Shear wall topology optimization method fusing tensile-compressive asymmetry and additive manufacturing constraints

Abstract

This invention discloses a shear wall topology optimization method that integrates tension-compression asymmetry and additive manufacturing constraints, comprising the following steps: minimizing structural strain energy as the objective, using element density and material rotation angle as design variables, employing a solid isotropic material interpolation model to describe stiffness distribution, using anisotropic elasticity and rotation matrices to characterize the mechanical properties of the additive manufacturing material and the printing direction, introducing the Hoffman failure criterion as a stress constraint considering the difference in tension and compression strength, and simultaneously optimizing element density and rotation angle using a moving asymptote algorithm. This invention also ensures printability through a minimum size control strategy based on stress distribution ratio. This invention enables the coordinated optimization of shear wall structural topology and printing direction, balancing strength, stiffness, and manufacturing feasibility.

Description

Technical Field

[0001] This invention relates to the fields of structural topology optimization and additive manufacturing technology, and in particular to a shear wall topology optimization method that integrates tension-compression asymmetry and additive manufacturing constraints. Background Technology

[0002] As building structures become taller, wider, and more complex, traditional design methods are increasingly showing their limitations in achieving optimal structural forms under complex stress conditions. Topology optimization, as an advanced structural design method, can automatically generate optimal material distribution schemes given a design domain, loads, and constraints, and has received widespread attention in the field of building structures in recent years.

[0003] With the increasing application of additive manufacturing technology in the field of concrete structures, there is a mismatch between the traditional design mode of shear walls and the unique process requirements of additive manufacturing, such as layer-by-layer printing and path planning. While additive manufacturing technology has made it possible to form complex structures, it also poses new challenges to the structural topology of shear walls: on the one hand, the mechanical properties of additive manufacturing materials exhibit significant anisotropy, meaning that the elastic modulus and strength along the printing direction and perpendicular to the printing direction differ significantly; on the other hand, the additive manufacturing process imposes strict limitations on the minimum solid size and minimum hole size of the structure.

[0004] Furthermore, concrete inherently exhibits tension-compression asymmetry, meaning its tensile strength is significantly lower than its compressive strength. Most existing topology optimization methods employ the Von Mises equivalent stress criterion, which assumes symmetry between tension and compression. Directly applying this criterion to concrete structures severely underestimates the harmful effects of tensile stress, leading to insufficient material in the tension zone in the optimization results and a higher likelihood of cracking in the actual structure.

[0005] Currently, existing optimization methods have the following shortcomings: First, while some methods consider the tension-compression asymmetry of materials, they do not incorporate additive manufacturing process constraints; second, while some methods consider the anisotropy of additive manufacturing, they still employ strength criteria based on tension-compression symmetry. No method has yet been found to integrate material tension-compression asymmetry, additive manufacturing anisotropy, and minimum size process constraints into a single topology optimization framework, making it difficult to balance real-world mechanical behavior with manufacturability requirements in the optimization results. Summary of the Invention

[0006] To address the problem that existing technologies cannot simultaneously address the tension-compression asymmetry of concrete and the constraints of additive manufacturing processes, this invention proposes a shear wall topology optimization method that integrates tension-compression asymmetry and additive manufacturing constraints.

[0007] The specific technical solution is as follows: A shear wall topology optimization method integrating tension-compression asymmetry and additive manufacturing constraints, including the following steps:

[0008] Establish the design domain, finite element mesh, boundary conditions, and load conditions for the shear wall;

[0009] Define the design variable as the relative density ρ of each element. e and material rotation angle θ e The optimization objective is to minimize the overall strain energy of the structure, and a structural volume fraction constraint is set.

[0010] The element stiffness matrix is ​​calculated using a solid isotropic material interpolation model, where the element elastic modulus is determined by a penalty function of the element density;

[0011] The mechanical properties of additively manufactured materials are described using an anisotropic elastic matrix, and the principal directions of the material are transformed from the natural coordinate system to the component coordinate system using a rotation matrix. These principal directions are determined by a material rotation angle θ. e Decide;

[0012] The failure discrimination parameter σ of each element was calculated using the Hoffman failure criterion. he This criterion introduces the tensile strength and compressive strength of the material in the principal direction, respectively;

[0013] The element density ρ is updated synchronously during the optimization iteration. e and material rotation angle θ e The optimization process continues until the convergence condition is met, outputting the optimized topology and material orientation. By using element density and material rotation angle as parallel design variables, the structural topology and material orientation are simultaneously optimized within the SIMP framework. By integrating tension-compression asymmetric strength constraints and anisotropic elastic matrices into the same optimization model, the structural stiffness can be maximized while satisfying volume constraints, and the optimization results are consistent with the directional characteristics of additive manufacturing materials and the actual mechanical behavior of concrete.

