A method for optimizing the surface texture topography of a friction pair

Abstract

This invention discloses an optimization design method for the surface texture morphology of friction pairs. First, by establishing a Reynolds equation considering microscopic cavitation and coupling it with a film thickness correction factor, the lubrication characteristic parameters of the friction pair are obtained using the finite difference method, providing precise physical support for optimization. Second, the texture shape profile is parametrically characterized using Bézier curves, and flexible control of arbitrarily complex morphologies is achieved by adjusting the coordinates of control points. Subsequently, a particle swarm optimization algorithm is introduced to optimize the cluster of control points. By introducing three types of dynamic adjustment strategies, the global optimization capability of the algorithm is significantly improved. Finally, with the goal of achieving a multi-objective balance between load-bearing capacity and friction coefficient, the optimal texture morphology is obtained. This method effectively improves the lubrication performance and operational stability of friction pairs, and is particularly suitable for extreme conditions such as high loads and high speeds, possessing broad engineering practical value.

Description

Technical Field

[0001] This invention belongs to the field of friction pair surface texture morphology optimization, and particularly relates to an optimization design method for friction pair surface texture morphology. Background Technology

[0002] In mechanical transmission systems, the surface lubrication performance of friction pairs (such as bearings, piston rings and cylinder liners) directly affects the energy efficiency and service life of the equipment. By using surface texturing technology to create micron-level pits or grooves on the surface of friction pairs, the flow state of the lubricating medium can be effectively improved. Therefore, how to enhance the oil film carrying capacity and reduce friction loss has become an important research direction in the field of tribology.

[0003] Currently, the main methods for optimizing the surface texture morphology of friction pairs include experimental design, topology optimization, and genetic algorithms. Experimental design primarily explores the key factors affecting the performance of friction pairs and their interactions through system-designed experiments or simulations. Its core lies in carefully selecting factors and levels, designing an experimental matrix, and then establishing a response model using response surface methodology for optimization. Topology optimization transforms the morphology optimization problem into a continuous variable optimization problem, allowing design variables (such as surface texture depth and shape) to vary freely throughout the design space. It solves the physical equations (such as the Reynolds equation) and corresponding adjoint equations related to the friction pair performance, using gradient information to guide the optimization process and ultimately obtain the optimal surface texture distribution. Genetic algorithms are a heuristic global optimization method capable of effective searching within a broad design space, particularly suitable for optimization problems with a mixture of discrete and continuous design variables. By simulating biological evolution processes such as natural selection, crossover, and mutation, it selects the most fit individuals in each generation and progressively optimizes the design scheme.

[0004] However, experimental design methods may face problems such as excessively large sample sizes and model fitting distortion when dealing with high-dimensional complex morphologies and strongly nonlinear relationships; topology optimization methods are computationally intensive and require high model accuracy, often necessitating high-performance computing support; genetic algorithms have high computational overhead and are sensitive to the selection of initial populations and evolutionary operators, resulting in low optimization efficiency. Traditional surface texture design methods are mostly based on experience or single-factor optimization, making it difficult to achieve synergistic optimization of lubrication performance under multivariate coupling effects. Furthermore, complex texture morphologies lack efficient parametric modeling methods, and commonly used optimization algorithms are prone to getting trapped in local optima and are computationally inefficient.

[0005] In summary, establishing a high-precision parameterized model of microtexture morphology under multivariable coupling conditions, and achieving collaborative calculation of global optimization and local fine-tuning based on tribological performance indicators, is key to improving the surface lubrication performance of friction pairs. It is necessary to develop a surface texture optimization design method that combines multi-objective collaborative optimization, global search capability, and high computational efficiency. Summary of the Invention

[0006] To address the problems of traditional surface texture design, such as monotonous geometry, strong parameter coupling, and difficulty in multi-objective optimization, this invention proposes an optimization design method for the surface texture morphology of friction pairs to improve their lubrication characteristics, such as load-bearing capacity and friction coefficient.

