A method and device for predicting contact resistance of rough surfaces based on semi-analytical solution

Abstract

The embodiment of the present application provides a method for predicting contact resistance of a rough surface based on a semi-analytical solution, which relates to the field of electrical contact technology. The method includes: numerically generating a rough surface based on the surface morphology, statistical characteristics, and fractal characteristics of the rough surface; the surface morphology of the rough surface includes a morphology function; the statistical characteristics include a height distribution law; the fractal characteristics include a power spectrum density function; determining electrical contact load conditions, material parameters, and contact parameters, and deriving a semi-analytical solution for contact resistance; deriving a contact resistance prediction model based on the generated rough surface and the semi-analytical solution; and using the contact resistance prediction model to numerically predict the contact resistance of different rough surfaces. This solution takes into account the influence of various surface parameters on contact resistance, performs fitting analysis on all parameters with greater influence, and extracts a contact resistance prediction model, which can greatly improve the prediction efficiency and accuracy of contact resistance of rough surfaces.

Description

Technical Field

[0001] The present application relates to the field of electrical contact technology, and in particular to a method and device for predicting rough surface contact resistance based on a semi-analytical solution. Background Art

[0002] Electrical contact is a ubiquitous phenomenon in electronics and power systems. Modern industrial systems rely on electrical contact to connect components, transmit signals across entire machines, and even between systems. The electrical resistance between contacting components is called contact resistance and is a key performance indicator of these components. The roughness of the contact interface and the presence of multiple coupling effects, including Joule heating and thermal expansion, make the calculation of contact resistance challenging.

[0003] Existing research primarily focuses on mechanical contact mechanics and experimental studies related to contact resistance. However, existing contact resistance models often ignore multi-field coupling effects when solving for contact resistance, treating electrical contact problems as mechanical ones. Furthermore, modeling random rough surfaces is difficult, leading to inaccurate contact resistance predictions. Therefore, there is an urgent need to develop an effective rough surface contact resistance prediction model that can efficiently and accurately predict the contact resistance of random rough surfaces. Summary of the Invention

[0004] The purpose of the embodiments of the present application is to provide a method for predicting the contact resistance of a rough surface based on a semi-analytical solution, so as to improve the efficiency and accuracy of predicting the contact resistance of a random rough surface. The specific technical solution is as follows:

[0005] In a first aspect of the present application, a method for predicting contact resistance of a rough surface based on a semi-analytical solution is provided, comprising:

[0006] The rough surface is numerically generated according to the surface morphology, statistical characteristics and fractal characteristics of the rough surface; wherein the surface morphology of the rough surface includes a morphology function; the statistical characteristics include a height distribution law; and the fractal characteristics include a power spectrum density function;

[0007] Determine the electrical contact load conditions, material parameters, and contact parameters, and derive the semi-analytical solution of contact resistance;

[0008] determining a contact resistance prediction model based on the generated rough surface and the semi-analytical solution;

[0009] The contact resistance prediction model is used to numerically predict the contact resistance of different rough surfaces.

[0010] Optionally, the semi-analytical solution of the contact resistance includes the following equations and conditions:

[0011]

[0012] The constraints are:

[0013]

[0014] Among them, R c is the contact resistance, is the average potential in the contact area, I is the total current passing through the contact interface, J(k,l) represents the current density at each node on the contact surface, V(k,l) represents the potential distribution, and the subscripts k and l represent the node numbers of the discrete rough surface in the x and y directions, respectively, satisfying 1≤k≤N x ,1≤l≤N y ,l x With l y are the lengths of the discrete grid in two directions, N x ×N y Characterizes the number of discrete nodes on the rough surface.

[0015] Optionally, determining a contact resistance prediction model based on the generated rough surface and the semi-analytical solution includes:

[0016] Determining surface parameters of the generated rough surface, wherein the surface parameters include root mean square roughness, Hurst index, and maximum and minimum cutoff wave numbers of the surface;

[0017] Generate corresponding contact resistance data using the surface parameters, target parameters and the semi-analytical solution, wherein the target parameters are the electrical contact load conditions, material parameters and contact parameters corresponding to the generated contact resistance data;

[0018] The contact resistance data are analyzed and fitted to extract a contact resistance prediction model.