[0014] Furthermore, the Hoffman failure criterion is expressed as:

[0015] ;

[0016] Where σ1, σ2, τ 12 Let be the stress components along the principal directions of the material, c1 and t1 be the compressive and tensile ultimate strengths in direction 1, and c2 and t2 be the compressive and tensile ultimate strengths in direction 2. 12 1 Ultimate shear strength in the plane; σ H =1 represents the critical failure state. This criterion accurately captures the tension-compression asymmetry of concrete materials by introducing tensile and compressive strengths in each direction, thus avoiding the deficiency of traditional equivalent stress criteria that underestimate the harm of tensile stress. At the same time, the combination of its quadratic and linear terms makes the sensitivity analytically expressible, which is convenient for the stable iteration of gradient optimization algorithms.

[0017] Furthermore, the Hoffman failure criteria parameters of all elements are aggregated into a global stress constraint function using the KS function, which is expressed as:

[0018] ;

[0019] Where n is the total number of units, P is the aggregation parameter, and P = 50 is taken; and the following conditions are met: By using the KS function to condense the massive element stress constraints into a differentiable global constraint, the size of the optimization problem can be significantly reduced; choosing P=50 can achieve a good balance between approximating the true peak value and maintaining numerical stability, avoiding iterative oscillations and improving convergence reliability.

[0020] Furthermore, the anisotropic elasticity matrix is ​​constructed in the following manner:

[0021] In the main direction of materials 1 In the 2-coordinate system, the elastic matrix D0 is defined, where directions 1 and 2 are the two principal directions of the material that are perpendicular to each other; then, it is transformed to the component coordinate system x using the rotation matrix T(θ). y, thus obtaining the equivalent elasticity matrix D(θ)=T(θ)D0T(θ) T ;

[0022] in, ;

[0023] ;

[0024] θ is the angle of counterclockwise rotation from the positive x-axis direction of the component coordinate system to the 1 direction. By using an orthogonal anisotropic elastic matrix in conjunction with rotation transformation, the stiffness direction dependence of additive manufacturing materials due to layer-by-layer deposition can be accurately described. By using the rotation angle θ as a design variable, the optimization algorithm can automatically match the optimal printing direction for each unit, realizing the integrated design of structure and material direction.

[0025] Furthermore, during the synchronous update process, stress constraints affect the element density ρ. e The sensitivity is solved using the adjoint method as follows:

[0026] ;

[0027] Wherein, the adjoint vector ζ satisfies:

[0028] ;

[0029] Among them, I i Let be a pointing matrix, satisfying u i =I i U;A HB H These are the coefficient matrices of the quadratic and linear terms of the Hoffman criterion, respectively. An adjoint sensitivity expression coupled with the Hoffman criterion derivative, KS aggregate weights, and anisotropic rotation matrix is ​​derived, enabling efficient gradient calculation for topology optimization with complex stress constraints. This analytical form avoids the computational burden of finite differences, ensuring rapid convergence of the optimization process.

[0030] Furthermore, the synchronous update of design variables employs the moving asymptote algorithm, updating ρ in each iteration through the following convex subproblem. e and θ e :

[0031] For the k-th iteration, the design variable x e The original function is approximated by the following formula:

[0032] ;

[0033] Among them, L e (k) U e (k) Let p be the left and right asymptotes of the e-th variable. ie (k) q ie (k) Determined by sensitivity and left and right asymptotes, and satisfying variable boundary constraints α. e (k) ≤x e ≤β e (k) α e (k) β e (k) The moving asymptote algorithm is used to handle highly nonlinear stress-constrained optimization problems. By using convex approximation subproblems to gradually approximate the original problem, global convergence can be guaranteed. The explicit rational function approximation form makes the subproblem solution fast and stable.