[0007] To achieve the above-mentioned objectives, the present invention specifically adopts the following technical solution:

[0008] In a first aspect, the present invention provides a method for optimizing the surface texture morphology of a friction pair, comprising the following steps:

[0009] S1. Under the parameter conditions including the size and structure of the friction pair, the type of lubricating oil, and the operating conditions, a Reynolds equation considering microcavitation is established; the oil film thickness at each node is obtained based on the correction factors including the clearance height, surface texture depth, comprehensive elastic deformation, and thermal deformation of the friction pair structure; the solution domain of the friction pair is meshed using the finite difference method, thereby transforming the Reynolds equation into a difference form; using the difference form of the Reynolds equation, combined with the boundary lubrication conditions of the solution domain, the oil film pressure at each node is iteratively solved; then, based on the oil film thickness and oil film pressure at each node, the lubrication characteristic parameters of the friction pair, including load-bearing capacity, shear force distribution, friction force, and friction coefficient, are solved;

[0010] S2. The shape profile of the friction pair surface texture is characterized by Bézier curves, and the number and coordinates of the control points of the Bézier curves are dynamically adjusted to provide initial condition parameters for the optimization of the shape profile of the friction pair surface texture.

[0011] S3. Select the cluster of control points along the Bézier curve as the target particle swarm. Use the particle swarm optimization algorithm to optimize the coordinates of the control point cluster. Each particle represents a set of candidate control point coordinate values. In each iteration, calculate the fitness and select the particle position corresponding to the smaller fitness value as the individual optimal position and the global optimal position of the particle swarm. Update the position and velocity of the particles at the current moment. Introduce three dynamic adjustment strategies for particle update: asymmetric learning mechanism of learning factor, adaptive dynamic adjustment of inertia weight, and application of Gaussian noise to the particle swarm position. Repeat the iteration until the number of iterations exceeds the preset maximum number of iterations. The final global optimal position is then output as the final result.

[0012] S4. Convert the final global optimal position into the coordinates of the control points of the Bézier curve. Use the converted control point coordinates to draw the Bézier curve that represents the shape profile of the optimal surface texture of the friction pair. Output the optimal surface texture of the friction pair, thus completing the optimization of the surface texture of the friction pair.

[0013] Based on the above scheme, each step can be implemented in the following preferred manner.

[0014] As a preferred embodiment of the first aspect mentioned above, during the optimization process of step S2, the oil film pressure distribution gradient is considered. Dynamically adjust the control point density of the Bézier curve:

[0015]

[0016] in, The updated number of control points; The number of control points before the update; This is the sensitivity coefficient; The modulus represents the gradient of oil film pressure distribution.

[0017] As a preferred embodiment of the first aspect mentioned above, in step S3, a fitness function is defined for each particle. :

[0018]

[0019] in, , , These are the weighting coefficients for the bearing capacity, friction coefficient, and lubrication stability of the friction pair. The bearing capacity of the friction pair after surface texture processing; The load-bearing capacity of the friction pair when the surface texture is unprocessed; The friction coefficient of the friction pair after surface texturing; The friction coefficient of the friction pair when the surface texture is unprocessed; For lubrication stability, the lubrication stability is based on the variance of oil film thickness fluctuation. calculate:

[0020]

[0021] in, This indicates the average oil film thickness.

[0022] As a preferred embodiment of the first aspect mentioned above, the step S3 is... In each iteration, the velocity and position of each particle are updated using the following formula:

[0023]

[0024]

[0025] in, For the first In the nth iteration The updated velocity of each particle; For the first In the nth iteration The velocity of each particle before the update; Inertial weight; For individual learning factors; As a social learning factor; A random number between 0 and 1; The optimal position for the individual; The globally optimal position; For the first In the nth iteration The updated positions of the particles; For the first In the nth iteration The position of each particle before the update;

[0026] When the distance between adjacent control points is less than the preset distance threshold When this occurs, the control point rejection mechanism is triggered:

[0027]

[0028] in, For the first In the nth iteration The position of each particle before the update; Repulsion strength; This represents the L2 norm.

[0029] As a preferred embodiment of the first aspect mentioned above, the asymmetric learning mechanism of the learning factor in step S3 is specifically as follows:

[0030]

[0031]

[0032] in, A stage index for dynamically adjusting learning factors; This is the preset maximum value for the stage index; For the first Individual learning factors; For the first Modern social learning factors; and These are the preset maximum and minimum values ​​of the individual learning factor, respectively. and These are the preset maximum and minimum values ​​of the social learning factor, respectively.