[0019] Optionally, the load conditions include contact force, total voltage or total current, the material parameters include elastic modulus, Poisson's ratio, yield stress, hardening modulus, thermal expansion coefficient, heat transfer coefficient and resistivity, and the contact parameters include contact conductance and contact thermal conductance.

[0020] Optionally, the height distribution law includes a probability density distribution law; the power spectrum density function includes a power function form and an exponential function form;

[0021] The power function form includes:

[0022]

[0023] The exponential function form includes:

[0024]

[0025] Among them, q x and q y are the frequencies in two directions respectively, H is the Husrt index, q0 and q1 are low-frequency and high-frequency cutoffs, respectively, and β is the power spectrum density correction parameter.

[0026] In a second aspect of the present application, a device for predicting contact resistance of a rough surface based on a semi-analytical solution is provided, comprising:

[0027] A numerical generation module is used to numerically generate a rough surface based on the surface morphology, statistical characteristics and fractal characteristics of the rough surface; wherein the surface morphology of the rough surface includes a morphology function; the statistical characteristics include a height distribution law; and the fractal characteristics include a power spectrum density function;

[0028] The first derivation module is used to determine the electrical contact load conditions, material parameters and contact parameters, and derive the semi-analytical solution of the contact resistance;

[0029] a second derivation module, configured to determine a contact resistance prediction model based on the generated rough surface and the semi-analytical solution;

[0030] The numerical prediction model is used to numerically predict the contact resistance of different rough surfaces using the contact resistance prediction model.

[0031] Optionally, the semi-analytical solution of the contact resistance includes the following equations and conditions:

[0032]

[0033] The constraints are:

[0034]

[0035]

[0036] Among them, R c is the contact resistance, is the average potential in the contact area, I is the total current passing through the contact interface, J(k,l) represents the current density at each node on the contact surface, V(k,l) represents the potential distribution, and the subscripts k and l represent the node numbers of the discrete rough surface in the x and y directions, respectively, satisfying 1≤k≤N x ,1≤l≤N y ,l x With l y are the lengths of the discrete grid in two directions, N x ×N y Characterizes the number of discrete nodes on the rough surface.

[0037] Optionally, the second derivation module is specifically configured to:

[0038] Determining surface parameters of the generated rough surface, wherein the surface parameters include root mean square roughness, Hurst index, and maximum and minimum cutoff wave numbers of the surface;

[0039] Generate corresponding contact resistance data using the surface parameters, target parameters and the semi-analytical solution, wherein the target parameters are the electrical contact load conditions, material parameters and contact parameters corresponding to the generated contact resistance data;

[0040] The contact resistance data are analyzed and fitted to extract a contact resistance prediction model.

[0041] Optionally, the load conditions include contact force, total voltage and total current, the material parameters include elastic modulus, Poisson's ratio, yield stress, hardening modulus, thermal expansion coefficient, heat transfer coefficient and resistivity, and the contact parameters include contact conductance and contact thermal conductance.

[0042] In another aspect of the implementation of the present application, an embodiment of the present application provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0043] Memory for storing computer programs;

[0044] The processor is configured to implement the steps of a method for predicting contact resistance of a rough surface based on a semi-analytical solution when executing a program stored in the memory.

[0045] Beneficial effects of this application:

[0046] The embodiment of the present application provides a method for predicting contact resistance of rough surfaces based on a semi-analytical solution. The method numerically generates a rough surface based on the surface morphology, statistical characteristics, and fractal characteristics of the rough surface. The surface morphology of the rough surface includes a morphology function. The statistical characteristics include a height distribution pattern. The fractal characteristics include a power spectrum density function. The method determines the electrical contact load conditions, material parameters, and contact parameters to derive a semi-analytical solution for the contact resistance. Based on the generated rough surface and the semi-analytical solution, a contact resistance prediction model is derived. The contact resistance of different rough surfaces is numerically predicted using the contact resistance prediction model. In this solution, the influence of various surface parameters on the contact resistance is taken into account. All parameters with significant influence are fitted and analyzed to extract a contact resistance prediction model. This method can greatly improve the efficiency and accuracy of predicting the contact resistance of rough surfaces. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art.

