A Modeling Method, System and Performance Prediction Method for Particle-Dispersed Conductive Adhesive

Abstract

The present invention belongs to the field of three-dimensional modeling of conductive adhesives, and specifically discloses a modeling method, system and performance prediction method for granular dispersed conductive adhesives, which include the following steps: preset the number and radius of particles, the number and radius range of pores, and the model size in the conductive adhesive; based on the preset number and radius of particles, set the positions of each particle one by one, and ensure that the current particle is in contact with at least one of the already set particles during the setting, and the last two particles are respectively in contact with the upper and lower surfaces of the model; based on the preset number and radius range of pores, randomly set the positions and radii of each pore, and ensure that there is no contact between pores and between pores and particles; perform Boolean operations on the particles, then create a matrix based on the model size, and perform Boolean operations on the matrix with the particles and pores to obtain the conductive adhesive model. The modeling method of the present invention has fillers and pores that conform to reality, controllable model parameters, and simple operation, realizing the simulation analysis of conductive adhesives.

Description

Technical Field

[0001] The present invention belongs to the field of three-dimensional modeling of conductive adhesives, and more specifically, relates to a modeling method, system and performance prediction method for granular dispersed conductive adhesives. Background Art

[0002] Regarding the performance research of conductive adhesives, most of them are analyzed from an experimental perspective, and rarely simulated through simulation. Due to the great randomness of the distribution of conductive particles and pores in conductive adhesives, through finite element modeling and solving operations, multiple situations can be analyzed simultaneously.

[0003] Patent CN116525041A discloses a porous structure modeling method based on matlab, which defines the position coordinates of spherical units in a matrix of algorithm software and generates a porous subsurface structure through Boolean operations. The pore position information of this model is manually input and only contains pores, which cannot meet the random distribution characteristics of the filler particles in conductive adhesives. Summary of the Invention

[0004] In view of the above defects or improvement requirements of the prior art, the present invention provides a modeling method, system and performance prediction method for granular dispersed conductive adhesives, aiming to achieve a conductive adhesive simulation modeling that can truly simulate the distribution of filler particles and pores in conductive adhesives and whose model parameters are controllable.

[0005] To achieve the above object, according to the first aspect of the present invention, a modeling method for granular dispersed conductive adhesives is proposed, including the following steps:

[0006] S1. Preset the number and radius of particles, the number and radius range of pores, and the model size in the conductive adhesive;

[0007] S2. Based on the preset number and radius of particles, set the positions of each particle one by one, and ensure that the current particle is in contact with at least one of the already set particles during the setting process, and the last two particles are in contact with the upper and lower surfaces of the model respectively;

[0008] S3. Based on the preset number and radius range of pores, randomly set the positions and radii of each pore, and ensure that there is no contact between pores and between pores and particles;

[0009] S4. Perform Boolean operations on the particles, then create a matrix based on the model size, and perform Boolean operations on the matrix with the particles and pores to obtain a conductive adhesive model.

[0010] As a further preferred option, step S2 includes the following steps:

[0011] Set the first particle in the center of the model;

[0012] For the i-th particle, where 2 ≤ i ≤ N-2: Using any one of the previously set i-1 particles as the base particle, and setting transformation coordinates, the coordinates of the base particle are transformed through the transformation coordinates to obtain the position coordinates of the i-th particle; N is the preset number of particles.

[0013] Set the last two particles so that they are in contact with the upper and lower surfaces of the model respectively, and in contact with at least one of the previously set particles.

[0014] As a further preference, in step S2, the determination method of the transformation coordinates is as follows:

[0015] radii = (2 - m) × r + m × r × rand()

[0016] angle1 = rand() × 2π

[0017] angle2 = rand() × 2π

[0018] x = radii × cos(angle2) × cos(angle1)

[0019] y = radii × cos(angle2) × sin(angle1)

[0020] z = radii × sin(angle2)

[0021] Where r is the preset particle radius, m is the weight coefficient, rand() represents a random number between [0, 1], radii is the transformation distance, angle1 is the azimuth angle, angle2 is the tilt angle, and (x, y, z) are the transformation coordinates.

[0022] As a further preference, the value range of the weight coefficient m is 0.05 to 0.2.

[0023] As a further preference, in step S4, after performing Boolean operations on the particles, the particles are trimmed by the matrix frame so that the upper and lower surface particles are in the same plane as the upper and lower surfaces of the matrix.