[0033] As a preferred embodiment of the first aspect mentioned above, the step S3 is... In the next iteration, the inertia weight The adaptive dynamic adjustment is specifically as follows:

[0034]

[0035]

[0036] in, For the first Dispersion at the next iteration; The average position of all particles in the particle swarm; Represents the square of the L2 norm; and These are the preset maximum and minimum inertia weight values, respectively. The preset maximum dispersion threshold; For particle swarm dimension; For the first In the nth iteration The position of each particle before the update.

[0037] As a preferred embodiment of the first aspect mentioned above, in step S3, when the individual's optimal position is continuous... When the average is the same and no update is performed, the particle swarm position is... Apply Gaussian noise:

[0038]

[0039] in, The positions of the particle swarm after adding noise; This serves as the variation scale used to control the amplitude of Gaussian noise perturbations; These are random numbers that follow a standard normal distribution. This indicates the preset threshold number of times.

[0040] In a second aspect, the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, enables the optimization design method for the surface texture morphology of the friction pair as described in any of the solutions of the first aspect above.

[0041] Thirdly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the optimized design method for the surface texture morphology of the friction pair as described in any of the solutions of the first aspect above.

[0042] Fourthly, the present invention provides a computer electronic device, which includes a memory and a processor;

[0043] The memory is used to store computer programs;

[0044] The processor is configured to, when executing the computer program, implement the optimized design method for the surface texture morphology of the friction pair as described in any of the embodiments of the first aspect above.

[0045] Compared with the prior art, the present invention has the following advantages:

[0046] This invention proposes an optimized design method for the surface texture morphology of friction pairs. By innovatively combining Bézier curves and the PSO algorithm, a novel texture optimization framework is constructed, significantly improving the load-bearing capacity and friction coefficient. In the texture morphology design process, Bézier curves are used for the first time to accurately model the surface texture. Complex and diverse smooth geometric shapes are generated flexibly and continuously through control points, thereby controlling the complexity of the texture morphology and breaking through the limitations of traditional regular shapes (such as circles and squares). During the optimization process, the particle swarm optimization algorithm ensures global optimization capability and convergence speed, avoiding premature convergence and achieving a multi-objective optimization balance between load-bearing capacity and friction coefficient. This method provides optimal texture design schemes under various working conditions for the first time, greatly improving the lubrication performance and operational stability of friction pairs. Furthermore, this optimization framework has good versatility and is applicable to the lubrication performance optimization design of various friction pairs, especially suitable for applications under extreme conditions such as high load and high speed, demonstrating significant engineering practical value. Attached Figure Description

[0047] Figure 1 This is a flowchart of the method of the present invention;

[0048] Figure 2 This is a schematic diagram of the friction pair structure in this embodiment;

[0049] Figure 3 A schematic diagram of the surface texture shape determined based on Bézier curve control points;

[0050] Figure 4 This is a schematic diagram illustrating the evolution of the dimensionless bearing capacity and surface texture area ratio of the lubricating oil film with the number of iterations.

[0051] Figure 5 This is a schematic diagram illustrating the evolution of surface texture shape with the number of iterations. Detailed Implementation

[0052] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below. Technical features in the various embodiments of the present invention can be combined accordingly without mutual conflict.

[0053] In the description of this invention, it should be understood that the terms "first" and "second" are used only for descriptive purposes and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first" and "second" may explicitly or implicitly include at least one of those features.

[0054] like Figure 1 As shown, in a preferred embodiment of the present invention, the optimized design method for the surface texture morphology of the friction pair includes the following steps S1 to S4. The specific implementation process of each step will be described in detail below.

[0055] S1. Under the parameter conditions including the size and structure of the friction pair, the type of lubricating oil, and the operating conditions, a Reynolds equation considering microscopic cavitation is established. The oil film thickness at each node is obtained based on the correction factors including the clearance height, surface texture depth, comprehensive elastic deformation, and thermal deformation of the friction pair structure. The solution domain of the friction pair is meshed using the finite difference method, thereby transforming the Reynolds equation into a difference form. Using the difference form of the Reynolds equation, combined with the boundary lubrication conditions of the solution domain, the oil film pressure at each node is iteratively solved. Then, based on the oil film thickness and oil film pressure at each node, the lubrication characteristic parameters of the friction pair, including load-bearing capacity, shear force distribution, friction force, and friction coefficient, are solved.

