An interface friction performance characterization and testing method based on the optimization of meta-interface topography characteristic parameters

Abstract

The present application discloses a method for characterizing and testing interface friction performance based on the optimization of meta-interface topography feature parameters, which relates to the field of geotechnical engineering and includes: fabricating a variety of meta-interfaces with different topography feature parameters by using 3D microprinting technology; constructing a meta-interface friction performance database based on a variety of meta-interfaces with different topography feature parameters; based on the meta-interface friction performance database and the target interface friction performance, using machine learning to extract features from the data, and combining a multi-objective optimization algorithm to adjust and optimize the topography feature parameters to determine the optimal topography feature parameters corresponding to the target interface friction performance; printing the meta-interface with the optimal topography feature parameters and applying it to subsequent interface friction performance tests. The present application can be applied to subsequent accurate and repeatable interface shear-related tests by designing a set of meta-interface micro-protrusions that are not limited by scale and material, do not interfere with nuclear magnetic signals, but can characterize the friction performance between soil and structure.

Description

Technical Field

[0001] The present application relates to the technical field of geotechnical engineering, and particularly to a method for characterizing and testing interface friction performance optimized based on meta-interface topography characteristic parameters. Background Art

[0002] The soil-structure interface widely exists in different fields of civil engineering, such as retaining walls, foundations and substructures, pile foundations, tunnel linings, slope reinforcement, etc. Among them, the frictional characteristics of the soil-structure interface directly affect the stability and safety of the structure. Especially under the action of freeze-thaw cycles in cold region projects, the physical and mechanical properties inside the soil will change significantly. For example, the rearrangement of particle structures, the changes in pores and water content, and the formation and ablation of ice lenses will further affect the frictional characteristics of the soil-structure interface.

[0003] For the soil-structure interfaces of different structural materials, their interaction mechanisms and frictional properties are not the same. The test results under different sizes of interface shear devices, different actual stress states and loading conditions are diverse and complex. Often, it is necessary to prepare soil samples and different structural materials multiple times, and they cannot be reused after the test, with poor repeatability, which reduces the overall efficiency of the test. At the same time, the existing technologies are difficult to obtain high-precision and highly consistent test data, and cannot effectively combine the shear performance of the macroscopic interface with the frictional characteristics of the microscopic interface units. Summary of the Invention

[0004] The purpose of the present application is to provide a method for characterizing and testing interface friction performance optimized based on meta-interface topography characteristic parameters. By designing a set of meta-interface asperities that are not limited by scale and material, do not interfere with nuclear magnetic signals, but can characterize the frictional performance between soil and structure, the frictional performance of the target interface is characterized, which can be applied to subsequent accurate and repeatable interface shear-related tests.

[0005] To achieve the above purpose, the present application provides the following solutions.

[0006] The present application provides a method for characterizing and testing the interfacial friction performance optimized based on the morphological feature parameters of a meta-interface. The method for characterizing and testing the interfacial friction performance optimized based on the morphological feature parameters of a meta-interface includes: fabricating multiple meta-interfaces with different morphological feature parameters by using 3D microprinting technology; the meta-interface includes a substrate and a set of micro-convex bodies with different shapes and different arrangements disposed on the substrate; the morphological feature parameters at least include the number, diameter, height, and spacing of different micro-convex bodies in the set of micro-convex bodies; the set of micro-convex bodies is used to characterize specific friction performance; based on multiple meta-interfaces with different morphological feature parameters, constructing a meta-interface friction performance database by means of experimental and numerical simulation tests; the meta-interface friction performance database includes multiple sets of meta-interface morphological feature parameter - friction performance data; based on the meta-interface friction performance database and the target interfacial friction performance, using machine learning to extract features from the data, and combining a multi-objective optimization algorithm to adjust and optimize the morphological feature parameters to determine the optimal morphological feature parameters corresponding to the target interfacial friction performance; printing the meta-interface with the optimal morphological feature parameters and applying it to subsequent interfacial friction performance tests.

[0007] Optionally, the size of the micro-convex body is in the micron range; the shape of the micro-convex body is a sphere, a hemisphere, or a quasi-sphere.

[0008] Optionally, before fabricating multiple meta-interfaces with different morphological feature parameters by using 3D microprinting technology, it further includes: designing multiple morphological feature parameter schemes according to the types of different soil samples and the sizes of soil particles.