[0048] Figure 1 This is a flowchart of a method for predicting contact resistance of rough surfaces based on a semi-analytical solution for this application.

[0049] Figure 2 This is the effect of 9 groups of different rough surface parameters on the contact resistance-contact force curve taken in this application;

[0050] Figure 3 This is the influence of various surface parameters on the contact resistance-contact force curve in this application;

[0051] Figure 4 This is a physical picture of the experimental instruments and test pieces used in this application;

[0052] Figure 5 is the actual surface morphology of the specimen used in this application;

[0053] Figure 6 It is the height probability density distribution diagram and power spectrum density curve diagram of the actual surface morphology of the test piece of this application;

[0054] Figure 7 This is an example diagram of a rough surface profile generated according to the statistical characteristics and fractal feature values ​​of a real rough surface in this application;

[0055] Figure 8 This is a comparison chart of the calculation results and experimental results of this application. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0057] In order to solve the problems in the prior art, the present application provides a rough surface contact resistance prediction method based on a semi-analytical solution to improve the efficiency and accuracy of predicting the contact resistance of random rough surfaces.

[0058] It should be noted that the rough surface electrical contact behavior prediction method based on the boundary element method provided in the embodiment of the present application can be applied to electronic devices. In practical applications, the electronic devices can be: smart phones, tablet computers, laptop computers and other devices, which are all reasonable.

[0059] In order to better illustrate the embodiments of the present application, it is assumed that: a rigid body with a rough surface compresses a deformable elastic-plastic block B under the action of a contact force, and the block has a height of h0 and a nominal area of ​​L x ×L y Apply a total current I (or a total voltage ΔV) at each contact roughness peak. When a current flows (current density is J), a surface heat flux Q will be generated due to the effect of Joule heat. s , the heat flow is evenly divided between the upper and lower surfaces. Therefore, the electrical contact process is a multi-field coupling problem of electricity, heat, and force, and the main heat generation location is on the contact surface.

[0060] The following first introduces a rough surface contact resistance prediction method based on a semi-analytical solution provided in an embodiment of the present application.

[0061] like Figure 1 As shown, a method for predicting contact resistance of a rough surface based on a semi-analytical solution provided in an embodiment of the present application may include the following steps:

[0062] S101, numerically generating a rough surface according to the surface morphology, statistical characteristics and fractal characteristics of the rough surface.

[0063] The surface topography of the rough surface includes a topography function; the statistical characteristics include a height distribution law; and the fractal characteristics include a power spectrum density function. In numerical calculations, the rough surface is represented as a set of discrete nodes.

[0064] For example, in one implementation, numerically generating a rough surface based on the surface morphology, statistical characteristics, and fractal characteristics of the rough surface may include: using statistical distribution to determine the regular characteristics of the surface height distribution, determining the fractal characteristics using a power spectrum density function, numerically generating the rough surface, and simultaneously determining the height h0 of the block and the nominal area L of the contact surface. x ×L y .

[0065] Exemplarily, in one implementation, the height distribution law includes a probability density distribution law, which may include a Gaussian distribution and a Weibull distribution; the power spectrum density function includes a power function form and an exponential function form;

[0066] The power function form includes:

[0067]

[0068] The exponential function form includes:

[0069]

[0070] Among them, q x and q y are the frequencies in two directions respectively, H is the Hurst exponent, q0 and q1 are the low-frequency and high-frequency cutoffs, respectively, and β is the power spectral density correction parameter.

[0071] S102, determining electrical contact load conditions, material parameters, and contact parameters, and deriving a semi-analytical solution for contact resistance;

[0072] It should be noted that in order to obtain the contact resistance of the rough surface, relevant conditions or parameters can be determined first, such as electrical contact load conditions, material parameters and contact parameters. Among them, the electrical contact load conditions may include: contact force and total current (or total voltage), and the material parameters may include elastic modulus, Poisson's ratio, plasticity parameters, heat transfer coefficient, thermal expansion coefficient and resistivity; contact parameters may include contact conductance and contact thermal conductance. In addition, in order to obtain the electrical contact pressure distribution under different materials and contact states, the settings of material parameters and contact parameters may be different, which is reasonable.

