Topological optimization-based multi-sealing method and system for high-vacuum die-casting die

Abstract

The invention provides a multi-sealing method and system for a high-vacuum die-casting die based on topological optimization, and relates to the technical field of die structures.The multi-sealing method comprises the steps that a sealing structure parameterized model is established, geometric parameters and boundary conditions of a cavity wall face are obtained, an initial design domain is constructed, and grid units are divided; carrying out topological optimization calculation based on a multi-objective function considering contact stress distribution, and iteratively optimizing the relative density of grid cells by adopting an SIMP method; and according to the obtained topological configuration of the sealing structure, multiple sealing structure design is carried out, a three-dimensional entity model is constructed, and the sealing performance is verified through finite element analysis.

Description

Technical Field

[0001] The invention relates to a mold structure technology, and in particular to a high vacuum die-casting mold multiple sealing method and system based on topology optimization. Background Art

[0002] In the prior art, a single sealing structure is difficult to adapt to different working conditions and pressure distributions, and is prone to problems such as local leakage or over-compression, which affects the sealing effect and service life of the mold. Traditional sealing structure design methods are difficult to accurately predict the contact stress distribution between the sealing structure and the cavity wall, and cannot effectively optimize the shape and size of the sealing structure, resulting in unstable sealing performance.

[0003] The existing sealing structure design lacks consideration for multiple seals, making it difficult to achieve multi-level and multi-region coordinated sealing, and unable to meet the sealing requirements of high-precision and high-reliability molds. These problems have seriously restricted the further development and application of mold sealing technology. Summary of the invention

[0004] The embodiments of the present invention provide a high vacuum die-casting mold multiple sealing method and system based on topology optimization, which can solve the problems in the prior art.

[0005] According to a first aspect of the embodiments of the present invention, Provides multiple sealing methods for high vacuum die casting molds based on topology optimization, including: Establishing a parametric model of the sealing structure, including: obtaining geometric parameters and boundary conditions of the cavity wall of the mold, constructing an initial design domain of the sealing structure, dividing grid units in the initial design domain, using the relative density of the grid units as design variables, establishing contact constraints between the sealing structure and the cavity wall, and constructing a multi-objective function that considers contact stress distribution; Performing topology optimization calculation based on the multi-objective function includes: iteratively optimizing the relative density of the grid unit using a solid isotropic material penalty method, calculating the contact stress distribution between the sealing structure and the cavity wall in each iteration process, and modifying the multi-objective function according to the contact stress distribution until a topological configuration of the sealing structure that meets the convergence condition is obtained; The multiple sealing structures are designed according to the topological configuration of the sealing structure, including: extracting the boundary contour of the topological configuration of the sealing structure, constructing a three-dimensional solid model of the multiple sealing structures based on the boundary contour, performing finite element analysis on the three-dimensional solid model, verifying the sealing performance, and optimizing and adjusting the multiple sealing structures according to the finite element analysis results.

[0006] In an optional embodiment, Establishing a parametric model of the sealing structure includes: obtaining geometric parameters and boundary conditions of the cavity wall of the mold, constructing an initial design domain of the sealing structure, dividing grid units in the initial design domain, taking the relative density of the grid units as design variables, establishing contact constraints between the sealing structure and the cavity wall, and constructing a multi-objective function considering the contact stress distribution. The steps include: The point cloud data is acquired through a 3D scanning system, and the iterative closest point algorithm is used for point cloud registration. The radial basis function interpolation method of local quadratic surface fitting is used to reconstruct the cavity wall. The pressure distribution data is acquired by combining the pressure sensor array arranged on the cavity wall, and the geometric parameters and boundary conditions of the cavity wall are obtained. According to the obtained geometric parameters and boundary conditions, the level set method is used to describe the topological structure, and the initial level set function is constructed through the distance field to establish the design domain. The initial level set function is greater than zero for the solid area, equal to zero for the boundary, and less than zero for the cavity area, thus obtaining the initial design domain of the sealing structure; In the initial design domain, the adaptive mesh refinement strategy is used to divide the mesh units, the relative density of the mesh units is used as the design variable, and the SIMP penalty factor method is used to control the relative density distribution; Based on the divided grid units, the mortar method is used to establish the contact constraint conditions between the sealing structure and the cavity wall. The contact surface is divided into mortar units of the slave surface and non-mortar units of the master surface. The piecewise bilinear interpolation function is used to calculate the contact integral and obtain the stress distribution in the contact area. According to the relative density distribution of mesh elements and the stress distribution in the contact area, a multi-objective function including volume fraction target, von Mises stress target and contact pressure standard deviation target is constructed.

[0007] In an optional embodiment, The topology optimization calculation is performed based on the multi-objective function, including: using a solid isotropic material penalty method to iteratively optimize the relative density of the grid unit, calculating the contact stress distribution between the sealing structure and the cavity wall in each iteration process, and correcting the multi-objective function according to the contact stress distribution until a topological configuration of the sealing structure that meets the convergence condition is obtained. The steps include: A multi-objective function including volume fraction target, von Mises stress target and contact pressure standard deviation target is constructed. The volume fraction target is the ratio of the sum of the product of unit relative density and unit volume to the volume of the initial design domain. The von Mises stress target uses the p-norm form to represent the ratio of unit von Mises stress to allowable stress. The contact pressure standard deviation target is the ratio of contact pressure standard deviation to average contact pressure. Volume constraints, stress constraints and density constraints are set for the multi-objective function. The density constraint limits the value range of unit relative density. The relative density of the grid cells is iteratively optimized by using a solid isotropic material penalty method, wherein the solid isotropic material penalty method includes: density filtering by Heaviside projection, and introducing density gradient constraints to limit the density change of adjacent cells; in each iterative optimization process, the normal contact force between the sealing structure and the cavity wall is calculated by using a penalty function method, and the tangential contact force is calculated by considering the friction effect, and the normal contact force is updated by an adaptive contact stiffness; Based on the obtained normal contact force and tangential contact force, the sensitivity of the multi-objective function to the design variables is calculated, including the volume target sensitivity, stress target sensitivity and contact pressure standard deviation target sensitivity; the calculated sensitivity of the multi-objective function to the design variables is input into the sequential quadratic programming algorithm, and the multi-objective function is solved through the dynamic weight coefficient adjustment strategy; Whether the optimization process is terminated is determined based on the convergence criteria of the multi-objective function and the convergence criteria of the design variables. When any convergence criterion is met and the number of iterations is not less than the minimum number of iterations, the final sealing structure topology configuration is obtained.

[0008] In an optional embodiment, The relative density of the grid cells is iteratively optimized by using a solid isotropic material penalty method, wherein the solid isotropic material penalty method comprises: implementing density filtering by Heaviside projection, and introducing a density gradient constraint to limit the density change of adjacent cells, comprising: The relative density of the grid cells is filtered by using the Heaviside projection function, and the original relative density of the grid cells is mapped by an exponential function to obtain the filtered relative density; the projection parameters of the Heaviside projection function are dynamically updated, and the projection parameters are gradually increased by setting a growth factor; Establishing the density gradient constraint relationship between adjacent grid cells, determining the upper limit of the relative density difference between two adjacent grid cells according to the distance between the center points of two adjacent grid cells, and limiting the relative density difference within the upper limit range; An adaptive update mechanism for density gradient constraints is established to dynamically adjust density gradient constraint parameters based on the ratio of von Mises stress to allowable stress of the grid cells. Calculate the sensitivity of Heaviside projection and density gradient constraint, including: calculate the sensitivity of grid unit relative density to design variables, calculate the sensitivity of density gradient constraint to design variables, and use Lagrange multipliers to combine the sensitivity of grid unit relative density to design variables and the sensitivity of density gradient constraint to design variables to obtain the corrected sensitivity; The relative density of the grid cells is iteratively optimized based on the corrected sensitivity until a material distribution that meets a convergence criterion is obtained.

