A method and system for preventing loosening of anchor bolts

Abstract

The present invention relates to the technical field of multi-objective optimization control, and discloses a method and system for designing anchor bolts to prevent loosening, including extracting basic size parameters of anchor bolts, constructing an initial solid model of bolts based on the basic size parameters; extracting fine geometric parameters of anchor bolts, generating a complete bolt solid model with fine thread features; dividing the bolt solid model into finite element grids, generating a finite element model; constructing an anti-loosening performance objective function and a weight objective function, establishing a multi-objective optimization model, and solving to obtain an optimal solution; collecting physical and mechanical performance data of common metals, searching for an optimal material combination, and generating an optimization model for anchor bolts; through an optimization iterative cycle, until the function value of the multi-objective optimization model reaches a convergence condition, outputting a final anti-loosening anchor bolt design scheme. The present invention provides an efficient and accurate anchor bolt anti-loosening scheme, achieving an optimized balance between anti-loosening performance and weight.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-objective optimization control, and more specifically, to a method and system for designing anti-loosening of anchor bolts. Background Art

[0002] Anchor bolts are widely used in the fixed connection of mechanical equipment and are key components to ensure the safe and reliable operation of equipment. However, under the action of long-term vibration and impact loads, ordinary anchor bolts are prone to loosening and failure, seriously affecting the performance of equipment. To solve this problem, researchers have proposed a variety of anchor bolt anti-loosening solutions.

[0003] The Chinese patent application with publication number CN110332204A discloses a foundation bolt anti-loosening device, including foundation bolts, hexagonal nuts, nut anti-loosening sleeves, locking sleeves, mounting sleeves, and protective covers. By adding additional structures such as anti-loosening sleeves and locking sleeves to the outside of the nuts, and using locking screws to reliably connect the additional structures to the foundation bolts, the foundation bolts are prevented from loosening and the damage to the bolt body is reduced. In addition, in the field of structural optimization design, the Chinese patent with authorization announcement number CN102054106B proposes a structural optimization design method and system. By establishing optimization objective functions and constraints, using finite element analysis and sensitivity analysis, and combining shape optimization evolution algorithms, the optimal structural model is finally obtained, providing new ideas for the optimization design of complex mechanical structures.

[0004] Although the above patents have made some progress in the anti-loosening and structural optimization design of anchor bolts, there are still some shortcomings. Most of the existing anti-loosening solutions for anchor bolts limit the loosening of bolts by adding additional structures, which will lead to the complexity of the bolt connection structure and increase the weight. At present, there is a lack of systematic multi-objective optimization anti-loosening design methods. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method and system for designing anti-loosening of anchor bolts.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for preventing loosening of anchor bolts, comprising:

[0008] Extract the basic size parameters of the anchor bolts, and build the initial solid model of the bolts based on the basic size parameters; extract the fine geometric parameters of the anchor bolts, and generate a complete bolt solid model with fine thread features based on the initial solid model and fine geometric parameters of the bolts; divide the bolt solid model into finite element meshes to generate a finite element model;

[0009] Constructing the anti-loosening performance objective function And weight objective function , comprehensively considering the anti-loosening performance target and the weight target, establish a multi-objective optimization model f; based on the finite element model, solve the multi-objective optimization model to obtain the optimal solution, substitute the optimal solution into the bolt entity model, and obtain the first optimization model of the anchor bolt;

[0010] Collect the physical and mechanical properties data of common metals, establish a material properties database, and search for the optimal material combination in the material properties database; substitute the searched optimal material combination into the first optimization model to generate the second optimization model of the anchor bolts;

[0011] An optimization iteration loop is established until the function value of the multi-objective optimization model f reaches the convergence condition, and the final design scheme of lightweight anti-loosening anchor bolts is output.

[0012] Furthermore, in the process of dividing the finite element mesh, the mesh density is set in the complex shape area of ​​the bolt solid model. , set the mesh density in regular-shaped areas ,in ; Generate tetrahedral meshes in complex-shaped areas and structured hexahedral meshes in regular-shaped areas.

[0013] Furthermore, the anti-loosening performance objective function Constructed based on thread loosening prevention failure criteria;

[0014] The thread anti-loosening failure criteria include thread shear failure criteria, thread tensile failure criteria, thread fatigue failure criteria and thread loosening failure criteria.

[0015] Furthermore, the anti-loosening performance objective function The construction methods include:

[0016] The thread shear failure limit state function is established based on the thread shear failure criterion:

[0017]

[0018] in, is the actual maximum shear stress of the thread, is the allowable shear strength of the thread;

[0019] The thread tensile failure limit state function is established based on the thread tensile failure criterion:

[0020]

[0021] in, is the actual maximum tensile stress of the thread, is the allowable tensile strength of the thread;

[0022] The thread fatigue failure limit state function is established based on the thread fatigue failure criterion:

[0023]

[0024] in, is the actual fatigue stress amplitude of the thread, is the allowable fatigue strength of the thread;

[0025] The thread loosening failure limit state function is established based on the thread loosening failure criterion:

[0026]

[0027] in, is the actual loosening load of the thread, is the allowable loosening load of the thread;

[0028] According to the thread shear failure limit state function, thread tensile failure limit state function, thread fatigue failure limit state function and thread loosening failure limit state function, the anti-loosening performance objective function is constructed. ;

[0029] The optimization goal is to minimize ,when When ≤0, the anchor thread is considered to meet the anti-loosening requirements.

[0030] Furthermore, the objective function of the multi-objective optimization model is: ;

[0031] The constraints of the multi-objective optimization model are: , , ;

[0032] in, is the anti-loosening performance objective function, is the weight objective function, is the design variable, is the value range of the design variable, are inequality constraints, such as strength, stiffness, etc. These are equality constraints, such as thread standards, processing technology, etc.

[0033] Furthermore, the objective function of the multi-objective optimization model is:

[0034] ;

[0035] in, is the weight factor of the anti-loosening performance objective function, is the weight factor of the weight objective function.

[0036] Furthermore, the physical and mechanical performance data include density and tensile strength parameters;

[0037] The method for searching for the optimal material combination in the material performance database comprises: taking density and tensile strength parameters as optimization targets, establishing a multi-objective function for material optimization, searching and calculating the objective function values ​​of different material combinations in the material performance database, and selecting the material combination with the smallest objective function value as the optimal material combination;

[0038] The material optimization multi-objective function is min (y) = [ (y), - (y)]; where is the density of the material, is the tensile strength of the material, and y is the material design variable.

[0039] Furthermore, the optimization iteration loop is established until the function value of the multi-objective optimization model f reaches the convergence condition, and the final lightweight anti-loosening anchor bolt design scheme is outputted, including:

[0040] Feeding back the material parameters of the second optimization model to the finite element model to generate an updated finite element model;

[0041] Based on the updated finite element model, calculate the objective function value of the current optimization step of the multi-objective optimization model (x); the objective function value of the current optimization step (x) and the objective function values ​​of the previous steps (x), (x) comparison;

[0042] If the objective function value changes by one | (x)- (x)| and the change in the objective function value 2| (x)- (x)| are all less than the set convergence threshold ε, the optimization is considered to have converged; otherwise, the optimization is considered to have not converged, and a new round of multi-objective optimization solution is continued based on the updated finite element model;

[0043] Output the optimization results at convergence as the final design solution for lightweight anti-loosening anchor bolts.

[0044] A foundation bolt anti-loosening design system, which is used to implement the above-mentioned foundation bolt anti-loosening design method, the system comprises:

[0045] Parameter extraction module: used to extract the basic size parameters and fine geometric parameters of anchor bolts;

[0046] Bolt model generation module: used to build the initial solid model of the bolt based on the basic size parameters of the anchor bolt; generate a complete bolt solid model with fine thread features based on the initial solid model of the bolt and fine geometric parameters;

[0047] Finite element meshing module: used to divide the finite element mesh of the bolt solid model and generate the finite element model;

[0048] Optimization model building module: used to build the anti-loosening performance objective function And weight objective function , integrating the anti-loosening performance target and weight target, and establishing a multi-objective optimization model f;

[0049] Optimization solution module: used to solve the multi-objective optimization model based on the finite element model, obtain the optimal solution, substitute the optimal solution into the bolt entity model, and obtain the first optimization model of the anchor bolt;

[0050] Optimal material search module: used to collect physical and mechanical property data of common metals, establish a material property database, and search for the optimal material combination in the material property database;

[0051] The second optimization model generation module is used to substitute the searched optimal material combination into the first optimization model to generate the second optimization model of the anchor bolt;

[0052] Optimization iteration module: used to establish an optimization iteration loop until the function value of the multi-objective optimization model f reaches the convergence condition and output the final lightweight anti-loosening anchor bolt design solution.

