Method and electronic device for the optimization of an aeronautical structure for directed energy deposition additive manufacturing

Abstract

The application relates to an aviation structure optimization method and electronic equipment for directed energy deposition additive manufacturing, comprising dividing a geometric space into a design domain and a non-design domain and constructing a digital twin model containing material properties and a multi-degree-of-freedom dynamic inertial force field; adopting a multi-stage topology iteration strategy, performing overall stiffness maximization optimization according to a SIMP method, introducing a Mises stress threshold and displacement constraint to guide material migration to a stress concentration area, combining process physical boundary conditions to detect an inclination angle of a overhanging surface and correct self-supporting and implement curvature continuous smoothing processing to generate a solid model, and outputting a manufacturing solid model and forming path data under the conditions of meeting support-free manufacturing and strength stiffness constraint conditions through grid optimization, virtual slicing and support generation simulation verification. According to the execution process of physical field definition, topology optimization, performance correction, entity reconstruction and simulation verification, evolution from discrete mathematical grid to continuous engineering entity is realized.

Description

Technical Field

[0001] This application relates to the field of aerospace equipment structural optimization technology, and more specifically to a method for optimizing aerospace structural components, electronic devices, and computer-readable storage media for directional energy deposition additive manufacturing. Background Technology

[0002] In the modification and rapid development of modern aero-engineering test aircraft, load-bearing structural components such as airborne equipment mounting brackets face extremely stringent spatiotemporal and load constraints. On the one hand, such airborne equipment is typically installed in irregular, narrow spaces such as the nose fairing and must withstand extreme inertial overloads during flight maneuvers, placing extremely high demands on the structure's stiffness and strength. On the other hand, test aircraft modification tasks are characterized by small batches and short cycles, requiring manufacturing processes to have extremely high responsiveness. Traditional subtractive manufacturing methods combining die forging and CNC milling are limited by physical bottlenecks such as long mold opening cycles and poor tooling accessibility, making it difficult to manufacture irregularly shaped load-bearing structures that meet extreme lightweight requirements and possess complex topological configurations in a short time. Therefore, design based on metal additive manufacturing has become a key path to solve this contradiction.

[0003] However, when using existing commercial topology optimization software for design, engineers face the practical dilemma that algorithm results cannot be directly applied. Although topology algorithms based on the variable density method can find the theoretically optimal distribution of materials, the default output of the software is usually a discrete STL mesh model. These models are filled with jagged noise and irregular patches, which are essentially mathematical approximations rather than engineering solid solutions. Such coarse mesh models cannot be directly used for subsequent precise finite element secondary verification, nor can they be recognized by CAM software for CNC programming or 3D printing path planning. Engineers often have to spend a lot of time manually reverse-engineering based on the mesh outline. This process is not only inefficient, but also prone to loss of the original optimized mechanical characteristics due to human error, forming a gap between conceptual design and manufacturing model.

[0004] Furthermore, for Directed Energy Deposition (DED) processes suitable for large-size forming, general algorithms lack physical perception of the rheological properties of the molten pool. The DED process is extremely sensitive to the overhang angle. When the structure growth angle is less than the critical threshold, gravity will cause the molten metal to flow and collapse. The topology branches generated by existing software often contain a large number of horizontal cantilevered arms that cannot be self-supported. If printed directly, it will lead to forming failure. However, adding supports will cause the parts to be scrapped due to the difficulty of removing supports in the DED process. Summary of the Invention

[0005] This application provides an optimization method and electronic device for aerospace structural components in directional energy deposition additive manufacturing (DOD). It can deeply integrate the dual verification of stiffness and strength under multiple working conditions with the self-supporting physical constraints of the DOD process, and use parametric modeling techniques such as PolyNURBS to achieve rapid transformation from coarse meshes to smooth solids. By integrating digital twin modeling, multi-objective topology iteration, and parametric geometric reconstruction techniques, it solves the technical problem that traditional design cannot simultaneously achieve extreme lightweighting and manufacturability of the DOD process.

[0006] In a first aspect, this application provides an optimization method for aerospace structural components in directed energy deposition additive manufacturing. The method includes: dividing the geometric space of the aerospace structural component into a design domain and a non-design domain according to the target functional attributes; constructing a digital twin model and introducing material properties and a multi-degree-of-freedom dynamic inertial force field into the digital twin model; performing one or more stages of topology iteration on the digital twin model to generate a solid model of the aerospace structural component, wherein the topology iteration includes at least maximizing the overall structural stiffness using a solid isotropic material penalty method, guiding the material to migrate to the stress concentration area using a Mises stress threshold and displacement constraints, detecting the tilt angle of a preset overhanging surface according to the physical boundary conditions of the directed energy deposition additive manufacturing process, correcting the target tilt angle region into a self-supporting curved surface structure, and simultaneously performing curvature continuous smoothing processing on the preset region; and performing mesh optimization on the solid model and using virtual slicing and support generation simulation verification to output solid manufacturing data that meets the conditions for unsupported manufacturing of directed energy deposition.

[0007] In one alternative embodiment of the first aspect, dividing the geometric space into design domains and non-design domains based on the target functional attributes includes: dividing the load input end of the aerospace structural component into a first non-design domain and establishing a solid retention ring zone in the hole location region of the load input end; dividing the boundary constraint end of the aerospace structural component into a second non-design domain and applying full-degree-of-freedom or preset-degree-of-freedom constraints to it; dividing the functional interference sensitive region of the aerospace structural component into a third non-design domain and applying absolute geometric exclusion constraints; constructing a continuous closed design domain outside the non-design domains and setting the material distribution variable within the design domain as a continuous density field; wherein the division of each non-design domain is performed through parametric modeling and Boolean operations, and each non-design domain maintains its geometric characteristics unchanged throughout the entire topology optimization and geometric reconstruction process.

[0008] In one alternative embodiment of the first aspect, the introduction of material properties and a multi-degree-of-freedom dynamic inertial force field into the digital twin model includes: setting multiple ultimate overload directions and amplitudes and converting acceleration into inertial force according to Newton's second law, and converting the inertial force into distributed volume forces acting on the structure; wherein the multiple ultimate overload directions include at least ultimate acceleration directions along the flight direction, in the opposite direction, vertically, and laterally; and, based on the spatial offset of the equipment's center of mass relative to the structural installation reference, using coordinate transformation to decompose the global inertial load into bending moment, shear force, and axial force components in the local coordinate system, and superimposing the structural self-weight field to form a composite load boundary.

[0009] In one alternative embodiment of the first aspect, the topological iteration of the digital twin model in one or more stages includes: employing a solid isotropic material penalty method and taking the maximization of the overall structural stiffness as the objective function, performing a first-stage topological iteration under preset constraints to generate a first topological configuration; introducing a Mises stress threshold and displacement constraints to the first topological configuration and promoting the material to migrate to the stress concentration region through a second-stage topological iteration to generate a second topological configuration; and, based on the physical boundary conditions of the directional energy deposition additive manufacturing process, performing tilt angle detection on the overhanging surface of the second topological configuration and correcting regions with tilt angles less than a preset self-supporting threshold relative to the construction direction into self-supporting curved surface structures, while performing curvature continuous smoothing processing on topological intersection nodes and sharp turning regions to generate a solid model.

[0010] In one alternative of the first aspect, the method of employing a solid isotropic material penalty method and taking the maximization of the overall structural stiffness as the objective function, and performing a first-stage topology iteration under preset constraints to generate a first topology configuration, includes: applying nonlinear suppression to the material properties of each finite element in the design domain through a penalty factor; setting the objective function as maximizing the overall structural stiffness and simultaneously applying volume retention rate constraints and minimum member size constraints; and performing the first-stage topology iteration and using the strain energy density field as the material migration criterion to identify and retain the force transmission path with the highest strain energy density to form the first topology configuration.

[0011] In one alternative of the first aspect, the step of introducing a Mises stress threshold and displacement constraints into the first topological configuration and promoting the migration of material to the stress concentration region through a second-stage topological iteration to generate a second topological configuration includes: introducing strength constraints and displacement constraints as criteria in the first topological configuration; adjusting the penalty factor and manufacturing constraint parameters of the topology optimization solver; performing a second-stage topological iteration on the material distribution to promote the migration of material to the stress concentration region and suppress the generation of discrete fine branches, thereby forming a second topological configuration of a continuous plate shell or robust rod structure, reducing local overheating of the molten pool and improving structural stiffness.