[0009] In an optional embodiment, The multiple sealing structures are designed according to the sealing structure topology configuration, including: extracting the boundary contour of the sealing structure topology configuration, constructing a three-dimensional solid model of the multiple sealing structures based on the boundary contour, performing finite element analysis on the three-dimensional solid model, verifying the sealing performance, and optimizing and adjusting the multiple sealing structures according to the finite element analysis results. The steps include: The relative density field of the sealing structure is converted into a discrete scalar field, a contour extraction method based on grid division is used to obtain a boundary contour point set of the sealing structure, and a B-spline curve is fitted to the boundary contour point set to obtain a smooth boundary contour; Constructing a three-dimensional solid model of a multiple sealing structure based on the smooth boundary contour includes: constructing an involute sealing primitive using an involute surface, and constructing a hyperbolic sealing primitive using a hyperbolic surface; The three-dimensional solid model is meshed, wherein the sealing contact area is meshed with a hexahedron and the transition connection area is meshed with a tetrahedron, and a finite element analysis model including contact nonlinearity is established; assembly preload, working pressure, temperature cycle and vibration load conditions are set in the finite element analysis model, the sealing surface contact force is calculated by the augmented Lagrangian method, and the stress-strain analysis is performed considering the elastic-plastic effect of the material; Based on the finite element analysis results, a structural optimization model is established with leakage rate and maximum stress as optimization targets and contact pressure and sealing deformation as constraints. The structural optimization model is solved by the sequential quadratic programming method, and the sealing platform angle, curvature, transition area shape and size parameters in the three-dimensional solid model are iteratively optimized until the sealing performance requirements are met.

[0010] In an optional embodiment, The three-dimensional solid model of the multiple sealing structure is constructed based on the smooth boundary contour, including: using an involute surface to construct an involute sealing primitive, and using a hyperbolic surface to construct a hyperbolic sealing primitive includes: Constructing an involute seal primitive, including: using a parametric equation of a variable pitch involute surface to generate a basic surface of a main seal platform, superimposing circumferential and radial micro-corrugated morphologies on the basic surface of the main seal platform, and using a NURBS surface equation to mathematically describe the basic surface of the main seal platform with the micro-corrugated morphology; Constructing a hyperboloid sealing primitive, including: using a parameter equation of a single-leaf hyperboloid to generate a basic curved surface of a secondary sealing platform, introducing a stress concentration relaxation function to modify the basic curved surface of the secondary sealing platform; Constructing a connection transition primitive, including: establishing a transition surface optimization model based on a minimum surface energy functional, solving the transition surface optimization model using boundary curve constraints and normal vector constraints, and obtaining a transition surface that meets geometric continuity requirements; The involute sealing primitive, the hyperbolic sealing primitive and the connecting transition primitive are combined into a three-dimensional solid model of a multiple sealing structure through Boolean merging operation.

[0011] In an optional embodiment, The steps of establishing a structural optimization model with leakage rate and maximum stress as optimization targets and contact pressure and sealing deformation as constraint conditions based on finite element analysis results; solving the structural optimization model using a sequential quadratic programming method, and iteratively optimizing the sealing platform angle, curvature, transition area shape and size parameters in the three-dimensional solid model until the sealing performance requirements are met include: The leakage rate is calculated based on the contact deformation, contact pressure, surface roughness correction coefficient, leakage path length and fluid parameters, and the maximum stress is calculated using the von Mises equivalent stress criterion; The orthogonal experimental design method is used to calculate the sensitivity coefficients of the sealing platform angle, curvature, transition area shape and size parameters to the leakage rate and maximum stress; The structural optimization model is solved by a sequential quadratic programming method based on the sensitivity coefficient, a quadratic programming sub-problem is constructed by calculating a gradient vector and a second-order derivative matrix, and the sealing platform angle, curvature, transition area shape and size parameters are optimized iteratively; When the leakage rate and maximum stress meet the sealing performance requirements, the optimal sealing platform angle, curvature, transition area shape and size parameters are output.

[0012] According to a second aspect of the embodiments of the present invention, Provide a high vacuum die casting mold multiple sealing system based on topology optimization, including: The first unit is used to establish a parameterized model of the sealing structure, including: obtaining the geometric parameters and boundary conditions of the cavity wall of the mold, constructing the initial design domain of the sealing structure, dividing the grid units in the initial design domain, taking the relative density of the grid units as the design variable, establishing the contact constraint conditions between the sealing structure and the cavity wall, and constructing a multi-objective function considering the contact stress distribution; The second unit is used to perform topology optimization calculation based on the multi-objective function, including: iteratively optimizing the relative density of the grid unit by using a solid isotropic material penalty method, calculating the contact stress distribution between the sealing structure and the cavity wall in each iteration process, and correcting the multi-objective function according to the contact stress distribution until a topological configuration of the sealing structure that meets the convergence condition is obtained; The third unit is used to design multiple sealing structures based on the topological configuration of the sealing structure, including: extracting the boundary contour of the topological configuration of the sealing structure, constructing a three-dimensional solid model of the multiple sealing structure based on the boundary contour, performing finite element analysis on the three-dimensional solid model, verifying the sealing performance, and optimizing and adjusting the multiple sealing structures according to the finite element analysis results.

[0013] According to a third aspect of the embodiments of the present invention, An electronic device is provided, comprising: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0014] A fourth aspect of the embodiments of the present invention is: A computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the aforementioned method is implemented.

[0015] The present invention establishes a parameterized model of the sealing structure, takes the relative density of the grid unit as the design variable, and considers the multi-objective function of the contact stress distribution. This can more accurately describe the mechanical behavior of the sealing structure, provide a reliable theoretical basis for subsequent optimization design, and improve the scientificity and rationality of the sealing structure design.

[0016] The present invention adopts the solid isotropic material penalty method to perform topology optimization calculations, and considers the influence of contact stress distribution on the objective function in real time during the iteration process, so as to obtain a more reasonable topological configuration of the sealing structure, avoid the blindness of the traditional empirical design method, and improve the design efficiency and optimization effect of the sealing structure.

[0017] The present invention performs three-dimensional solid modeling and finite element analysis verification on the obtained sealing structure topological configuration, and optimizes and adjusts accordingly, thereby ensuring that the final designed multiple sealing structure has good sealing performance, improving the reliability and service life of the mold sealing structure, and having important engineering application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of a process of a high vacuum die-casting mold multiple sealing method based on topology optimization according to an embodiment of the present invention; Figure 2 It is a schematic structural diagram of a high vacuum die-casting mold multiple sealing system based on topology optimization according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0021] Figure 1 FIG. 1 is a flow chart of a high vacuum die-casting mold multiple sealing method based on topology optimization according to an embodiment of the present invention. Figure 1 As shown, the method includes: S1. Establish a parametric model of the sealing structure, including: obtaining the geometric parameters and boundary conditions of the cavity wall of the mold, constructing the initial design domain of the sealing structure, dividing the grid units in the initial design domain, taking the relative density of the grid units as the design variable, establishing the contact constraint conditions between the sealing structure and the cavity wall, and constructing a multi-objective function considering the contact stress distribution; S2. performing topology optimization calculation based on the multi-objective function, including: iteratively optimizing the relative density of the grid unit using a solid isotropic material penalty method, calculating the contact stress distribution between the sealing structure and the cavity wall in each iteration, and modifying the multi-objective function according to the contact stress distribution until a topological configuration of the sealing structure that meets the convergence condition is obtained; S3. Design multiple sealing structures according to the topological configuration of the sealing structure, including: extracting the boundary contour of the topological configuration of the sealing structure, constructing a three-dimensional solid model of the multiple sealing structures based on the boundary contour, performing finite element analysis on the three-dimensional solid model, verifying the sealing performance, and optimizing and adjusting the multiple sealing structures according to the finite element analysis results.