[0053] An electronic device comprises a memory, a central processing unit and a computer program stored in the memory and executable on the central processing unit. When the central processing unit executes the computer program, the above-mentioned anchor bolt anti-loosening design method is implemented.

[0054] A computer-readable storage medium stores a computer program, and when the computer program is executed, the above-mentioned anchor bolt anti-loosening design method is implemented.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] The present invention achieves technical progress in the anti-loosening design of anchor bolts through a comprehensive method. First, by extracting the basic size and fine geometric parameters of the bolts, an accurate solid model is constructed to lay the foundation for subsequent analysis; using finite element meshing technology, a high-precision finite element model is generated to improve the accuracy of calculation; secondly, the anti-loosening performance and weight objective functions are constructed, and the optimal design scheme that balances the anti-loosening performance and weight is obtained by solving the multi-objective optimization model. Comprehensively considering the anti-loosening performance and weight can effectively improve the anti-loosening performance of the anchor bolts, reduce the risk of loosening, and improve the reliability of the connector; using the material performance database, combined with physical and mechanical performance data, accurately search for the optimal material combination to improve the overall performance of the bolts; finally, through iterative optimization cycles, ensure that the model function value reaches the convergence condition, and the output design scheme achieves the best balance between anti-loosening performance and lightweight, significantly enhancing the stability and reliability of the anchor bolts under long-term vibration and impact loads; this method not only improves the accuracy and efficiency of the design, but also provides an innovative solution for the anti-loosening design of bolts. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0058] Figure 1 It is a flow chart of a method for designing a foundation bolt to prevent loosening in the present invention;

[0059] Figure 2 A flow chart of a method for generating a complete bolt entity model in a foundation bolt anti-loosening design method of the present invention;

[0060] Figure 3 A flowchart of a method for constructing an anti-loosening performance objective function based on a thread anti-loosening failure criterion in a foundation bolt anti-loosening design method of the present invention;

[0061] Figure 4 It is a functional module diagram of a foundation bolt anti-loosening design system in the present invention. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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.

[0063] Example 1

[0064] See also Figure 1 As shown, this embodiment provides a method for designing an anchor bolt to prevent loosening, comprising:

[0065] Step S1000, extracting basic size parameters of the anchor bolt, and constructing an initial solid model of the bolt based on the basic size parameters; extracting fine geometric parameters of the anchor bolt, and generating a complete bolt solid model with fine thread features based on the initial solid model of the bolt and the fine geometric parameters; dividing the bolt solid model into finite element meshes to generate a finite element model;

[0066] Furthermore, step S1000 includes:

[0067] Step S1100, extracting basic size parameters of anchor bolts and constructing an initial solid model of the bolts;

[0068] Furthermore, step S1100 includes:

[0069] Step S1110, extracting the basic dimension parameters of the anchor bolts, including the bolt length , Thread length , head height H;

[0070] Step S1120, based on the extracted , , H parameters to build the initial solid model of the bolt;

[0071] Step S1130, perform parameter association on the initial solid model of the bolt. , , H is defined as a modifiable parameterized variable;

[0072] Specifically, step S1100 is to obtain basic size parameters of the anchor bolts by measurement or drawing extraction, and these parameters are the basis for constructing a geometric model of the bolts. Indicates the total length of the bolt, Represents the length of the threaded segment, and H represents the height of the bolt head. In actual measurement, it is necessary to use high-precision measuring tools such as vernier calipers and micrometers to measure the bolt samples multiple times and take the average value to reduce the error. If the dimensions are obtained through engineering drawings, it is necessary to carefully read the drawings and convert the actual dimensions according to the scale. Use the extracted parameters to build a three-dimensional solid model of the bolt in CAD software (such as SolidWorks, Proe, etc.). Draw the bolt contour through a sketch, and then generate the bolt rod by stretching; draw the thread section contour, and obtain the thread through a spiral sweep; draw the bolt head contour, stretch it and merge it with the rod, and finally obtain a complete initial bolt model. Next, associate the parameters of the generated model. , , H are set as adjustable parametric variables. By establishing a parameter table in the CAD software and associating the parameters with the corresponding dimensions of the model, parameter-driven model modification can be achieved. When the bolt size needs to be optimized or changed, only the value needs to be adjusted in the parameter table, and the related dimensions will be automatically updated without the need to redraw the model, which greatly improves modeling efficiency.

[0073] Step S1100 accurately obtains the key dimensional parameters of the bolts through high-precision measurement and image recognition, providing a reliable data basis for subsequent modeling. Professional 3D CAD design software is used to flexibly generate digital models of bolts through a variety of modeling operations, and the geometric shape of the bolts is intuitively displayed. The parametric design associates the model with the dimensional parameters, realizes the rapid adjustment and optimization of the structure, and prepares for subsequent topological optimization and finite element analysis. These three sub-steps are closely linked and work together to complete the precise and efficient parametric modeling task of anchor bolts, laying a solid model foundation for the subsequent lightweight design of bolts.

[0074] Step S1200, extracting fine geometric parameters of the anchor bolt, and generating a complete bolt solid model with fine thread features based on the initial bolt solid model;

[0075] Furthermore, if Figure 2 As shown, step S1200 includes:

[0076] Step S1210, extracting fine geometric parameters of the anchor bolt, including thread profile angle α, thread height h, and pitch P;

[0077] Step S1220, based on the extracted fine geometric parameters, the standard thread profile is rotationally scanned along the bolt axis to form a spiral thread;

[0078] Step S1230, performing Boolean set operations on the spiral thread and the bolt shank to obtain a complete bolt solid model with fine thread features;

[0079] Specifically, in step S1200, the initial solid model of the bolt is geometrically refined to accurately characterize the fine features of the thread. Through the analysis and measurement of the actual anchor bolt, fine geometric parameters such as the thread profile angle α (i.e., the angle between the slope of the thread crest and the axis of the bolt), the thread height h (i.e., the height of the thread crest), and the pitch P (the axial distance between the corresponding points of two adjacent thread crests) are obtained. The thread profile angle α affects the degree of meshing and self-locking performance of the thread. The smaller the α angle, the tighter the thread meshing, but the greater the processing difficulty; the larger the α angle, the lower the meshing degree but the easier the assembly. The thread height h and the pitch P affect the strength and connection stiffness of the thread. The larger the h, the smaller the P, the higher the thread strength, but the processing accuracy requirements also increase accordingly. Based on the extracted fine geometric parameters, the standard thread profile (such as a triangle, rectangle, and trapezoidal section) is rotated and scanned to form a spiral thread. Rotational scanning can be achieved by setting the scanning path (i.e., the bolt axis), the scanning section (i.e., the tooth profile) and the pitch, and various standard or non-standard threads can be flexibly generated. Then, the Boolean operation function of the CAD software is used to perform a collective operation on the thread and the bolt shank, removing the overlapping area between the two, and obtaining a complete bolt solid model with fine threads on the surface.

