the support area are evaluated to ensure that all support structures can be safely and efficiently removed after manufacturing to avoid damage to the main structure or residual stress concentration.

[0117] In sub-step S550, the core parameters of the manufacturing process are set, including but not limited to laser power, scanning speed, scanning spacing, layer thickness, scanning strategy, powder laying thickness and platform preheating temperature. These parameters determine the energy input distribution, molten pool stability, interlayer fusion degree and forming density. Based on the previous process knowledge base data and structural response analysis results, this embodiment dynamically adjusts the above parameter combination, establishes a mapping relationship between structure, process and performance, and verifies whether the manufacturing accuracy, surface quality and internal integrity meet the design goals through physical testing of printed feature parts. If the verification results are out of tolerance or defective, they are automatically transmitted back to the S520 topology model, S530 component layout or S540 support setting for correction, forming a complete closed-loop optimization system.

[0118] In a specific embodiment, a certain type of aircraft engine bearing seat is designed using a thin-walled structure lightweight design method based on additive manufacturing.

[0119] 1) Initial structural design based on function-performance mapping: The bearing housing is a major load-bearing component of an aircraft engine, subject to aerodynamic, thermal, inertial, and rotor-transmitted vibration loads. Its complex structure and critical functions are key. The bearing housing integrates lubricating oil supply and return lines, cooling air lines, and emergency oil lines. A reliable bearing housing plays a vital role in mitigating eccentric shaft vibration and ensuring proper operation of the aircraft engine.

[0120] Based on the principles of gas dynamics and structural mechanics, functional requirements are quantified into specific mechanical performance indicators: the maximum stress of the bearing seat under the extreme working load shall not exceed 400MPa, and the overall deformation shall not exceed 0.3mm.

[0121] With lightweighting as the goal, a topology optimization algorithm based on additive manufacturing process constraints was employed to optimize the initial structure of the bearing housing while meeting the aforementioned performance constraints. Through multiple rounds of complex calculations, material was removed from non-critical stress-bearing areas within the casing, resulting in a preliminary model of the bearing housing with an optimized topology. Compared to conventional designs, this design achieved a 5% weight reduction.

[0122] 2) Construction of manufacturing process knowledge base and process pre-screening:

[0123] The topologically optimized bearing seat features a complex structure with extremely high requirements for dimensional accuracy, internal quality, and weight. The main structure is a thin-walled, reinforced structure: the outer wall is decorated with diamond-shaped weight-reducing grooves of varying depths, ranging from 0.5mm to 1mm. TC4 was selected as the bearing seat material, and the SLM process was chosen as the primary process due to its widespread application, high forming accuracy, and stable TC4 printing performance.

[0124] 3) Process-Structure Bidirectional Coupling Finite Element Simulation Analysis: Simufact finite element simulation software was used to establish a bidirectional process-structure coupling analysis model for the additive manufacturing process. This model simulated the effects of process parameters such as the laser scanning path, laser power, and scanning speed on the temperature and stress fields during the additive manufacturing process. This bidirectional coupling simulation revealed that during the manufacturing of the bearing seat, thermal stress concentration during additive manufacturing can cause minor deformations in certain corners and thin-walled areas of the casing.

[0125] 4) Multi-objective optimization algorithm-driven process-structure collaborative optimization: With the multi-objective goals of lightweighting the bearing housing, optimizing mechanical properties, and minimizing manufacturing costs, this collaborative optimization of the bearing housing was achieved using ANSYS software, utilizing advanced sampling techniques, efficient optimization algorithms, and adaptive surrogate models. For example, while maintaining strength and stiffness, the weight of the housing was further reduced by optimizing wall thickness and rib width. Simultaneously, adjustments to laser power and scanning speed were made to reduce thermal stress concentration and minimize the risk of bearing housing deformation. Optimizing layer thickness and scanning strategies also improved manufacturing efficiency and reduced costs. After multiple rounds of iterative calculations, a set of optimized design-process parameters was determined. The optimized bearing housing achieved a further 3% weight reduction compared to the initial design, while fully meeting all mechanical performance requirements.

[0126] 5) Feature Part Manufacturing Verification and Iterative Optimization of the Design-Process Solution: Typical feature parts were designed and physically manufactured to verify the optimized parameters from the previous step. Based on the verification results, the design-process parameters were fine-tuned and virtual manufacturing verification was performed again. After three rounds of iterative optimization, the virtual manufacturing verification results demonstrated that the bearing seat could be manufactured with high quality and efficiency under the pre-set additive manufacturing process conditions, with all performance indicators far exceeding the design requirements.

[0127] 6) Generate a design and process integration plan and quality control plan: After feature part manufacturing verification and iterative optimization, a detailed aircraft engine bearing seat design and process integration plan is generated. The plan includes:

[0128] (1) The optimized three-dimensional model of the bearing seat is marked with detailed dimensions of each part, layout, size and shape of the reinforcement ribs and other design parameters.

[0129] (2) Precise additive manufacturing process parameters, solid area (laser power: 200~400W, scanning speed: 800~1200mm / s, spot diameter: 90~120μm, powder thickness: 60~90μm, platform temperature: 120~200℃); support area (laser power: 200~350W, scanning speed: 900~1350mm / s, spot diameter: 90~120μm, powder thickness: 60~90μm, platform temperature: 120~200℃).

[0130] (3) Product quality testing methods and standards: such as furnace mechanical properties testing (GB / T228.1) and fluorescence testing (GJB2367A).

[0131] Through actual manufacturing verification, the aircraft engine bearing seat manufactured using the design method of the present invention has stable and reliable quality, the structural weight is reduced by 8% compared with traditional design and manufacturing methods, and the manufacturing efficiency is increased by 15%. It fully meets the high performance requirements of aircraft engines and provides strong support for the performance improvement and reliability enhancement of aircraft engines.