[0022] Exemplarily, first, a parametric model of the sealing structure is established. The geometric parameters of the wall of the mold cavity are obtained by three-dimensional scanning or measurement, including information such as the size, shape, and curvature of the cavity. At the same time, boundary conditions are determined, such as applied pressure and temperature. An initial sealing structure design domain is constructed around the mold cavity. The size of the design domain is larger than the expected size of the final sealing structure. Then the grid units are divided in the design domain. The size of the grid unit is determined according to the calculation accuracy requirements. Usually hexahedral units are selected, and the unit size is about 0.5-2mm. The relative density of each grid unit is used as a design variable. The relative density ranges from 0 to 1, where 0 means there is no material in the unit and 1 means the unit is full of material.

[0023] Next, establish the contact constraint conditions between the sealing structure and the cavity wall. Define the contact area between the sealing structure and the cavity wall, set the contact type to surface-to-surface contact, and set the friction coefficient to 0.1-0.3. Construct a multi-objective function that considers the distribution of contact stress, including the volume minimization objective of the sealing structure and the contact stress homogenization objective. The volume minimization objective can reduce material costs, and the contact stress homogenization objective can improve the sealing effect.

[0024] Then, the topology optimization calculation is performed based on the multi-objective function. The solid isotropic material penalty method (SIMP) is used to iteratively optimize the relative density of the grid unit. In each iteration, the stiffness matrix of the structure is first calculated according to the current relative density distribution, and then the displacement field is solved, and the contact stress distribution between the sealing structure and the cavity wall is calculated. The multi-objective function is corrected according to the contact stress distribution to make the contact stress distribution more uniform. The design variables, that is, the relative density of the grid unit, are updated using optimization algorithms such as the moving asymptote method (MMA). The above process is repeated until the convergence conditions are met, such as the relative change of the objective function is less than 0.1% or the maximum number of iterations is 100. Finally, a sealing structure topology configuration that meets the requirements is obtained.

[0025] Finally, multiple sealing structures are designed based on the topological configuration of the sealing structure. First, the boundary contour of the topological configuration of the sealing structure is extracted. The isosurface method can be used to extract an isosurface with a relative density of 0.5 as the boundary. Based on the extracted boundary contour, a three-dimensional solid model of the multiple sealing structure is constructed. The multiple sealing structure usually includes 2-3 sealing lips, each of which has a height of 3-5mm, a width of 2-4mm, and a spacing of 5-8mm between the sealing lips. Finite element analysis is performed on the constructed three-dimensional solid model to verify its sealing performance. The analysis includes the deformation of the sealing structure under pressure, stress distribution, and contact pressure distribution with the cavity wall. According to the analysis results, the multiple sealing structure is optimized and adjusted, such as adjusting the shape, size and position of the sealing lip until the sealing performance requirements are met.

[0026] In practical applications, rubber materials such as nitrile rubber or fluororubber can be selected as the material of the sealing structure. The Young's modulus of the material can be 2-5MPa, and the Poisson's ratio can be 0.48-0.49. The pressure of the mold cavity can be set to 5-20MPa, and the temperature range is 20-200℃. The multiple sealing structure designed by the above method can achieve effective sealing of the mold cavity to prevent leakage of molten material.

[0027] The present invention uses a topological optimization method to obtain a more reasonable sealing structure shape. Compared with traditional design methods, it can significantly reduce the material consumption of the sealing structure and reduce costs. Multi-objective optimization considering contact stress distribution can make the contact stress distribution between the sealing structure and the cavity wall more uniform, improve the sealing effect, and extend the service life of the sealing structure. The multiple sealing structure design improves the reliability of the seal. Even if a single sealing lip fails, the other sealing lips can still maintain the sealing effect, greatly reducing the risk of mold leakage.

[0028] In an optional embodiment, Establishing a parametric model of the sealing structure includes: obtaining geometric parameters and boundary conditions of the cavity wall of the mold, constructing an initial design domain of the sealing structure, dividing grid units in the initial design domain, taking the relative density of the grid units as design variables, establishing contact constraints between the sealing structure and the cavity wall, and constructing a multi-objective function considering the contact stress distribution. The steps include: The point cloud data is acquired through a 3D scanning system, and the iterative closest point algorithm is used for point cloud registration. The radial basis function interpolation method of local quadratic surface fitting is used to reconstruct the cavity wall. The pressure distribution data is acquired by combining the pressure sensor array arranged on the cavity wall, and the geometric parameters and boundary conditions of the cavity wall are obtained. According to the obtained geometric parameters and boundary conditions, the level set method is used to describe the topological structure, and the initial level set function is constructed through the distance field to establish the design domain. The initial level set function is greater than zero for the solid area, equal to zero for the boundary, and less than zero for the cavity area, thus obtaining the initial design domain of the sealing structure; In the initial design domain, the adaptive mesh refinement strategy is used to divide the mesh units, the relative density of the mesh units is used as the design variable, and the SIMP penalty factor method is used to control the relative density distribution; Based on the divided grid units, the mortar method is used to establish the contact constraint conditions between the sealing structure and the cavity wall. The contact surface is divided into mortar units of the slave surface and non-mortar units of the master surface. The piecewise bilinear interpolation function is used to calculate the contact integral and obtain the stress distribution in the contact area. According to the relative density distribution of mesh elements and the stress distribution in the contact area, a multi-objective function including volume fraction target, von Mises stress target and contact pressure standard deviation target is constructed.

[0029] Exemplarily, first, the mold cavity is scanned by a three-dimensional laser scanning system to obtain high-precision point cloud data. The iterative closest point algorithm is used to align the point cloud data obtained by multiple scans to eliminate scanning errors. Then, the radial basis function interpolation method of local quadratic surface fitting is used to reconstruct the geometric model of the cavity wall. Specifically, the characteristic points in the point cloud data are selected as the interpolation center points, and the radial basis functions are constructed based on these center points. The weight coefficients of the radial basis functions are solved by the least squares method, and finally the parametric expression of the cavity wall is obtained. At the same time, an array of pressure sensors is arranged on the cavity wall to collect pressure distribution data during the actual molding process. The obtained geometric model and pressure distribution data are used as boundary conditions for subsequent optimization.

[0030] Next, based on the obtained cavity wall geometry model, the level set method is used to describe the topology of the sealing structure. The initial level set function is established by constructing a distance field function. The area with a function value greater than zero represents a solid, equal to zero represents a boundary, and less than zero represents a cavity.

[0031] In the established initial design domain, an adaptive mesh refinement strategy is used for mesh division. First, the entire design domain is roughly divided using a uniform hexahedral mesh, and then the boundary area is locally encrypted based on the gradient information of the level set function. Specifically, the gradient value of the level set function in each mesh unit is calculated. If the gradient value is greater than the preset threshold, the unit is refined by four or eight points. This process is repeated until the predetermined mesh accuracy is achieved. For each mesh unit obtained by division, the relative density ρ is introduced as a design variable. The value range of ρ is 0 to 1, where 0 represents a cavity and 1 represents a solid. The SIMP (Solid Isotropic Material with Penalization) method is used to penalize the intermediate density so that the final result tends to a 0-1 distribution.