[0024] According to the second aspect of the present invention, there is provided a modeling system for particle-dispersed conductive adhesive, including a processor, and the processor is used to execute the above-mentioned modeling method of particle-dispersed conductive adhesive.

[0025] According to the third aspect of the present invention, there is provided a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned modeling method of particle-dispersed conductive adhesive is implemented.

[0026] According to the fourth aspect of the present invention, there is provided a conductive adhesive model constructed by using the above-mentioned modeling method of particle-dispersed conductive adhesive.

[0027] According to the fifth aspect of the present invention, a method for predicting the performance of a conductive adhesive is provided. In finite element software, the performance of the conductive adhesive is predicted based on the above conductive adhesive model.

[0028] As a further preference, predicting the performance of the conductive adhesive includes:

[0029] By applying different voltages to the upper and lower surfaces of the conductive adhesive model, the volume resistivity of the conductive adhesive is predicted;

[0030] By applying different temperatures to the upper and lower surfaces of the conductive adhesive model, the thermal conductivity of the conductive adhesive is predicted;

[0031] By applying a load to the upper surface of the conductive adhesive model, the Young's modulus and shear modulus of the conductive adhesive are predicted.

[0032] Generally speaking, compared with the prior art by the above technical solution conceived by the present invention, the following technical advantages are mainly possessed:

[0033] 1. The present invention designs a method for generating the positions of filler particles and pores, and through Boolean operations, the outer model is dug and filled with particles to form a three-dimensional model of the conductive adhesive, which can more realistically simulate the distribution of filler particles and pores in the conductive adhesive, including the randomness and complexity of the particles; at the same time, the independent regulation of the relevant parameters of the particles and pores is realized, so that the parameters of the model are controllable, and the model can be adjusted more finely to meet specific research needs.

[0034] 2. Based on the conductive adhesive model obtained by the present invention, different physical property parameters can be further predicted through finite element software, and the behavior of the conductive adhesive under multiple physical fields such as electricity, heat, and force can be simulated. The physical property prediction under the coupling action of multiple fields such as heat, force, and electricity can be realized, greatly reducing the experimental cost, which is very beneficial to engineering problems that need to comprehensively consider various factors in practical applications. Description of the Drawings

[0035] Figure 1 It is a schematic flow chart of the modeling method and performance prediction method of the particle-dispersed conductive adhesive in the embodiment of the present invention;

[0036] Figure 2 It is a three-dimensional model diagram of the conductive adhesive constructed in the embodiment of the present invention;

[0037] Figure 3 It is the boundary condition of the conductive performance simulation analysis of the conductive adhesive constructed in the embodiment of the present invention;

[0038] Figure 4 It is the boundary condition of the thermal conductivity simulation analysis of the conductive adhesive constructed in the embodiment of the present invention;

[0039] Figure 5Boundary conditions for the simulation analysis of the mechanical properties of the conductive adhesive constructed for the embodiments of the present invention.

[0040] In all the drawings, the same reference numerals are used to represent the same elements or structures, where: 1 - filler particles, 2 - pores, 3 - matrix. Detailed implementation manners

[0041] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0042] A modeling method for a particulate-dispersed conductive adhesive provided by an embodiment of the present invention includes the following steps:

[0043] S1. According to the actual filler mass fraction requirement and porosity requirement, preset the number and radius of filler particles, the number and radius range of pores, and the model size in the conductive adhesive.

[0044] S2. Assign a preset radius to the particles, and the radii of all particles are the same; based on the preset number of particles, set the positions of each particle one by one, and ensure that the current particle is in contact with at least one of the previously set particles during the setting process, and the last two particles are respectively in contact with the upper and lower surfaces of the model.

[0045] Further, setting the positions of each particle includes the following steps:

[0046] Set the first particle at the center of the model size;

[0047] Then generate the second to the penultimate third particles, that is, for the i-th particle (2 ≤ i ≤ N - 2, N is the total number of particles): take any one of the previous i - 1 particles as the base particle, and set the transformation coordinates, and transform the coordinates of the base particle through the transformation coordinates to obtain the position coordinates of the i-th particle;

[0048] Finally, generate the penultimate second particle, which is in contact with the upper surface of the model, and generate the last particle, which is in contact with the lower surface of the model, and both the last two particles are in contact with at least one of the previously set particles.