[0009] Optionally, the material for printing the meta-interface is a material without nuclear magnetic signal; the material without nuclear magnetic signal at least includes polytetrafluoroethylene.

[0010] Optionally, based on multiple meta - interfaces with different morphological characteristic parameters, a meta - interface friction performance database is constructed by means of experimental and numerical simulation tests, specifically including: combining soil samples with meta - interfaces of different morphological characteristic parameters respectively to obtain a variety of soil - meta - interface specimens and conducting interface shear tests to obtain the results of the shear tests; using numerical simulation software to establish a soil - meta - interface numerical model corresponding to each soil - meta - interface specimen, and conducting interface shear simulations on the soil - meta - interface numerical model to obtain the results of the shear simulations; comparing the shear simulation results with the shear test results; when the shear stress - shear displacement curve in the shear simulation results is inconsistent with the corresponding shear stress - shear displacement curve in the shear test results, adjusting the system parameters of the numerical simulation software until the shear stress - shear displacement curve in the shear simulation results tends to be consistent with the corresponding shear stress - shear displacement curve in the shear test results; after the system parameter adjustment is completed, continuously changing the values of the morphological characteristic parameters within a set range, and using the numerical simulation software to determine the friction performance data under different values of the morphological characteristic parameters, matching multiple groups of meta - interface morphological characteristic parameter - friction performance data to obtain the meta - interface friction performance database.

[0011] Optionally, the system parameters at least include contact parameters; the contact parameters at least include contact type and friction coefficient.

[0012] Optionally, based on the meta - interface friction performance database and the target interface friction performance, machine learning is used to extract features from the data, and combined with a multi - objective optimization algorithm to adjust and optimize the morphological characteristic parameters to determine the optimal morphological characteristic parameters corresponding to the target interface friction performance, specifically including: training a machine learning model based on the meta - interface friction performance database, and optimizing the hyperparameters of the machine learning model by means of cross - validation; the machine learning model is used to construct a mapping relationship between interface friction performance and morphological characteristic parameters; using the trained machine learning model to determine multiple alternative morphological characteristic parameters corresponding to the target interface friction performance; using a multi - objective optimization algorithm to optimize the multiple alternative morphological characteristic parameters to obtain the optimal morphological characteristic parameters corresponding to the target interface friction performance; the multi - objective optimization algorithm is used to improve the efficiency and accuracy of capturing the optimal morphological characteristic parameters.

[0013] Optionally, the machine learning model at least includes any one of a BP neural network, a support vector machine, and a random forest model.

[0014] According to the specific embodiments provided in the present application, the following technical effects are disclosed in the present application.

[0015] This application innovatively introduces the concept of a meta-interface, precisely constructs a meta-interface with specific morphological features through 3D microprinting technology, and uses its morphological features to characterize the friction performance of the corresponding interface. Compared with traditional interfaces, the meta-interface has significant advantages: its manufacturing process is not restricted by materials, sizes, and environmental conditions, and can flexibly adapt to different research needs; the meta-interface can be reused, significantly reducing test costs and time consumption; at the same time, to further improve the characterization design efficiency and accuracy of the meta-interface, this application also effectively captures the complex non-linear mapping relationship between the interface friction performance and morphological feature parameters through machine learning, breaks through the limitations of traditional empirical formulas, and improves the capture efficiency and accuracy of optimal morphological feature parameters based on a multi-objective optimization algorithm, providing an intelligent solution for precisely controlling the interface friction performance. This innovative method not only provides a new technical path for the research of the friction performance of soil-structure interfaces, but also helps with the multi-scale evolution analysis in related fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0017] Figure 1 It is a flowchart of the method for characterizing and testing the interface friction performance based on the optimization of the morphological feature parameters of the meta-interface provided by the embodiment of this application.

[0018] Figure 2 It is the front view of the meta-interface provided by the embodiment of this application.

[0019] Figure 3 It is the three-dimensional structure diagram of the meta-interface provided by the embodiment of this application.

[0020] Figure 4 It is the internal structure diagram of the computer system provided by the embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] The following will clearly and completely describe the technical solutions in the embodiments of this application in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only some embodiments of this application, rather than all embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of this application.

[0022] The purpose of this application is to provide a method for characterizing and testing the interfacial friction performance optimized based on the morphological characteristic parameters of the meta-interface. By designing a meta-interface micro-convex body set that is not restricted by scale and material, does not interfere with nuclear magnetic signals, but can characterize the friction performance between soil and structure, the friction performance of the target interface is characterized, which can be applied to subsequent accurate and repeatable interface shear-related tests.

