High-flux component gradient change copper-based material as well as preparation method and application thereof

Abstract

The invention provides a high-flux component gradient change copper-based material and a preparation method and application thereof. According to the preparation method, the high-flux component gradient change copper-based material with components in gradient change along the axis is prepared through a traditional powder metallurgy pressing and sintering method. The high-flux component gradient change copper-based material prepared by the preparation method is combined with a corresponding friction performance test, so that data of component characteristics and friction performance can be efficiently obtained in a large scale, the previous long-flow and low-efficiency process of preparing a sample from each component and carrying out a friction experiment is changed, and the friction performance of the copper-based material is greatly improved. And the development period of the brake pad material is greatly shortened.

Description

Technical Field

[0001] The present invention belongs to the technical field of brake pads, and in particular relates to a high-flux component gradient-changing copper-based material and a preparation method and application thereof. Background Art

[0002] Copper-based brake pads are made of copper matrix, hard friction components and lubricating components through traditional powder metallurgy methods. They have become the core key components of the current train braking system with a speed of 160 km / h and above. They can produce physical contact and friction with the dual disc to generate friction resistance, ensuring that the train stops within a safe distance. Copper-based brake pads have the following advantages: (1) heat resistance of friction materials; (2) stability of friction coefficient; (3) anti-adhesion of friction materials; (4) wear resistance of friction materials; (5) strong thermophysical properties; (6) sufficient mechanical strength; (7) adaptability to environmental requirements; (8) smooth braking and low noise, and the friction pair can quickly recover performance after periodic overload; (9) economy and process reliability during mass production. At present, high-speed EMU trains, intercity EMU trains and ordinary EMU trains are equipped with copper-based brake pads.

[0003] However, the copper-based brake pads used in many train models are also different. This is because the operating environment and braking conditions of these trains are often quite different, and the range of friction coefficients required for braking is inconsistent. Therefore, the development of a specific model is often accompanied by the design and development of the composition of a new generation of copper-based brake pad materials, which increases the development cycle and cost of new models. In addition, the components that make up the copper-based brake pads often include more than ten components. For example, the pure copper matrix ensures the excellent thermal conductivity and formability of the brake pads, the hard wear-resistant components (iron, chromium iron and silicon carbide, etc.) improve the strength, wear resistance and resistance between the friction interfaces of the matrix, and the lubricating components (graphite and molybdenum disulfide, etc.) relieve the friction degree of the interface and the smooth friction surface to reduce the friction coefficient. Therefore, the previous method to improve the performance of copper-based brake pads is often to optimize more than ten components through a large number of experiments. Although this can obtain data results with strong practical reference significance, it takes a lot of time and resources. Therefore, there is an urgent need for a high-efficiency and low-cost copper-based brake pad development method.

[0004] However, the preparation of multi-component copper-based brake pads by powder metallurgy and the subsequent friction brake performance testing is a complex process of multiple physical fields and multiple scales. How to establish an accurate relationship between composition, microstructure and braking performance is still a huge challenge. The emergence of machine learning allows researchers to use computers as tools and strive to simulate human learning methods in real time, which provides a powerful tool for the rapid development, optimization and iteration of materials. However, machine learning is based on data, and how to obtain sufficient and reliable experimental test data has become a prerequisite for using machine learning methods.

[0005] However, the traditional friction material preparation and friction and wear performance testing paths are relatively inefficient. It is very urgent to provide a high-throughput material preparation method for friction experimental methods to obtain a large amount of reliable data. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention proposes a high-throughput component gradient-changing copper-based material and a preparation method and application thereof.

[0007] The technical solution of the present invention is as follows:

[0008] The first aspect of the present invention provides a preparation method for a high-throughput component gradient-changing copper-based material, the preparation method comprising: powder mixing treatment, powder pressing treatment and sintering treatment; wherein the step of the powder mixing treatment comprises: preparing a mixed powder whose component mass fraction changes gradiently along an axis in a powder mixing device; the powder mixing device comprises a cube or a rectangular mold, and a baffle arranged along the diagonal direction of the mold, and the baffle is detachable; the baffle divides the mold into two straight triangular prism areas, one of which is used to load matrix powder, and the other is used to load a uniform mixture of one or more component powders that need to be screened; after the two straight triangular prism areas are loaded, the baffle is removed, and the mold is horizontally rotated along the central axis parallel to the side length direction, so that the matrix powder and the uniform mixture of one or more component powders that need to be screened are uniformly mixed only on the cross section of the mold, and a mixed powder whose component mass fraction changes gradiently along the axis is obtained.