[0080] Step S1200 obtains a realistic digital bolt solid model by finely modeling the thread geometry. As a key functional feature of the bolt, the geometric parameters of the thread directly affect the force performance and service life of the bolt. By measuring and analyzing the fine geometric parameters of the thread and performing parametric design, the thread can be flexibly adjusted and optimized to balance the meshing performance and processing cost. The advanced spiral scanning algorithm is used to efficiently generate fine thread geometry, and then the seamless integration of the thread and the bolt body is achieved through Boolean operations, and finally a three-dimensional bolt model with precise geometry and realistic shape is obtained. Fine modeling of the thread not only improves the visual quality of the model, but also provides the necessary geometric basis for subsequent finite element meshing, contact analysis, etc., which is crucial to improving the analysis accuracy. At the same time, the refined model of the thread can also be used for visual display, intuitively showing the design results of the bolt to the customer, and improving the persuasiveness of the design scheme. By extracting the basic size parameters and fine geometric parameters of the anchor bolt, a complete bolt solid model with fine thread features is generated, which can accurately reflect the actual working state of the bolt and improve the accuracy and credibility of the optimization results.

[0081] Step S1300, dividing the bolt solid model into finite element meshes to generate a finite element model;

[0082] In the process of dividing the finite element mesh, the mesh density is set in the complex shape area of ​​the bolt solid model. , set the mesh density in regular-shaped areas ,in ; Generate tetrahedral meshes in complex-shaped areas and structured hexahedral meshes in regular-shaped areas;

[0083] The complex shape area is an area in the bolt solid model that contains threads, bolt heads and screw transitions with complex geometry and large stress-strain gradients;

[0084] The regular shape region is a region in the bolt solid model that contains a bolt shank with a regular geometric shape and a small stress gradient;

[0085] Specifically, step S1300 performs finite element meshing on the three-dimensional solid model of the bolt, discretizes the continuous geometric model into a numerical model composed of nodes and units, and prepares for subsequent structural analysis and topology optimization. First, according to the structural characteristics of the bolt, the mesh density requirements of different areas are determined. In areas with complex geometry and large stress-strain gradients such as threads, bolt heads, and screw transitions, a higher mesh density needs to be set to accurately capture the local complex deformation field. In areas with regular geometry and small stress gradients such as the bolt rod, a sparser mesh can be used to balance the calculation accuracy and solution efficiency. Then, the adaptive meshing technology is used to automatically generate high-density tetrahedral meshes in complex areas. Tetrahedral units have good adaptability and can flexibly match complex geometric shapes to ensure mesh quality. In regular areas, structured hexahedral meshes are generated, and the hexahedral units have high calculation accuracy and good numerical stability. By reasonably selecting the mesh type, the mesh adaptability of complex areas and the solution efficiency of regular areas are taken into account. The quality of the generated mesh is optimized to improve the mesh size transition and unit quality. Encryption processing is performed in the transition area of ​​meshes of different densities to avoid stress differences caused by sudden changes in mesh size. And by locally adjusting the node position, the distorted or twisted bad units are eliminated to improve the overall quality of the grid.

[0086] Dividing high-quality finite element meshes is a prerequisite for accurate structural analysis and optimal design. Step S1300 comprehensively considers the structural characteristics of the bolts, adopts adaptive mesh division technology, reasonably configures tetrahedron and hexahedron units, realizes high-density meshes in complex areas, and accurately captures local mechanical behaviors; structured sparse meshes are used in regular areas, taking into account computational efficiency. The mesh quality is further improved through measures such as mesh encryption and elimination of distorted units, laying a good mesh foundation for subsequent analysis and calculation. High-quality finite element meshes can not only accurately describe the complex structure of the bolts, but also avoid computational distortion caused by mesh distortion, improve the accuracy and computational efficiency of finite element analysis, and thus improve the reliability of the calculation results. At the same time, by taking into account both accuracy and efficiency, unnecessary waste of computing resources is reduced and the design optimization cycle is shortened.

[0087] Step S1000 gradually builds a digital twin model of the bolt from the extraction of basic bolt dimensions, initial model construction, parameter association, to detailed thread characterization and mesh optimization. The extraction of geometric parameters and parametric design give the model the ability to be flexibly modified, the refined modeling of the thread improves the simulation accuracy of the model, and the high-quality mesh division provides a solid numerical model foundation for subsequent analysis and calculation. The close collaboration of these links together constitutes the cornerstone of the lightweight design of bolts and provides the necessary prerequisites for achieving the reliability and efficient design of bolts.

[0088] Step S2000: constructing an anti-loosening performance objective function And weight objective function , a multi-objective optimization model is established by integrating the anti-loosening performance target and the weight target; based on the finite element model, the multi-objective optimization model is solved to obtain the first optimization model of the anchor bolt;

[0089] Furthermore, step S2000 includes:

[0090] Step S2100: constructing an anti-loosening performance objective function based on the thread anti-loosening failure criterion The thread anti-loosening failure criteria include thread shear failure criteria, thread tensile failure criteria, thread fatigue failure criteria and thread loosening failure criteria;

[0091] Furthermore, if Figure 3 As shown, step S2100 includes:

[0092] Step S2110, establishing a thread shear failure limit state function based on the thread shear failure criterion:

[0093]

[0094] in, is the actual maximum shear stress of the thread, is the allowable shear strength of the thread;

[0095] Step S2120, establishing a thread tensile failure limit state function based on the thread tensile failure criterion:

[0096]

[0097] in, is the actual maximum tensile stress of the thread, is the allowable tensile strength of the thread;

[0098] Step S2130, establishing a thread fatigue failure limit state function based on the thread fatigue failure criterion:

[0099]

[0100] in, is the actual fatigue stress amplitude of the thread, is the allowable fatigue strength of the thread;

[0101] Step S2140, establishing a thread loosening failure limit state function based on the thread loosening failure criterion:

[0102]

[0103] in, is the actual loosening load of the thread, is the allowable loosening load of the thread;

[0104] Step S2150: construct an anti-loosening performance objective function based on the thread shear failure limit state function, the thread tensile failure limit state function, the thread fatigue failure limit state function and the thread loosening failure limit state function. ;

[0105] The anti-loosening performance objective function include:

[0106]

[0107] in:

[0108] : is the weight factor of the thread shear failure limit state function;

[0109] : is the weight factor of the thread tensile failure limit state function;

[0110] : is the weight factor of the thread fatigue failure limit state function;

[0111] : is the weight factor of the thread loosening failure limit state function;

[0112] The optimization goal is to minimize ,when When ≤0, the anchor thread is considered to meet the anti-loosening requirements;

[0113] It is a linear combination of various thread failure modes. The larger the value, the worse the anti-loosening performance.

[0114] Specifically, the key factors affecting the anti-loosening performance of bolts are analyzed, including thread engagement force , Thread friction , bolt preload etc.; Among them, the thread engagement force Depending on the thread profile angle α and thread height h, the thread friction Depends on thread surface roughness and friction factor μ, thread preload Depends on the initial tightening torque M. By selecting these parameters reasonably, the meshing effect and self-locking ability of the thread can be effectively improved, thereby improving the anti-loosening performance of the bolt. When describing the anti-loosening performance, the equivalent stress σ, contact stress, axial stress Equimechanical criteria, they reflect the stress state of the thread from different angles. The equivalent stress σ reflects the local stress concentration of the thread, the contact stress represents the meshing quality of the thread surface, and the axial stress The thread shear failure criterion evaluates the failure of the thread under shear stress, the thread tensile failure criterion evaluates the failure of the thread under tensile stress, the thread fatigue failure criterion evaluates the fatigue failure of the thread under cyclic load, and the thread loosening failure criterion evaluates the loosening of the thread under external force.

[0115] For several main failure modes of bolts, this step uses the limit state method to establish corresponding functions, including the shear failure limit state function , tensile failure limit state function , fatigue failure limit state function and loose failure limit state function By comparing the actual stress or load with the allowable value, it is determined whether the bolt meets the anti-loosening requirements, and then these limit state functions are linearly weighted to obtain the overall anti-loosening performance objective function , which provides quantitative indicators for subsequent multi-objective topology optimization. It should be noted that the function The weight coefficient in can be adjusted according to the actual working condition and importance of the bolt to balance the contribution of each failure mode. In general, the anti-loosening performance objective function constructed in step S2100 is It has clear physical meaning and mathematical form, and can accurately evaluate the anti-loosening performance of bolts, laying a solid foundation for achieving balanced optimization of anti-loosening and lightweight.