[0056] It should be noted that, in this invention, surface texture refers to the formation of an array of microscopic geometric morphologies with specific size, shape and distribution on the surface of a material through an active and controllable processing method. Common types of surface textures include pits and grooves.

[0057] It should be noted that the specific process of step S1 of the present invention is as follows:

[0058] S11. First, establish the Reynolds equations considering microscopic cavitation:

[0059]

[0060] in, This indicates the partial derivative; These represent the horizontal and vertical coordinates in the solution domain of the friction pair, respectively. Indicates the density of the lubricating oil; Indicates the oil film thickness at the node; Indicates the viscosity of the lubricating oil; Indicates the oil film pressure at the node; This represents the relative velocity on the horizontal axis; Represents the relative velocity along the vertical axis; Indicates time; Denotes the cavitation factor, satisfying:

[0061]

[0062] in, This is a correction factor within the cavitation region; This indicates cavitation pressure.

[0063] S12. Divide the solution domain along the horizontal and vertical axes into... The grid has a horizontal spacing of [value]. The vertical spacing of the grid is Each grid node uses Indicates; among which, These represent the horizontal and vertical coordinate numbers of the grid nodes, respectively. , ; This indicates the maximum value of the horizontal coordinate of the grid. This indicates the maximum value of the horizontal coordinate of the grid.

[0064] The oil film pressure can be obtained using the intermediate difference formula. In grid nodes Partial derivative at:

[0065]

[0066]

[0067] in, , , , , Representing grid nodes respectively , , , , Oil film pressure at the location.

[0068] Therefore, the Reynolds equation can be transformed into a difference form:

[0069]

[0070] Simplifying and rearranging the difference form of the Reynolds equation, it can finally be expressed as:

[0071]

[0072] in, , , , , , All are used A mathematical expression for a combination of known quantities.

[0073] S13. The finite difference method is used for iterative solution during calculation. The boundary lubrication conditions of the solution domain are as follows:

[0074]

[0075]

[0076] in, , , , Grid nodes , , , Oil film pressure at the location; and These represent the minimum and maximum values ​​of the vertical boundary coordinates of the solution domain, respectively. and These represent the minimum and maximum values ​​of the lateral boundary coordinates of the solution domain, respectively.

[0077] S14. The iterative equations are corrected using the relaxation iteration method to accelerate convergence and improve accuracy. The corrected equations are as follows:

[0078]

[0079] Among them, superscript Indicates the iteration count index; Indicates the relaxation factor; Indicates the first In the next iteration, the grid nodes Oil film pressure at the location; , , , , Indicates the first In the next iteration, the grid nodes , , , , Oil film pressure at the location.

[0080] S15. When the pressure value change between two consecutive iterations is less than a preset pressure change threshold. Stop iteration when:

[0081]

[0082] in, This represents the cumulative pressure difference at each grid node during the two iterations; Indicates the first The sum of pressure on all grid nodes at the next iteration.

[0083] S16. Correct the oil film thickness:

[0084]

[0085] in, The clearance height is determined by the friction pair structure. This refers to the increase in oil film thickness caused by surface texturing. This represents the combined elastic deformation of the upper and lower surfaces of the friction pair. This refers to the thermal deformation of the friction pair material; and They respectively satisfy:

[0086]

[0087]

[0088] in, Represents the external force load matrix; Represents the stiffness matrix of the friction pair; Indicates the coefficient of thermal expansion of the friction pair material; The thickness of the friction pair structure; These are coordinate variables along the thickness direction; For temperature.

[0089] In actual operating conditions, external conditions such as temperature and load can cause deformation of the friction pair structure, thereby changing the oil film thickness distribution. Therefore, in this embodiment, the lubricating oil film thickness distribution of the friction pair is modified, as shown in S16, and will not be described in detail here.

[0090] S17. The formula for the effect of temperature on lubricating oil viscosity is as follows:

[0091]

[0092] in, Indicates temperature The viscosity of the lubricating oil is as follows; The reference temperature indicating the known viscosity of the lubricating oil; Indicates temperature as The viscosity of the lubricating oil at that time; This represents the viscosity-temperature decay coefficient; This represents the thermal gradient correction factor.