[0023] To make the above objects, features, and advantages of this application more obvious and understandable, the following further details this application in conjunction with the accompanying drawings and specific embodiments.

[0024] As Figure 1 shown, this embodiment provides a method for characterizing and testing the interfacial friction performance optimized based on the morphological characteristic parameters of the meta-interface. The method for characterizing and testing the interfacial friction performance optimized based on the morphological characteristic parameters of the meta-interface is specifically as follows.

[0025] Step S1: Use 3D microprinting technology to fabricate multiple meta-interfaces with different morphological characteristic parameters.

[0026] As Figure 2 and Figure 3 shown, the meta-interface includes: a substrate and a set of micro-convex bodies disposed on the substrate with different shapes and different arrangements and combinations. The shapes of different micro-convex bodies in the set of micro-convex bodies are generally set as spheres, hemispheres, or sphere-like bodies, but can also be other shapes (such as cylinders, cubes, etc.). However, regardless of the shape, their sizes are at the micron level. The morphological characteristic parameters at least include the number m, diameter d (radius r), height h, and spacing n of different micro-convex bodies in the set of micro-convex bodies. The set of micro-convex bodies is used to characterize specific friction performance.

[0027] Since most structural specimens contain metal elements, these metal elements will interfere with the nuclear magnetic signals emitted by the shear nuclear magnetic equipment in the shear test of the soil-structure interface, which will also affect the shear results and the accuracy of macro-micro multi-scale correlation analysis to a certain extent. Therefore, this embodiment uses a material without nuclear magnetic signals to print the meta-interface, such as polytetrafluoroethylene, etc.

[0028] Since shear tests need to be carried out on multiple soil - meta - interface specimens composed of soil samples and meta - interfaces with different morphological characteristic parameters in the follow - up, in order to save time and improve efficiency, the number of printed meta - interfaces should not be too large, generally several or a dozen or so is appropriate. And before printing the meta - interfaces, it is necessary to design multiple morphological characteristic parameter schemes according to the types of different soil samples and the sizes of soil particles. For example, the size of the micro - protrusions on the meta - interface should match the size of the corresponding soil particles and should not significantly exceed the soil particle size. The finally designed morphological characteristic parameters should be as representative as possible. At the same time, the contact friction between the soil particles and the structure at the interface is also the basic unit affecting the overall friction performance of the interface. Therefore, when designing the micro - protrusions on the meta - interface, considering that the common soil particle sizes are in the micron and millimeter levels, the designed micro - protrusions can characterize the responses at least at the micron level by changing the morphological characteristic parameters. In addition, the diameter of the micro - protrusions of the reference group of the designed meta - interface is 600 μm, the height is 300 μm, and the spacing is 500 μm. By changing each parameter and comparing with the reference group, the variation law of the friction performance between the meta - interface and different soil properties is analyzed. The specific morphological characteristic parameter schemes of the meta - interface are as follows.

[0029] Table 1 Morphological characteristic parameter schemes of the meta - interface

[0030]

[0031] Step S2: Based on multiple meta - interfaces with different morphological characteristic parameters, construct a meta - interface friction performance database by means of experimental and numerical simulation tests.

[0032] In this embodiment, step S2 is specifically as follows.

[0033] Step S21: Combine the soil samples with the meta - interfaces with different morphological characteristic parameters respectively to obtain multiple soil - meta - interface specimens and conduct interface shear tests to obtain the shear test results.

[0034] In the actual operation process, first put the meta - interface into the shear box (the size of the meta - interface should be adapted to the size of the shear box), and then cover the soil sample (such as cohesive soil, sandy soil, etc.) on the meta - interface to obtain the soil - meta - interface specimen. During actual shearing, a certain normal stress needs to be applied to the soil - meta - interface specimen, and then the soil - meta - interface specimen is sheared at a constant shear rate. At the same time, it is also necessary to record the shear data (i.e., shear stress and shear displacement) generated in the shear test for the subsequent generation of the shear stress - shear displacement curve. Finally, based on the above - mentioned shear data, use drawing software such as Origin to generate the shear stress - shear displacement curves corresponding to different soil - meta - interface specimens to obtain the shear test results.