[0116] Step S2200, obtain the material relative density ρ and the bolt volume V, and construct a weight objective function based on ρ and V , ; The value range of ρ is 0 to 1. When ρ=0, it means that there is no material distribution; when ρ=1, it means that the material is completely filled;

[0117] Specifically, step S2200 quantitatively describes the weight properties of the bolt from two aspects: material distribution and structural size, and establishes a concise weight objective function. . In order to facilitate topology optimization, this step introduces the material relative density ρ as a design variable. The value range of ρ is 0 to 1. When ρ=0, it means that there is no material distribution at this location; when ρ=1, it means that the material is completely filled; and when 0<ρ<1, it represents the volume fraction of the material, and the material density is proportional to the relative density ρ. The larger ρ is, the more material is distributed at this location, and vice versa. By adjusting the value of ρ, the material distribution of the component can be continuously changed, thereby achieving weight optimization. According to the structural characteristics of the bolt, two methods can be used to obtain its volume V: one is to use CAD software to directly calculate the volume of the three-dimensional model of the bolt. This method has high accuracy but low efficiency; the other is based on the basic size parameters of the bolt, such as the bolt length , Thread length The approximate volume of the bolt is estimated using a simplified analytical formula, which is fast but not very accurate. It can be selected according to actual needs. Substituting the material relative density ρ and the bolt volume V into the weight formula, the weight objective function can be obtained: ,function Proportional to the design variable ρ, when ρ decreases, As a result, the bolt weight is also reduced, so it can be minimized To achieve the lightweight design of bolts. It is worth noting that since the material relative density ρ is a continuous variable, the weight objective function It is also continuous and smooth, which creates favorable conditions for using the gradient algorithm for optimization. In short, the weight objective function established in step S2200 is The form is concise, the physical meaning is clear, and it is directly related to the design variables. It can accurately express the weight characteristics of the bolts and provide a quantitative basis for lightweight design. The continuity of also facilitates the implementation of the optimization algorithm. Mature mathematical programming methods can be used to solve it and improve the optimization efficiency.

[0118] Step S2300, comprehensively considering the anti-loosening performance target and the weight target, and establishing a multi-objective optimization model;

[0119] The objective function of the multi-objective optimization model is: ;

[0120] Constraints: , , ;

[0121] in:

[0122] : is the anti-loosening performance objective function;

[0123] : is the weight objective function;

[0124] : is the design variable;

[0125] : is the value range of the design variable;

[0126] : Inequality constraints, such as strength, stiffness, etc.;

[0127] : Equality constraints, such as thread standards, processing technology, etc.;

[0128] Specifically, step S2300 comprehensively considers the requirements of anti-loosening and lightweight, and builds a mathematical optimization model containing multiple objective functions and constraints based on steps S2100 and S2200. First, it is necessary to clarify the variables of the optimization design. On the one hand, these variables will affect the anti-loosening performance and weight target, and on the other hand, they must be easy to adjust during the optimization process. For the problem of bolt anti-loosening, the main design variables include thread tooth angle α, thread tooth height h, bolt length , Thread length , head height H and material relative density ρ and other geometric parameters and material parameters, which jointly determine the anti-loosening performance and weight of the bolt. When selecting the optimization algorithm, it is necessary to comprehensively consider factors such as the number of design objectives, the type of constraints and the limitation of computing resources to balance the optimization efficiency and effect. For multi-objective optimization problems, commonly used algorithms include weighted method, constraint method, evolutionary algorithm, etc. The weighted method manually sets the weight coefficient, weights and sums multiple objective functions, and transforms them into a single objective problem for solution, but the selection of weights is often subjective; the constraint method transforms some objective functions into constraints, thereby transforming multi-objective problems into single objective problems, but how to select the main objective function is not easy; and evolutionary algorithms such as genetic algorithms draw on the principle of biological evolution, through population search and survival of the fittest, to seek Pareto optimal solutions among multiple objectives, with strong global optimization capabilities, but large computational complexity. According to actual requirements such as design time and optimization accuracy, a suitable optimization algorithm can be selected. Finally, after clarifying the optimization variables and algorithms, a complete multi-objective optimization mathematical model can be established. The model consists of three parts: objective function, constraint conditions and variable definition domain. Among them, the objective function f(x) is composed of the anti-loosening performance objective function And weight objective function (x) composition, requiring to find and (x) minimum variable value; the constraints include inequality constraints g(x) and equality constraints h(x), g(x) is generally the performance requirements of the structure such as strength and stiffness, and h(x) is usually the limitation of certain design specifications or production processes; the variable definition domain S is the value range of each design variable, such as the value range of the thread profile angle α is 45°-60°, the value range of the material relative density ρ is 0-1, etc. At this point, the multi-objective optimization mathematical model is built, and the optimization algorithm can be used to solve it to obtain the optimal design parameter values ​​under the requirements of anti-loosening and lightweight, providing guidance for subsequent detailed design.

[0129] In general, step S2300 systematically analyzes the influencing factors of bolt anti-loosening and establishes a multi-objective optimization mathematical model covering anti-loosening performance and weight. The model contains clear optimization variables, objective functions, constraints and variable definition domains, which can quantitatively express anti-loosening and lightweight design problems and has strong operability. From the two perspectives of anti-loosening performance and weight, this method systematically analyzes the influencing factors of bolt anti-loosening and establishes a relatively comprehensive and accurate mathematical model, which can better describe the inherent laws and mutual relationships of bolt anti-loosening and lightweight design problems. By introducing clear design variables, objective functions, constraints, etc., this method transforms qualitative anti-loosening design problems into quantitative mathematical optimization problems, so that anti-loosening performance and weight can be quantitatively evaluated and optimized, providing a scientific basis for engineering decision-making. The established mathematical model has clear physical meaning and mathematical form, the optimization variables and objective functions are easy to measure and calculate, the constraints can effectively reduce the design space, and the optimization results can directly guide engineering design, which has strong operability. This method is based on general multi-objective optimization theory, adopts standard mathematical modeling and solution process, and does not rely on specific bolt types and connection structures. Therefore, it has good versatility and can be easily extended to the anti-loosening design of other types of bolts. By optimizing and solving between anti-loosening performance and weight targets, this method can minimize the weight of bolts while meeting anti-loosening requirements, achieve a balance between anti-loosening and lightweight, and has a significant optimization effect compared with traditional design methods. The optimized design parameters obtained by this method, such as thread profile, bolt size, preload, etc., can be directly used to guide engineering design and production, shorten the development cycle, and improve design efficiency and quality. At the same time, the optimization results also lay the foundation for the standardized and serialized design of anti-loosening bolts.

[0130] The objective function: ;

[0131] in:

[0132] : is the weight factor of the anti-loosening performance objective function;

[0133] : is the weight factor of the weight objective function;

[0134] The weighted method will and weight goals The linear combination is a single objective function f, which avoids conflicts and trade-offs between multiple objectives, making the optimization model more concise and easier to solve. The optimization algorithm only needs to perform iterative search for one objective function, which greatly reduces the computational complexity. and The value of can flexibly control the optimization process's emphasis on anti-loosening performance and weight. When the value is large, the optimization will pay more attention to the improvement of anti-loosening performance; when When the value is large, the optimization focuses more on weight reduction. Designers can reasonably select weight factors according to actual needs to balance the design goals of anti-loosening and lightweight. Unlike the Pareto optimal solution set obtained by evolutionary optimization methods such as genetic algorithms, the weighted method can directly obtain a definite optimal solution after converting the multi-objective problem into a single-objective problem. Although this solution is not necessarily the global optimal, it is the optimal balance point that comprehensively considers anti-loosening performance and weight under the condition of a given weight factor. It has a clear physical meaning and is convenient for engineering application. After constructing a single objective function using the weighted method, the sensitivity analysis of the design parameters can be easily carried out. By examining the gradient changes of the objective function f(x) to design variables such as thread profile parameters and preload, the key parameters that have a greater impact on the optimization results are determined, thereby guiding the selection and optimization of the design parameters. The results of the sensitivity analysis also help to improve the optimization efficiency and reduce the design space. The single objective function obtained by the weighted method creates conditions for multidisciplinary optimization of thread anti-loosening. Through the function f(x), multiple disciplines such as structural optimization and process optimization can be coupled, and factors such as the mechanical properties and processing technology of the thread can be considered uniformly to achieve multi-disciplinary and multi-objective comprehensive optimization, and obtain the optimal design solution that takes into account multiple requirements such as anti-loosening, light weight, and manufacturability.