[0073] Exemplarily, the semi-analytical solution of the contact resistance may include the following equations and conditions:

[0074]

[0075] The constraints may include:

[0076]

[0077] Among them, R c is the contact resistance, is the average potential in the contact area, I is the total current passing through the contact interface, J(k,l) represents the current density at each node on the contact surface, V(k,l) represents the potential distribution, and the subscripts k and l represent the node numbers of the discrete rough surface in the x and y directions, respectively, satisfying 1≤k≤N x ,1≤l≤N y ,l x With l y are the lengths of the discrete grid in two directions, N x ×N y Characterizes the number of discrete nodes on the rough surface.

[0078] S103, determining a contact resistance prediction model based on the generated rough surface and the semi-analytical solution;

[0079] Exemplarily, in one implementation, determining a contact resistance prediction model based on the generated rough surface and the semi-analytical solution may include:

[0080] Determining surface parameters of the generated rough surface, wherein the surface parameters include root mean square roughness, Hurst index, and maximum and minimum cutoff wave numbers of the surface;

[0081] Using the surface parameters, target parameters and the semi-analytical solution, focusing on the influence of the surface parameters on the contact resistance, generating corresponding contact resistance data, wherein the target parameters are the electrical contact load conditions, material parameters and contact parameters corresponding to the generated contact resistance data;

[0082] The contact resistance data are analyzed and fitted, and the surface parameters with greater influence are considered to obtain a contact resistance prediction model.

[0083] Considering the influence of different surface parameters on contact resistance, multiple surface parameters can be comprehensively considered when deriving the contact resistance prediction model, such as the height distribution of the rough surface morphology, root mean square roughness, Hurst index (or fractal dimension), and the maximum and minimum cutoff wave numbers of the surface.

[0084] Considering that different surface parameters and target parameters can generate different contact resistance data, in order to improve the prediction efficiency and accuracy of the contact resistance prediction model, as well as the applicability of the prediction model, multiple sets of different surface parameters and target parameters can be selected to generate contact resistance data.

[0085] In the embodiments of the present application, the electrical contact problem can be solved using existing theoretical or numerical methods. Furthermore, multiple sets of surface parameters and semi-analytical solutions can be used to generate multiple sets of contact resistance data. By combining the influence of different surface roughness parameters on contact resistance, a fitting analysis is performed on all the parameters with the greatest impact, and a contact resistance prediction model is derived to achieve numerical prediction of contact resistance for different surface roughnesses.

[0086] S104 , using the contact resistance prediction model, numerically predicting the contact resistance of different rough surfaces.

[0087] The contact resistance prediction model of the present application is a simple and easy-to-use contact resistance prediction model formed after analyzing and fitting the rough surface parameters that have a greater impact. It needs to be used when the material parameters and surface parameters are known, without the need to give contact parameters or use it in conjunction with other algorithms.

[0088] This application numerically generates a rough surface based on its surface morphology, statistical characteristics, and fractal features. The surface morphology of the rough surface includes a morphology function; the statistical characteristics include a height distribution pattern; and the fractal characteristics include a power spectral density function. Electrical contact load conditions, material parameters, and contact parameters are determined to derive a semi-analytical solution for contact resistance. Based on the generated rough surface and the semi-analytical solution, a contact resistance prediction model is derived. Using the contact resistance prediction model, the contact resistance of different rough surfaces is numerically predicted. This solution considers the influence of various surface parameters on contact resistance, performs a fitting analysis on all parameters with significant influence, and derives a contact resistance prediction model, which can greatly improve the efficiency and accuracy of predicting contact resistance on rough surfaces.

[0089] In order to further illustrate the solution of the present application, a rough surface contact resistance prediction method based on a semi-analytical solution of the present application is introduced below with reference to specific examples.