[0012] In one alternative embodiment of the first aspect, the step of detecting the tilt angle of the overhanging surface of the second topological configuration based on the physical boundary conditions of the directional energy deposition additive manufacturing process includes: performing global overhang angle detection on all topological branches of the second topological configuration based on the physical boundary conditions of the directional energy deposition additive manufacturing process, and identifying regions where the tilt angle of each topological branch relative to the construction direction is less than a preset self-supporting threshold as rheological instability regions; adjusting the growth direction of the structural branches in the rheological instability regions by using a strategy of adjusting parametric surface control points or local geometric stretching, so that the surface tilt angle is greater than the preset self-supporting threshold; the step of correcting regions where the tilt angle relative to the construction direction is less than the preset self-supporting threshold into self-supporting surface structures includes: using a split construction strategy to convert regions that cannot be constructed into self-supporting surface structures. The structural features that meet the self-supporting conditions are decoupled into independent printed substructures and weld bevels are reserved through local modification to generate a third topological configuration. Furthermore, the curvature continuity smoothing of topological intersection nodes and sharp turning areas to generate a solid model includes: using a large-radius rounding method to smooth all sharp corners of the openings on the wall of the third topological configuration, topological branch intersection nodes, and high-curvature abrupt change areas, thereby eliminating stress concentration sources and thermal effects; performing Boolean summation on the reconstructed main surface of the third topological configuration and the solid features of the non-design domain, and using a mesh processing algorithm to identify and stitch non-manifold edges and open boundaries; and using a subdivision algorithm and Laplace smoothing method to improve mesh resolution, thereby forming and outputting a complete and closed parametric solid model.

[0013] In one alternative of the first aspect, the step of decoupling structural features that cannot meet the self-support conditions through local modification into independent printed substructures and reserving welding bevels includes: determining structural features in the second topology that still cannot meet the self-support forming conditions after local modification as features that cannot be directly formed; and decoupling the features that cannot be directly formed from the main structure of the second topology to construct independent printed substructures, and designing welding bevels, overlapping interfaces or mechanical connection features between the main structure and the independent printed substructures.

[0014] In one optional embodiment of the first aspect, the step of mesh optimization of the solid model and simulation verification using virtual slicing and support generation to output solid manufacturing data that meets the conditions for unsupported manufacturing of directional energy deposition includes: re-meshing the solid model using additive manufacturing preprocessing software and performing virtual slicing and support generation simulations, while performing linear static analysis and modal analysis under a multi-degree-of-freedom dynamic inertial force field; determining whether the maximum Mises stress of the solid model structure under any ultimate load is lower than the safety threshold of the material yield strength, and whether the absolute displacement of key functional points is within a preset displacement threshold range; if any indicator is not satisfied, parameter adjustment is performed according to the judgment and identification results, wherein the parameter adjustment includes adjusting one or more of the following parameters: volume constraint, penalty factor, local material density, or stiffness weight parameter; if all indicators are satisfied, the manufacturing solid model and directional energy deposition additive manufacturing process path data are output.

[0015] In a second aspect, this application provides an electronic device, comprising: a processor adapted to execute a computer program; and a computer-readable storage medium storing a computer program, wherein when executed by the processor, the computer program implements the method described in any one of the first aspects.

[0016] Thirdly, this application provides a computer-readable storage medium for storing a computer program that, when run on a computer, causes the computer to perform the method described in any one of the first aspects.

[0017] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit this application. Attached Figure Description

[0018] The accompanying drawings, which are incorporated herein and form part of this specification, illustrate one or more embodiments of the present application and, together with the description, serve to explain the principles of the present application and to enable those skilled in the art to make and use the present application.

[0019] Figure 1 This is a first flowchart illustrating an exemplary method for optimizing aerospace structural components according to some embodiments of this application.

[0020] Figure 2 This is a second flowchart illustrating an exemplary method for optimizing aerospace structural components according to some embodiments of this application.

[0021] Figure 3 This is a schematic diagram of an exemplary aerospace structural component according to some embodiments of this application.

[0022] Figure 4This is a flowchart illustrating an exemplary method for introducing material properties and multi-degree-of-freedom dynamic inertial positions according to some embodiments of this application.

[0023] Figure 5 This is an exemplary initial model and iterative optimization model baseline finite element analysis result diagram based on some embodiments of this application.

[0024] Figure 6 This is a flowchart illustrating an exemplary topology iterative optimization method according to some embodiments of this application.

[0025] Figure 7 This is a schematic diagram of an exemplary first topological configuration according to some embodiments of this application.

[0026] Figure 8 This is a schematic diagram of an exemplary second topology configuration according to some embodiments of this application.

[0027] Figure 9 This is a flowchart illustrating an exemplary entity model generation method according to some embodiments of this application.

[0028] Figure 10 This is a schematic diagram of an exemplary third topology configuration according to some embodiments of this application.

[0029] Figure 11 This is a flowchart illustrating an exemplary optimization and simulation verification method according to some embodiments of this application.

[0030] Figure 12 This is a schematic diagram of an exemplary optimization model and a design model according to some embodiments of this application; wherein, (a) is an optimization model and (b) is a design model.

[0031] Figure 13 This is a schematic diagram comparing the maximum Mises stress of an exemplary optimization model and a design model according to some embodiments of this application; wherein, (a) is the optimization model and (b) is the design model.

[0032] Figure 14 This is a schematic diagram comparing the displacements of an exemplary optimization model and a design model according to some embodiments of this application; wherein, (a) is the optimization model and (b) is the design model.

[0033] Figure 15 This is a schematic diagram of an exemplary feature simulation component according to some embodiments of this application.

[0034] Figure 16 This is a stress-strain diagram of an exemplary feature simulation element according to some embodiments of this application.

[0035] Figure 17 This is a schematic diagram of the module connections of an exemplary aerospace structural component optimization device according to some embodiments of this application.

[0036] Figure 18 This is a schematic diagram of the structure of an exemplary electronic device according to some embodiments of this application. Detailed Implementation

[0037] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments may be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, the description of these embodiments is intended to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to provide a deeper understanding of embodiments of this application.

[0038] To facilitate understanding of the technical solutions provided in this application, the relevant terms are explained below.

[0039] It should be noted that the terminology used in the implementation section of this application is only for explaining the embodiments of this application and is not intended to limit this application.

[0040] For example, the term "and / or" in this article simply describes the relationship between related objects, indicating that three relationships can exist. For instance, A and / or B can represent: A alone, A and B simultaneously, and B alone. The term "at least one" simply describes the combination relationship of listed objects, indicating that one or more can exist. For instance, at least one of the following: A, B, C can represent the following combinations: A alone, B alone, C alone, A and B simultaneously, A and C simultaneously, B and C simultaneously, and A, B, and C simultaneously. The term "multiple" refers to two or more. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0041] For example, the term "correspondence" can indicate a direct or indirect correspondence between two things, or an association between them, or a relationship of instruction and being instructed, configuration and being configured, etc. The term "instruction" can be direct, indirect, or indicate an association. For example, A instructing B can mean A directly instructs B, for example, B can be obtained through A; it can also mean A indirectly instructs B, for example, A instructs C, B can be obtained through C; or it can mean an association between A and B. The terms "predefined" or "preconfigured" can refer to pre-stored codes, tables, or other relevant information that can be used for instruction in the device, or it can refer to something agreed upon by a protocol. "Protocol" can refer to standard protocols in the field. The term "when..." can be interpreted as "if," "when," or "in response," etc. Similarly, depending on the context, the phrases "if determined" or "if detected (the condition or event of the statement)" can be interpreted as "when determined" or "in response to determined" or "when detected (the condition or event of the statement)" or "in response to detected (the condition or event of the statement)" and similar descriptions. The terms "first," "second," "third," "fourth," "A," "B," etc., are used to distinguish different objects, not to describe a specific order. The terms "includes" and "has," and any variations thereof, are intended to cover non-exclusive inclusion.

[0042] In current aircraft test aircraft modification and rapid development tasks, the design of load-bearing structural components faces a profound contradiction between ultimate performance requirements and manufacturing process constraints. Although commercial topology optimization software is widely available, a significant gap still exists between software calculation results and manufacturable physical parts in practical engineering applications. First, traditional topology optimization algorithms are based on solving mathematical stiffness matrices, and their default output is often a discrete STL mesh model with jagged noise on the surface. This coarse geometry is essentially a mathematical approximation, which cannot be directly recognized by CAM software for CNC programming or additive manufacturing path planning, and it also cannot meet the stringent requirements of aerospace structures for surface finish and fatigue performance. Secondly, general optimization processes lack an understanding of the physical characteristics of Directed Energy Deposition (DED) additive manufacturing processes. DED relies on a high-energy laser beam to melt metal powder, forming a dynamic molten pool. Influenced by gravity and fluid surface tension, this process imposes strict physical limitations on the overhang angle of the structure. Existing topology algorithms often generate numerous horizontal cantilevered arms or small-angle branches. Direct printing in this case can lead to molten pool collapse, while adding external supports is difficult to remove due to the metallurgical bonding characteristics of DED, easily damaging the part itself. Furthermore, a single stiffness optimization objective cannot simultaneously meet the dual requirements of maximum displacement and safety factor in aerospace standards, resulting in repeated iterations between achieving stiffness targets and overcoming strength failures, leading to low efficiency.

[0043] Therefore, in order to solve the above-mentioned technical problems, some embodiments of this application provide an optimization method for aerospace structural components oriented to directional energy deposition additive manufacturing. This method can realize the evolution from discrete mathematical grids to continuous engineering entities by following the logical main line of physical field definition, conceptual skeleton optimization, engineering performance correction, process entity reconstruction, and multi-physics field verification.