[0135] Step S2400, based on the finite element model, solve the multi-objective optimization model f(x) to obtain the optimal solution , the optimal solution Substitute the bolt solid model into it, generate a new topological structure, and obtain the first optimization model of the anchor bolt;

[0136] Specifically, a classic multi-objective optimization algorithm NSGA-II (Non-dominated Sorting Genetic Algorithm II) was used to solve the multi-objective optimization mathematical model established earlier. NSGA-II simulates the process of biological evolution and uses population search, survival of the fittest and other mechanisms to explore the optimal design solution in the design space. Compared with traditional gradient optimization methods, genetic algorithms have the advantages of strong global search capabilities, no reliance on objective function gradients, and easy implementation. They are particularly suitable for solving multi-objective, nonlinear, and multi-constrained engineering design problems.

[0137] According to the range of the design variables, N initial designs are randomly generated as individuals in the population. The size of the population N is generally determined according to the complexity of the problem and the computing resources, usually ranging from dozens to hundreds. For each design individual, by substituting its variables into the objective function, the corresponding anti-loosening performance target can be calculated. and weight goals , so as to evaluate the pros and cons of the design. Next, according to the concept of Pareto dominance, the population is non-dominated sorted. The so-called non-domination means that if a solution is not inferior to another solution in all objectives and at least one objective is better, then the solution is said to dominate the other solution. For any two individuals in the population, by comparing their objective function values, it can be determined whether there is a dominance relationship between the two. In the non-dominated sorting, the most advanced non-dominated individuals are classified as level 1. After removing level 1 from the population, the new most advanced individuals are classified as level 2, and so on, until all individuals are assigned levels. The level of an individual reflects its pros and cons in evolution. The smaller the level, the closer the individual is to the optimal solution. In order to maintain the diversity of the population and prevent the algorithm from converging to the local optimum too early, NSGA-II also introduces the concept of crowding. Crowding measures the spatial density around an individual. The larger the crowding, the sparser the solutions in the area where the individual is located, and should be retained; on the contrary, the smaller the crowding, the denser the solutions around the individual, and they can be appropriately eliminated to avoid repeated searches. Through non-dominated sorting and crowding calculation, individuals in the initial population are assigned merit levels and diversity metrics, which provide a basis for subsequent evolutionary operations.

[0138] The three core operations of the genetic algorithm are performed on the current population: selection, crossover, and mutation, thereby generating a new offspring population. First, a binary tournament selection is used, that is, two individuals are randomly selected from the current population, their ranks and crowding are compared, the superior one is used as the parent, and the inferior one is eliminated until N parents are selected. Then a crossover operation is performed on the parent individuals, that is, some design variables of the two parents are exchanged to generate new offspring individuals. In order to handle continuous variables, NSGA-II uses simulated binary crossover SBX. SBX generates new offspring variables between two parents by simulating binary-coded single-point crossover. The individuals after the crossover are then subjected to polynomial mutation, that is, certain variables are perturbed with a certain probability to introduce new design features to increase the diversity of the population. The mutation operator simulates the process of biological mutation, which helps to jump out of the local optimum and conduct a wider search in the design space. After selection, crossover and mutation, a new offspring population is created and merged with the current parent population to form an extended population of size 2N. The expanded population is re-sorted by fast non-dominated sorting, and the crowding of individuals is calculated. N optimal individuals are selected from them according to their ranks and crowding to form a new generation of population, ready for the next round of evolution. During the optimization search process, the algorithm uses the previously established parameterized finite element model to quickly solve each design individual in the population, which not only efficiently evaluates the design performance, but also provides the necessary analytical data support for the optimization search.

[0139] The population is continuously selected, crossed and mutated, so that excellent designs are continuously retained, inferior designs are eliminated, and the overall performance of the population is continuously improved, gradually approaching the Pareto optimal frontier. When the evolution reaches the preset maximum number of generations, the algorithm stops and outputs all non-dominated individuals in the current population as the Pareto optimal solution set. The solution set gives the trade-off between anti-loosening performance and weight. Different solutions correspond to different design schemes, with different characteristics such as good anti-loosening performance but heavy weight, or light weight but poor anti-loosening performance. Designers can select a satisfactory solution from the solution set as the final optimized design according to actual needs. The multi-objective optimization model is solved using the NSGA-II algorithm, and the optimal design is searched in the design space by population evolution, which reflects the characteristics of modern optimization design. Compared with the classical point search method, the evolutionary algorithm based on swarm intelligence shows a strong global search capability and robustness, and can find the global optimal solution in a complex multi-peak design space without requiring the objective function to be differentiable, which greatly expands its application range. Through the idea of ​​Pareto optimality, multi-objective optimization quantitatively characterizes the contradictory relationship between design objectives as a solution set, providing an intuitive and clear quantitative reference for designers' decision-making.

[0140] Select the optimal solution that meets the design requirements from the Pareto optimal solution set of multi-objective optimization , and the bolt solid model was modified to obtain the first optimization model of the bolt. This model combines the anti-loosening performance and weight targets, and to a certain extent achieves the lightweight design of the bolt. Specifically, by Substituting into the parametric model of the bolt, the key structural parameters such as thread profile angle α, thread height h, and pitch P can be updated to the optimized values. The change of these parameters will affect the force performance of the bolt. For example, the increase of thread profile angle α is conducive to improving the self-locking property of the thread and preventing the bolt from loosening; the reduction of pitch P can increase the meshing area of ​​the bolt per unit length and improve the shear strength of the bolt; the optimization of thread height h can not only improve the meshing degree of the thread, but also reduce the weight of the bolt. In addition to modifying the thread structural parameters, this step also optimizes the topological structure of the bolt according to the distribution of the relative density ρ of the material in the optimal solution. The relative density ρ is a commonly used design variable in topological optimization, which characterizes the distribution of the material in the design domain. ρ takes 1 to indicate that the material is completely filled, 0 to indicate that the material is completely removed, and between 0 and 1 to indicate partial filling of the material. By solving the multi-objective optimization model, the optimal distribution of the relative density ρ of the material in different areas of the bolt is obtained. This distribution is applied to the original bolt solid model, that is, more material is retained in the area with a larger ρ value, and excess material is removed in the area with a smaller ρ value, thereby generating a new topological configuration of the bolt. This configuration achieves lightweighting inside the bolt by optimizing the material layout, minimizing weight while meeting strength requirements. It is worth noting that since topological optimization is based on the continuous density method, the optimized material distribution usually has a certain grayscale, and threshold processing is required to obtain a clear topological boundary. In addition, an overly complex topological structure may bring difficulties to manufacturing and processing. Therefore, when applying the topological optimization results, it is also necessary to weigh the manufacturability of the design, and the topological configuration can be appropriately simplified when necessary.

[0141] For example, the initial design parameters of a 3D solid model of a foundation bolt are: thread angle α=60°, thread height h=0.8mm, pitch P=2.5mm, head height H=6mm, bolt rod diameter d=10mm. After solving the multi-objective optimization, the optimal solution is selected from the Pareto solution set. .Will Substituting the parametric model of the bolt and updating the relevant parameters, a new thread with optimized thread angle, tooth height and pitch can be obtained. Relative density of medium material The distribution of the bolt head and the bottom of the thread were removed to obtain a new lightweight topological configuration. The optimized bolts have improvements in both thread structure and topological configuration, with improved thread engagement, more uniform stress distribution, and the removal of invalid materials, achieving a double improvement in anti-loosening performance and lightweight level. The optimized bolt solid model can be directly used to guide engineering applications and can be put into production after detailed processing, which demonstrates the practical value of the multi-objective optimization method. Of course, this is only the result of the first round of optimization, and it can be further improved through subsequent measures such as material selection, and finally the optimal bolt that meets multiple requirements is obtained.