[0049] Further, the determination method of the transformation coordinates is:

[0050] radii = (2 - m) × r + m × r × rand()

[0051] angle1 = rand() × 2π

[0052] angle2 = rand() × 2π

[0053] x = radii × cos(angle2) × cos(angle1)

[0054] y = radii × cos(angle2) × sin(angle1)

[0055] z = radii × sin(angle2)

[0056] Among them, r is the preset particle radius, rand() represents a random number between [0, 1], radii is the transformation distance, m is the weight coefficient, angle1 is the azimuth angle, angle2 is the tilt angle, and (x, y, z) are the transformation coordinates.

[0057] Specifically, two random angles are generated through angle1 and angle2, in radians, covering all possible angles on the circle / sphere. The distance of the new position is calculated through radii, which uses (2 - m) × r as the base radius and adds a part of the random quantity m × r × rand(), and m is preferably set to 0.05 - 0.2, so as to ensure that the new particle contacts the base particle and maintains an appropriate distance.

[0058] In the spherical coordinate system, the position of a point is determined by three values: the radius radii, the tilt angle angle2, and the azimuth angle angle1. The tilt angle is the angle measured downward from the z-axis, and the azimuth angle is the angle measured counterclockwise from the x-axis. It is necessary to convert the spherical coordinate system to the Cartesian coordinate system, that is, comprehensively use radii, angle1, and angle2 to calculate the transformation coordinates of the new particle. In the spherical coordinate system, radii affects the distance of the point, and angle1 and angle2 determine its direction, and the cos and sin functions of the angles are used to ensure that the point is correctly placed on the sphere.

[0059] Finally, xcoor(i) = xcoor(p) + x, ycoor(i) = ycoor(p) + y, zcoor(i) = zcoor(p) + z: The calculated offset (x, y, z) is added to the coordinates (xcoor(p), ycoor(p), zcoor(p)) of the base particle p through the above formula to obtain the x, y, and z coordinates (xcoor(i), ycoor(i), zcoor(i)) of the i-th particle, effectively placing the particle in the correct position relative to the base particle in three-dimensional space.

[0060] S3. Based on the preset number of pores and the radius range, randomly set the positions and radii of each pore, and ensure that there is no contact between pores and between pores and particles.

[0061] Specifically, when setting the pores, randomly assign values within the preset pore radius range, and set multiple (such as 300,000 times) loops to find the pore positions, ensuring that they do not contact other pores and do not contact the particles.

[0062] S4. Perform Boolean operations on the particles, then create a matrix based on the model size, and perform Boolean operations on the matrix, particles, and pores to obtain the conductive adhesive model.

[0063] Furthermore, write all the particle position information, perform Boolean addition on the particles, and then subtract the particles with the matrix frame so that the upper and lower surface particles and the upper and lower surfaces of the matrix are in the same plane without protrusions, ensuring that the upper and lower particles are in surface contact with the upper and lower surfaces and ensuring their conductivity. Write the pore position information, then create a matrix, and perform Boolean operations on the matrix, particles, and pores to obtain the conductive adhesive model. In actual operation, what is obtained here is the command stream for establishing the conductive adhesive model, and this command stream is input into the finite element software to establish and form a visual three-dimensional model.

[0064] The embodiment of the present invention also provides a method for predicting the performance of a conductive adhesive, that is, predicting the electrical conductivity, thermal conductivity, and mechanical properties of the conductive adhesive through the above-mentioned conductive adhesive model, specifically including:

[0065] By applying different voltages on the upper and lower surfaces of the conductive adhesive model to generate a voltage difference between the upper and lower surfaces, predicting the volume resistivity of the conductive adhesive;

[0066] By applying different temperatures on the upper and lower surfaces of the conductive adhesive model to generate a temperature difference between the upper and lower surfaces, predicting the thermal conductivity coefficient of the conductive adhesive;

[0067] By applying a load on the upper surface of the conductive adhesive model, predicting the Young's modulus and shear modulus of the conductive adhesive.

[0068] It should be noted that based on the conductive adhesive model, voltage, temperature, and load can be applied simultaneously to realize the prediction of physical properties under the coupling action of multiple fields of heat, force, and electricity.

[0069] The following are specific embodiments:

[0070] Establish a conductive adhesive model and perform performance prediction through this conductive adhesive model, as Figure 1 shown, including the following steps:

[0071] Determine the number and radius of the filler particles, the number and radius range of the pores, and the model size in the algorithm software editor.

[0072] Take the weight coefficient m = 0.1, generate the coordinate positions of the filler particles and pores, and the distribution graph of the contacting particles and pores can be drawn through a function and distinguished by different colors.