[0044] Figure 1 The illustration shows a first flowchart of an exemplary aerospace structural component optimization method according to some embodiments of this application.

[0045] refer to Figure 1As shown. Specifically, this application abandons the traditional serial mode of design first and then verification, and establishes a fully parameterized physical evolution environment for the binary model of the reserved domain and the design domain. First, it constructs a high-fidelity digital twin model, which not only accurately defines the immutable non-design domains such as the upper flange equipment interface, the lower flange fuselage connection point, and the electrical connector plug-in envelope of the aerospace structural components, but also needs to reproduce the real flight mechanical environment in virtual space. Based on Newton's second law, a six-degree-of-freedom dynamic inertial force field including forward 2g, backward 6g, downward 3g, lateral 4g, and the most stringent upward 9g ultimate overload is applied, and the nonlinear constitutive relation of 316L stainless steel in the printed state is introduced to provide a physical benchmark; at the physical edge After the boundary is established, the first topology iteration stage begins with conceptual framework optimization. This stage employs the Solid Isotropic Material Penalty Model (SIMP) algorithm, with the goal of maximizing the overall structural stiffness. A volume retention rate is set as a macroscopic constraint, driving the material to self-organize within the design domain. The SIMP algorithm automatically identifies and retains force transmission paths with high strain energy density, eliminating inefficient elements, thus generating an initial mesh model reflecting the core force flow direction. The second topology iteration stage then proceeds to engineering performance correction, introducing more stringent aerospace engineering constraints. This mandates that the maximum displacement of key functional points (such as the upper flange center) under ultimate load must not exceed 0.5 mm, while safety limits are set based on the material's yield strength. A threshold of 1.5 is used as the criterion for successful optimization. Then, the third stage of topology iteration involves geometric reconstruction, transforming the mathematical mesh into an engineering entity. Specifically, this application uses NURBS modeling technology to perform a wrapping-style solidification reconstruction of the discrete mesh model generated in the second stage, using PolyNURBS tool blocks to wrap the rough topological skeleton. By adjusting control points, the geometry closely follows the contour of the optimization result, thus transforming discrete triangular facets into continuous, smooth mathematical surfaces. This process deeply integrates the self-supporting criterion of the DED process, which performs overhang angle detection on all generated topological branches. For regions with an inclination angle less than 55° relative to the construction direction, no further overhang is detected. Instead of the traditional strategy of passively adding supports, this method actively alters the growth topology of the structure by manually or parametrically adjusting PolyNURBS control points, stretching it into an arched structure that meets the self-support threshold or adding diagonal bracing. For physically unavoidable large-angle overhangs, a split manufacturing strategy is employed, decoupling the feature into independent printed parts with pre-reserved weld bevels. Furthermore, the mathematical continuity features of the NURBS surface are used to automatically smooth all topological intersection nodes with high curvature, eliminating sharp features that could induce thermal cracks during the rapid thermal cycling of the DED process from the geometric source. Finally, the reconstructed solid model undergoes Boolean operations and detailed feature refinement, outputting standard format solid data.

[0046] Figure 2The illustration shows a second flowchart of an exemplary aerospace structural component optimization method according to some embodiments of this application.

[0047] refer to Figure 1 and Figure 2 As shown. Therefore, the optimization method for aerospace structural components includes at least the following steps 101 to 103.

[0048] 101: Divide the geometric space of the aerospace structural component into a design domain and a non-design domain according to the target functional attributes, construct a digital twin model, and introduce material properties and multi-degree-of-freedom dynamic inertial force field into the digital twin model.

[0049] Step 101 establishes the physical and geometric foundation environment for subsequent topology optimization and geometric reconstruction. Step 101 includes at least three sub-processes: geometric domain partitioning of the digital twin model, material property definition, and introduction of a multi-degree-of-freedom dynamic inertial force field.

[0050] Specifically, during the geometric domain partitioning process, aerospace structural component data is imported into a 3D parametric design environment (e.g., a CAD / CAE integrated platform with Boolean operations and constraint modeling capabilities). This data can be imported using STEP, IGES, or the original parametric model format. Then, based on the functional attributes, load transfer paths, and assembly relationships of the aerospace structural components, the overall geometric space is divided into a design domain and a non-design domain. The non-design domain, partitioned through parametric modeling and Boolean operations, is a rigid region whose geometry, dimensions, and topological connections remain unchanged during topology optimization. The design domain is an optimizable volumetric region that allows for material density redistribution.

[0051] The geometric domain partitioning of the three-dimensional model of the aerospace structural component includes at least the following steps 101a to 101d.

[0052] 101a: The load input end of the aerospace structural component is divided into a first non-design domain and a solid retention ring zone is established in the hole area of ​​the load input end, wherein the retention ring zone is set as a non-removable material area.

[0053] Specifically, the division of the first non-design domain ensures the effective transmission of bolt preload and shear load and provides a stable load input boundary, as well as prevents connection failure caused by weakening of the hole edge material during the optimization process.

[0054] 101b: Divide the boundary constraint end of the aerospace structural component into a second non-design domain and apply full or preset degree of freedom constraints to it, wherein the preset degree of freedom constraint can be set to restrict only the translational degree of freedom.

[0055] In this process, the real boundary conditions are constructed by dividing the second non-design domain and the shape of the connecting reference surface is avoided during the optimization process, thereby ensuring that the final structure of the aerospace structural component can be directly assembled.

[0056] 101c: Divide the functional interference sensitive area of ​​the aerospace structural component into a third non-design domain and apply absolute geometric exclusion constraints; wherein, the division of the third non-design domain includes establishing a cuboid solid envelope space on the back of the equipment as the electrical connector insertion and removal travel area, establishing a cylindrical channel of a preset diameter on the belly of the bracket as a cable passage hole, and setting the above-mentioned insertion and removal travel area and cable passage hole area as an absolute geometric exclusion area, that is, material is not allowed to enter during the optimization process.

[0057] Specifically, the division into a third non-design domain prevents the optimization process from generating structural interference in this domain and ensures the maintenance space and functional realization.

[0058] 101d: Construct a continuous closed design domain outside the non-design domain and set the material distribution variable within the design domain as a continuous density field; wherein, the construction of the design domain includes constructing the remaining available space outside the non-design domain into a continuous closed entity through Boolean filling and defining the material properties within the design domain as continuous density variables, which are used as subsequent optimization variables.

[0059] Therefore, after completing the above geometric domain division, the three-dimensional model of the aerospace structural component is converted into a computable finite element digital twin model. The mesh of the digital twin model uses tetrahedral or hexahedral elements and the element size is set according to the structural feature size. In addition, a higher mesh density is used in the non-design domain to ensure stress accuracy.

[0060] Figure 3 A schematic diagram of an exemplary aerospace structural component according to some embodiments of this application is shown.

[0061] refer to Figure 3As shown. For example, import the internal surface data of the nose fairing and the geometric interface data of the fuselage frame into a 3D design environment. Divide the geometric space into a non-design domain and a design domain according to functional attributes. The non-design domain is a rigid region that must maintain its geometric configuration and topological connection relationship during topological evolution. In this application example, the equipment mounting flange, which serves as the load input end, is defined as the first non-design domain. Its geometric features include eight rectangularly distributed M6 bolt fixing holes and two φ10 locating pin holes for ensuring equipment installation accuracy. A solid retention area with a double diameter is set around these hole features to ensure the effective transmission of bolt preload. This solid retention area is formed by operation and its thickness is twice the hole diameter to allow for effective transmission of bolt preload, avoid excessive concentration of contact stress, and prevent local material from being eroded during topological evolution. The lower flange, serving as the boundary constraint end, is defined as the second non-design domain. This region contains 24 M5 bracket nut mounting points connected to the fuselage frame section, with their spatial positions strictly adhering to the fuselage drawing coordinates. Furthermore, based on electromagnetic compatibility (EMC) and field maintainability requirements, a rectangular solid envelope space is reserved on the back of the equipment as the insertion and removal travel area for electrical connectors, and a cylindrical channel with a diameter of 20mm is constructed on the belly of the bracket as a cable passage hole. The areas of this insertion and removal travel area and the cable passage hole are designated as absolute geometric interference no-go zones. In addition to the aforementioned non-design domains, all available gaps between the inner envelope of the nose fairing and the equipment mounting surface are filled as design domains, thereby forming the initial volume for material distribution searching. Moreover, this design domain satisfies the requirements of continuous closure, non-overlapping with the third non-design domain, and non-disruption of the connection topology of the first and second non-design domains.

[0062] Specifically, in defining the material properties and introducing the multi-degree-of-freedom dynamic inertial position in the digital twin model of the aerospace structural component, 316L austenitic stainless steel suitable for directional energy deposition (DED) additive manufacturing is selected as the structural material. This application includes material properties such as elastic modulus, Poisson's ratio, density, and yield strength to reflect the influence of rapid interlayer thermal cycling on material properties. Furthermore, to simulate the inertial response of the aerospace structural component under extreme maneuvering conditions, this application adopts a dynamic inertial loading method instead of the traditional static concentrated force loading method.