[0009] Furthermore, the base powder includes base pure copper powder; the uniform mixture of one or more component powders that need to be screened includes one or more mixtures of solid lubrication components and hard friction components; wherein the solid lubrication component range includes one or more of graphite, metal sulfide and metal fluoride; the hard friction component range includes one or more of hard metal and ceramic.

[0010] Further, in the mixed powder in which the mass fraction of the components changes along the axis gradient, the contents of the following components are all in mass fractions: 5 to 20 parts of graphite, 1 to 10 parts of metal sulfide, 1 to 10 parts of metal fluoride, 20 to 35 parts of hard metal, and 0 to 4 parts of ceramic; optionally, the graphite includes one or more of granular graphite, flake graphite and colloidal graphite; wherein the particle size of the granular graphite is 75 μm to 180 μm, the particle size of the flake graphite is 210 μm to 500 μm, and the particle size of the colloidal graphite is 1 μm to 10 μm; optionally, the particle size of the metal sulfide is 10 μm to 25 μm m; Optionally, the metal sulfide includes one or more of molybdenum disulfide, tungsten disulfide, ferrous sulfide, zinc sulfide and copper sulfide; Optionally, the particle size of the metal fluoride is 1μm~6.5μm; Optionally, the metal fluoride includes one or more of calcium fluoride, barium fluoride, lanthanum trifluoride and cerium trifluoride; Optionally, the powder particle size of the hard metal is 53μm~150μm; Optionally, the hard metal includes one or more of iron, chromium and ferrochrome; Optionally, the powder particle size of the ceramic is 0.5μm~2.6μm; Optionally, the ceramic includes one or more of silicon dioxide, silicon carbide, aluminum oxide and molybdenum trioxide.

[0011] Furthermore, the rotation speed of the powder mixing process is 50r / min~200r / min, and the time is 0.5h~2h; optionally, the powder pressing process includes placing the mixed powder whose component mass fraction changes gradiently along the axis in a pressing mold of the same shape and size for pressing to obtain a green body; the pressing pressure is 150MPa~500MPa, and the holding time is 30s~120s; optionally, the sintering process includes sintering the green body in an atmosphere hot pressing sintering furnace, the sintering temperature is 800℃~980℃, the sintering pressure is 1MPa~3MPa, the holding sintering time is 1h~3h, and the sintering atmosphere is H2.

[0012] The second aspect of the present invention provides a high-flux component gradient-changing copper-based material, which is prepared by the above-mentioned preparation method of the high-flux component gradient-changing copper-based material; the composition of the high-flux component gradient-changing copper-based material changes gradiently along the axis.

[0013] The third aspect of the present invention provides the use of the high-throughput component gradient-changing copper-based material as described above in the field of preparing brake pads and designing brake pad components.

[0014] Furthermore, the method for designing brake pad components includes: step one, performing friction performance testing on the high-throughput component gradient-changing copper-based material to obtain a component characteristic-friction performance data set; step two, obtaining a brake pad component combination by performing machine learning on the component characteristic-friction performance data set, generating a model, and training the model; wherein, step one includes: performing linear reciprocating friction on the high-throughput component gradient-changing copper-based material perpendicular to the component gradient direction to obtain friction coefficient and stability data caused by the same material; performing linear reciprocating friction on the high-throughput component gradient-changing copper-based material parallel to the component gradient direction to obtain friction coefficient change data caused by component mass fraction change; obtaining a component characteristic-friction performance data set based on the friction coefficient and stability data caused by the same material and the friction coefficient change data caused by the component mass fraction change.

[0015] Furthermore, the step one is specifically carried out on a pin-disc friction testing machine, wherein the material of the disk in the pin-disc friction testing machine is consistent with the material of the high-throughput component gradient-changing copper-based material, and the material of the pin is one of cast iron, cast steel and forged steel.