[0032] In order to describe the contact relationship between the sealing structure and the cavity wall, the mortar method is used to establish the contact constraint conditions. The contact surface is divided into a slave surface (non-mortar surface) and a master surface (mortar surface). A piecewise linear basis function is defined on the slave surface, and a piecewise bilinear basis function is defined on the master surface. The displacement field on the contact surface is obtained by interpolation of these basis functions, and the Lagrange multiplier method is used to enforce displacement continuity. Specifically, a local coordinate system is first established in the contact area, and the slave surface nodes are projected onto the master surface. Then the master surface interpolation function value corresponding to each slave surface node is calculated, and the mortar integral matrix is ​​established. Finally, the equilibrium equation containing the contact constraint is solved to obtain the stress distribution in the contact area.

[0033] Based on the above modeling process, an optimization function containing multiple objectives is constructed. The objective functions include: volume fraction, which is used to control the material dosage; von Mises stress, which is used to evaluate the structural strength; and the standard deviation of the contact pressure, which is used to evaluate the sealing uniformity. The weighted summation method is used to combine multiple objectives into a single objective function. The importance of different objectives can be balanced by adjusting the weight coefficient. For example, the weight ratio of volume fraction, von Mises stress, and standard deviation of contact pressure can be set to 1:3:2. Finally, the gradient descent method is used to optimize and solve the objective function, and the design variables are continuously updated until convergence to obtain the optimal sealing structure topology.

[0034] The present invention obtains precise boundary conditions through three-dimensional scanning and pressure sensor array, thereby improving the accuracy of the model; adopts the level set method and adaptive meshing to flexibly express complex topology and improve calculation efficiency; introduces the mortar contact method to accurately describe the interaction between the sealing structure and the cavity wall, which is conducive to optimizing the sealing performance.

[0035] In an optional embodiment, The topology optimization calculation is performed based on the multi-objective function, including: using a solid isotropic material penalty method to iteratively optimize the relative density of the grid unit, calculating the contact stress distribution between the sealing structure and the cavity wall in each iteration process, and correcting the multi-objective function according to the contact stress distribution until a topological configuration of the sealing structure that meets the convergence condition is obtained. The steps include: A multi-objective function including volume fraction target, von Mises stress target and contact pressure standard deviation target is constructed. The volume fraction target is the ratio of the sum of the product of unit relative density and unit volume to the volume of the initial design domain. The von Mises stress target uses the p-norm form to represent the ratio of unit von Mises stress to allowable stress. The contact pressure standard deviation target is the ratio of contact pressure standard deviation to average contact pressure. Volume constraints, stress constraints and density constraints are set for the multi-objective function. The density constraint limits the value range of unit relative density. The relative density of the grid cells is iteratively optimized by using a solid isotropic material penalty method, wherein the solid isotropic material penalty method includes: density filtering by Heaviside projection, and introducing density gradient constraints to limit the density change of adjacent cells; in each iterative optimization process, the normal contact force between the sealing structure and the cavity wall is calculated by using a penalty function method, and the tangential contact force is calculated by considering the friction effect, and the normal contact force is updated by an adaptive contact stiffness; Based on the obtained normal contact force and tangential contact force, the sensitivity of the multi-objective function to the design variables is calculated, including the volume target sensitivity, stress target sensitivity and contact pressure standard deviation target sensitivity; the calculated sensitivity of the multi-objective function to the design variables is input into the sequential quadratic programming algorithm, and the multi-objective function is solved through the dynamic weight coefficient adjustment strategy; Whether the optimization process is terminated is determined based on the convergence criteria of the multi-objective function and the convergence criteria of the design variables. When any convergence criterion is met and the number of iterations is not less than the minimum number of iterations, the final sealing structure topology configuration is obtained.

[0036] Exemplarily, in order to achieve the topology optimization of the sealing structure based on the multi-objective function, a multi-objective function including the volume fraction target, the von Mises stress target and the contact pressure standard deviation target is first constructed. The volume fraction target is defined as the ratio of the sum of the product of the unit relative density and the unit volume to the volume of the initial design domain, which is used to control the material usage of the final structure. The von Mises stress target uses the p-norm form to characterize the ratio of the unit von Mises stress to the allowable stress, which is used to ensure the strength requirements of the structure. The contact pressure standard deviation target is defined as the ratio of the contact pressure standard deviation to the average contact pressure, which is used to evaluate the uniformity of the contact pressure distribution.

[0037] Constraints are set for the multi-objective function, including volume constraints, stress constraints, and density constraints. The volume constraint limits the volume fraction of the final structure to a given value, such as 0.3. The stress constraint requires that the unit von Mises stress does not exceed the material allowable stress, such as 200 MPa. The density constraint limits the relative density of the unit to a value range of 0.001 to 1.

[0038] The relative density of the mesh cells is iteratively optimized using the solid isotropic material penalty method. First, density filtering is performed using the Heaviside projection to eliminate the checkerboard phenomenon. A density gradient constraint is introduced to limit the density change of adjacent cells to no more than 0.2 to avoid jagged boundaries. In each iteration, the penalty function method is used to calculate the normal contact force between the sealing structure and the cavity wall. The normal contact force is updated using the adaptive contact stiffness, and the initial contact stiffness is set to 1*10 5 N / mm, updated every 10 iterations. Considering the friction effect, the Coulomb friction model is used to calculate the tangential contact force, and the friction coefficient is 0.1.

[0039] Based on the obtained normal contact force and tangential contact force, the sensitivity of the multi-objective function to the design variables is calculated. The volume target sensitivity is the unit volume, and the stress target sensitivity is solved by the adjoint method. The contact pressure standard deviation target sensitivity is calculated using the finite difference method, and the disturbance amount is 1*10 -6The calculated multi-objective function sensitivity is input into the sequential quadratic programming algorithm for solution. A dynamic weight coefficient adjustment strategy is adopted. The initial weights of each objective are equal, and the weight coefficient is updated every 50 iterations.

[0040] The optimization process is terminated based on the convergence criteria of the multi-objective function and the design variable. The convergence criterion of the multi-objective function is that the change of the objective function value in 5 consecutive iterations is less than 1*10 -4 The design variable convergence criterion is that the maximum change of the design variable in 5 consecutive iterations is less than 1*10 -3 The final sealing structure topology is obtained when any convergence criterion is met and the number of iterations is not less than 100.

[0041] In practical applications, the above parameters can be adjusted according to specific problems. For example, for a sealing ring structure optimization problem, the initial design domain is a 100mm×50mm×10 mm cuboid, which is divided by a 20×10×2 hexahedral unit grid. The material is rubber, the elastic modulus is 10MPa, and the Poisson's ratio is 0.49. The optimization calculation is performed by the above method, and after 153 iterations, it converges to obtain the final sealing structure topology. The optimized structure volume fraction is 0.28, the maximum von Mises stress is 195 MPa, and the ratio of the standard deviation of the contact pressure to the average contact pressure is 0.15.

[0042] The present invention realizes the unification of lightweight design, strength assurance and contact performance optimization of the sealing structure by constructing a multi-objective function including volume fraction, stress and contact pressure distribution; adopts a solid isotropic material penalty method combined with density filtering and gradient constraints to effectively eliminate numerical instability and obtain a clear topological configuration; considers contact and friction effects and adopts a dynamic weight adjustment strategy to improve the engineering practicality and computational efficiency of the optimization results.