[0063] Figure 4 The diagram illustrates a flowchart of an exemplary method for introducing material properties and multi-degree-of-freedom dynamic inertial positions according to some embodiments of this application.

[0064] refer to Figure 4 As shown, the material property definition and the introduction of multi-degree-of-freedom dynamic inertial stance in the digital twin model of this aerospace structural component include at least the following steps 101e to 101f.

[0065] 101e: Set multiple ultimate overload directions and amplitudes and convert acceleration into inertial force according to Newton's second law, and convert the inertial force into distributed volume force acting on the structure; wherein, the multiple ultimate overload directions include at least the ultimate acceleration directions along the flight direction, in the opposite direction, vertically and laterally.

[0066] Specifically, it includes six directions: forward, backward, downward, left, right, and upward. To avoid local numerical singularities caused by concentrated forces, this application equates inertial forces to distributed volume forces. Then, in the finite element digital twin model, the equivalent volume forces are converted into inertial volume forces of finite element elements through the mass density field. This allows the inertial volume forces to be loaded onto all finite element elements, thus aligning with the actual inertial distribution and avoiding stress singularities caused by concentrated loads at nodes.

[0067] 101f: Based on the spatial offset of the equipment's center of mass relative to the structural installation reference, coordinate transformation is used to decompose the global inertial load into bending moment, shear force, and axial force components in the local coordinate system, and the structural self-weight field is superimposed to form a composite load boundary.

[0068] In actual assembly, the center of mass of the equipment usually does not coincide with the installation reference. Therefore, it is necessary to perform coordinate transformation and moment decomposition on the global inertial load. The specific process includes decomposing the load into bending moment, shear force and axial force in the local coordinate system, and then considering the self-weight volume force generated by the mass of the structure itself and superimposing the self-weight volume force with the inertial volume force to form a composite load boundary.

[0069] For example, considering the thermophysical characteristics of rapid melting and solidification by high-energy laser beams in the DED additive manufacturing process, this application uses 316L austenitic stainless steel as the reference material, unlike traditional forged materials. The nonlinear constitutive parameters of this material in the DED printed state are input, specifically setting its elastic modulus to 195 GPa, Poisson's ratio to 0.28, density to 7.98 g / cm³, and setting the yield strength threshold to 470 MPa, thus establishing a realistic material stiffness matrix. Subsequently, abandoning the traditional static force loading mode, this application establishes a dynamic inertial load field with six degrees of freedom based on Newton's second law, simplifying the 25 kg mass of the airborne equipment to a point mass. The loads are applied to the geometric center of the upper flange and set according to the acceleration response of the aircraft under extreme maneuvers. Six independent load conditions are set: a 2g forward takeoff / landing overload, a 6g backward drag parachute opening overload, a 3g downward negative overload, a 4g sideslip overload on each side, and the most severe 9g upward extreme maneuver overload. These accelerations are converted into volume forces acting on the structure. Considering that there is a 480mm cantilever arm of the equipment's center of mass relative to the leading edge of the fuselage mounting frame, and that the equipment's own coordinate system has a 10-degree rightward tilt and a 15-degree downward tilt, this application decomposes the inertial load projection in the global coordinate system into bending moment, shear force, and axial force in the local coordinate system, and superimposes the structure's own gravity field.

[0070] The above descriptions of material parameter selection and multi-degree-of-freedom dynamic inertial load conditions are merely exemplary, illustrating the implementation of the method in a typical application scenario, and are not intended to limit the scope of this application. In practical applications, the material type can be selected as other metallic or alloy materials according to the applicable scope of the directional energy deposition additive manufacturing process, and its corresponding elastic modulus, Poisson's ratio, density, and strength parameters can also be adjusted according to the actual material properties. The mass, center of mass position, and installation attitude angle of the airborne equipment can be set according to the specific structural layout. The ultimate overload direction and amplitude can also be adjusted according to different aircraft types, mission envelopes, or airworthiness regulations, including but not limited to different combinations of load conditions and different acceleration value ranges. The method of applying the inertial load is not limited to simplifying the equipment mass into a concentrated point mass and converting it into a volume force, but can also be implemented using equivalent nodal forces, surface loads, or multi-source coupled loads as needed. The decomposition method of the global load into the local coordinate system can also be adjusted according to the specific coordinate definition relationship. Therefore, any equivalent substitutions or modifications made to the load modeling method, parameter value range, and working condition combination without departing from the technical concept of this application shall fall within the protection scope of this application.

[0071] Figure 5 The diagram shows the baseline finite element analysis results of an exemplary initial model and iterative optimization model for some embodiments of this application.

[0072] refer to Figure 5 As shown. Before formally initiating topology iteration in this application, in order to establish the physical baseline of the design and quantify the necessity of optimization, the embodiments of this application first performed a baseline finite element analysis under all working conditions on the fully filled initial design domain (i.e., a solid envelope without any material removal), and the stress and strain are as follows. Figure 5 In the a1 to a2 sections, after applying the most stringent upward ultimate inertial overload of 9g, the calculation results show that the initial model has a serious contradiction between performance and weight redundancy. First, the risk of strength failure is significant. The maximum Mises stress of the initial model reaches 598MPa, which significantly exceeds the yield strength threshold (470MPa) of 316L stainless steel in the DED printed state. This indicates that under extreme conditions, even a solid structure faces the risk of plastic deformation or even fracture in stress concentration areas such as flange connections. Second, the structural stiffness is seriously insufficient. Although the solid volume is huge, the maximum absolute displacement of the structure is low. The thickness reached 0.22mm, indicating that simple material stacking did not effectively translate into structural stiffness. Instead, the excessive weight led to a multiplication of inertial forces, exacerbating deformation. Finally, there is the issue of weight redundancy. The initial design domain's solid weight reached 18.8kg, which is unacceptable for aerospace modification and repair tasks that pursue extreme lightweighting. Therefore, the initial design domain analysis shows that the solid filling mode cannot resolve the triple contradiction of strength failure, insufficient stiffness, and excessive weight. It is necessary to find the optimal force transmission path through subsequent topology iterations and establish a physical connection between the microscopic molten pool and the macroscopic structure in order to improve performance while reducing weight.

[0073] 102: Perform one or more stages of topology iteration on the digital twin model to generate a solid model of the aerospace structural component. The topology iteration includes at least the following: maximizing the overall structural stiffness using a solid isotropic material penalty method; guiding the material to migrate to the stress concentration area using a Mises stress threshold and displacement constraints; detecting the tilt angle of the preset overhanging surface according to the physical boundary conditions of the directional energy deposition additive manufacturing process and correcting the target tilt angle region into a self-supporting curved surface structure; and performing curvature continuous smoothing on the preset region.

[0074] Figure 6 A flowchart illustrating an exemplary topology iterative optimization method according to some embodiments of this application is shown.

[0075] refer to Figure 6 As shown. Specifically, in step 102, the digital twin model undergoes one or more phases of topological iteration, including at least the following steps 102a to 102c.

[0076] 102a: Using a solid isotropic material penalty method and taking the maximization of the overall structural stiffness as the objective function, the first stage of topological iteration is performed under preset constraints to generate the first topological configuration.

[0077] Specifically, in step 102a, nonlinear suppression is applied to the material properties of each finite element in the design domain by a penalty factor. The objective function is set to maximize the overall stiffness of the structure, while simultaneously applying volume retention rate constraints and minimum member size constraints. The volume retention rate constraint can be set to 40% to 70% of the original volume, and the minimum member size constraint can be set to not less than 1.2 to 2 times the diameter of the directional energy deposition molten pool. The specific values ​​can be set by the designer according to actual needs. Then, the first stage of topology iteration is performed, and the strain energy density field is used as the material migration criterion to identify and retain the force transmission path with the highest strain energy density to form the first topology configuration.

[0078] Figure 7 A print preview schematic diagram of an exemplary first topology configuration according to some embodiments of this application is shown.

[0079] refer to Figure 7As shown, for example, the objective function of the first-stage topology iteration is set to maximize the overall stiffness, the constraint conditions focus on significant weight reduction, the percentage of the retained volume of the design domain is set to 60%, and a minimum member size constraint of 3 mm is introduced. Then, through iterative calculation using the Solid Isotropic Material with Penalization (SIMP) algorithm, the first topology configuration is generated. In terms of strength verification, under the most stringent upward 9g ultimate overload condition, the maximum Mises stress of the structure is 424 MPa, which is successfully controlled below the safe threshold of 470 MPa for the material yield strength. However, when the rheological properties of the DED molten pool were introduced for process review, this first topological configuration revealed fatal flaws. Finite element displacement mapping showed that the maximum deformation of the structure under an upward 9g overload reached 0.16mm, and the large deformation area was mainly concentrated on the slender rib structures generated on the sidewalls. From a deeper rheological perspective, these dispersed, slender, fibrous material branches had an excessively large aspect ratio and lacked continuous underlying support. Under the action of the high-energy laser beam in the DED process, the volume of the molten pool was too large relative to the small cross-section. At this time, the liquid metal tended to flow under gravity. The surface tension will be exceeded; this physical phenomenon will lead to severe instability of the molten pool, which manifests as interlayer stacking misalignment, sidewall collapse, and even dripping during the forming process. In addition, this discrete network structure will cause frequent interruptions in the laser scanning path, and the heat input will be extremely discontinuous in time and space, which will easily induce huge thermal stress gradients at the root of the thin rod, leading to crack initiation. Therefore, although the first-stage topology optimization meets the static strength requirements at the mathematical level, it violates the rheological stability principle of the directional energy deposition process, and the stiffness index does not meet the requirements, so subsequent physical corrections are necessary.