[0016] Furthermore, the step 2 includes: preprocessing and feature selecting the component feature-friction performance data set to obtain a screened training data set; standardizing the screened training data set to obtain a standardized training data set; establishing a model with the standardized training data set and evaluating it to obtain a trained standard prediction model; inputting the required condition parameters and the expected friction coefficient value or stability range into the trained standard prediction model to obtain a brake pad component combination; wherein the condition parameters include one or more of speed, pressure and temperature; the brake pad component combination includes the type, mass fraction and particle size of the component.

[0017] Furthermore, the step 2 specifically includes: using the component feature-friction performance data set to divide the machine learning training data set and the test data set, using the Pearson correlation coefficient to analyze the correlation between the feature data X, and removing redundant features with a correlation greater than 0.9; applying the ReliefF algorithm to further screen the features that have an important impact on the target attribute Y to obtain a screened training data set; wherein the feature data X includes the speed, pressure and temperature during the friction test, the type, mass fraction and particle size of the component, and the target attribute Y includes the friction coefficient value and stability; the screened training data set is standardized using the Z-score method to obtain a standardized training data set; random forest regression, artificial neural network, XGBoost regression, gradient boosting regression tree Four models are built, and the key parameters of each model are searched and optimized using the GridSearchCV automatic parameter adjustment tool, including the number of trees in the random forest, the hidden layer size and activation function of the artificial neural network, the learning rate of XGBoost, and the maximum depth of the tree; each model is trained using the standardized training data set and adjusted to the optimal parameter configuration; an evaluation standard for model prediction accuracy is constructed, and mean square error, root mean square error, and R² score are used as evaluation indicators; based on the evaluation results, a trained standard prediction model is obtained; the required conditional parameters and the expected friction coefficient value or stability range are input into the trained standard prediction model, and the model is used to predict the copper-based brake pad component combination that meets specific goals; wherein the specific goals include optimal friction performance.

[0018] Furthermore, the ratio of training dataset to test dataset is 6:4 to 8:2.

[0019] Compared with the prior art, the technical solution provided by the present invention has at least the following advantages:

[0020] The present invention provides a high-throughput component gradient-changing copper-based material and a preparation method and application thereof. The preparation method prepares a high-throughput component gradient-changing copper-based material whose components change gradiently along an axis by a traditional powder metallurgy pressing and sintering method. The high-throughput component gradient-changing copper-based material prepared by the preparation method is combined with corresponding friction performance tests to efficiently and in large quantities obtain component characteristics and friction performance data, changing the long and inefficient process of preparing a sample for each component and conducting a friction test. Secondly, the correlation between performance and composition is fully considered, and a machine learning prediction model for brake pad composition is established and trained from a data-driven perspective. The obtained training model is used to predict the new brake pad composition required to obtain the target friction coefficient size and its stability, greatly reducing the cycle of brake pad material development. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplifications do not constitute limitations on the embodiments. Unless otherwise stated, the pictures in the drawings do not constitute proportional limitations.

[0022] Figure 1 A schematic diagram of the structure of a powder mixing device provided in an embodiment of the present invention;

[0023] Figure 2 A flow chart of a brake pad component design method provided by an embodiment of the present invention;

[0024] Figure 3 A schematic diagram of a high-throughput component gradient-varied copper-based material sample and a friction performance test provided by an embodiment of the present invention, wherein arrows 1 and 2 in the figure respectively indicate the directions of the friction test;

[0025] Figure 4 A copper-25 wt.% iron composite material prepared in an embodiment of the present invention, wherein the composition changes gradually along the axis;

[0026] Figure 5 A graph showing the relationship between the friction coefficient and time during a friction test along the direction of continuous change of the copper-8 wt.% molybdenum disulfide composition provided in an embodiment of the present invention;

[0027] Figure 6 A graph showing the relationship between the friction coefficient obtained during the friction test along the direction of continuous change of the copper-25 wt.% iron composition provided in the embodiment of the present invention and the change over time;

[0028] Figure 7 A graph showing the relationship between the friction coefficient obtained during the friction test along the direction of continuous change of the copper-22 wt.% chromium composition provided in an embodiment of the present invention and the time;

[0029] Figure 8 A graph showing the relationship between the friction coefficient obtained during the friction test along the direction of continuous change of the composition of the copper-30 wt.% (graphite and CrFe) premixed powder provided in an embodiment of the present invention and the time;

[0030] Fig. 9 A graph of average friction coefficients of brake pad component combinations obtained by a design method for brake pad components based on high-throughput experiments and machine learning provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The present invention is described in detail below in conjunction with specific embodiments, with the base powder being copper powder as an example.