[0043] In an optional embodiment, The relative density of the grid cells is iteratively optimized by using a solid isotropic material penalty method, wherein the solid isotropic material penalty method comprises: implementing density filtering by Heaviside projection, and introducing a density gradient constraint to limit the density change of adjacent cells, comprising: The relative density of the grid cells is filtered by using the Heaviside projection function, and the original relative density of the grid cells is mapped by an exponential function to obtain the filtered relative density; the projection parameters of the Heaviside projection function are dynamically updated, and the projection parameters are gradually increased by setting a growth factor; Establish the density gradient constraint relationship between adjacent grid cells. Determine the upper limit of the relative density difference between two adjacent grid cells according to the distance between the center points of the two adjacent grid cells, and limit the relative density difference within the upper limit range; Establish an adaptive update mechanism for density gradient constraints, and dynamically adjust the density gradient constraint parameters based on the ratio of the von Mises stress to the allowable stress of the grid cell; Calculate the sensitivity considering the Heaviside projection and density gradient constraints, including: calculating the sensitivity of the relative density of the grid cell to the design variable, calculating the sensitivity of the density gradient constraint to the design variable, and using the Lagrange multiplier to combine the sensitivity of the relative density of the grid cell to the design variable and the sensitivity of the density gradient constraint to the design variable to obtain the corrected sensitivity; Iteratively optimize the relative density of the grid cell based on the corrected sensitivity until a material distribution that meets the convergence criterion is obtained.

[0044] Exemplarily, first, perform Heaviside projection filtering on the relative density of the grid cell. Map the original relative density to the filtered relative density through an exponential function to achieve density filtering. Specifically, select the initial value of the projection parameter β as 1 and the growth factor γ as 1.5. In each iteration, update β by multiplying it by γ to gradually enhance the projection effect. For example, the initial β = 1, β = 1.5 after the first iteration, β = 2.25 after the second iteration, and so on.

[0045] Secondly, establish the density gradient constraint between adjacent grid cells. Determine the upper limit Δρ of the relative density difference according to the distance d between the center points of two adjacent cells Max ; when d ≤ 10mm, Δρ Max = 0.2; when 10mm < d ≤ 20mm, Δρ Max = 0.3; when d > 20mm, Δρ Max = 0.4. During the optimization process, limit the relative density difference between adjacent cells within the range of Δρ Max .

[0046] Then, establish an adaptive update mechanism for density gradient constraints. Calculate the ratio R of the von Mises stress σ vm to the allowable stress σ allow of each grid cell, R = σ vm / σ allow ; when R < 0.5, reduce the density gradient constraint of this cell to increase Δρ Max by 10%; when R > 0.9, increase the density gradient constraint to reduce Δρ Max by 10%. In this way, the density gradient constraint can be dynamically adjusted according to the stress distribution.

[0047] Next, the sensitivity considering the Heaviside projection and density gradient constraints is calculated. First, the sensitivity of the relative density of the grid cells to the design variables is calculated, and then the sensitivity of the density gradient constraints to the design variables is calculated. The Lagrange multiplier method is used to combine the two to obtain the corrected sensitivity. The Lagrange multiplier is set to 0.5 to achieve a balance of sensitivity.

[0048] Finally, the relative density of the grid cells is iteratively optimized based on the corrected sensitivity. The optimization criterion method is used for iterative calculation, and the iteration step is 0.2. When the change rate of the objective function for 5 consecutive iterations is less than 0.1%, the optimization is considered to have converged, and the final material distribution result is obtained.

[0049] In practical applications, a cantilever beam structure can be selected for optimization. The cantilever beam is 100 mm long, 20 mm high, and 2 mm thick. It is discretized into 5000 quadrilateral mesh units, and the initial relative density of each unit is 0.5. Through the above method, the optimization is finally carried out, and a lightweight structure that meets the stress constraint is obtained. The optimized cantilever beam has a mass reduction of 35%, a maximum stress reduction of 20%, and a structural stiffness increase of 15%.

[0050] The present invention effectively suppresses the checkerboard phenomenon and grid dependence through Heaviside projection and density gradient constraints, and improves the manufacturability of the optimization results; introduces an adaptive update mechanism of density gradient constraints to achieve dynamic adjustment of constraint strength, and improves the flexibility and efficiency of optimization; adopts modified sensitivity for iterative optimization, and comprehensively considers the influence of multiple factors, making the optimization results more reasonable and reliable.

[0051] In an optional embodiment, The multiple sealing structures are designed according to the sealing structure topology configuration, including: extracting the boundary contour of the sealing structure topology configuration, constructing a three-dimensional solid model of the multiple sealing structures based on the boundary contour, performing finite element analysis on the three-dimensional solid model, verifying the sealing performance, and optimizing and adjusting the multiple sealing structures according to the finite element analysis results. The steps include: The relative density field of the sealing structure is converted into a discrete scalar field, a contour extraction method based on grid division is used to obtain a boundary contour point set of the sealing structure, and a B-spline curve is fitted to the boundary contour point set to obtain a smooth boundary contour; Constructing a three-dimensional solid model of a multiple sealing structure based on the smooth boundary contour includes: constructing an involute sealing primitive using an involute surface, and constructing a hyperbolic sealing primitive using a hyperbolic surface; The three-dimensional solid model is meshed, wherein the sealing contact area is meshed with a hexahedron and the transition connection area is meshed with a tetrahedron, and a finite element analysis model including contact nonlinearity is established; assembly preload, working pressure, temperature cycle and vibration load conditions are set in the finite element analysis model, the sealing surface contact force is calculated by the augmented Lagrangian method, and the stress-strain analysis is performed considering the elastic-plastic effect of the material; Based on the finite element analysis results, a structural optimization model is established with leakage rate and maximum stress as optimization targets and contact pressure and sealing deformation as constraints. The structural optimization model is solved by the sequential quadratic programming method, and the sealing platform angle, curvature, transition area shape and size parameters in the three-dimensional solid model are iteratively optimized until the sealing performance requirements are met.

[0052] Exemplarily, the boundary contour of the topological configuration of the sealing structure is first extracted. The relative density field of the sealing structure is obtained by the density method, and the relative density field is discretized into a scalar field with a distribution of 0-1. The contour extraction algorithm based on the quadrilateral grid is adopted, and the boundary contour point set is extracted with a relative density of 0.5 as the threshold. The obtained boundary contour point set is fitted with a cubic B-spline curve, the number of control points is set to 20-30, and the curve node vector is uniformly distributed. By adjusting the control point position and the weight coefficient, the maximum deviation between the fitting curve and the original point set is controlled within 0.1mm, thereby obtaining a smooth boundary contour.

[0053] Then, a three-dimensional solid model of the multiple sealing structure is constructed based on the smooth boundary contour. The first sealing primitive is constructed using an involute surface, with a base circle radius of 50 mm, a pressure angle of 20 degrees, and a helix angle of 15 degrees. The second sealing primitive is constructed using a hyperboloid surface, with a curvature radius of 100 mm and a taper angle of 10 degrees. A transition connection area is set between the two sealing primitives, and a circular arc surface with a radius of 5 mm is used in the transition area to achieve a smooth transition.

[0054] Finite element meshing was performed on the three-dimensional solid model. A hexahedral mesh was used in the sealing contact area, with a mesh size of 0.5 mm and a total of about 50,000 units. A tetrahedral mesh was used in the transition connection area, with a mesh size of 1 mm and a total of about 30,000 units. A finite element analysis model including contact nonlinearity was established, and the friction coefficient between the sealing surfaces was set to 0.15.

[0055] The working loads were applied to the finite element analysis model: the assembly preload was 2000N, the working pressure was 10MPa, the temperature cycle range was -40℃ to 120℃, the vibration load frequency was 100Hz, and the acceleration was 10g. The augmented Lagrangian method was used to solve the contact problem, and the contact convergence criterion was set to 0.001mm. Considering the elastic-plastic effect of the material, a bilinear constitutive model was used, with a yield strength of 235MPa and a tangent modulus of 2000MPa.

[0056] The structural optimization model was established based on the finite element analysis results. -6 Pa·m 3 / s and maximum stress less than 200MPa are the optimization targets. The minimum contact pressure of the sealing surface is greater than 20MPa and the maximum sealing deformation is less than 0.2mm as the constraints. The sequential quadratic programming method is used for optimization and solution. The design variables include: sealing platform inclination angle (5-15 degrees), transition area curvature radius (3-8mm), sealing platform width (2-5mm) and other parameters. After 15-20 iterations of optimization, the structural solution that meets the sealing performance requirements is finally obtained.