[0093] S18. After completing the calculations of oil film thickness and oil film pressure, obtain the shear force distribution of the friction pair. Bearing capacity Friction and coefficient of friction The calculation formula is as follows:

[0094]

[0095]

[0096]

[0097]

[0098] in, Represents grid nodes Shear stress at the point; Represents grid nodes Oil film thickness at the location; Indicates the solution domain; and Represents the differential symbol.

[0099] S2. The shape profile of the friction pair surface texture is characterized by Bézier curves, and the number and coordinates of the control points of the Bézier curves are dynamically adjusted to provide initial condition parameters for the optimization of the shape profile of the friction pair surface texture.

[0100] It should be noted that in step S2 of this invention, Bézier curves are used to characterize the shape of the surface texture of the friction pair. In mathematical definition, Bézier curves are described using control point parameterization, and their mathematical expression is based on Bernstein polynomial basis functions. For a given... Control points any point on the Bézier curve It can be represented as:

[0101]

[0102] in, Indicates the number of control points; For control point indexing; For parameters; For the first Coordinates of control points; It is a combination number; Bernstein polynomial This is the weighting function.

[0103] It should be noted that in the optimization process of step S2 of the present invention, the oil film pressure distribution gradient is considered. Dynamically adjust the control point density of the Bézier curve:

[0104]

[0105] in, The updated number of control points; The number of control points before the update; This is a sensitivity coefficient, used to achieve fine-tuning in high-stress areas; The modulus represents the gradient of oil film pressure distribution.

[0106] S3. Select the cluster of control points along the Bézier curve as the target particle swarm. Use the Particle Swarm Optimization (PSO) algorithm to optimize the coordinates of the control point cluster. Each particle represents a set of candidate control point coordinate values. In each iteration, calculate the fitness and select the particle position corresponding to the smaller fitness value as the individual optimal position and the global optimal position of the particle swarm. Update the position and velocity of the particles at the current moment. Introduce three dynamic adjustment strategies for particle update: asymmetric learning mechanism of learning factor, adaptive dynamic adjustment of inertia weight, and application of Gaussian noise to the particle swarm position. Repeat the iteration until the number of iterations exceeds the preset maximum number of iterations. The final global optimal position is then output as the final result.

[0107] It should be noted that in step S3 of this invention, a fitness function is defined for each particle. :

[0108]

[0109] in, , , These are the weighting coefficients for the bearing capacity, friction coefficient, and lubrication stability of the friction pair. The bearing capacity of the friction pair after surface texture processing; The load-bearing capacity of the friction pair when the surface texture is unprocessed; The friction coefficient of the friction pair after surface texturing; The friction coefficient of the friction pair when the surface texture is unprocessed; For lubrication stability, it is based on the variance of oil film thickness fluctuation. calculate:

[0110]

[0111] in, This indicates the average oil film thickness.

[0112] In this embodiment, the weighting coefficients of the fitness function can be adjusted according to design requirements. , Adjustments can be made, such as increasing the load capacity if higher performance is required. The demand for better friction reduction performance increases. .

[0113] It should be noted that in step S3 of this invention, the velocity of the particle swarm... and location Represented as:

[0114]

[0115]

[0116] in, They represent the first and second particles in the swarm. The position of each particle; They represent the i-th particles in the i-th swarm. The velocity of each particle; The particle swarm dimension.

[0117] It should be noted that in step S3 of the present invention In each iteration, the velocity and position of each particle are updated using the following formula:

[0118]

[0119]

[0120] in, For the first In the nth iteration The updated velocity of each particle; For the first In the nth iteration The velocity of each particle before the update; Inertial weight; For individual learning factors; As a social learning factor; A random number between 0 and 1; The optimal position for the individual; The globally optimal position; For the first In the nth iteration The updated positions of the particles; For the first In the nth iteration The position of each particle before the update.

[0121] Furthermore, when the distance between adjacent control points is less than a preset distance threshold... When this occurs, the control point rejection mechanism is triggered:

[0122]

[0123] in, For the first In the nth iteration The position of each particle before the update; To determine the repulsion strength, in this embodiment, ; This represents the L2 norm.