[0132] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A lightweight design method for thin-walled structures based on additive manufacturing, characterized by: The steps include: S100: Design the initial structure based on the mapping of function and performance. Obtain the mechanical performance indicators according to the functional requirements of the thin-walled structure. Based on the mechanical performance indicators, use the topology optimization method to perform the initial structural design and obtain the initial lightweight structural model that meets the performance constraints. S200, additive manufacturing process knowledge base construction and additive manufacturing process adaptability assessment, constructing an additive manufacturing process knowledge base, performing process adaptability assessment on the initial lightweight structure model based on the additive manufacturing process knowledge base, and identifying areas and features that do not meet the additive manufacturing process requirements; S300, process and structure bidirectional coupling finite element analysis, establishes a bidirectional coupling finite element simulation model between the structural model and the manufacturing process parameters, and analyzes the impact of process parameters on structural performance and manufacturing quality; S400, a multi-objective optimization algorithm-driven collaborative optimization of process and structure, uses a multi-objective optimization algorithm to collaboratively optimize structural and process parameters with the goals of structural quality, performance stability, and manufacturing efficiency; S500: Feature Part Manufacturing Verification and Iterative Optimization of Design and Process Solutions: Select representative feature structures for physical manufacturing verification to assess manufacturing defects, deformation levels, and performance to see if they meet design goals. Based on the verification results, iteratively optimize design and process parameters until the structural performance and manufacturing process requirements are met. S600: Generates a design and process integration plan, which includes design parameters and manufacturing parameters; The topology optimization method in step S100 includes the following steps: S110, Digital representation of additive manufacturing process constraints; S120, Multi-objective topology optimization with weak coupling of force and heat; S130, structural reinforcement and grid layout optimization design; Step S120 includes the following steps: S121; Determine two basic constraints: volume constraint and external interface constraint; S122; constructing an objective function and normalizing it, converting multiple design objectives into a unified optimization objective function through dimensionless normalization processing; S123; selecting and applying an optimization algorithm to obtain an optimal topology structure and transmission path, the optimization algorithm including at least one of a return matrix method, an adjoint method, and an MMA algorithm.

2. The method for lightweight design of thin-walled structures based on additive manufacturing according to claim 1, characterized in that: Step S110 includes the following steps: S111, Analyze and identify process constraints that affect the forming success rate and structural quality during additive manufacturing, including self-supporting forming constraints and preparation process constraints; Self-supporting forming constraints include overhang angle constraints and printing direction constraints; preparation process constraints include manufacturability constraints, machinability constraints, and connectivity constraints; S112, digitize all constraints.

3. The lightweight design method for thin-walled structures based on additive manufacturing according to claim 1, characterized in that: In step S200, the additive manufacturing process knowledge base includes: a process classification module, a typical feature structure module, a material matching information module, an additive manufacturing process parameter module, and a post-processing and inspection module.

4. The method for lightweight design of thin-walled structures based on additive manufacturing according to claim 3, characterized in that: Step S400 includes the following steps: S410, performing geometric restoration processing on the topology optimization results to obtain a structural solid model with engineering identifiability; S420, constructing an analytical proxy model for multi-physics performance prediction based on the structural entity model; S430: Based on the constructed analysis proxy model, the external boundary and geometric size parameters of the structure are further jointly optimized.

5. The method for lightweight design of thin-walled structures based on additive manufacturing according to claim 4, characterized in that: Step S410 includes the following steps: S411, uses non-uniform B-spline basis functions to perform continuous surface reconstruction on the topological structure boundary, achieving high smoothness modeling of complex topological contours; S412, based on the point cloud density distribution of the topological result, an automatic clipping and boundary extraction algorithm is executed to identify and separate the effective load path and redundant areas of the structure; S413, constructing the processed boundary data into a high-precision three-dimensional solid model to provide basic input for subsequent parametric expression and analytical modeling.

6. The method for lightweight design of thin-walled structures based on additive manufacturing according to claim 5, characterized in that: Step S420 includes the following steps: Step S421, using sampling technology to effectively cover the design variable space; Step S422, introducing an optimization algorithm and an adaptive agent modeling method to establish an approximate expression of the relationship between variables and responses; Step S423 , parametric modeling is performed on key features of the structure, expressed in the form of variables and imported into the optimization framework; In step S424, based on the parameter model, innovative structural feature templates of typical stator components are integrated to improve the functional adaptability and manufacturing rationality of the structure in specific application scenarios.

7. The method for lightweight design of thin-walled structures based on additive manufacturing according to claim 6, characterized in that: Step S430 includes the following steps: S431, uses a compromise programming method to synergistically balance multiple optimization objectives of the structure; S432, applying the Pointer intelligent optimization method, performs multiple rounds of rapid iteration on design variables to improve optimization efficiency and convergence stability; S433, input multiple optimization constraints, including: multi-point loading criteria, shape boundary control rules, and size parameter restrictions.

8. The method for lightweight design of thin-walled structures based on additive manufacturing according to claim 7, characterized in that: In step S500, the design parameters and process parameters are iteratively optimized based on the verification results, specifically including the following steps: S510: Clarify the requirements for printed parts, including mechanical performance requirements, geometric accuracy requirements, and functional integration. These requirements serve as the starting point of the process and drive the subsequent design and manufacturing strategy formulation; S520, topology optimization, minimizes material usage while meeting structural performance targets and preliminarily determines material distribution and component layout; S530, component layout, considers how parts are placed on the build platform, affecting residual stress, cooling paths, and powder usage; S540, support structure design, including type, layout, and removability analysis; S550, setting the core parameters of the manufacturing process, including controlling the energy input, printing path, printing spacing and layer thickness.

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