[0035] Step S22: Use numerical simulation software to establish a soil - meta - interface numerical model corresponding to each soil - meta - interface specimen, and conduct interface shear simulation on the soil - meta - interface numerical model to obtain the shear simulation results.

[0036] In the actual operation process, numerical simulation software such as ABAQUS and COMSOL needs to be used to establish the soil-element interface numerical model corresponding to each soil-element interface specimen. At the same time, in order to ensure the effectiveness of the soil-element interface numerical model, the corresponding system parameters (material property parameters, contact parameters, shear conditions, etc.) need to be reasonably set. For example: set the material property parameters and contact parameters according to the type of soil sample (cohesive soil, sandy soil, etc.) and the material of the element interface (polytetrafluoroethylene, etc.). The material property parameters include elastic modulus, Poisson's ratio, shear modulus, etc., and the contact parameters include contact type, friction coefficient, etc.; set the shear conditions according to the normal stress and shear rate applied in the shear test in step S21 above. The shear conditions include normal stress, shear rate, etc.; set the modeling parameters according to the different morphological characteristic parameters of the element interface in step S1 above. The modeling parameters include the size and thickness of the matrix in the element interface, the number, radius, height, spacing, etc. of the micro-protrusions on the matrix. After completing the setting of the above system parameters, the soil-element interface numerical model corresponding to each soil-element interface specimen can be constructed. Finally, use the numerical simulation software to perform interface shear simulation on each soil-element interface numerical model, and obtain the shear stress-shear displacement curve corresponding to each soil-element interface numerical model to obtain the shear simulation result.

[0037] Step S23: Compare the shear simulation result with the shear test result.

[0038] In the actual operation process, mainly compare the shear stress-shear displacement curves in the shear simulation result and the shear test result, and analyze the curve shapes, maximum shear stresses, corresponding shear displacements, shear stresses at stability and corresponding shear displacements, etc. of different shear stress-shear displacement curves. These indexes are all friction performance data of the corresponding interface.

[0039] Step S24: When the shear stress-shear displacement curve in the shear simulation result is inconsistent with the corresponding shear stress-shear displacement curve in the shear test result, adjust the system parameters of the numerical simulation software until the shear stress-shear displacement curve in the shear simulation result tends to be consistent with the corresponding shear stress-shear displacement curve in the shear test result.

[0040] Since there is no completely absolute basis for setting the contact type, friction coefficient, etc. in the above contact parameters, the initially set contact parameters may not be appropriate. Therefore, it is necessary to further correct them in combination with the actual situation. By comparing the shear stress-shear displacement curves in the shear simulation result and the shear test result, observe whether they are consistent in terms of curve shape, maximum shear stress and other characteristics. When they are inconsistent, the values of the contact parameters need to be appropriately adjusted until they are consistent in terms of curve shape, maximum shear stress and other characteristics, and record the values of the contact parameters at this time.

[0041] Step S25: After the system parameters are adjusted, continuously change the values of the morphological feature parameters within a set range, and use numerical simulation software to determine the friction performance data of the (meta-interface) under different values of the morphological feature parameters, match multiple groups of morphological feature parameter - friction performance data of the meta-interface, and obtain the friction performance database of the meta-interface.

[0042] Since the morphological feature parameters and their corresponding friction performance data obtained through the above shear tests and numerical simulations are limited and not sufficient to establish the friction performance database of the meta-interface, in order to obtain more morphological feature parameters and their corresponding friction performance data, it is necessary to further construct more soil-meta-interface numerical models with the help of numerical simulation software and conduct shear simulations on them. After the contact parameters are adjusted, keep other system parameters in the numerical simulation software unchanged, continuously change the values of the modeling parameters (i.e., morphological feature parameters) within a set range, and regenerate multiple soil-meta-interface numerical models and their corresponding shear stress - shear displacement curves. Specifically, when designing the morphological feature parameters, a set of reference parameters can be determined first, such as the diameter of the asperities is 600 μm, the height is 300 μm, and the spacing is 500 μm, and then adjust them up and down based on this set of reference parameters to obtain multiple groups of morphological feature parameters. After continuous experiments, it is more appropriate to adjust the values of the morphological feature parameters within the set range given in Table 2.