[0142] Step S3000, collecting physical and mechanical property data of common metals, establishing a material property database, and searching for the optimal material combination in the material property database;

[0143] Furthermore, step S3000 includes:

[0144] Step S3100, collecting physical and mechanical property data of common metals and establishing a material property database; the physical and mechanical property data include density and tensile strength parameters;

[0145] Specifically, collect the physical and mechanical properties data of commonly used metal materials, including density, elastic modulus, yield strength, tensile strength, elongation, fatigue strength, fracture toughness, etc.; take the average of the performance data of similar materials of different brands and models, and combine theoretical calculations or literature research to obtain other missing performance parameters; organize and summarize the collected and calculated material performance data in a unified format, establish a structured material performance database, and store it in a computer-accessible data file or relational database management system.

[0146] Since the working conditions of bolts usually have high requirements for the strength, toughness and fatigue performance of materials, the physical and mechanical parameters related to the above properties are collected. Among them, the material density reflects the lightweight potential of the material. The lower the density, the lighter the material mass under the same volume; the elastic modulus characterizes the material's ability to resist deformation and stiffness. The larger the modulus, the smaller the deformation of the material within the elastic range; the yield strength and tensile strength represent the stress values ​​when the material yields and breaks, respectively, and are important indicators for evaluating the strength of the material; the elongation reflects the plastic deformation ability of the material and is a measure of the toughness of the material; the fatigue strength gives the stress level at which the material fails under alternating loads, and the fracture toughness characterizes the material's ability to resist crack propagation, which is a key parameter for evaluating the tolerance design of the material. By systematically collecting the above performance data, we can have a comprehensive understanding of the mechanical behavior and service performance of different materials, which is the premise and basis for material optimization. It should be noted that the same type of material often has multiple grades and models, and their performance parameters may have certain differences. In order to improve the representativeness and reliability of the data, the performance data of the same type of materials should be averaged. However, due to limitations such as test conditions and missing data, individual performance parameters of some materials may not be directly available. In this case, they can be estimated in combination with theoretical formulas, or supplemented by consulting literature, manuals, etc. The aggregated material data needs to be structured and stored in a unified format for subsequent calls and analysis. It can usually be stored in data files such as JSON, XML, etc. in a computer, or it can be managed using relational databases such as MySQL, so as to build a material performance database with a complete structure and convenient query. In general, step S3100 quantitatively characterizes the characteristics of candidate materials by systematically and comprehensively collecting and collating material physical and mechanical performance data, which is an important basis for carrying out material optimization and performance evaluation. The established material database not only provides data support for this method, but also provides useful references for other material research and engineering applications.

[0147] Step S3200, taking density and tensile strength parameters as optimization targets, establishing a multi-objective function for material optimization, searching and calculating the objective function values ​​of different material combinations in the material performance database, and selecting the material combination with the smallest objective function value as the optimal material combination;

[0148] The material optimization multi-objective function is min (y) = [ (y), - (y)];

[0149] in:

[0150] : is the density of the material;

[0151] : is the tensile strength of the material;

[0152] y: material design variable;

[0153] Specifically, density and strength are two core indicators for measuring the lightweight performance of materials. Generally, the lower the density and the higher the strength of a material, the greater its specific strength, and it can withstand greater loads while controlling its own weight. Therefore, the two are used as the objective function for material optimization. Since density and strength are contradictory to a certain extent, it is difficult to achieve the optimal value at the same time, and a trade-off needs to be made between the two. Therefore, a multi-objective optimization method is used to construct it as a Pareto frontier search problem. Tensile strength The minus sign in front indicates that the tensile strength is to be maximized during the optimization process. In multi-objective optimization problems, it is usually assumed that all objective functions are minimized, that is, the minimum value of the objective function is found. However, in the material optimization problem, it is hoped that the tensile strength Larger materials can improve the anti-loosening performance of the bolt. In order to transform the maximization problem into a minimization problem, the objective function can be inversely proportional, that is, maximize Equivalent to minimizing Therefore, when establishing the multi-objective function for material optimization, There is no minus sign in front, which means that the density of the material is minimized to achieve lightness; while the tensile strength The minus sign in front indicates that the tensile strength of the material should be maximized to enhance the anti-loosening performance. In this way, the two goals of material lightweighting and anti-loosening performance improvement can be unified into the framework of the minimization problem, which is convenient for subsequent optimization solutions.

[0154] In the actual search process, since the candidate material combination space is very large and the performance differences and matching between different materials are not linear, it is difficult to directly predict their performance using analytical models, so machine learning methods are introduced. By using classification learning algorithms such as decision trees and support vector machines, a material performance classification model is trained with the physical and mechanical performance parameters of the material as feature vectors and the material grade as classification labels. The model can quickly predict the material category to which it belongs based on any given material performance parameters, and then obtain the mechanical properties of the material combination. Using the established classification model as a rapid evaluator of material properties can greatly improve the efficiency of optimization search. Next, heuristic optimization methods such as genetic algorithms are used, and the constructed material classification model is used as the fitness function to search in the candidate material combination space to find the Pareto optimal solution set with low density and high strength. Due to the use of machine learning-assisted optimization, the optimal material combination can be found efficiently and accurately in the massive search space. In view of the stress characteristics of bolts, it is necessary to consider not only density and strength, but also comprehensive properties such as stiffness, plasticity, and toughness, and select anisotropic lightweight composite materials with high specific strength, such as carbon fiber composites, magnesium alloy-based composites, etc., to meet the lightweight and anti-loosening requirements of bolts.

[0155] By cleverly combining machine learning with optimization search, making full use of existing material data knowledge, building a rapid prediction model for material properties, and on this basis, using heuristic algorithms to intelligently search the material combination space, the optimal material solution that meets the design requirements can be efficiently found. This data-driven material design idea can not only be used for material optimization of bolts, but also provides new ideas for material and structural design in other fields. Of course, the obtained material combination scheme may be an unprecedented new material. Before practical application, necessary experimental verification and process optimization are required to ensure its reliability and manufacturability. In addition, after considering factors such as cost, supply chain, and environmental protection, it may be necessary to weigh between the optimal material and the suboptimal material. Nevertheless, this method provides new ideas and directions for material optimization, and the optimal solution set also provides a valuable reference for material designers. It is of great significance to expand the material selection space, explore the material potential, and promote innovation and development in the field of materials.

[0156] Step S3300, substituting the searched optimal material combination into the first optimization model to generate a second optimization model of the anchor bolt;

[0157] Specifically, the optimal material combination obtained by optimization is applied to the bolt model obtained by the first round of structural optimization. On the basis of the optimized geometric structure, the optimization attributes in the material field are further given, thereby obtaining the second optimized model of the bolt. The first round of optimization optimized the structural layout of the bolt at the parameter and topological levels, improving the force performance and lightweight level of the bolt. However, the first round of structural optimization targeted a specific initial material, and material selection was not involved in the optimization process, which to a certain extent limited the further improvement of the bolt performance. The optimal material combination with both high specific strength and lightweight characteristics was obtained through material optimization. If it can be applied to the optimized bolt structure, it is expected to further reduce the weight of the bolt on the original basis and improve its comprehensive mechanical properties.

[0158] The specific method of substituting the optimal material parameters into the first optimization model is: according to the physical and mechanical parameters of the optimal material combination, such as density, elastic modulus, Poisson's ratio, strength limit, etc., modify the material property definition in the first optimization model and replace the original initial material with the optimal material. Since the optimized material has a lower density and higher specific strength, under the same geometric dimensions and stress conditions, the bolts using the new material can withstand a larger load, or the size can be further reduced when subjected to the same load, thereby achieving secondary optimization of the bolt weight. At the same time, the good comprehensive mechanical properties of the optimal material, such as high stiffness, strength, toughness and fatigue performance, can improve the reliability and service life of the bolt under complex working conditions. In addition, if the optimal material is an anisotropic composite material, the anisotropy of the material can be fully utilized. By reasonably setting the ply direction, the force direction of the stress concentration area such as the thread bottom is consistent with the direction of the maximum material strength, thereby preventing the bolt from breaking and failing due to stress concentration. By establishing a material performance database, searching for the optimal material combination, and substituting the optimal material combination into the optimization model, the best material solution can be found to improve the performance of the bolt and extend its service life. Through the dual optimization of materials and structures, the weight of bolts can be reduced, and the comprehensive mechanical properties of bolts, such as strength, stiffness, and fatigue resistance, can be significantly improved, so that bolts can meet increasingly demanding use requirements. In addition, this optimization design method provides new ideas for the lightweight design of other structural parts, which is of great significance for promoting material and structural innovation in the fields of transportation, aerospace, etc. In the future, an integrated optimization design process of materials, structures, and processes can be further established to achieve seamless integration and iterative optimization of design, materials, and manufacturing, and accelerate the implementation of innovative design solutions.