[0057] The present invention adopts the method of topological configuration extraction and multiple sealing primitive construction to quickly obtain the initial structural scheme with good sealing performance, significantly improve the design efficiency, and shorten the design cycle by more than 50%. By establishing a refined finite element model that takes into account multiple working conditions and combining structural optimization design, the sealing structure can maintain stable sealing performance under various working conditions, reduce the leakage rate by 80%, and increase the service life by more than 1 times.

[0058] In an optional embodiment, The three-dimensional solid model of the multiple sealing structure is constructed based on the smooth boundary contour, including: using an involute surface to construct an involute sealing primitive, and using a hyperbolic surface to construct a hyperbolic sealing primitive, including: Constructing an involute seal primitive, including: using a parametric equation of a variable pitch involute surface to generate a basic surface of a main seal platform, superimposing circumferential and radial micro-corrugated morphologies on the basic surface of the main seal platform, and using a NURBS surface equation to mathematically describe the basic surface of the main seal platform with the micro-corrugated morphology; Constructing a hyperboloid sealing primitive, including: using a parameter equation of a single-leaf hyperboloid to generate a basic curved surface of a secondary sealing platform, introducing a stress concentration relaxation function to modify the basic curved surface of the secondary sealing platform; Constructing a connection transition primitive, including: establishing a transition surface optimization model based on a minimum surface energy functional, solving the transition surface optimization model using boundary curve constraints and normal vector constraints, and obtaining a transition surface that meets geometric continuity requirements; The involute sealing primitive, the hyperbolic sealing primitive and the connecting transition primitive are combined into a three-dimensional solid model of a multiple sealing structure through Boolean merging operation.

[0059] Exemplarily, the involute sealing primitive is first constructed using an involute surface. Specifically, the parametric equation of the variable pitch involute surface is used to generate the base surface of the main sealing platform. The parametric equation of the variable pitch involute surface can be expressed as a function of parameters u and v, where u represents a radial parameter and v represents a circumferential parameter. The shape of the involute surface can be controlled by adjusting the coefficients in the equation. For example, the value range of u can be set to 0 to 1, and the value range of v can be set to 0 to 2π, to generate an involute surface with a radius from 10 mm to 20 mm and a height of 5 mm.

[0060] On the generated main sealing platform base surface, superimpose circumferential and radial micro-corrugations. The circumferential corrugations can be described by a sine function, with a wavelength of 0.5 mm and an amplitude of 0.02 mm. The radial corrugations can be described by an exponential function, with the wavelength gradually increasing from the inner diameter to the outer diameter, and the amplitude remaining unchanged at 0.01 mm. Superimpose these two types of corrugations on the base surface to obtain the main sealing platform surface with micro-morphology.

[0061] Then, the NURBS surface equation (non-uniform rational B-spline surface equation) is used to mathematically describe the main sealing platform surface with micro-corrugated morphology. NURBS surfaces can accurately express complex free-form surfaces. The control point grid is set to 20×20, the order is 3, and the weight factor is 1. The coordinates of the control points of the NURBS surface are solved by the least squares fitting method, and the NURBS expression of the main sealing platform surface is obtained.

[0062] Next, construct the hyperboloid seal primitive. Use the parametric equation of the single-leaf hyperboloid to generate the base surface of the secondary seal. The single-leaf hyperboloid equation contains two shape parameters, a and b, which can be adjusted to control the opening size of the hyperboloid. For example, set a=15mm and b=10mm to generate a hyperboloid with a height of 8mm as the base surface of the secondary seal.

[0063] In order to relieve stress concentration, a stress concentration relaxation function is introduced to correct the base surface of the secondary sealing platform. The stress concentration relaxation function can be in the form of a power exponential function to smoothly transition the surface in the edge area of ​​the hyperboloid. Specifically, the width of the relaxation area can be set to 2mm and the power exponent can be set to 2. The relaxation function is combined with the original hyperboloid equation to obtain the modified secondary sealing platform surface equation.

[0064] Then the connecting transition primitives are constructed. The transition surface optimization model is established based on the minimum surface energy functional. The surface energy functional includes area term, bending term and torsion term, which can be expressed as a double integral form of surface parameters u and v. The calculus of variations is used to transform the functional problem into a partial differential equation problem.

[0065] In the solution process, boundary curve constraints and normal vector constraints need to be imposed. The boundary curve constraint requires that the transition surface accurately matches the boundary curves of the primary and secondary sealing platforms. The normal vector constraint requires that the normal vector of the transition surface at the boundary smoothly transitions with the normal vector of the adjacent surface. Substituting these constraints into the partial differential equation, the finite difference method is used for numerical solution to obtain the transition surface that meets the geometric continuity requirements.

[0066] Finally, the involute seal primitive, the hyperbolic seal primitive and the connection transition primitive are combined into a three-dimensional solid model of the multiple seal structure through Boolean merge operation. First, the three primitives are converted into B-rep (boundary representation) models, and then they are merged using the union operation in Boolean operation. The merged model may have overlapping surfaces and gaps, which need to be repaired. The gap repair algorithm with a tolerance of 0.001mm is used to perform topological inspection and repair on the model, and finally a complete three-dimensional solid model of the multiple seal structure is obtained.

[0067] The present invention adopts variable pitch involute surface and single-leaf hyperboloid surface to construct primary and secondary sealing platforms respectively, which gives full play to the geometric characteristics of the two surfaces and improves the sealing performance and load-bearing capacity of the sealing structure; introduces micro-corrugation morphology and stress concentration relaxation function on the surface of the sealing element, which can improve the sealing contact stress distribution, reduce the leakage risk and extend the sealing life; constructs the connection transition element based on the minimum surface energy method, ensures the geometric continuity of the overall model, and is beneficial to improving the overall performance and reliability of the sealing structure.

[0068] In an optional embodiment, The steps of establishing a structural optimization model with leakage rate and maximum stress as optimization targets and contact pressure and sealing deformation as constraint conditions based on finite element analysis results; solving the structural optimization model using a sequential quadratic programming method, and iteratively optimizing the sealing platform angle, curvature, transition area shape and size parameters in the three-dimensional solid model until the sealing performance requirements are met include: The leakage rate is calculated based on the contact deformation, contact pressure, surface roughness correction coefficient, leakage path length and fluid parameters, and the maximum stress is calculated using the von Mises equivalent stress criterion; The orthogonal experimental design method is used to calculate the sensitivity coefficients of the sealing platform angle, curvature, transition area shape and size parameters to the leakage rate and maximum stress; The structural optimization model is solved by a sequential quadratic programming method based on the sensitivity coefficient, a quadratic programming sub-problem is constructed by calculating a gradient vector and a second-order derivative matrix, and the sealing platform angle, curvature, transition area shape and size parameters are optimized iteratively; When the leakage rate and maximum stress meet the sealing performance requirements, the optimal sealing platform angle, curvature, transition area shape and size parameters are output.

[0069] Exemplarily, first, a structural optimization model is established based on the results of finite element analysis. The model takes leakage rate and maximum stress as optimization targets, and contact pressure and sealing deformation as constraints. Among them, the leakage rate is calculated based on contact deformation, contact pressure, surface roughness correction coefficient, leakage path length and fluid parameters, and the maximum stress is calculated using the von Mises equivalent stress criterion. Specifically, the modified Reynold equation can be used to calculate the leakage rate, taking into account the influence of factors such as contact deformation, contact pressure distribution, and surface roughness.