[0124] It should be noted that traditional particle swarm optimization algorithms are prone to getting stuck in local optima. To improve the global optimization performance of the algorithm, this invention introduces three dynamic adjustment strategies for particle updates during the iteration process: an asymmetric learning mechanism for the learning factor, adaptive dynamic adjustment of the inertia weight, and applying Gaussian noise to the particle swarm position.

[0125] It should be noted that, in step S3 of this invention, the asymmetric learning mechanism of the learning factor is specifically as follows:

[0126]

[0127]

[0128] in, The stage index for dynamically adjusting the learning factor starts from 1. This index is used after the particle swarm search has completed a fixed number of iterations. Increasing sequentially; This is the preset maximum value for the stage index; For the first Individual learning factors; For the first Modern social learning factors; and These are the preset maximum and minimum values ​​of the individual learning factor, respectively. and These are the preset maximum and minimum values ​​of the social learning factor, respectively.

[0129] It should be noted that the first step S3 of the present invention In the next iteration, the inertia weight The adaptive dynamic adjustment is specifically as follows:

[0130]

[0131]

[0132] in, For the first Dispersion at the next iteration; The average position of all particles in the particle swarm; Represents the square of the L2 norm; and These are the preset maximum and minimum inertia weight values, respectively. The preset maximum dispersion threshold; The particle swarm dimension.

[0133] It should be noted that in step S3 of the present invention, when the individual optimal position is continuous When the average values ​​are the same and no update is performed, Gaussian noise is applied to the particle swarm positions:

[0134]

[0135] in, The positions of the particle swarm after adding noise; This serves as the variation scale used to control the amplitude of Gaussian noise perturbations; These are random numbers that follow a standard normal distribution. This indicates the preset threshold number of times.

[0136] S4. Convert the final global optimal position into the coordinates of the control points of the Bézier curve. Use the converted control point coordinates to draw the Bézier curve that represents the shape profile of the optimal surface texture of the friction pair. Output the optimal surface texture of the friction pair, thus completing the optimization of the surface texture of the friction pair.

[0137] To better demonstrate the specific implementation and technical effects of the present invention, the optimization design method for the surface texture morphology of the friction pair shown in steps S1 to S4 of the above preferred implementation is applied to a specific example.

[0138] Example

[0139] The specific implementation process of the optimized design method for the surface texture morphology of the friction pair used in this embodiment is as described above and will not be repeated here.

[0140] First, specific friction pair conditions are selected, and the lubrication parameters are set as shown in Table 1. The friction pair structure is as follows. Figure 2 As shown, the main optimization focuses on the texture shape within the design unit area. The bottom edge of the surface texture unit is constructed using straight line segments to ensure the regularity of the shape and the feasibility of processing; the side contours are described by Bézier curves, and by adjusting the position of the curve control points, complex surface shapes can be flexibly generated.

[0141] Table 1. Lubrication parameters of friction pairs

[0142] To further improve the efficiency of the optimization process, this embodiment applies symmetrical constraints to the control points of the Bézier curves on both sides, so that the left and right contours are geometrically mirrored. This symmetry condition effectively reduces the number of design variables, halving the number of control point parameters that were originally optimized independently. This represents the total number of control points for the Bézier curve. For the first Control points Cartesian coordinates; and They are respectively The coordinate components; For the first Control points Cartesian coordinates.

[0143] The surface texture of the friction pair is parametrically modeled using Bézier curves, and the initial number of control points is set. The initial coordinates and initial velocities of each control point are randomly generated, wherein the first... Each control point is The initial coordinate range of the direction is ,exist The initial coordinate range of the direction is Initial velocity range , for The maximum value of the direction coordinates. for The maximum value of the directional coordinates. The particle swarm optimization algorithm parameters are shown in Table 2. The final optimized surface texture shape is the result of the Bézier curve intersecting the coordinate axes. The enclosed area, such as Figure 3 As shown. Furthermore, in this embodiment, only the friction pair requires a high load capacity, and the weighting coefficient of the fitness function is set to... .