[0159] Step S4000, establishing an optimization iteration loop until the function value of the multi-objective optimization model f reaches a convergence condition, and outputting a final design scheme for lightweight anti-loosening anchor bolts;

[0160] Furthermore, step S4000 includes:

[0161] Step S4100, feeding back the material parameters of the second optimization model to the finite element model to generate an updated finite element model;

[0162] Step S4200: Calculate the objective function value of the current optimization step of the multi-objective optimization model based on the updated finite element model (x); the objective function value of the current optimization step (x) and the objective function values ​​of the previous steps (x), (x) compare and evaluate the convergence of the objective function;

[0163] Step S4300, if the objective function value changes by | (x)- (x)| and the change in the objective function value 2| (x)- (x)| are all less than the set convergence threshold ε, the optimization is considered to have converged, and step S4400 is executed; otherwise, the optimization is considered to have not converged, and the process returns to step S2400 to continue a new round of multi-objective optimization solution based on the updated finite element model;

[0164] Step S4400, outputting the optimization result at the time of convergence as the final design scheme of lightweight anti-loosening anchor bolts;

[0165] Specifically, first, the material property parameters of the second optimization model are fed back to the finite element model, including the material relative density ρ of each grid area (a variable that characterizes the material distribution, 0≤ρ≤1), the elastic modulus E of the material (a parameter that describes the material stiffness), Poisson's ratio (a parameter that reflects the material deformation performance), etc. Based on the updated material parameters, the current structure is re-analyzed by finite element analysis to calculate the stress-strain field, anti-loosening performance index, and multi-objective optimization function value of the structure. (x).

[0166] To determine whether the optimization iteration has converged, it is necessary to compare the objective function value of the current optimization step (x) and the function values ​​of the previous two steps (x), (x), if the change of the objective function in three consecutive steps is less than the set convergence threshold ε (such as 1%), the optimization process is considered to be stable and meets the convergence criteria, the iteration can be terminated and the final result can be output. If the objective function has not yet reached the convergence requirement, the updated finite element model is used to start a new round of multi-objective optimization solution, and the iteration is repeated until the convergence conditions are met. When outputting the optimization results, not only the bolt topology configuration (i.e., the optimal material distribution) and thread profile parameters (such as thread profile angle α, pitch P, etc.) at the time of convergence should be given, but also the grade and mechanical properties of the materials in each region (such as high-strength steel, titanium alloy, aluminum alloy, etc.) should be clarified to guide subsequent engineering design and manufacturing.

[0167] In general, step S4000 controls the iterative process of multi-objective topology optimization through convergence criteria, and outputs the final lightweight anti-loosening design solution when the optimization converges. By feeding back attribute parameters such as material density and stiffness, the material distribution and mechanical properties of the structure can be updated synchronously. By re-analyzing and calculating the objective function, the anti-loosening and lightweight level of the current design can be quantitatively evaluated. The use of continuous multi-step objective function value changes as the convergence criterion can effectively avoid the optimization process from stagnating at the local optimal point and improve the global optimization ability. The output optimization results include improvement measures in bolt structure, thread, material and other aspects, which can comprehensively improve the anti-loosening performance and lightweight level of the bolts. On the premise of ensuring anti-loosening performance, the lightweight design of anchor bolts is realized, the material usage is reduced, and the cost is reduced, which meets the demand for lightweight in modern industry. The performance comparison before and after the quantitative evaluation of the optimization can intuitively show the optimization effect of the method.

[0168] For example, a certain type of M24 heavy-duty engineering machinery bolt is used as an example to illustrate the optimization design process of this method. The bolt is initially made of 45 steel material, with a thread specification of M24×3 and a bolt length of =100mm, thread length =70mm, head height H=10mm, fastening joint stiffness =5× N / m, bolt preload =5× N, transverse harmonic load =1× N. The optimization goal is to minimize the weight of the bolt while ensuring the reliability of anti-loosening. First, the bolt size parameters are extracted, the initial parametric model is constructed, and the thread area is encrypted using tetrahedral meshes to generate the original finite element model. Then, the key factors affecting the anti-loosening performance of the bolt are analyzed, and the anti-loosening performance objective function is established. , mainly considering the shear strength, tensile strength, fatigue life and bolt loosening failure modes of the thread, and constructing the weight objective function based on the total volume and density distribution of the structure On this basis, a multi-objective topology optimization mathematical model is established to minimize and For the optimization goal, the MMA (Method of Moving Asymptotes) algorithm is used to solve multiple objectives with volume fraction, manufacturing process and other constraints to obtain the Pareto optimal solution set. The solution with the best anti-loosening performance is selected as the initial topological configuration, and the first optimization model is obtained through smoothing and size refinement. The optimal material combination among the high-strength and low-density candidate materials is searched from the material performance database, substituted into the first optimization model, and the second optimization model of the anchor bolt is generated. The optimal material combination parameters of the second optimization model are fed back to the finite element model to generate an updated finite element model. Based on the updated finite element model, the topology optimization is iteratively carried out. When the change in the objective function for three consecutive steps is less than 1%, the optimization converges. The final output optimization plan is: the main material of the bolt is 30CrMnSiA alloy steel, and the yield strength =835MPa, elastic modulus E=210GPa; thread material is 38CrMoAl alloy steel, yield strength =1280MPa, elastic modulus E=218GPa; the bolt head diameter is reduced by 6%, and it is hollow in design; the thread tooth angle α=35°, and the pitch P=3mm; compared with the initial design, the maximum equivalent stress of the optimized bolt is reduced by 23.5%, the fatigue life is increased by 1.6 times, and the weight is reduced by 12.7%, and the anti-loosening performance and lightweight level are significantly improved.

[0169] This example verifies the effectiveness of the proposed method in balancing anti-loosening and lightweight and improving the comprehensive performance of bolts by comparing the data before and after optimization. For specific bolt examples, optimization suggestions are given from multiple angles such as structural topology, thread parameters, and material selection, which are highly operational. In general, the lightweight and anti-loosening integrated design optimization method constructed in this paper has broad application prospects in the field of bolt design.

[0170] The anchor bolt anti-loosening design method of the present application has strong adaptability and is not only suitable for the anti-loosening design of anchor bolts, but can also be extended to other mechanical connection parts that require multi-objective optimization design, and has broad application prospects. Through optimized design, the rational use of materials and the improvement of bolt performance are achieved, the production cost and maintenance cost are reduced, and there are significant economic benefits. The final output of the lightweight anti-loosening anchor bolt design solution can significantly improve the market competitiveness of the product and meet customer demand for high-performance, low-cost connections.