[0080] Among them, the solid isotropic material penalty algorithm is based on the concept of material density. It establishes the relationship between material properties (such as elastic modulus) and unit density by introducing a penalty factor. Its core idea is to use continuous material density variables as design variables and optimize the material to be reasonably distributed in the design space through iterative optimization.

[0081] For example, in the relationship between elastic modulus and density, a power-law interpolation formula can be used: ,in The elastic modulus of a solid material. For unit density, The penalty factor is defined as follows: its value range is [0,1]. When it is close to 1, it will completely reduce the influence of element density on the elastic modulus of the solid material and reduce the correlation characteristics of boundary density changes. The uniformity of the overall material distribution in the topology result will be reduced. Therefore, the default value is 0.5, and the value is 0.3 in the simulation. The topology optimization design of this application takes into account the constraints of 6-direction inertial acceleration, 25kg weight at the top and bottom flange. The total finite element voxel distribution of the topology result should be as uniform as possible and distributed along the main stress direction. The value of the penalty factor is reduced to enhance the correlation between stress and boundary. The test iterations of 0.1, 0.3, 0.4 and 0.5 were carried out with a step size of 0.1. Finally, the boundary penalty factor of 0.3, which is structurally complete and manufacturable, was selected.

[0082] 102b: Introduce the Mises stress threshold and displacement constraint to the first topological configuration, and promote the material to migrate to the stress concentration region through the second-stage topological iteration to generate the second topological configuration.

[0083] Specifically, in step 102b, strength constraints and displacement constraints are introduced as criteria in the first topological configuration. At the same time, the penalty factor of the topology optimization solver is adjusted, the minimum feature size constraint is increased, and the density gradient change rate of adjacent elements is restricted. The second-stage topological iteration of the material distribution promotes the migration of material to the stress concentration area and suppresses the generation of discrete fine branches, thereby forming a second topological configuration of continuous plate shell or robust rod structure, reducing local overheating of the molten pool and improving structural stiffness. The strength constraints, displacement constraints, penalty factor, increase of minimum feature size constraint, and restriction of density gradient change rate of adjacent elements can be set by the designer according to actual needs.

[0084] Figure 8 A print preview schematic diagram of an exemplary second topology configuration according to some embodiments of this application is shown.

[0085] refer to Figure 8As shown, for example, in response to the rheological instability and insufficient stiffness problems exposed in step 102a, step 102b implemented a second-stage iterative iteration. By suppressing the generation of discrete fine branches, the material was forced to aggregate along the main force transmission path, forming a plate or robust rod structure with sufficient heat capacity and support area to stabilize the molten pool and improve stiffness. At the operational level, the penalty factor and manufacturing constraint parameters of the topology optimization solver were adjusted, and consistent control over the member dimensions was increased. This essentially simulates the limitation of the spot diameter on the minimum feature in the directional energy deposition process at the algorithm level, forcing the algorithm to eliminate those fine structures that cannot support a stable molten pool. At the same time, the load boundary conditions of the previous round remained unchanged, and the evolution continued with the maximization of stiffness as the driving force. The simulation results after the second-stage topology iteration showed that the material distribution of the structure changed significantly. The originally discrete mesh-like ribs on the sidewalls were integrated into a plate-like structure with a certain width, and the aggregation effect of the material was significantly enhanced. From a physical property perspective, the optimized model weight... The weight was adjusted to 10.6 kg, a reduction of 14.2%. The maximum displacement under extreme conditions was adjusted from 0.16 mm in step 102a to 0.22 mm. However, with the change in material distribution, new thermo-mechanical coupling problems emerged. Static analysis indicated that the local maximum Mises stress rose to 478 MPa. Although still below the yield limit, the safety margin was compressed. Furthermore, from a geometric perspective, the optimized wall surface exhibited several sharp corners. In directional energy deposition processes, these sharp corners are not only mechanical stress concentration points but also thermal hotspots. When the laser scans to the sharp corners, the scanning speed vector changes drastically (deceleration and reversal), causing a sudden spike in local energy density. Due to the narrow heat dissipation path, the molten pool temperature rises sharply, viscosity decreases, and rheology increases dramatically, easily leading to edge collapse or grain coarsening and performance degradation due to overheating. This indicates that relying solely on the automatic evolution of the algorithm still has limitations in handling geometric continuity to adapt to the thermodynamic behavior of the molten pool, requiring subsequent engineering design intervention.

[0086] 102c: Based on the physical boundary conditions of the directional energy deposition additive manufacturing process, the tilt angle of the overhanging surface of the second topology is detected, and the region with a tilt angle less than the preset self-supporting threshold relative to the building direction is corrected into a self-supporting curved surface structure. At the same time, the curvature of the topology intersection nodes and sharp turning areas is continuously smoothed to generate a solid model.

[0087] Figure 9 A flowchart illustrating an exemplary entity model generation method according to some embodiments of this application is shown.

[0088] refer to Figure 9 As shown. Specifically, step 102c includes at least steps 1021 to 1025.

[0089] 1021: Based on the physical boundary conditions of the directional energy deposition additive manufacturing process, the overhang angle of all topological branches of the second topological configuration is detected globally, and the regions where the tilt angle of each topological branch relative to the construction direction is less than a preset self-supporting threshold are identified as rheological instability regions; wherein the preset self-supporting threshold can be set to 50° to 60°, and can be set by the designer according to actual needs, with 55° as an example.

[0090] 1022: By adjusting the parametric surface control points or using a local geometric stretching strategy, the growth direction of the structural branches in the rheological instability zone is adjusted so that the surface tilt angle is greater than the preset self-support threshold.

[0091] Specifically, strategies such as reverse offsetting of parametric surface control points along the construction direction, reconstructing branch growth direction, introducing arched transition surfaces, or adding oblique tie structures are implemented for the rheologically unstable region to make the surface tilt angle of the rheologically unstable region greater than the preset self-support threshold, so that the surface tension of the molten pool can resist gravity collapse and avoid interlayer sagging.

[0092] 1023: By adopting a split construction strategy, structural features that cannot meet the self-support conditions through local modification are decoupled into independent printed substructures and reserved for welding bevels to generate a third topological configuration.

[0093] Specifically, in step 1023, structural features in the second topology that still cannot meet the self-supporting forming conditions after local modification are determined to be non-directly formable features; the non-directly formable features are decoupled from the main structure of the second topology to construct independent printing substructures, and welding bevels, overlapping interfaces or mechanical connection features are designed between the main structure and the independent printing substructures to avoid the introduction of large-area support structures, thereby generating the third topology.

[0094] 1024: A large-radius rounding method is used to smooth the transition of all sharp corners, topological branch intersections, and high curvature abrupt change regions of the wall openings in the third topological configuration, thereby eliminating stress concentration sources and thermal effects.

[0095] Specifically, a large radius rounding method is used to apply a preset radius of fillet to all sharp corners, topological branch intersections, and high curvature abrupt change regions of the wall openings of the third topology configuration. This preset radius is set by the designer according to actual needs. For example, the fillet radius can be set to be greater than or equal to 1.5 times the minimum wall thickness, thereby reducing stress concentration and reducing the heat input peak caused by abrupt changes in the scanning path.

[0096] For example, in response to the localized thermal stress concentration problem exposed in step 102b, this application adopts a deep fusion strategy of algorithmic topology optimization combined with forward process enhancement in the third topology stage. The design criteria of this stage directly anchor the 55° self-support threshold of the directional energy deposition process, that is, when the structure overhang angle is greater than 55°, the surface tension of the molten metal is sufficient to resist gravity and maintain the stability of the molten pool shape. According to the analysis conclusions of steps 102a and 102b, the connection area between the bottom flange and the side wall is identified as a key part of stress transmission and also a weak link in stiffness. Therefore, a set of curved reinforcing ribs is designed and added between the upper surface of the bottom flange and the side wall plate using CAD forward modeling tools. The geometry of these reinforcing ribs is carefully designed, and their growth angle is strictly controlled within the range of less than 35° with the normal of the substrate to ensure that the molten pool can always obtain effective support from the underlying entity during the printing process and avoid sagging.