[0043] Table 2 Value Table of Morphological Feature Parameters

[0044]

[0045] Since multiple groups of morphological feature parameters of the meta-interface have been redesigned within a set range, and the corresponding friction performance data have been extracted according to the shear stress - shear displacement curves, it is necessary to match a set of morphological feature parameters with their corresponding friction performance data to obtain a complete set of morphological feature parameter - friction performance data of the meta-interface. After all the data are matched, the friction performance database of the meta-interface can be established, and the number of groups of morphological feature parameter - friction performance data in the friction performance database of the meta-interface should be greater than 500 groups.

[0046] Step S3: Based on the friction performance database of the meta-interface and the friction performance of the target interface, use machine learning to extract features from the data, and combine the multi-objective optimization algorithm to adjust and optimize the morphological feature parameters to determine the optimal morphological feature parameters corresponding to the friction performance of the target interface.

[0047] First, a machine learning model is trained based on the meta-interface friction performance database. The machine learning model can be selected from a BP neural network, a support vector machine, a random forest model, etc. Then, the hyperparameters of the machine learning model are optimized by means of cross-validation, mainly by using the machine learning model to construct the mapping relationship between the interface friction performance and the morphological feature parameters. Secondly, the trained machine learning model is used to determine multiple alternative morphological feature parameters corresponding to the target interface friction performance, and then a multi-objective optimization algorithm is used to optimize the multiple alternative morphological feature parameters to obtain the optimal morphological feature parameters corresponding to the target interface friction performance. The multi-objective optimization algorithm can be selected from a particle swarm algorithm, a genetic algorithm, etc. The use of the multi-objective optimization algorithm improves the efficiency and accuracy of capturing the optimal morphological feature parameters. The purpose of designing the intelligent optimization algorithm in this embodiment is that when the amount of data in the meta-interface friction performance database is insufficient, the mapping relationship captured by the machine learning model may not be accurate, so the multi-objective optimization algorithm is still needed to further capture the optimal morphological feature parameters; when the amount of data in the meta-interface friction performance database is sufficient, the machine learning model can accurately capture the above mapping relationship, so the morphological feature parameters corresponding to the target interface friction performance can also be directly obtained.

[0048] Step S4: Print the meta-interface with the optimal morphological feature parameters and apply it to subsequent interface friction performance tests.

[0049] Since the meta-interface fabricated by 3D microprinting does not have magnetism and can be applied to nuclear magnetic resonance tests, the meta-interface printed in step S4 and the soil sample are placed in a temperature-controlled direct shear tester, and the whole is placed in a low-field nuclear magnetic resonance instrument. A shear test of the soil-meta-interface is carried out during the cooling process of the specimen. Since the nuclear magnetic signal will basically disappear after water freezes, the H1 signal in the specimen is obtained through the nuclear magnetic equipment. According to the magnitude of this signal, the moisture migration, moisture content change and the formation of ice lenses in the specimen can be analyzed, and the freeze-thaw shear process of the soil-structure interface corresponding to the macroscopic clear boundary and loading conditions can be analyzed from the micro and microscopic aspects, as well as the influence mechanism of different temperatures and normal stresses on it.

[0050] Based on the above analysis, in this embodiment, a meta-interface that is not restricted by scale and material is designed, and it is combined with the soil sample to form a soil-structure interface that does not interfere with nuclear magnetic signals. At the same time, in-depth research on the shear friction characteristics of the frozen-thawed soil-structure interface under the action of different temperatures and different normal stresses is also realized in a low-field nuclear magnetic resonance instrument. Since the 3D-printed meta-interface has high repeatability, the shear test can be repeated multiple times without increasing additional complexity, significantly reducing the cumbersome problem of fabricating traditional structural specimens. Compared with the traditional soil-structure interface, the meta-interface can well replace the structural materials containing metal, so that it can be compatible with the signal test of the low-field nuclear magnetic resonance instrument, which helps to study the shear friction characteristics of the soil-structure interface at the microscopic level during the freeze-thaw process.

[0051] In an exemplary embodiment, a computer system is provided. The computer system can be a server or a terminal, and its internal structure diagram can be as Figure 4 shown. The computer system includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer system is used to provide computing and control capabilities. The memory of the computer system includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer system is used to exchange information between the processor and external devices. The communication interface of the computer system is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements the above-mentioned interface friction performance characterization and testing method based on the optimization of meta-interface morphology characteristic parameters.