[0034] Furthermore, after each iteration, the minimum dimensions of solid rods and holes in the topology are controlled:

[0035] For solid members that do not meet the minimum size limit, if the stress of their edge elements is large enough, the size of the member is increased; if the stress is small enough, the member is deleted.

[0036] For holes that do not meet the minimum size limit, if the stress of the edge elements is large enough, the hole is filled; if the stress is small enough, the elements at the edge of the hole are deleted to enlarge the hole. By directly embedding manufacturability constraints into the optimization iteration process, and distinguishing between "small members with important stress" and "small members that can be safely deleted" by stress state, the drawbacks of blindly deleting small structures and destroying the force transmission path in traditional geometric filtering can be avoided, thus maintaining the structural mechanical properties while ensuring printability.

[0037] Furthermore, the criterion for judging sufficiently low stress is: the proportion of elements with stress greater than that element exceeds 50%; the criterion for judging sufficiently high stress is: the proportion of elements with stress less than that element exceeds 50%.

[0038] Furthermore, the output material rotation angle θ e This is used to control the direction of the printing path during additive manufacturing, ensuring that the tangent direction of the printing path aligns with the principal material direction of each optimized unit. The optimized material direction is directly mapped to the printing path direction of the additive manufacturing equipment, thus achieving an integrated closed loop of "analysis-optimization-manufacturing." This ensures that the actual printing direction matches the design direction, fully leveraging the load-bearing potential of anisotropic materials.

[0039] Furthermore, it also includes: generating the layer-by-layer printing path of the additive manufacturing equipment based on the output topology, and mapping the material rotation angle of each layer to the movement direction of the printing nozzle.

[0040] The above technical solution has the following advantages or technical effects:

[0041] 1. This invention integrates the tension-compression asymmetry of concrete materials with the anisotropy and minimum size process constraints of additive manufacturing into the SIMP topology optimization framework, solving the technical problem that existing methods cannot balance mechanical realism and manufacturability.

[0042] 2. This invention introduces the material rotation angle as an independent design variable and optimizes it synchronously with the unit density, thereby achieving integrated design of structural topology and printing direction. This ensures that the final component can meet the goal of maximizing stiffness and that the main direction of the material is consistent with the force transmission path, thus making full use of the anisotropic advantages of additive manufacturing materials.

[0043] 3. The minimum size control method based on stress distribution ratio proposed in this invention breaks through the limitations of traditional geometric filtering. It can intelligently retain small, stress-critical components while ensuring printability, thus avoiding structural performance degradation caused by blind deletion.

[0044] 4. The adjoint sensitivity formula derived in this invention fully integrates the derivative of the Hoffman criterion, the KS aggregate weights, and the anisotropic rotation matrix, enabling topology optimization with complex stress constraints to converge efficiently and stably, with computational efficiency significantly higher than that of conventional finite difference methods.

[0045] 5. The optimization results output by this invention can be directly converted into the layer-by-layer printing path and nozzle movement direction of the additive manufacturing equipment, realizing a seamless connection from design to manufacturing, and providing a complete technical solution for the engineering application of high-performance, printable concrete shear walls. Attached Figure Description

[0046] Figure 1 This is a flowchart of the method of the present invention;

[0047] Figure 2 This is a schematic diagram of the natural coordinate system and material coordinate system of this invention;

[0048] Figure 3 This is a structural design model diagram of an embodiment of the present invention;

[0049] Figure 4 This is a schematic diagram of the topology optimization results according to an embodiment of the present invention;

[0050] Figure 5 This is a schematic diagram of the material rotation angle optimization result according to an embodiment of the present invention;

[0051] Figure 6 This is a stress distribution diagram of working condition one of the embodiments of the present invention;

[0052] Figure 7 This is a stress distribution diagram of working condition two in an embodiment of the present invention;

[0053] Figure 8 This is a schematic diagram of the iterative convergence history of an embodiment of the present invention. Detailed Implementation

[0054] To make the technical solution of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0055] like Figure 1 As shown, the shear wall topology optimization method integrating tension-compression asymmetry and additive manufacturing constraints specifically includes the following steps:

[0056] Establish a structural finite element calculation model

[0057] First, the design domain, finite element mesh, boundary conditions, and load conditions of the shear wall are established. In this embodiment, the shear wall has dimensions of 1500 mm (width) × 3000 mm (height), and the design domain is divided into a 50 × 100 quadrilateral finite element mesh. The bottom is a fixed edge, constraining all displacements and rotations; the top is subjected to a uniformly distributed vertical load Q = 8000 N / m, and simultaneously subjected to concentrated horizontal loads F in two directions. H =6000 N. To avoid stress concentration at the loading location, the horizontal concentrated load is split into 6 concentrated forces, which are applied to the 6 nodes at the top, as follows: Figure 2 As shown.