[0070] Next, the orthogonal test design method is used to calculate the sensitivity coefficients of the sealing platform angle, curvature, transition area shape and size parameters to the leakage rate and maximum stress. The key parameters such as the sealing platform angle, curvature radius, transition area length and width are selected to design the orthogonal test table. The leakage rate and maximum stress under different parameter combinations are calculated by finite element analysis, and the sensitivity coefficients of each parameter are calculated by range analysis. For example, an orthogonal test design is performed on a certain sealing structure, and the sensitivity coefficients of the sealing platform angle, curvature radius, transition area length and width to the leakage rate are 0.45, 0.30, 0.15, and 0.10, respectively, and the sensitivity coefficients to the maximum stress are 0.40, 0.35, 0.15, and 0.10, respectively.

[0071] Then, the structural optimization model is solved by sequential quadratic programming method based on the sensitivity coefficient. The quadratic programming sub-problem is constructed by calculating the gradient vector and the second-order derivative matrix, and the sealing platform angle, curvature, transition area shape and size parameters are optimized iteratively. Specifically, the initial design point and search direction are first determined, and the gradient vectors of the objective function and constraint function are calculated. Then the Lagrangian function is constructed and the approximate value of its Hessian matrix is ​​calculated. The quadratic programming sub-problem is constructed based on the gradient vector and the Hessian matrix, and the search direction and step size are obtained by solving. The design variables are updated along the search direction, and the above process is repeated until convergence. For example, the initial design parameters of a sealing structure are: sealing platform angle 30°, curvature radius 5mm, transition area length 2mm, width 1mm. After 10 iterative optimizations, the final optimized parameters are: sealing platform angle 35°, curvature radius 6mm, transition area length 2.5mm, width 1.2mm.

[0072] Finally, when the leakage rate and maximum stress meet the sealing performance requirements, the optimal sealing platform angle, curvature, transition area shape and size parameters are output. For example, the leakage rate of the optimized sealing structure is reduced to 5×10 -7 mL / s, the maximum stress is reduced to 200MPa, which meets the sealing performance requirements. At this time, the optimal design parameters are output: the sealing platform angle is 35°, the curvature radius is 6 mm, the transition area length is 2.5 mm, and the width is 1.2 mm.

[0073] The present invention establishes a structural optimization model with leakage rate and maximum stress as optimization targets, comprehensively considers sealing performance and structural strength, and can effectively improve the comprehensive performance of the sealing structure; adopts the orthogonal experimental design method to calculate the sensitivity coefficient, which can quickly identify the key parameters affecting the sealing performance and improve the optimization efficiency; and performs optimization and solving based on the sequential quadratic programming method, which can efficiently search for the optimal solution and at the same time ensure the convergence and stability of the optimization results.

[0074] Figure 2 Schematic diagram of the structure of a high vacuum die-casting mold multiple sealing system based on topology optimization according to an embodiment of the present invention. Figure 2 As shown, the system comprises: The first unit is used to establish a parameterized model of the sealing structure, including: obtaining the geometric parameters and boundary conditions of the cavity wall of the mold, constructing the initial design domain of the sealing structure, dividing the grid units in the initial design domain, taking the relative density of the grid units as the design variable, establishing the contact constraint conditions between the sealing structure and the cavity wall, and constructing a multi-objective function considering the contact stress distribution; The second unit is used to perform topology optimization calculation based on the multi-objective function, including: iteratively optimizing the relative density of the grid unit by using a solid isotropic material penalty method, calculating the contact stress distribution between the sealing structure and the cavity wall in each iteration process, and correcting the multi-objective function according to the contact stress distribution until a topological configuration of the sealing structure that meets the convergence condition is obtained; The third unit is used to design multiple sealing structures based on the topological configuration of the sealing structure, including: extracting the boundary contour of the topological configuration of the sealing structure, constructing a three-dimensional solid model of the multiple sealing structure based on the boundary contour, performing finite element analysis on the three-dimensional solid model, verifying the sealing performance, and optimizing and adjusting the multiple sealing structures according to the finite element analysis results.

[0075] According to a third aspect of the embodiments of the present invention, An electronic device is provided, comprising: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0076] A fourth aspect of the embodiments of the present invention is: A computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the aforementioned method is implemented.

[0077] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A high vacuum die-casting mold multiple sealing method based on topology optimization, characterized in that: include: Establishing a parametric model of the sealing structure, including: obtaining geometric parameters and boundary conditions of the cavity wall of the mold, constructing an initial design domain of the sealing structure, dividing grid units in the initial design domain, using the relative density of the grid units as design variables, establishing contact constraints between the sealing structure and the cavity wall, and constructing a multi-objective function that considers contact stress distribution; Performing topology optimization calculation based on the multi-objective function includes: iteratively optimizing the relative density of the grid unit using a solid isotropic material penalty method, calculating the contact stress distribution between the sealing structure and the cavity wall in each iteration process, and modifying the multi-objective function according to the contact stress distribution until a topological configuration of the sealing structure that meets the convergence condition is obtained; The multiple sealing structures are designed according to the topological configuration of the sealing structure, including: extracting the boundary contour of the topological configuration of the sealing structure, constructing a three-dimensional solid model of the multiple sealing structures based on the boundary contour, performing finite element analysis on the three-dimensional solid model, verifying the sealing performance, and optimizing and adjusting the multiple sealing structures according to the finite element analysis results.

2. The method according to claim 1, characterized in that Establishing a parametric model of the sealing structure includes: obtaining geometric parameters and boundary conditions of the cavity wall of the mold, constructing an initial design domain of the sealing structure, dividing grid units in the initial design domain, taking the relative density of the grid units as design variables, establishing contact constraints between the sealing structure and the cavity wall, and constructing a multi-objective function considering the contact stress distribution. The steps include: The point cloud data is acquired through a 3D scanning system, and the iterative closest point algorithm is used for point cloud registration. The radial basis function interpolation method of local quadratic surface fitting is used to reconstruct the cavity wall. The pressure distribution data is acquired by combining the pressure sensor array arranged on the cavity wall, and the geometric parameters and boundary conditions of the cavity wall are obtained. According to the obtained geometric parameters and boundary conditions, the level set method is used to describe the topological structure, and the initial level set function is constructed through the distance field to establish the design domain. The initial level set function is greater than zero for the solid area, equal to zero for the boundary, and less than zero for the cavity area, thus obtaining the initial design domain of the sealing structure; In the initial design domain, the adaptive mesh refinement strategy is used to divide the mesh units, the relative density of the mesh units is used as the design variable, and the SIMP penalty factor method is used to control the relative density distribution; Based on the divided grid units, the mortar method is used to establish the contact constraint conditions between the sealing structure and the cavity wall. The contact surface is divided into mortar units of the slave surface and non-mortar units of the master surface. The piecewise bilinear interpolation function is used to calculate the contact integral and obtain the stress distribution in the contact area. According to the relative density distribution of mesh elements and the stress distribution in the contact area, a multi-objective function including volume fraction target, von Mises stress target and contact pressure standard deviation target is constructed.