[0090] Follow the above steps to study the root mean square roughness h of the rough surface rms , Hurst index H and minimum cutoff wave number The influence of the rough surface parameters on the contact resistance is shown in Table 1. First, 9 groups of rough surface parameters are taken within the parameter range used to study the influence of the rough surface parameters on the contact resistance, such as Figure 2 As shown, it can be seen that in the double logarithmic coordinates, the contact resistance and contact force are close to a linear relationship. Therefore, it is assumed that the relationship between the contact resistance and the contact force (i.e., an example of the contact resistance prediction model in this application) is:

[0091]

[0092] Where C1 and C2 are unknown parameters, which are mainly related to surface parameters and material parameters, R * is the dimensionless contact resistance, calculated as R * =R c / (ρh rms / A0), ρ is the resistivity of the material, A0 is the nominal contact area, P * is the dimensionless contact force, calculated as E * is the comprehensive elastic modulus, is the root mean square gradient of the rough surface (▽h represents the gradient of the rough surface profile, and the symbol <·> represents the mathematical expectation of the parameters within it), which can usually be obtained using surface topography data or power spectrum density function. The remaining material parameters are shown in Table 2. The following focuses on the influence of the above three rough surface parameters on C1 and C2. When studying the effect of each surface parameter, 20 rough surfaces are generated for each set of parameters to eliminate the influence of surface randomness on the results. When each surface parameter changes within a given range, the values ​​of the other two parameters are fixed respectively. For example, when studying h rms When H is fixed at 0.7, When studying H, fix h rms =1μm, Research When, h rms =1μm, H=0.7, the influence of the three parameters on C1 and C2, such as Figure 3 According to the above research method, the fitting formula of C1 and C2 for the three surface parameters can be obtained as follows:

[0093]

[0094] Combining the relationship between dimensionless resistance and dimensionless contact force, the above two parameters are substituted into the effective contact resistance prediction model of the present invention. The present invention is verified using an experimental method below.

[0095] Table 1 Rough surface parameters

[0096]

[0097] Table 2 Material parameters

[0098]

[0099] The experimental method was used to compare and verify the present invention. A 10mm×10mm×10mm cube was made as a rough surface indenter, and a 20mm×20mm×10mm rectangular block was made as an elastic-plastic block. Both specimens were made of copper alloy. The surface morphology, statistical characteristics and fractal characteristics of the rough surface were obtained using a Zegage Plus, Zygo, USA white light interferometer. The surface morphology, statistical characteristics and fractal characteristics of the rough surface were further analyzed using a Keithley 6221 current source, a Keithley 2182A nanovoltmeter and a Shimadzu universal testing machine equipped with a 10kN force sensor. Figure 4 As shown, the four-wire method was used to measure contact resistance according to Ohm's law. A compressive displacement was applied at a constant speed, and a force sensor was used to obtain normal contact force data. After the experiment, the contact force and contact resistance results were extracted and compared with those determined using the present invention.

[0100] In this embodiment, a white light interferometer is used to measure the real rough surface, and the real surface morphology is as follows: Figure 5 , the rough surface is obtained by sandblasting on the smooth surface. According to the measurement data of the real surface morphology, its height probability density distribution and power spectrum density curve are obtained, such as Figure 6 , further analysis of the statistical characteristics and fractal characteristics of the rough surface shows that the surface morphology of the rough surface is close to Gaussian distribution, and the root mean square roughness h rms =4.5μm, Hurst index H=0.65, minimum cutoff wave number A rough surface is generated based on the obtained parameters, an example of which is Figure 7 This application forms a contact resistance prediction model by analyzing and fitting the contact resistance data of random rough surfaces. The prediction results include contact resistance-contact force curves, such as Figure 8 The results are in good agreement with the experimental results. By comparison, the effectiveness of the rough surface contact resistance prediction method based on semi-analytical solution proposed in this application is demonstrated.

[0101] Compared to the above-mentioned method embodiment, the present application embodiment provides a rough surface contact resistance prediction device based on a semi-analytical solution, which may include:

[0102] A numerical generation module is used to numerically generate a rough surface based on the surface morphology, statistical characteristics and fractal characteristics of the rough surface; wherein the surface morphology of the rough surface includes a morphology function; the statistical characteristics include a height distribution law; and the fractal characteristics include a power spectrum density function;

[0103] The first derivation module is used to determine the electrical contact load conditions, material parameters and contact parameters, and derive the semi-analytical solution of the contact resistance;

[0104] a second derivation module, configured to determine a contact resistance prediction model based on the generated rough surface and the semi-analytical solution;

[0105] The numerical prediction model is used to numerically predict the contact resistance of different rough surfaces using the contact resistance prediction model.