[0073] Data of the matrix 3 material, filler particles 1, and pores 2 are written through Boolean operations to generate a porous conductive adhesive model, that is, a command stream for establishing the conductive adhesive model is obtained and output in the form of a txt document.

[0074] The command stream for establishing the conductive adhesive model is input into finite element software to establish a visualized three-dimensional model, as Figure 2 shown, and exported in iges format; then the iges file is opened with modeling software, scaled to the required size, and exported in x_t format.

[0075] The obtained x_t format file is imported into finite element software, and the material parameters of the filler particles, pores, and matrix can be set, and then performance prediction can be carried out:

[0076] When solving the volume resistivity, as Figure 3 shown, a 0.02v voltage field is applied to the upper surface of the model, a 0.01v voltage field is applied to the lower surface of the model, and the surrounding walls are set as insulating surfaces. The current density output from the lower surface is read, and the volume resistivity of the model can be calculated through the formula;

[0077] When solving the thermal conductivity, as Figure 4 shown, a 30°C temperature field is applied to the upper surface of the model, a 20°C voltage field is applied to the lower surface of the model, and the surrounding walls are set as adiabatic surfaces. The heat flux density output from the lower surface is read, and the thermal conductivity of the model can be calculated through the formula;

[0078] When solving the Young's modulus, as Figure 5 shown, the upper surface of the model is vertically lifted by a certain distance, the lower surface of the model is set as a fixed surface that will not slip, the normal stress on the upper surface of the model is read, and the Young's modulus of the model can be calculated through the formula.

[0079] Those skilled in the art can easily understand that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A modeling method for particle dispersed conductive adhesive, characterized in that: The steps include: S1. Preset the number and radius of particles in the conductive glue, the number and radius range of pores, and the model size; S2. Based on the preset number of particles and radius, the positions of the particles are set one by one, and during the setting, it is ensured that the current particle contacts at least one of the set particles, and the last two particles contact the upper and lower surfaces of the model respectively; S3, based on the preset number of pores and radius range, randomly set the position and radius of each pore, and ensure that there is no contact between the pores and between the pores and the particles; S4, performing Boolean operations on the particles, trimming the particles through the matrix framework, so that the upper and lower surface particles and the upper and lower surfaces of the matrix are in the same plane, and then creating a matrix based on the model size, performing Boolean operations on the matrix, particles, and pores to obtain a conductive adhesive model; Step S2 includes the following steps: Set the first particle in the center of the model; For the i-th particle, 2≤i≤N-2: any one of the first i-1 particles that have been set is used as the basic particle, and the transformation coordinates are set. The coordinates of the basic particles are transformed by the transformation coordinates to obtain the position coordinates of the i-th particle; N is the preset number of particles; The last two particles are arranged so as to contact the upper and lower surfaces of the model respectively and to contact at least one of the arranged particles; The transformation coordinates are determined as follows: radii=(2-m)×r+m×r×rand() angle1=rand()×2π angle2=rand()×2π x=radii×cos(angle2)×cos(angle1) y=radii×cos(angle2)×sin(angle1) z = radii × sin(angle2) Among them, r is the preset particle radius, m is the weight coefficient, rand() represents a random number between [0,1], radii is the transformation distance, angle1 is the azimuth, angle2 is the inclination angle, and (x, y, z) is the transformation coordinate.

2. The modeling method of particle dispersed conductive adhesive according to claim 1, characterized in that: The value range of the weight coefficient m is 0.05 to 0.

2.

3. A modeling system for particle dispersed conductive adhesive, characterized in that: The method comprises a processor, wherein the processor is used to execute the modeling method of the particle dispersed conductive adhesive according to claim 1 or 2.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the modeling method of the particle dispersed conductive adhesive according to claim 1 or 2 is implemented.

5. A method for predicting the performance of a conductive adhesive, characterized in that: In finite element software, the conductive adhesive model constructed based on the modeling method of particle dispersed conductive adhesive as claimed in claim 1 or 2 is used to predict the performance of the conductive adhesive.

6. The conductive adhesive performance prediction method according to claim 5, characterized in that: Predict the performance of conductive adhesives, including: The volume resistivity of the conductive adhesive is predicted by applying different voltages to the upper and lower surfaces of the conductive adhesive model. The thermal conductivity of the conductive adhesive is predicted by applying different temperatures to the upper and lower surfaces of the conductive adhesive model; By applying a load on the surface of the conductive adhesive model, the Young's modulus and shear modulus of the conductive adhesive are predicted.

Images