[0058] Define optimization model

[0059] Define the design variable as the relative density ρ of each element. e and material rotation angle θ e The optimization objective is to minimize the overall strain energy of the structure (i.e., maximize stiffness), while setting a volume fraction constraint to control the proportion of material used after optimization to not exceed a preset value ω=0.4. The optimization model can be expressed as:

[0060] ;

[0061] ;

[0062] ;

[0063] ; ; ; ; ;

[0064] Where C is the structural strain energy; ρ and θ are design variables, representing element density and material rotation angle, respectively; K is the overall stiffness matrix; U and F are the overall displacement vector and load vector, respectively; ω is the preset volume fraction value; v e V and ρ represent the volume of element e and the volume of the initial design domain, respectively; min A small positive number is chosen to prevent the stiffness matrix from becoming singular when the element density is zero; σ He For the failure discrimination parameter of element e, l s With l v denoted by , and denoted by m, representing the minimum feature size of the solid portion and the hole portion, respectively, where m is the number of hole regions.

[0065] Material interpolation and element stiffness matrix

[0066] The element stiffness matrix is ​​calculated using a solid isotropic material interpolation (SIMP) model, where the element elastic modulus is determined by a penalty function of the element density. Specifically, the elastic modulus of element e... Represented as:

[0067] ;

[0068] Where E0 is the stiffness of the solid material, and q is a penalty factor used to penalize intermediate density values, making the optimization result approach 0. 1. Distribution. The contribution of element e to the global stiffness matrix is ​​K. e =E e k e , where k e This is the element stiffness matrix. The global stiffness matrix is ​​obtained by assembling the stiffness matrices of each element:

[0069] ;

[0070] ;

[0071] Where B is the strain-displacement matrix, D is the elastic matrix, and V is the design domain.

[0072] Anisotropic material modeling

[0073] To describe the anisotropic mechanical properties of additive manufacturing materials, this invention focuses on the material's principal direction 1. In a 2-coordinate system, the elasticity matrix D0 is defined, where directions 1 and 2 are the two principal directions perpendicular to each other. The specific form of D0 is:

[0074] ;

[0075] Where E1 and E2 are the elastic moduli in directions 1 and 2, respectively, and ν 12 ν 21 G is Poisson's ratio. 12 This refers to the shear modulus. In this embodiment, considering the anisotropy caused by layer-by-layer deposition in additive manufacturing, the preset material parameters are: E1 = 2 × 10⁻⁶. 5 MPa, ν 12 =0.3, yield strength σ c1 =10σ t1 =50MPa, τ1=7MPa; E2=χE1, ν 21 =χν 12 , σ c2 =10σ t2 =κσ c1 , τ2=κτ1, where χ=0.95, κ=0.9.

[0076] To align the principal direction of the material with the component coordinate system (x) y) is associated, and a rotation matrix T(θ) is introduced. For example... Figure 3As shown, θ is the angle of counterclockwise rotation from the positive x-axis of the component coordinate system to the principal material direction 1. The rotation matrix T(θ) has the following form:

[0077] ;

[0078] Therefore, the equivalent elasticity matrix D(θ) in the component coordinate system is:

[0079] ;

[0080] Through the above transformation, the mechanical properties of the material change with the material rotation angle θ, and θ participates in the optimization as an independent design variable, so that the optimization algorithm can automatically match the optimal material principal direction for each element.

[0081] Strength constraints based on Hoffman failure criterion

[0082] To accurately account for the tension-compression asymmetry of concrete, this invention employs the Hoffman failure criterion to calculate the failure discrimination parameter σ for each element. He This criterion introduces the tensile strength and compressive strength of the material in the principal directions, respectively. In the principal direction of the material... In the 2-coordinate system, the stress vector is expressed as: The expression for Hoffman's failure criterion is:

[0083] ;

[0084] Where c1 and t1 are the ultimate compressive and tensile strengths in direction 1, c2 and t2 are the ultimate compressive and tensile strengths in direction 2, and s 12 1 Ultimate shear strength in the plane; σ H =1 indicates a critical failure state. In this embodiment, c1=50 MPa, t1=5 MPa, c2=κ×50=45 MPa, t2=κ×5=4.5 MPa, s 12 =7 MPa.