[0106] This application numerically generates a rough surface based on its surface morphology, statistical characteristics, and fractal features. The surface morphology of the rough surface includes a morphology function; the statistical characteristics include a height distribution pattern; and the fractal characteristics include a power spectral density function. Electrical contact load conditions, material parameters, and contact parameters are determined to derive a semi-analytical solution for contact resistance. Based on the generated rough surface and the semi-analytical solution, a contact resistance prediction model is derived. Using the contact resistance prediction model, the contact resistance of different rough surfaces is numerically predicted. This solution considers the influence of various surface parameters on contact resistance, performs a fitting analysis on all parameters with significant influence, and derives a contact resistance prediction model, which can greatly improve the efficiency and accuracy of predicting contact resistance on rough surfaces.

[0107] Optionally, the semi-analytical solution of the contact resistance includes the following equations and conditions:

[0108]

[0109] The constraints are:

[0110]

[0111] Among them, R c is the contact resistance, is the average potential in the contact area, I is the total current passing through the contact interface, J(k,l) represents the current density at each node on the contact surface, V(k,l) represents the potential distribution, and the subscripts k and l represent the node numbers of the discrete rough surface in the x and y directions, respectively, satisfying 1≤k≤N x ,1≤l≤N y ,l x With l y are the lengths of the discrete grid in two directions, N x ×N y Characterizes the number of discrete nodes on the rough surface.

[0112] Optionally, the second derivation module is specifically configured to:

[0113] Determining surface parameters of the generated rough surface, wherein the surface parameters include root mean square roughness, Hurst index, and maximum and minimum cutoff wave numbers of the surface;

[0114] Using the surface parameters, target parameters and the semi-analytical solution, focusing on the influence of the surface parameters on the contact resistance, generating corresponding contact resistance data, wherein the target parameters are the electrical contact load conditions, material parameters and contact parameters corresponding to the generated contact resistance data;

[0115] The contact resistance data are analyzed and fitted, and the surface parameters with greater influence are considered to obtain a contact resistance prediction model.

[0116] Optionally, the load conditions include contact force, total voltage and total current, the material parameters include elastic modulus, Poisson's ratio, yield stress, hardening modulus, thermal expansion coefficient, heat transfer coefficient and resistivity, and the contact parameters include contact conductance and contact thermal conductance.

[0117] The embodiment of the present application further provides an electronic device, comprising a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus.

[0118] Memory for storing computer programs;

[0119] The processor is used to implement a rough surface contact resistance prediction method based on a semi-analytical solution provided in an embodiment of the present application when executing a program stored in the memory.

[0120] The communication bus mentioned in the terminal can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. This communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is used in the figure, but this does not mean that there is only one bus or only one type of bus.

[0121] The communication interface is used for communication between the above terminal and other devices.

[0122] The memory may include random access memory (RAM) or non-volatile memory, such as at least one disk storage. Alternatively, the memory may be at least one storage device located away from the processor.

[0123] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components.

[0124] In another embodiment provided in the present application, a computer-readable storage medium is also provided, in which a computer program is stored. When the computer program is executed by a processor, the rough surface contact resistance prediction method based on a semi-analytical solution described in any of the above embodiments is implemented.

[0125] In another embodiment provided in the present application, a computer program product comprising instructions is also provided, which, when executed on a computer, enables the computer to execute the rough surface contact resistance prediction method based on a semi-analytical solution as described in any of the above embodiments.

[0126] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When software is used for implementation, it can 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. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can 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 can be transmitted from a website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrations. The available medium can 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 (e.g., a solid-state drive (SSD)).

[0127] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0128] Each embodiment in this specification is described in a related manner. Similar portions between the embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences from other embodiments. In particular, the device embodiments are generally similar to the method embodiments, so their description is relatively simple. For related portions, refer to the description of the method embodiments.

[0129] The above description is only a preferred embodiment of the present application and is not intended to limit the scope of protection of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application are included in the scope of protection of the present application.