[0097] Meanwhile, to address the sharp corner hot spot problem that appeared in step 102b, a full-domain feature smoothing process was implemented. This involved using a large-radius rounding technique to smoothly transition all sharp corners and intersections of topological branches at the openings on the wall. This process mechanically eliminated geometric abrupt change points and significantly reduced the stress concentration factor. In terms of process rheology, it ensured that the laser cladding head could maintain a constant scanning speed and uniform power output, thus maintaining the stability of the molten pool temperature field and flow field and avoiding forming defects caused by heat input fluctuations. Therefore, the final model in the third topology optimization stage demonstrated the required comprehensive performance. In terms of weight, the final weight was controlled at 8.7kg, compared with the original design of 18.8kg, achieving a weight reduction of 46.27%, accurately meeting the weight reduction target. In terms of mechanical properties, under the extreme working condition of 9g upward, the maximum Mises stress of the structure dropped significantly to 378MPa, compared with the original model (598MPa), the stress level was significantly reduced, far below the yield strength of 316L material, the minimum safety factor was increased to 1.91, the stiffness index also met the requirements, and the maximum displacement was strictly controlled at 0.14mm, breaking through the design red line of 0.5mm.

[0098] 1025: Perform Boolean summation on the main surface and non-design domain entity features after reconstructing the third topology configuration, identify and stitch non-manifold edges and open boundaries, and improve mesh resolution to form and output a complete closed parametric solid model.

[0099] Specifically, the Boolean summation operation is set by the designer using the existing Boolean summation operation process. Through the Boolean summation operation, the main surface after the reconstruction of the third topology configuration is fused with high-precision flange interfaces, locating pin holes and other non-design domain features. The identification and stitching of non-manifold edges and open boundaries is performed by the designer using the existing mesh processing algorithm. The improvement of mesh resolution is achieved by the designer using the existing subdivision algorithm to improve the surface resolution and using the Laplacian smoothing method to reduce local jagged noise. This forms a complete, closed, non-self-intersecting surface with correct surface normals that meets the requirements of additive manufacturing slicing. The format of this parametric solid model can be set to STL or an equivalent manufacturable format.

[0100] For example, after generating the third topological configuration and performing a smooth transition, PolyNURBS wrap-around reverse reconstruction is implemented based on the rheological constraints of the directional energy deposition process. This transforms the discrete optimization results into continuous solid geometry. The designers use the third topological configuration output from the third stage as a reference base map and generate NURBS surfaces by setting control points. During this process, a real-time sag analysis tool is used for full-domain scanning. For unstable regions with growth angles less than 55 degrees, the slope of the topological branches is actively corrected by stretching the control points to ensure that all forming surfaces are within the stable range of the molten pool. Then, through Boolean summation, the reconstructed main surface is fused with high-precision flange interfaces, locating pin holes, and other non-design domain features. The final solid model is then discretized and exported as high-fidelity STL format data as a benchmark for subsequent process processing.

[0101] Furthermore, the exported STL model is automatically identified and stitched with non-manifold edges and open boundaries using a mesh processing algorithm, and degenerate patches are removed to ensure that the model meets strict watertightness requirements. On this basis, subdivision algorithm and Laplacian smoothing technology are applied to improve mesh resolution and eliminate tiny jagged features on the surface to avoid high-frequency trajectory jitter generated by the directional energy deposition laser cladding head during scanning.

[0102] 103: Optimize the mesh of the solid model and use virtual slicing and support generation simulation to verify the output of solid manufacturing data that meets the conditions for supportless manufacturing of directional energy deposition.

[0103] The entity manufacturing data includes at least a directional energy deposition additive manufacturing printing path code containing laser power, scanning speed, and layer thickness strategy.

[0104] Figure 11 A flowchart illustrating an exemplary optimization and simulation verification method according to some embodiments of this application is shown.

[0105] refer to Figure 11As shown. Specifically, in step 103, the solid model is meshed and simulated using virtual slicing and support generation to verify the results, and solid manufacturing data that meets the conditions for supportless manufacturing of directional energy deposition is output, including at least the following steps 103a to 103c.

[0106] 103a: The solid model is re-meshed using additive manufacturing preprocessing software, and virtual slicing and support generation simulation are performed. At the same time, linear static analysis and modal analysis are carried out under a multi-degree-of-freedom dynamic inertial force field.

[0107] Specifically, the parametric solid model output in step 102 is imported into existing additive manufacturing preprocessing software. Then, tetrahedral or hexahedral solid mesh reconstruction is performed to generate a uniform-sized finite element mesh, eliminating high aspect ratio elements, repairing non-manifold edges and overlapping surfaces, ensuring model sealing and normal consistency, and controlling the mesh size to be no greater than 1 / 3 of the minimum wall thickness and no less than twice the printing layer thickness. Then, virtual slicing is performed according to the pre-set construction direction and layer thickness parameters to generate the profile path of each layer. Through layer-by-layer slicing analysis, it is detected whether there are fault areas, whether there are suspended island areas, and whether there are red warning areas requiring additional support. Then, a sag detection is performed on each layer slice. If the automatically generated support volume is 0, there are no isolated suspended areas, and the profiles of each layer are continuous, it is determined that the layer meets the unsupported manufacturing conditions; otherwise, it is marked as a potentially unstable layer and support is added. At the same time, finite element linear static analysis and modal analysis are performed on the solid model based on the multi-degree-of-freedom dynamic inertial load field established in step 101.

[0108] 103b: Determine whether the maximum Mises stress of the solid model structure under any ultimate load is lower than the safety threshold of the material yield strength, and whether the absolute displacement of the key functional points is within the preset displacement threshold range.

[0109] 103c: If any indicator is not met (i.e., if the maximum Mises stress is higher than the safety threshold of the material yield strength and / or the absolute displacement of the key functional point is not within the preset displacement threshold range), then the parameters are adjusted according to the judgment and identification results. The parameter adjustment includes adjusting one or more of the volume constraints, penalty factors, local material density or stiffness weight parameters. If all indicators are met, then the final manufacturing entity model and directional energy deposition additive manufacturing process path data are output.

[0110] Specifically, if the maximum Mises stress exceeds the safety threshold of the material's yield strength, the volume fraction is increased and / or the penalty factor is decreased; if the absolute displacement of a critical functional point is outside the preset displacement threshold range, the local material density is increased or the stiffness weight is increased; if suspended islands exist, the self-support threshold is adjusted; if mesh hot spots are significant, the curvature smoothing parameter is increased; after the above parameter adjustments, the topology optimization process can be re-executed by returning to step 102 or step 102c. This directional energy deposition additive manufacturing process path data generates a scanning path based on the virtual slicing results. Each layer includes the construction direction, layer profile scanning path, filling path, power modulation strategy, energy beam power, scanning speed, powder feed rate, layer thickness, and inter-pass overlap.

[0111] Figure 12 The diagram illustrates an exemplary optimization model and design model of some embodiments of this application, wherein, Figure 12 (a) is the optimization model. Figure 12 (b) is the design model; Figure 13 A schematic diagram comparing the maximum Mises stress of an exemplary optimization model and a design model according to some embodiments of this application is shown, wherein, Figure 13 (a) is the optimization model. Figure 13 (b) is the design model; Figure 14 This illustration shows a comparative displacement diagram of an exemplary optimization model and a design model based on some embodiments of this application, wherein... Figure 14 (a) is the optimization model. Figure 14 (b) is the design model.

[0112] refer to Figure 12 As shown, to verify the optimization effect of this application, a performance comparison analysis of the optimized model and the design model after optimization is first performed, referring to... Figure 13 and Figure 14 As shown, the simulation results of stress and displacement distribution of the optimized solid model and the design model of this application are listed respectively. The maximum Mises stress of the optimized solid model of this application is 378 MPa, while the maximum Mises stress of the design model reaches 598 MPa, which is 1.5 times that of the optimized solid model of this application. The maximum displacement of the optimized solid model of this application is 0.14 mm, while the maximum displacement of the design model is 0.22 mm, which is also 1.5 times that of the optimized solid model of this application. Comparing the design model and the optimized solid model of this application, the topology optimization technology has a significant advantage in determining the material distribution mode according to the actual working condition load. Therefore, the solid model after topology optimization iteration of this application is determined to be the final manufacturing part model.

[0113] Figure 15 A schematic diagram of an exemplary feature simulation component according to some embodiments of this application is shown. Figure 16A stress-strain schematic diagram of an exemplary feature simulation element according to some embodiments of this application is shown.