[0052] Those skilled in the art can understand that Figure 4 the structure shown in

[0053] is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer system to which the solution of the present application is applied. The specific computer system may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0054] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0055] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the various embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random-access memory (ReRAM), magnetoresistive random-access memory (MRAM), ferroelectric random-access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0056] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the various embodiments can be referred to each other.

[0057] Specific examples are used in this article to elaborate on the principles and implementation manners of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation on this application.

Claims

1. An interface friction performance characterization and testing method based on the optimization of meta-interface topography feature parameters, characterized in that The interface friction performance characterization and testing method based on the optimization of meta-interface morphology feature parameters includes: Using 3D microprinting technology to fabricate multiple meta-interfaces with different morphology feature parameters; the meta-interface includes a substrate and a set of micro-convex bodies with different shapes and different arrangements on the substrate; the morphology feature parameters at least include the number, diameter, height, and spacing of different micro-convex bodies in the set of micro-convex bodies; the set of micro-convex bodies is used to characterize specific friction performance; the size of the micro-convex bodies is in the micron scale; the shape of the micro-convex bodies is spherical, hemispherical, or quasi-spherical; Based on multiple meta-interfaces with different morphology feature parameters, constructing a meta-interface friction performance database by means of experimental and numerical simulation tests; the meta-interface friction performance database includes multiple groups of meta-interface morphology feature parameter - friction performance data; Based on the meta-interface friction performance database and the target interface friction performance, using machine learning to extract features from the data, and combining with a multi-objective optimization algorithm to adjust and optimize the morphology feature parameters, and determining the optimal morphology feature parameters corresponding to the target interface friction performance, specifically including: training a machine learning model based on the meta-interface friction performance database, and optimizing the hyperparameters of the machine learning model by means of cross-validation; the machine learning model is used to construct a mapping relationship between the interface friction performance and the morphology feature parameters; using the trained machine learning model to determine multiple alternative morphology feature parameters corresponding to the target interface friction performance; using a multi-objective optimization algorithm to optimize the multiple alternative morphology feature parameters to obtain the optimal morphology feature parameters corresponding to the target interface friction performance; the multi-objective optimization algorithm is used to improve the efficiency and accuracy of capturing the optimal morphology feature parameters; Printing the meta-interface with the optimal morphology feature parameters and applying it to subsequent interface friction performance tests; the material for printing the meta-interface is a material without nuclear magnetic signal; the material without nuclear magnetic signal at least includes polytetrafluoroethylene.

2. The method for characterizing and testing the interfacial friction performance optimized based on the meta-interface topography characteristic parameters according to claim 1, wherein Before using 3D microprinting technology to fabricate multiple meta-interfaces with different morphology feature parameters, it further includes: Designing multiple morphology feature parameter schemes according to the types of different soil samples and the sizes of soil particles.

3. The interface friction performance characterization and testing method based on the optimization of meta-interface topography feature parameters according to claim 1, characterized in that Based on multiple meta-interfaces with different morphology feature parameters, constructing a meta-interface friction performance database by means of experimental and numerical simulation tests, specifically including: Combining soil samples with meta-interfaces with different morphology feature parameters respectively to obtain multiple soil-meta-interface specimens and conducting interface shear tests to obtain shear test results; Using numerical simulation software to establish a soil-meta-interface numerical model corresponding to each soil-meta-interface specimen, and conducting interface shear simulation on the soil-meta-interface numerical model to obtain shear simulation results; Comparing the shear simulation results with the shear test results; When the shear stress-shear displacement curve in the shear simulation result is inconsistent with the corresponding shear stress-shear displacement curve in the shear test result, adjust the system parameters of the numerical simulation software until the shear stress-shear displacement curve in the shear simulation result tends to be consistent with the corresponding shear stress-shear displacement curve in the shear test result; After the system parameters are adjusted, continuously change the values of the morphological feature parameters within a set range, and use the numerical simulation software to determine the friction performance data under different values of the morphological feature parameters, match multiple groups of morphological feature parameter-friction performance data of the multi-component interface, and obtain the friction performance database of the multi-component interface.

4. The interface friction performance characterization and testing method based on the optimization of meta-interface topography feature parameters according to claim 3, characterized in that, The system parameters at least include contact parameters; the contact parameters at least include contact type and friction coefficient.

5. The interface friction performance characterization and testing method based on the optimization of the meta-interface topography feature parameters according to claim 1, wherein The machine learning model at least includes any one of BP neural network, support vector machine and random forest model.

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