Claims

1. A method for predicting contact resistance of rough surfaces based on semi-analytical solutions, characterized in that: include: The rough surface is numerically generated according to the surface morphology, statistical characteristics and fractal characteristics of the rough surface; wherein the surface morphology of the rough surface includes a morphology function; the statistical characteristics include a height distribution law; and the fractal characteristics include a power spectrum density function; Determine the electrical contact load conditions, material parameters, and contact parameters, and derive the semi-analytical solution of contact resistance; determining a contact resistance prediction model based on the generated rough surface and the semi-analytical solution; Using the contact resistance prediction model, the contact resistance of different rough surfaces is numerically predicted; The semi-analytical solution of the contact resistance includes the following equations and conditions: The constraints are: Among them, R c is the contact resistance, is the average potential in the contact area, I is the total current passing through the contact interface, J(k,l) represents the current density at each node on the contact surface, V(k,l) represents the potential distribution, and the subscripts k and l represent the node numbers of the discrete rough surface in the x and y directions, respectively, satisfying 1≤k≤N x ,1≤l≤N y ,l x With l y are the lengths of the discrete grid in two directions, N x ×N y Characterizing the number of discrete nodes on the rough surface; determining a contact resistance prediction model based on the generated rough surface and the semi-analytical solution, including: Determining surface parameters of the generated rough surface, wherein the surface parameters include root mean square roughness, Hurst index, and maximum and minimum cutoff wave numbers of the surface; Generate corresponding contact resistance data using the surface parameters, target parameters and the semi-analytical solution, wherein the target parameters are the electrical contact load conditions, material parameters and contact parameters corresponding to the generated contact resistance data; The contact resistance data are analyzed and fitted to extract a contact resistance prediction model.

2. The method according to claim 1, characterized in that The load conditions include contact force, total voltage or total current, the material parameters include elastic modulus, Poisson's ratio, yield stress, hardening modulus, thermal expansion coefficient, heat transfer coefficient and resistivity, and the contact parameters include contact conductance and contact thermal conductance.

3. The method according to claim 1, characterized in that The height distribution law includes a probability density distribution law; the power spectrum density function includes a power function form and an exponential function form; The power function form includes: The exponential function form includes: Among them, q x and q y are the frequencies in two directions respectively, H is the Hurst exponent, q0 and q1 are the low-frequency and high-frequency cutoffs, respectively, and β is the power spectral density correction parameter.

4. A rough surface contact resistance prediction device based on a semi-analytical solution, characterized in that: include: A numerical generation module is used to numerically generate a rough surface based on the surface morphology, statistical characteristics and fractal characteristics of the rough surface; wherein the surface morphology of the rough surface includes a morphology function; the statistical characteristics include a height distribution law; and the fractal characteristics include a power spectrum density function; The first derivation module is used to determine the electrical contact load conditions, material parameters and contact parameters, and derive the semi-analytical solution of the contact resistance; a second derivation module, configured to determine a contact resistance prediction model based on the generated rough surface and the semi-analytical solution; A numerical prediction model is used to numerically predict the contact resistance of different rough surfaces using the contact resistance prediction model; The semi-analytical solution of the contact resistance includes the following equations and conditions: The constraints are: Among them, R c is the contact resistance, is the average potential in the contact area, I is the total current passing through the contact interface, J(k,l) represents the current density at each node on the contact surface, V(k,l) represents the potential distribution, and the subscripts k and l represent the node numbers of the discrete rough surface in the x and y directions, respectively, satisfying 1≤k≤N x ,1≤l≤N y ,l x With l y are the lengths of the discrete grid in two directions, N x ×N y Characterize the number of discrete nodes on the rough surface; The second derivation module is specifically used for: Determining surface parameters of the generated rough surface, wherein the surface parameters include root mean square roughness, Hurst index, and maximum and minimum cutoff wave numbers of the surface; Generate corresponding contact resistance data using the surface parameters, target parameters and the semi-analytical solution, wherein the target parameters are the electrical contact load conditions, material parameters and contact parameters corresponding to the generated contact resistance data; The contact resistance data are analyzed and fitted to extract a contact resistance prediction model.

5. The device according to claim 4, characterized in that The load conditions include contact force, total voltage and total current; the material parameters include elastic modulus, Poisson's ratio, yield stress, hardening modulus, thermal expansion coefficient, heat transfer coefficient and resistivity; and the contact parameters include contact conductance and contact thermal conductance.

6. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory for storing computer programs; A processor, configured to implement the method steps described in any one of claims 1 to 3 when executing a program stored in a memory.

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