[0114] refer to Figure 15 and Figure 16 As shown in some embodiments of this application, feature simulations can be used for verification during the model design process. The specific process is as follows:

[0115] Taking the optimized directional energy deposition (DED) aerospace structural component of 316L stainless steel as an example, a simplified specimen design was created by extracting key stress concentration areas (such as around bolt holes) to accurately replicate the mechanical response under actual working conditions. For the bolt hole area of ​​the lower flange of the test machine part, finite element analysis showed a maximum stress of 246 MPa under an upward 9g load, indicating a potential failure point. The simulated component design adopted the principles of structural fidelity and load equivalence. The middle section was 2 mm thick (consistent with the actual flange bearing thickness), and 7 mm wide to simulate a narrow stress zone. Three 5.2 mm diameter holes (corresponding to actual bolt holes) were evenly distributed along the axial direction, with a total length of 100 mm and a mass of only 0.024 kg. The design process underwent finite element pre-verification to ensure that the stress distribution of the simulated component was consistent with the full-size part and that it was compatible with universal testing machine clamping. The verification test applied an axial tensile force of 884 N, equivalent to a 9g acceleration inertial force (calculated based on a part mass of 10 kg). A universal testing machine was used with a loading rate of 5 mm / min to avoid dynamic effect interference, and stress was monitored in real time using strain gauges.

[0116] The results showed that the maximum Mises stress predicted by the finite element method was 252.4 MPa, while the measured value in the physical experiment was 258.9 MPa, with a relative error of 2.6%, which is lower than the engineering tolerance of 5%, proving that the simulated part effectively represents the real working condition. In the experiment, the load-displacement curve showed a linear increase, indicating that the area around the hole was in an elastic state without plastic deformation. Failure mode analysis showed that cracks initiated at the edge of the hole, consistent with the simulation of the full-size part, confirming the bolt hole as a risk point. By verifying the characteristic simulated part, the time and economic costs required for manufacturing the full-size part are avoided while basically reproducing the mechanical response of the real part, thus achieving rapid verification of the mechanical properties of the part.

[0117] Therefore, this application introduces PolyNURBS geometric reconstruction technology to transform rough, unmanufacturable topological meshes into smooth, feature-continuous parametric solid models. This not only eliminates the time-consuming and laborious point cloud fitting process in traditional reverse modeling but also ensures that the final solid model can be directly recognized by mainstream CAM software, achieving seamless data flow from design to manufacturing and greatly improving R&D efficiency. Furthermore, unlike the traditional passive approach of optimizing first and then adding support, this application internalizes the overhang angle constraint of the DED process as an active design variable in the geometric reconstruction process. Through self-support correction of the topological branch shape, it eliminates the dependence on external process support. This not only saves metal powder materials but also avoids the extremely difficult support removal process in the DED process, preventing surface damage and precision loss due to post-processing and reducing manufacturing costs and cycle time. Moreover, by introducing multiple constraint mechanisms of stiffness, strength, and displacement in the optimization iteration and eliminating sharp thermal stress concentration points in the geometric reconstruction, the final structure not only meets the lightweight requirements but also exhibits excellent structural stiffness and fatigue resistance under a 9g ultimate overload. This in-depth optimization based on physical substance enables the test machine modification parts to safely pass airworthiness certification.

[0118] Figure 17 A schematic diagram of the module connections of an exemplary aerospace structural component optimization device 200 according to some embodiments of this application is shown.

[0119] refer to Figure 17 As shown; some embodiments of this application provide an optimization device 200 for aerospace structural components in directional energy deposition additive manufacturing. The device includes: a design construction module 201, used to divide the geometric space of the aerospace structural component into a design domain and a non-design domain according to the target functional attributes, construct a digital twin model, and introduce material properties and a multi-degree-of-freedom dynamic inertial force field into the digital twin model; an iterative optimization module 202, used to perform one or more stages of topological iteration on the digital twin model to generate a solid model of the aerospace structural component, wherein the topological iteration includes at least maximizing the overall structural stiffness using a solid isotropic material penalty method, guiding the material to migrate to the stress concentration area using a Mises stress threshold and displacement constraints, and detecting the tilt angle of a preset overhanging surface according to the physical boundary conditions of the directional energy deposition additive manufacturing process and correcting the target tilt angle region into a self-supporting curved surface structure, while performing curvature continuous smoothing processing on the preset region; and a verification output module 203, used to perform mesh optimization on the solid model and verify it using virtual slicing and support generation simulation, and output solid manufacturing data that meets the conditions for unsupported manufacturing of directional energy deposition.

[0120] It should be understood that the device embodiments and method embodiments can correspond to each other, and similar descriptions can be referred to the method embodiments. To avoid repetition, they will not be repeated here. Specifically, the aerospace structural component optimization device 200 can correspond to the corresponding subject in the aerospace structural component optimization method 100 of this application embodiment, and each unit in the aerospace structural component optimization device 200 is for implementing the corresponding process in the aerospace structural component optimization method 100. For the sake of brevity, they will not be repeated here.

[0121] It should also be understood that the various units in the aerospace structural component optimization device 200 involved in the embodiments of this application are based on logical functional division. In practical applications, the function of one unit can also be implemented by multiple units, or the function of multiple units can be implemented by one unit. Furthermore, these functions can also be implemented with the assistance of one or more other units. For example, some or all of the aerospace structural component optimization device 200 can be merged into one or more additional units. As another example, some units(s) in the aerospace structural component optimization device 200 can be further divided into multiple functionally smaller units, which can achieve the same operation without affecting the technical effects of the embodiments of this application. Furthermore, the aerospace structural component optimization device 200 may also include other units. In practical applications, these functions can also be implemented with the assistance of other units, and can be implemented collaboratively by multiple units.

[0122] It should also be understood that the terms "module" or "unit" used in the embodiments of this application refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0123] For example, the aerospace structural component optimization device 200 according to the embodiments of this application, and the method of the embodiments of this application, can be constructed and implemented by running a computer program (including program code) capable of executing the steps involved in the corresponding method on a general-purpose computing device including processing elements and storage elements such as a central processing unit (CPU), random access memory (RAM), and read-only memory (ROM). The computer program can be recorded on, for example, a computer-readable storage medium and loaded into an electronic device through the computer-readable storage medium. The computer program is used to implement the corresponding method of the embodiments of this application. In other words, the units mentioned above can be implemented in hardware, in software instructions, or in a combination of hardware and software. Specifically, the steps of the method embodiments in the embodiments of this application can be completed by the integrated logic circuits of the hardware in the processor and / or in software instructions. The steps of the method disclosed in the embodiments of this application can be directly embodied as being executed by a hardware decoding processor, or by a combination of hardware and software in the decoding processor. Optionally, the software can be located in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The software in the memory can be run by a processor to perform the steps described in the method embodiments above.

[0124] Figure 18 A schematic diagram of the structure of an exemplary electronic device 300 according to some embodiments of this application is shown.

[0125] like Figure 18 As shown, the electronic device 300 includes at least a processor 310 and a computer-readable storage medium 320. The processor 310 and the computer-readable storage medium 320 can be connected via a bus or other means. The computer-readable storage medium 320 stores a computer program 321, which includes computer instructions. The processor 310 executes the computer instructions stored in the computer-readable storage medium 320. The processor 310 is the computing and control core of the electronic device 300, and is suitable for implementing one or more computer instructions, specifically for loading and executing one or more computer instructions to achieve a corresponding method flow or function.

[0126] As an example, processor 310 may also be referred to as a central processing unit (CPU). Processor 310 may include, but is not limited to: general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete component gate or transistor logic devices, discrete hardware components, etc.

[0127] As an example, the computer-readable storage medium 320 may be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device; optionally, it may also be at least one computer-readable storage medium located remotely from the aforementioned processor 310. Specifically, the computer-readable storage medium 320 includes, but is not limited to, volatile memory and / or non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0128] like Figure 18 As shown, the electronic device 300 may also include a transceiver 330.

[0129] The processor 310 can control the transceiver 330 to communicate with other devices; specifically, it can send information or data to other devices or receive information or data sent by other devices. The transceiver 330 may include a transmitter and a receiver. The transceiver 330 may further include antennas, and the number of antennas may be one or more.

[0130] It should be understood that the various components in the electronic device 300 are connected through a bus system, which includes a data bus, a power bus, a control bus, and a status signal bus. It is worth noting that the electronic device 300 can be any type of electronic device with data processing capabilities; the computer-readable storage medium 320 stores first computer instructions; the processor 310 loads and executes the first computer instructions stored in the computer-readable storage medium 320 to implement the corresponding steps in the method embodiments of this application; in specific implementations, the first computer instructions in the computer-readable storage medium 320 are loaded and executed by the processor 310, and to avoid repetition, this will not be described further here.

[0131] According to another aspect of this application, embodiments of this application provide a chip. This chip can be an integrated circuit chip with signal processing capabilities, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The chip can also be referred to as a system-on-a-chip (SoC), system-on-a-chip (SoC), chip system, or system-on-chip, etc. This chip can be applied to various electronic devices capable of mounting chips, enabling the device with the chip mounted to execute the corresponding steps in the methods or logic block diagrams disclosed in the embodiments of this application. For example, the chip may be suitable for implementing one or more computer instructions, specifically suitable for loading and executing one or more computer instructions to achieve a corresponding method flow or corresponding function.