[0144] Table 2. Particle Swarm Optimization Algorithm Parameter Settings

[0145] Figure 4The convergence characteristics of the dimensionless bearing capacity and surface texture area ratio of the lubricating oil film as a function of iteration number are demonstrated. The results show that in the initial iteration stage, the dimensionless bearing capacity rapidly increases from 1.8 to 2.4, accompanied by a linear increase in the surface texture area ratio from 0.25 to 0.65, exhibiting significant parameter optimization characteristics. When the number of iterations is between 20 and 100, the dimensionless bearing capacity and surface texture area ratio show convergent oscillation characteristics. After reaching 100 iterations, a stable convergence state is reached, with the dimensionless bearing capacity eventually stabilizing at 2.62, corresponding to an equilibrium point where the surface texture area ratio remains at 66.4%.

[0146] Optimized surface texture shape evolution process, such as Figure 5 As shown, with the increase of the number of iterations, the surface texture shape gradually becomes flatter, demonstrating the effectiveness of the method in the global search stage. When the number of iterations reaches 60, the surface texture region is close to stable, indicating that the method has found a relatively optimal solution at this stage. However, as the number of iterations further increases to 70 and 100, the surface texture shape continues to be fine-tuned, demonstrating the fine optimization capability of the method in the local search stage. Finally, when the number of iterations reaches 150, the surface texture shape tends to stabilize, indicating that the method has basically converged.

[0147] It is understood that the optimization design method for the surface texture morphology of the friction pair described in S1 to S4 above can essentially be implemented by a computer program. Therefore, based on the same inventive concept, another preferred embodiment of the present invention also provides a computer program product corresponding to the optimization design method for the surface texture morphology of the friction pair provided in the above embodiments. This product includes a computer program / instruction, which, when executed by a processor, can implement the optimization design method for the surface texture morphology of the friction pair as described in the above embodiments.

[0148] Similarly, based on the same inventive concept, another preferred embodiment of the present invention also provides a computer electronic device corresponding to the optimized design method for the surface texture morphology of the friction pair provided in the above embodiment, which includes a memory and a processor;

[0149] The memory is used to store computer programs;

[0150] The processor is configured to implement the optimized design method for the surface texture morphology of the friction pair in the above embodiments when executing the computer program.

[0151] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0152] Therefore, based on the same inventive concept, another preferred embodiment of the present invention also provides a computer-readable storage medium corresponding to the optimization design method for the surface texture morphology of the friction pair provided in the above embodiments. The storage medium stores a computer program, which, when executed by a processor, can realize the optimization design method for the surface texture morphology of the friction pair in the above embodiments.

[0153] Specifically, in the computer-readable storage medium of the above three embodiments, the stored computer program is executed by a processor, which can perform the aforementioned steps S1 to S4.

[0154] It is understood that the aforementioned storage media may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Furthermore, the storage media may also be various media capable of storing program code, such as USB flash drives, external hard drives, magnetic disks, or optical discs.

[0155] It is understood that the processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0156] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the invention. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention. Therefore, all technical solutions obtained through equivalent substitution or transformation fall within the protection scope of the present invention.

Claims

1. A method for optimizing the surface texture morphology of a friction pair, characterized in that, Includes the following steps: S1. Under the parameter conditions including the size and structure of the friction pair, the type of lubricating oil, and the operating conditions, the Reynolds equation considering microcavitation is established; the oil film thickness of each node is obtained according to the correction factors including the clearance height, surface texture depth, comprehensive elastic deformation, and thermal deformation of the friction pair structure; the solution domain of the friction pair is meshed using the finite difference method, thereby transforming the Reynolds equation into a finite difference form; The oil film pressure at each node is iteratively solved using the difference form of the Reynolds equation and the boundary lubrication conditions of the solution domain. Then, based on the oil film thickness and oil film pressure at each node, the lubrication characteristic parameters of the friction pair are solved, including load capacity, shear force distribution, friction force and friction coefficient. S2. The shape profile of the friction pair surface texture is characterized by Bézier curves, and the number and coordinates of the control points of the Bézier curves are dynamically adjusted to provide initial condition parameters for the optimization of the shape profile of the friction pair surface texture. S3. Select the cluster of control points of the Bézier curve as the target particle swarm. Use the particle swarm optimization algorithm to optimize the coordinates of the control point cluster. Each particle represents a set of candidate control point coordinate values. In each iteration, calculate the fitness and select the particle position corresponding to the smaller fitness value as the individual optimal position and the global optimal position of the particle swarm. Update the position and velocity of the particles at the current time. In addition, introduce three types of dynamic adjustment particle update strategies: asymmetric learning mechanism of learning factor, adaptive dynamic adjustment of inertia weight, and application of Gaussian noise to the position of particle swarm. Repeat the iteration until the number of iterations exceeds the preset maximum number of iterations, and then output the final global optimal position as the final result; S4. Convert the final global optimal position into the coordinates of the control points of the Bézier curve. Use the converted control point coordinates to draw the Bézier curve that represents the shape profile of the optimal surface texture of the friction pair. Output the optimal surface texture of the friction pair, thus completing the optimization of the surface texture of the friction pair.