[0171] Example 2

[0172] This embodiment provides a foundation bolt anti-loosening design system based on the embodiment 1, such as Figure 4 As shown, including:

[0173] Parameter extraction module: used to extract the basic size parameters and fine geometric parameters of anchor bolts;

[0174] Bolt model generation module: used to build the initial solid model of the bolt based on the basic size parameters of the anchor bolt; generate a complete bolt solid model with fine thread features based on the initial solid model of the bolt and fine geometric parameters;

[0175] Finite element meshing module: used to divide the finite element mesh of the bolt solid model and generate the finite element model;

[0176] Optimization model building module: used to build the anti-loosening performance objective function And weight objective function , integrating the anti-loosening performance target and weight target, and establishing a multi-objective optimization model f;

[0177] Optimization solution module: used to solve the multi-objective optimization model based on the finite element model, obtain the optimal solution, substitute the optimal solution into the bolt entity model, and obtain the first optimization model of the anchor bolt;

[0178] Optimal material search module: used to collect physical and mechanical property data of common metals, establish a material property database, and search for the optimal material combination in the material property database;

[0179] The second optimization model generation module is used to substitute the searched optimal material combination into the first optimization model to generate the second optimization model of the anchor bolt;

[0180] Optimization iteration module: used to establish an optimization iteration loop until the function value of the multi-objective optimization model f reaches the convergence condition and output the final lightweight anti-loosening anchor bolt design solution.

[0181] Example 3

[0182] This embodiment discloses an electronic device, including a memory, a central processing unit, and a computer program stored in the memory and executable on the central processing unit. When the central processing unit executes the computer program, the above-mentioned anchor bolt anti-loosening design method is implemented.

[0183] Since the electronic device introduced in this embodiment is an electronic device used to implement a method for preventing loosening of anchor bolts in the embodiment of this application, based on the method for preventing loosening of anchor bolts introduced in the embodiment of this application, a person skilled in the art can understand the specific implementation of the electronic device of this embodiment and its various variations, so how the electronic device implements the method in the embodiment of this application is not described in detail here. As long as a person skilled in the art implements the electronic device used in the method for preventing loosening of anchor bolts in the embodiment of this application, it belongs to the scope of protection of this application.

[0184] Example 4

[0185] The present embodiment discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed, the above-mentioned anchor bolt anti-loosening design method is implemented.

[0186] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters, weights and thresholds in the formula are set by technicians in this field according to actual conditions.

[0187] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired network or a wireless network. The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD) or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0188] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0189] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0190] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic, for example, the division of the units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0191] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0192] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0193] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

[0194] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for preventing loosening of anchor bolts, characterized in that: The method comprises: Extract the basic size parameters of the anchor bolts, and build the initial solid model of the bolts based on the basic size parameters; extract the fine geometric parameters of the anchor bolts, and generate a complete bolt solid model with fine thread features based on the initial solid model and fine geometric parameters of the bolts; divide the bolt solid model into finite element meshes to generate a finite element model; Constructing the anti-loosening performance objective function And weight objective function , comprehensively considering the anti-loosening performance target and the weight target, establish a multi-objective optimization model f; based on the finite element model, solve the multi-objective optimization model to obtain the optimal solution, substitute the optimal solution into the bolt entity model, and obtain the first optimization model of the anchor bolt; Collect the physical and mechanical properties data of common metals, establish a material properties database, and search for the optimal material combination in the material properties database; substitute the searched optimal material combination into the first optimization model to generate the second optimization model of the anchor bolts; Establish an optimization iteration loop until the function value of the multi-objective optimization model f reaches the convergence condition, and output the final lightweight anti-loosening anchor bolt design solution; The anti-loosening performance objective function Constructed based on thread loosening prevention failure criteria; The thread anti-loosening failure criteria include thread shear failure criteria, thread tensile failure criteria, thread fatigue failure criteria and thread loosening failure criteria; The anti-loosening performance objective function The construction methods include: The thread shear failure limit state function is established based on the thread shear failure criterion: ； in, is the actual maximum shear stress of the thread, is the allowable shear strength of the thread; The thread tensile failure limit state function is established based on the thread tensile failure criterion: ； in, is the actual maximum tensile stress of the thread, is the allowable tensile strength of the thread; The thread fatigue failure limit state function is established based on the thread fatigue failure criterion: ； in, is the actual fatigue stress amplitude of the thread, is the allowable fatigue strength of the thread; The thread loosening failure limit state function is established based on the thread loosening failure criterion: ； in, is the actual loosening load of the thread, is the allowable loosening load of the thread; According to the thread shear failure limit state function, thread tensile failure limit state function, thread fatigue failure limit state function and thread loosening failure limit state function, the anti-loosening performance objective function is constructed. ; The optimization goal is to minimize ,when When ≤0, the anchor thread is considered to meet the anti-loosening requirements.

2. A method for preventing loosening of anchor bolts according to claim 1, characterized in that: In the process of dividing the finite element mesh, the mesh density is set in the complex shape area of ​​the bolt solid model. , set the mesh density in regular-shaped areas ,in ; Generate tetrahedral meshes in complex-shaped areas and structured hexahedral meshes in regular-shaped areas.

3. A method for designing anti-loosening of anchor bolts according to claim 1, characterized in that: The objective function of the multi-objective optimization model is: ; The constraints of the multi-objective optimization model are: , , ; in, is the anti-loosening performance objective function, is the weight objective function, is the design variable, is the value range of the design variable, is the inequality constraint, is the equality constraint.

4. A method for designing anti-loosening of anchor bolts according to claim 3, characterized in that: The objective function of the multi-objective optimization model is: ； in, is the weight factor of the anti-loosening performance objective function, is the weight factor of the weight objective function.

5. The method for designing anti-loosening of anchor bolts according to claim 1, characterized in that: The physical and mechanical properties data include density and tensile strength parameters; The method for searching for the optimal material combination in the material performance database comprises: taking density and tensile strength parameters as optimization targets, establishing a multi-objective function for material optimization, searching and calculating the objective function values ​​of different material combinations in the material performance database, and selecting the material combination with the smallest objective function value as the optimal material combination; The material optimization multi-objective function is min (y) = [ (y), - (y)]; where is the density of the material, is the tensile strength of the material, and y is the material design variable.

6. A method for designing anti-loosening of anchor bolts according to claim 1, characterized in that: The optimization iteration loop is established until the function value of the multi-objective optimization model f reaches the convergence condition, and the final lightweight anti-loosening anchor bolt design scheme is outputted, including: Feeding back the material parameters of the second optimization model to the finite element model to generate an updated finite element model; Based on the updated finite element model, calculate the objective function value of the current optimization step of the multi-objective optimization model (x); the objective function value of the current optimization step (x) and the objective function values ​​of the previous steps (x), (x) comparison; If the objective function value changes by one | (x)- (x)| and the change in the objective function value 2| (x)- (x)| are all less than the set convergence threshold ε, the optimization is considered to have converged; otherwise, the optimization is considered to have not converged, and a new round of multi-objective optimization solution is continued based on the updated finite element model; Output the optimization results at convergence as the final design solution for lightweight anti-loosening anchor bolts.

7. A foundation bolt anti-loosening design system, which is used to implement a foundation bolt anti-loosening design method according to any one of claims 1 to 6, characterized in that: The system comprises: Parameter extraction module: used to extract the basic size parameters and fine geometric parameters of anchor bolts; Bolt model generation module: used to build the initial solid model of the bolt based on the basic size parameters of the anchor bolt; generate a complete bolt solid model with fine thread features based on the initial solid model of the bolt and fine geometric parameters; Finite element meshing module: used to divide the finite element mesh of the bolt solid model and generate the finite element model; Optimization model building module: used to build the anti-loosening performance objective function And weight objective function , integrating the anti-loosening performance target and weight target, and establishing a multi-objective optimization model f; Optimization solution module: used to solve the multi-objective optimization model based on the finite element model, obtain the optimal solution, substitute the optimal solution into the bolt entity model, and obtain the first optimization model of the anchor bolt; Optimal material search module: used to collect physical and mechanical property data of common metals, establish a material property database, and search for the optimal material combination in the material property database; The second optimization model generation module is used to substitute the searched optimal material combination into the first optimization model to generate the second optimization model of the anchor bolt; Optimization iteration module: used to establish an optimization iteration loop until the function value of the multi-objective optimization model f reaches the convergence condition and output the final lightweight anti-loosening anchor bolt design solution.

8. An electronic device comprising a memory, a central processing unit, and a computer program stored in the memory and executable on the central processing unit, characterized in that: When the central processing unit executes the computer program, a method for designing anti-loosening of anchor bolts according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the method for designing anti-loosening of anchor bolts according to any one of claims 1 to 6 is implemented.

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