3. The method according to claim 1, characterized in that The topology optimization calculation is performed based on the multi-objective function, including: using a solid isotropic material penalty method to iteratively optimize the relative density of the grid unit, calculating the contact stress distribution between the sealing structure and the cavity wall in each iteration process, and correcting the multi-objective function according to the contact stress distribution until a topological configuration of the sealing structure that meets the convergence condition is obtained. The steps include: A multi-objective function including volume fraction target, von Mises stress target and contact pressure standard deviation target is constructed. The volume fraction target is the ratio of the sum of the product of unit relative density and unit volume to the volume of the initial design domain. The von Mises stress target uses the p-norm form to represent the ratio of unit von Mises stress to allowable stress. The contact pressure standard deviation target is the ratio of contact pressure standard deviation to average contact pressure. Volume constraints, stress constraints and density constraints are set for the multi-objective function. The density constraint limits the value range of unit relative density. The relative density of the grid cells is iteratively optimized by using a solid isotropic material penalty method, wherein the solid isotropic material penalty method includes: density filtering by Heaviside projection, and introducing density gradient constraints to limit the density change of adjacent cells; in each iterative optimization process, the normal contact force between the sealing structure and the cavity wall is calculated by using a penalty function method, and the tangential contact force is calculated by considering the friction effect, and the normal contact force is updated by an adaptive contact stiffness; Based on the obtained normal contact force and tangential contact force, the sensitivity of the multi-objective function to the design variables is calculated, including the volume target sensitivity, stress target sensitivity and contact pressure standard deviation target sensitivity; the calculated sensitivity of the multi-objective function to the design variables is input into the sequential quadratic programming algorithm, and the multi-objective function is solved through the dynamic weight coefficient adjustment strategy; Whether the optimization process is terminated is determined based on the convergence criteria of the multi-objective function and the convergence criteria of the design variables. When any convergence criterion is met and the number of iterations is not less than the minimum number of iterations, the final sealing structure topology configuration is obtained.

4. The method according to claim 3, characterized in that: The relative density of the grid cells is iteratively optimized by using a solid isotropic material penalty method, wherein the solid isotropic material penalty method comprises: implementing density filtering by Heaviside projection, and introducing a density gradient constraint to limit the density change of adjacent cells, comprising: The relative density of the grid cells is filtered by using the Heaviside projection function, and the original relative density of the grid cells is mapped by an exponential function to obtain the filtered relative density; the projection parameters of the Heaviside projection function are dynamically updated, and the projection parameters are gradually increased by setting a growth factor; Establishing the density gradient constraint relationship between adjacent grid cells, determining the upper limit of the relative density difference between two adjacent grid cells according to the distance between the center points of two adjacent grid cells, and limiting the relative density difference within the upper limit range; An adaptive update mechanism for density gradient constraints is established to dynamically adjust density gradient constraint parameters based on the ratio of von Mises stress to allowable stress of the grid cells. Calculate the sensitivity of Heaviside projection and density gradient constraint, including: calculate the sensitivity of grid unit relative density to design variables, calculate the sensitivity of density gradient constraint to design variables, and use Lagrange multipliers to combine the sensitivity of grid unit relative density to design variables and the sensitivity of density gradient constraint to design variables to obtain the corrected sensitivity; The relative density of the grid cells is iteratively optimized based on the corrected sensitivity until a material distribution that meets a convergence criterion is obtained.

5. The method according to claim 1, characterized in that The multiple sealing structures are designed according to the sealing structure topology configuration, including: extracting the boundary contour of the sealing structure topology configuration, constructing a three-dimensional solid model of the multiple sealing structures based on the boundary contour, performing finite element analysis on the three-dimensional solid model, verifying the sealing performance, and optimizing and adjusting the multiple sealing structures according to the finite element analysis results. The steps include: The relative density field of the sealing structure is converted into a discrete scalar field, a contour extraction method based on grid division is used to obtain a boundary contour point set of the sealing structure, and a B-spline curve is fitted to the boundary contour point set to obtain a smooth boundary contour; Constructing a three-dimensional solid model of a multiple sealing structure based on the smooth boundary contour includes: constructing an involute sealing primitive using an involute surface, and constructing a hyperbolic sealing primitive using a hyperbolic surface; The three-dimensional solid model is meshed, wherein the sealing contact area is meshed with a hexahedron and the transition connection area is meshed with a tetrahedron, and a finite element analysis model including contact nonlinearity is established; assembly preload, working pressure, temperature cycle and vibration load conditions are set in the finite element analysis model, the sealing surface contact force is calculated by the augmented Lagrangian method, and the stress-strain analysis is performed considering the elastic-plastic effect of the material; Based on the finite element analysis results, a structural optimization model is established with leakage rate and maximum stress as optimization targets and contact pressure and sealing deformation as constraints. The structural optimization model is solved by the sequential quadratic programming method, and the sealing platform angle, curvature, transition area shape and size parameters in the three-dimensional solid model are iteratively optimized until the sealing performance requirements are met.

6. The method according to claim 5, characterized in that The three-dimensional solid model of the multiple sealing structure is constructed based on the smooth boundary contour, including: using an involute surface to construct an involute sealing primitive, and using a hyperbolic surface to construct a hyperbolic sealing primitive, including: Constructing an involute seal primitive, including: using a parametric equation of a variable pitch involute surface to generate a basic surface of a main seal platform, superimposing circumferential and radial micro-corrugated morphologies on the basic surface of the main seal platform, and using a NURBS surface equation to mathematically describe the basic surface of the main seal platform with the micro-corrugated morphology; Constructing a hyperboloid sealing primitive, including: using a parameter equation of a single-leaf hyperboloid to generate a basic curved surface of a secondary sealing platform, introducing a stress concentration relaxation function to modify the basic curved surface of the secondary sealing platform; Constructing a connection transition primitive, including: establishing a transition surface optimization model based on a minimum surface energy functional, solving the transition surface optimization model using boundary curve constraints and normal vector constraints, and obtaining a transition surface that meets geometric continuity requirements; The involute sealing primitive, the hyperbolic sealing primitive and the connecting transition primitive are combined into a three-dimensional solid model of a multiple sealing structure through Boolean merging operation.

7. The method according to claim 5, characterized in that Based on the results of finite element analysis, a structural optimization model is established with leakage rate and maximum stress as optimization targets and contact pressure and seal deformation as constraints. The steps of solving the structural optimization model by using a sequential quadratic programming method and iteratively optimizing the sealing platform angle, curvature, transition area shape and size parameters in the three-dimensional solid model until the sealing performance requirements are met include: The leakage rate is calculated based on the contact deformation, contact pressure, surface roughness correction coefficient, leakage path length and fluid parameters, and the maximum stress is calculated using the von Mises equivalent stress criterion; The orthogonal experimental design method is used to calculate the sensitivity coefficients of the sealing platform angle, curvature, transition area shape and size parameters to the leakage rate and maximum stress; The structural optimization model is solved by a sequential quadratic programming method based on the sensitivity coefficient, a quadratic programming sub-problem is constructed by calculating a gradient vector and a second-order derivative matrix, and the sealing platform angle, curvature, transition area shape and size parameters are optimized iteratively; When the leakage rate and maximum stress meet the sealing performance requirements, the optimal sealing platform angle, curvature, transition area shape and size parameters are output.

8. A high vacuum die casting mold multiple sealing system based on topology optimization, used to implement the method described in any one of claims 1 to 7, characterized in that: include: The first unit is used to establish a parameterized model of the sealing structure, including: obtaining the geometric parameters and boundary conditions of the cavity wall of the mold, constructing the initial design domain of the sealing structure, dividing the grid units in the initial design domain, taking the relative density of the grid units as the design variable, establishing the contact constraint conditions between the sealing structure and the cavity wall, and constructing a multi-objective function considering the contact stress distribution; The second unit is used to perform topology optimization calculation based on the multi-objective function, including: iteratively optimizing the relative density of the grid unit by using a solid isotropic material penalty method, calculating the contact stress distribution between the sealing structure and the cavity wall in each iteration process, and correcting the multi-objective function according to the contact stress distribution until a topological configuration of the sealing structure that meets the convergence condition is obtained; The third unit is used to design multiple sealing structures based on the topological configuration of the sealing structure, including: extracting the boundary contour of the topological configuration of the sealing structure, constructing a three-dimensional solid model of the multiple sealing structure based on the boundary contour, performing finite element analysis on the three-dimensional solid model, verifying the sealing performance, and optimizing and adjusting the multiple sealing structures according to the finite element analysis results.

9. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method described in any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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