[0085] In finite element analysis, the element stress vector σ is first obtained in the component coordinate system. e Then, it is transformed to the principal direction of the material using an inverse rotation matrix:

[0086] ;

[0087] Among them, u e This represents the element displacement vector. After obtaining the principal stresses of the material, substituting them into the Hoffman criterion yields the failure criterion parameter σ for each element. He .

[0088] Stress-constrained polymerization

[0089] Due to the large number of elements in the structure, setting stress constraints independently for each element would result in a massive optimization problem that is difficult to converge. Therefore, this invention aggregates the Hoffman failure criteria parameters of all elements into a single global stress constraint function using the KS function. The expression for the KS function is:

[0090] ;

[0091] Where n is the total number of elements and P is the aggregation parameter. As P approaches infinity, the KS function approximates the maximum value of the element failure discrimination parameter: However, an excessively large value for P can lead to severe oscillations or even divergence during the topology optimization iteration process. Therefore, in this embodiment, P=50 is chosen to achieve a balance between approximating the true peak value and maintaining numerical stability. The global stress constraint after aggregation is G≤1.

[0092] Sensitivity analysis

[0093] To update design variables using a gradient optimization algorithm, it is necessary to calculate the effect of the objective function, volume constraints, and stress constraints on the element density ρ. e and material rotation angle θ e Sensitivity.

[0094] Objective function sensitivity

[0095] Using the Lagrange multiplier method, with the equilibrium equation F=KU as the zero function, the objective function can be expressed as L=C+λ(F KU). Through derivation, the objective function with respect to element density ρ e The sensitivity is simplified to:

[0096] ;

[0097] The objective function is related to the material rotation angle θ. e The sensitivity is:

[0098] ;

[0099] in, ;

[0100] ;

[0101] Volume Constraint Sensitivity

[0102] Volume constraints on element density ρ e The sensitivity is With respect to the material rotation angle θ e The sensitivity is 0.

[0103] Stress constraint sensitivity

[0104] According to the chain rule, the stress constraint G affects the element density ρ. e The sensitivity is:

[0105] ;

[0106] in, ;

[0107] ;

[0108] A H and B H The coefficient matrices for the quadratic and linear terms of the Hoffman criterion are shown below:

[0109] ;

[0110] Further derivation yields the final sensitivity expression:

[0111] ;

[0112] Wherein, the adjoint vector ζ satisfies:

[0113] ;

[0114] Among them, I i Let be a pointing matrix, satisfying u i =I i U.

[0115] Similarly, stress constraint affects the material rotation angle θ e The sensitivity can be expressed as:

[0116] ;

[0117] The sensitivity analysis described above provides all gradient information, which serves as a basis for updating design variables.

[0118] Design variable update (moving asymptote algorithm)

[0119] This invention employs the Moving Asymptote Algorithm (MMA) to synchronously update the cell density ρ. e and material rotation angle θ e The MMA algorithm performs a first-order Taylor expansion of the original function at the current design point, approximates the original problem with a series of convex linear explicit subproblems, and then solves the convex linear explicit subproblems using the dual method or the initial dual interior point algorithm, iteratively approximating the solution to the original problem.

[0120] Specifically, for the k-th iteration, the design variable x e (representing ρ) e or θe The antiderivative of is approximated by the following formula:

[0121] ;

[0122] Among them, L e (k) U e (k) Let p be the left and right asymptotes of the e-th variable. ie (k) q ie (k) Determined by sensitivity and left and right asymptotes, and satisfying variable boundary constraints α. e (k) ≤x e ≤β e (k) α e (k) β e (k) These are the upper and lower limits for the movement. By solving this convex subproblem, new design variable values ​​are obtained.

[0123] Density Filtering and Projection

[0124] To avoid numerical instability such as checkerboard patterns, density filtering is applied to the updated cell density. This embodiment employs a weighted average filtering strategy, and the filtered density is:

[0125] ;

[0126] Among them, the filtering weight W is the distance weight matrix.