2. The method for optimizing the surface texture morphology of a friction pair as described in claim 1, characterized in that, In the optimization process of step S2, based on the oil film pressure distribution gradient... Dynamically adjust the control point density of the Bézier curve: ； in, The updated number of control points; The number of control points before the update; This is the sensitivity coefficient; The modulus represents the gradient of oil film pressure distribution.

3. The method for optimizing the surface texture morphology of a friction pair as described in claim 1, characterized in that, In step S3, for each particle, its fitness function is defined. : ； in, , , These are the weighting coefficients for the bearing capacity, friction coefficient, and lubrication stability of the friction pair. The bearing capacity of the friction pair after surface texture processing; The load-bearing capacity of the friction pair when the surface texture is unprocessed; The friction coefficient of the friction pair after surface texturing; The friction coefficient of the friction pair when the surface texture is unprocessed; For lubrication stability, the lubrication stability is based on the variance of oil film thickness fluctuation. calculate: ； in, This indicates the average oil film thickness.

4. The method for optimizing the surface texture morphology of a friction pair as described in claim 1, characterized in that, Step S3 In each iteration, the velocity and position of each particle are updated using the following formula: ； ； in, For the first In the nth iteration The updated velocity of each particle; For the first In the nth iteration The velocity of each particle before the update; Inertial weight; For individual learning factors; As a social learning factor; A random number between 0 and 1; The optimal position for the individual; The globally optimal position; For the first In the nth iteration The updated positions of the particles; For the first In the nth iteration The position of each particle before the update; When the distance between adjacent control points is less than the preset distance threshold When this occurs, the control point rejection mechanism is triggered: ； in, For the first In the nth iteration The position of each particle before the update; Repulsion strength; This represents the L2 norm.

5. The method for optimizing the surface texture morphology of a friction pair as described in claim 1, characterized in that, In step S3, the asymmetric learning mechanism of the learning factor is specifically as follows: ； ； in, A stage index for dynamically adjusting learning factors; This is the preset maximum value for the stage index; For the first Individual learning factors; For the first Modern social learning factors; and These are the preset maximum and minimum values ​​of the individual learning factor, respectively. and These are the preset maximum and minimum values ​​of the social learning factor, respectively.

6. The method for optimizing the surface texture morphology of a friction pair as described in claim 1, characterized in that, Step S3 In the next iteration, the inertia weight The adaptive dynamic adjustment is specifically as follows: ； ； in, For the first Dispersion at the next iteration; The average position of all particles in the particle swarm; Represents the square of the L2 norm; and These are the preset maximum and minimum inertia weight values, respectively. The preset maximum dispersion threshold; For particle swarm dimension; For the first In the nth iteration The position of each particle before the update.

7. The method for optimizing the surface texture morphology of a friction pair as described in claim 1, characterized in that, In step S3, when the individual's optimal position is continuous When the average is the same and no update is performed, the particle swarm position is... Apply Gaussian noise: ； in, The positions of the particle swarm after adding noise; This serves as the variation scale used to control the amplitude of Gaussian noise perturbations; These are random numbers that follow a standard normal distribution. This indicates the preset threshold number of times.

8. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it can realize the optimized design method of the surface texture morphology of the friction pair as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the optimized design method for the surface texture morphology of the friction pair as described in any one of claims 1 to 7.

10. A computer electronic device, characterized in that, Including memory and processor; The memory is used to store computer programs; The processor is configured to, when executing the computer program, implement the optimized design method for the surface texture morphology of the friction pair as described in any one of claims 1 to 7.

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