[0132] According to another aspect of this application, embodiments of this application provide a computer-readable storage medium (Memory). This computer-readable storage medium is a computer's memory device used to store programs and data. It is understood that the computer-readable storage medium here may include both built-in storage media within the computer and, of course, extended storage media supported by the computer. The computer-readable storage medium provides storage space that stores the operating system of an electronic device. This storage space contains computer instructions suitable for loading and execution by a processor. When these computer instructions are read and executed by the processor of the computer device, they cause the computer device to perform the corresponding steps in the methods or logic diagrams disclosed in the embodiments of this application.

[0133] According to another aspect of this application, embodiments of this application provide a computer program product or computer program. The computer program product or computer program includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform corresponding steps in the methods or logic block diagrams disclosed in the embodiments of this application. In other words, when the solutions provided in this application are implemented using software, they can be implemented in whole or in part as a computer program product or computer program. The computer program product or computer program includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the processes of the embodiments of this application are run or the functions of the embodiments of this application are implemented.

[0134] It is worth noting that the computer involved in this application can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions involved in this application can be stored in a computer-readable storage medium, or can be transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0135] Those skilled in the art will recognize that the units and process steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. In other words, those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of protection of this application.

[0136] Finally, it should be noted that the above content is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims. For example, the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. Furthermore, various different embodiments of this application can also be arbitrarily combined, as long as they do not violate the basic idea of ​​this application, and they should also be considered as the content disclosed in this application.

Claims

1. An optimization method for aerospace structural components oriented towards directional energy deposition additive manufacturing, characterized in that, The method includes: Based on the target functional attributes, the geometric space of the aerospace structural component is divided into the design domain and the non-design domain. A digital twin model is constructed, and material properties and a multi-degree-of-freedom dynamic inertial force field are introduced into the digital twin model. The digital twin model undergoes one or more stages of topology iteration to generate a solid model of the aerospace structural component. The topology iteration includes at least maximizing the overall structural stiffness using a solid isotropic material penalty method, guiding material migration towards stress concentration areas using a Mises stress threshold and displacement constraints, detecting the tilt angle of a preset overhanging surface based on the physical boundary conditions of the directional energy deposition additive manufacturing process, correcting the target tilt angle region into a self-supporting curved surface structure, and simultaneously performing curvature continuity smoothing on the preset region; and... The solid model is mesh optimized and simulated and verified using virtual slicing and support generation, outputting solid manufacturing data that meets the conditions for supportless manufacturing of directional energy deposition.

2. The method according to claim 1, characterized in that, The process of dividing the geometric space into design domain and non-design domain based on the target functional attributes includes: The load input end of the aerospace structural component is divided into a first non-design domain, and a solid retention ring zone is established in the hole area of ​​the load input end; The boundary constraint end of the aerospace structural component is divided into a second non-design domain and subjected to full or preset degree of freedom constraints. The functional interference sensitive area of ​​the aerospace structural component is divided into a third non-design domain and an absolute geometric exclusion constraint is applied. A continuous, closed design domain is constructed outside the non-design domain, and the material distribution variable within the design domain is defined as a continuous density field; wherein... The partitioning of each non-design domain is performed through parametric modeling and Boolean operations, and each non-design domain retains its geometric features throughout the entire topology optimization and geometric reconstruction process.

3. The method according to claim 1 or 2, characterized in that, The introduction of material properties and multi-degree-of-freedom dynamic inertial force fields into the digital twin model includes: Multiple ultimate overload directions and amplitudes are defined, and acceleration is converted into inertial force according to Newton's second law, and the inertial force is converted into distributed volume forces acting on the structure; wherein, the multiple ultimate overload directions include at least ultimate acceleration directions along the flight direction, in the opposite direction, vertically, and laterally; and, Based on the spatial offset of the equipment's center of mass relative to the structural installation reference, coordinate transformation is used to decompose the global inertial load into bending moment, shear force, and axial force components in the local coordinate system, and the structural self-weight field is superimposed to form a composite load boundary.

4. The method according to claim 1 or 3, characterized in that, The topological iteration of the digital twin model in one or more stages includes: A solid isotropic material penalty method is adopted, and the overall stiffness of the structure is maximized as the objective function. Under preset constraints, the first stage of topology iteration is performed to generate the first topology configuration. A Mises stress threshold and displacement constraint are introduced into the first topological configuration, and a second-stage topological iteration is used to induce material migration towards the stress concentration region, generating a second topological configuration; and, Based on the physical boundary conditions of the directional energy deposition additive manufacturing process, the tilt angle of the overhanging surface of the second topology is detected, and the region with a tilt angle less than a preset self-supporting threshold relative to the construction direction is corrected into a self-supporting curved surface structure. At the same time, the curvature of the topology intersection nodes and sharp turning regions is continuously smoothed to generate a solid model.

5. The method according to claim 4, characterized in that, The method employs a solid isotropic material penalty approach, with the objective function of maximizing the overall structural stiffness. Under preset constraints, a first-stage topological iteration is performed to generate a first topological configuration, including: Nonlinear suppression is applied to the material properties of each finite element element in the design domain by a penalty factor. The objective function is set to maximize the overall structural stiffness while simultaneously imposing volume retention constraints and minimum member size constraints; and, The first stage of topology iteration is performed, and the strain energy density field is used as the material migration criterion to identify and retain the force transmission path with the highest strain energy density, thus forming the first topology configuration.

6. The method according to claim 5, characterized in that, The process of introducing a Mises stress threshold and displacement constraints to the first topological configuration and then using a second-stage topological iteration to induce material migration towards stress concentration regions to generate a second topological configuration includes: In the first topological configuration, strength constraints and displacement constraints are introduced as criteria; and... By adjusting the penalty factor and manufacturing constraint parameters of the topology optimization solver, a second-stage topology iteration is performed on the material distribution to encourage the material to migrate to the stress concentration area and suppress the generation of discrete fine branches, thereby forming a second topological configuration of continuous plate shell or robust bar structure, reducing local overheating of the molten pool and improving structural stiffness.

7. The method according to claim 6, characterized in that, The step of detecting the tilt angle of the overhanging surface of the second topological configuration based on the physical boundary conditions of the directional energy deposition additive manufacturing process includes: Based on the physical boundary conditions of the directional energy deposition additive manufacturing process, the overhang angle of all topological branches of the second topological configuration is detected globally, and the regions where the tilt angle of each topological branch relative to the construction direction is less than the preset self-support threshold are identified as rheological instability zones. By adjusting the parametric surface control points or using a local geometric stretching strategy, the growth direction of the structural branches in the rheological instability zone is adjusted so that the surface tilt angle is greater than the preset self-supporting threshold. The step of correcting regions with tilt angles less than a preset self-supporting threshold relative to the construction direction into self-supporting curved surface structures includes: A modular construction strategy is adopted to decouple structural features that cannot meet self-support conditions through local modifications into independent printed substructures and reserve welding bevels, generating a third topological configuration; and, The process of performing curvature continuity smoothing on topological intersection nodes and sharp turning regions to generate a solid model includes: A large-radius rounding method is used to smooth the transition of all sharp corners, topological branch intersections, and high curvature abrupt change regions of the wall openings in the third topological configuration, thereby eliminating stress concentration sources and thermal effects. The main surface after the reconstruction of the third topological configuration is subjected to Boolean summation with the solid features of the non-design domain. A mesh processing algorithm is used to identify and stitch non-manifold edges and open boundaries. A subdivision algorithm and Laplacian smoothing method are used to improve the mesh resolution, thereby forming and outputting a complete closed parametric solid model.

8. The method according to claim 7, characterized in that, The process of decoupling structural features that cannot meet self-support conditions through local modifications into independent printed substructures and reserving weld bevels includes: Structural features in the second topological configuration that still cannot meet the self-supporting forming conditions after local modification are determined to be features that cannot be directly formed; and... The non-directly formable features are decoupled from the main structure of the second topology to construct an independent printing substructure, and welding bevels, overlap interfaces or mechanical connection features are designed between the main structure and the independent printing substructure.

9. The method according to claim 2 or 8, characterized in that, The process of mesh optimization of the solid model and simulation verification using virtual slicing and support generation, outputting solid manufacturing data that meets the conditions for unsupported manufacturing of directional energy deposition, includes: The solid model was re-meshed and virtual slicing and support generation simulation was performed using additive manufacturing preprocessing software. At the same time, linear static analysis and modal analysis were performed under a multi-degree-of-freedom dynamic inertial force field. Determine whether the maximum Mises stress of the structure of the solid model under any ultimate load is lower than the safety threshold of the material yield strength, and whether the absolute displacement of the key functional points is within the preset displacement threshold range. If any indicator is not met, the parameters are adjusted according to the judgment and identification results. The parameter adjustment includes adjusting one or more of the following parameters: volume constraint, penalty factor, local material density or stiffness weight parameter. If all indicators are met, the manufacturing entity model and directional energy deposition additive manufacturing process path data are output.

10. An electronic device, characterized in that, include: A processor, adapted to execute computer programs; as well as A computer-readable storage medium storing a computer program that, when executed by the processor, implements the method of any one of claims 1 to 9.

Images