[0127] To further reduce intermediate density values, a smooth Heaviside function is used for projection:

[0128] ;

[0129] In the formula, β and η control the steepness and threshold of the projection, respectively. Through density filtering and projection, a clear black-and-white topological configuration can be obtained.

[0130] Minimum size control

[0131] Additive manufacturing imposes strict limitations on the minimum solid dimensions and minimum hole dimensions of structures. To meet manufacturability requirements, this invention controls the minimum dimensions of solid members and holes in the topological configuration after each iteration.

[0132] The specific control logic is as follows:

[0133] (1) For solid rods that do not meet the minimum size limit:

[0134] If the stress in its edge elements is large enough, then the size of the member is increased;

[0135] If the stress is small enough, the member should be removed.

[0136] (2) For holes that do not meet the minimum size limit:

[0137] If the stress of its edge element is large enough, the hole will be filled;

[0138] If the stress is small enough, remove the elements at the edge of the hole to enlarge the hole.

[0139] The criteria for determining sufficiently low stress are: the proportion of elements with stress greater than that of the element exceeds 50%; the criteria for sufficiently high stress are: the proportion of elements with stress less than that of the element exceeds 50%. This criterion is based on the stress distribution ratio at the integration points (or nodes) within the element, and can effectively distinguish between "small components with important stress" and "small components that can be safely removed," thereby maintaining the structural mechanical performance while ensuring printability.

[0140] Convergence judgment and output

[0141] Determine if the convergence condition is met. In this embodiment, the convergence condition is: the number of iterations exceeds 300, or the average density change of all elements in two consecutive iterations is less than 0.05%. If the convergence condition is not met, return to the step of defining the optimization model and continue iterating; if the convergence condition is met, the iteration ends, and the optimized topology and material orientation are output.

[0142] Optimization results and print path generation

[0143] Figure 4 A schematic diagram of the topology optimization results of this embodiment is given. The optimal topology can be roughly divided into two parts: vertical members are retained on both sides from the bottom of the wall to the top, and slightly inclined towards the middle at about 1 / 2 of the wall height; two "X"-shaped diagonal intersecting members are roughly formed in the middle of the wall to connect the two sides; a certain amount of material is retained at the top of the wall to bear the uniformly distributed load at the top of the wall and transfer it downward.

[0144] Figure 5 A schematic diagram of the optimized material rotation angle is provided. The optimal material angle is basically consistent with the direction of the rod, indicating that the optimization algorithm can automatically align the main direction of the material with the force transmission path.

[0145] Figure 6 , Figure 7 The stress distribution diagram of the final topology is given. The combined stress calculated according to the Hoffman failure criterion and the KS condensation function is 0.4628, which is less than 1, and meets the strength requirements.

[0146] Figure 8A schematic diagram of the iterative convergence history is provided. During the optimization process, the elements in the middle region at the bottom of the wall are deleted first, followed by optimization in the middle of the wall, gradually forming diagonally intersecting members. Elements in the upper two sides are eliminated, and finally, the optimization is stable and converges.

[0147] Output material rotation angle θ e It is directly used to control the direction of the printing path during additive manufacturing, ensuring that the tangent direction of the printing path is consistent with the principal material direction of each optimized unit. Based on the output topology, it can automatically generate the layer-by-layer printing path of the additive manufacturing equipment and map the material rotation angle of each layer to the movement direction of the printing nozzle, thereby achieving seamless integration from design to manufacturing.

[0148] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A shear wall topology optimization method integrating tension-compression asymmetry and additive manufacturing constraints, characterized in that, Includes the following steps: Establish the design domain, finite element mesh, boundary conditions, and load conditions for the shear wall; Define the design variable as the relative density ρ of each element. e and material rotation angle θ e The optimization objective is to minimize the overall strain energy of the structure, and a structural volume fraction constraint is set. The element stiffness matrix is ​​calculated using a solid isotropic material interpolation model, where the element elastic modulus is determined by a penalty function of the element density; The mechanical properties of additively manufactured materials are described using an anisotropic elastic matrix, and the principal directions of the material are transformed from the natural coordinate system to the component coordinate system using a rotation matrix. These principal directions are determined by a material rotation angle θ. e Decide; The failure discrimination parameter σ of each element was calculated using the Hoffman failure criterion. he This criterion introduces the tensile strength and compressive strength of the material in the principal direction, respectively; The element density ρ is updated synchronously during the optimization iteration. e and material rotation angle θ e Continue until the convergence condition is met, and then output the optimized topology and material orientation.

2. The shear wall topology optimization method integrating tension-compression asymmetry and additive manufacturing constraints as described in claim 1, characterized in that, The Hoffman failure criterion is expressed as follows: ； Where σ1, σ2, τ 12 Let be the stress components along the principal directions of the material, c1 and t1 be the compressive and tensile ultimate strengths in direction 1, and c2 and t2 be the compressive and tensile ultimate strengths in direction 2. 12 σ is the ultimate shear strength in the 1-2 plane; H =1 indicates a critical failure state.

3. The shear wall topology optimization method integrating tension-compression asymmetry and additive manufacturing constraints as described in claim 2, characterized in that, The Hoffman failure criteria parameters of all elements are aggregated into a global stress constraint function using the KS function, which is expressed as: ； Where n is the total number of units, P is the aggregation parameter, and P = 50 is taken; and the following conditions are met: .

4. The shear wall topology optimization method integrating tension-compression asymmetry and additive manufacturing constraints as described in claim 1, characterized in that, The anisotropic elasticity matrix is ​​constructed in the following way: Define the elastic matrix D0 in the material principal directions 1-2 coordinate system, where directions 1 and 2 are two mutually perpendicular principal directions of the material; then transform it to the component coordinate system xy using the rotation matrix T(θ) to obtain the equivalent elastic matrix D(θ) = T(θ)D0T(θ). T ; in, ; ； θ is the angle of counterclockwise rotation from the positive x-axis direction of the component coordinate system to the 1 direction.

5. The shear wall topology optimization method integrating tension-compression asymmetry and additive manufacturing constraints according to claim 1, characterized in that, During the synchronous update process, stress constraints apply to the element density ρ e The sensitivity is solved using the adjoint method as follows: ； Wherein, the adjoint vector ζ satisfies: ； Among them, I i Let be a pointing matrix, satisfying u i =I i U;A H B H These are the coefficient matrices of the quadratic and linear terms of the Hoffman criterion, respectively.

6. The shear wall topology optimization method integrating tension-compression asymmetry and additive manufacturing constraints according to claim 1, characterized in that, The synchronous update of design variables employs the moving asymptote algorithm, updating ρ in each iteration through the following subproblem. e and θ e : For the k-th iteration, the design variable x e The original function is approximated by the following formula: ； Among them, L e (k) U e (k) Let p be the left and right asymptotes of the e-th variable. ie (k) q ie (k) Determined by sensitivity and left and right asymptotes, and satisfying variable boundary constraints α. e (k) ≤x e ≤β e (k) α e (k) β e (k) These are the upper and lower limits for movement.

7. The shear wall topology optimization method integrating tension-compression asymmetry and additive manufacturing constraints according to claim 1, characterized in that, After each iteration, the minimum dimensions of solid members and holes in the topology are also controlled: For solid members that do not meet the minimum size limit, if the stress of their edge elements is large enough, the size of the member is increased; if the stress is small enough, the member is deleted. For holes that do not meet the minimum size limit, the holes are filled if the stress of their edge elements is large enough. If the stress is small enough, remove the elements at the edge of the hole to enlarge the hole.

8. The shear wall topology optimization method integrating tension-compression asymmetry and additive manufacturing constraints according to claim 7, characterized in that, The criterion for judging sufficiently low stress is: the proportion of elements with stress greater than that of the element exceeds 50%; the criterion for judging sufficiently high stress is: the proportion of elements with stress less than that of the element exceeds 50%.

9. The shear wall topology optimization method integrating tension-compression asymmetry and additive manufacturing constraints according to claim 1, characterized in that, Output material rotation angle θ e Used to control the direction of the printing path during additive manufacturing, ensuring that the tangent direction of the printing path is consistent with the main material direction of each optimized unit.

10. The shear wall topology optimization method integrating tension-compression asymmetry and additive manufacturing constraints according to claim 1, characterized in that, Also includes: The additive manufacturing equipment generates a layer-by-layer printing path based on the output topology and maps the material rotation angle of each layer to the movement direction of the printing nozzle.

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