[0032] Example

[0033] The present invention provides a method for preparing a high-throughput component gradient-changing copper-based material, the method comprising: powder mixing, powder pressing and sintering. The step of powder mixing comprises: preparing a mixed powder with a component mass fraction gradiently changing along an axis in a powder mixing device.

[0034] like Figure 1 As shown, the powder mixing device includes a cube or rectangular mold, and a baffle arranged along the diagonal direction of the mold, and the baffle is removable; the baffle divides the mold into two right triangular prism areas, one of which is used to load the matrix powder, and the other is used to load a uniform mixture of one or more component powders that need to be screened.

[0035] After the two straight triangular prism areas are loaded, remove the baffle and make the mold rotate horizontally along the central axis parallel to the length direction (rotation method is as follows Figure 1 As indicated by the arrow in the figure, the matrix powder and the uniform mixture of one or more component powders to be screened are uniformly mixed only on the cross section of the mold, but no mixing occurs along the axis of the cubic mold, thereby obtaining a mixed powder with a gradient change in the mass fraction of the component along the axis.

[0036] Among them, the base powder includes base pure copper powder; the uniform mixture of one or more component powders that need to be screened includes one or more mixtures of solid lubrication components and hard friction components; wherein the solid lubrication component range includes one or more of graphite, metal sulfide and metal fluoride; the hard friction component range includes one or more of hard metal and ceramic.

[0037] In a specific embodiment, graphite is 5-20 parts, metal sulfide is 1-10 parts, metal fluoride is 1-10 parts, hard metal is 20-35 parts, and ceramic is 0-4 parts.

[0038] Specifically, graphite includes one or more of granular graphite, flake graphite and colloidal graphite; the particle size of granular graphite is 75μm~180μm, the particle size of flake graphite is 210μm~500μm, and the particle size of colloidal graphite is 1μm~10μm. The particle size of metal sulfide is 10μm~25μm; metal sulfide includes one or more of molybdenum disulfide, tungsten disulfide, ferrous sulfide, zinc sulfide and copper sulfide. The particle size of metal fluoride is 1μm~6.5μm; metal fluoride includes one or more of calcium fluoride, barium fluoride, lanthanum trifluoride and cerium trifluoride. The powder particle size of hard metal is 53μm~150μm; hard metal includes one or more of iron, chromium and ferrochrome. The powder particle size of ceramic is 0.5μm~2.6μm; optionally, ceramic includes one or more of silicon dioxide, silicon carbide, aluminum oxide and molybdenum trioxide.

[0039] More specifically, the rotation speed of the powder mixing process is 50r / min~200r / min, and the time is 0.5h~2h. The powder pressing process includes placing the mixed powder with the component mass fraction gradient changing along the axis in a pressing mold of the same shape and size for pressing to obtain a green body; the pressing pressure is 150MPa~500MPa, and the holding time is 30s~120s. The sintering process includes sintering the green body in an atmosphere hot pressing sintering furnace, the sintering temperature is 800℃~980℃, the sintering pressure is 1MPa~3MPa, the holding time is 1h~3h, and the sintering atmosphere is H2.

[0040] like Figure 2 The embodiment of the present invention also provides a design method for brake pad components, specifically a design method for brake pad components based on high-throughput experiments and machine learning, the design method comprising three main steps:

[0041] S1. Preparation of high-throughput component gradient-changing copper-based materials: Copper powder and another component powder are mixed in a mold to form a powder with continuously changing composition, and then the required high-throughput component gradient-changing copper-based material sample is prepared through two steps of pressing and hot pressing sintering.

[0042] S2. High-throughput friction experiment to obtain component characteristics - friction performance data set: A linear reciprocating friction tester is used to conduct friction experiments along the direction in which the sample composition changes continuously or uniformly.

[0043] S3. Use machine learning methods for component design: Select the components required for copper-based brake pads, and then use the corresponding experimental data set to establish and train the machine learning model. Input the expected friction coefficient value or stability range, and use the final optimized model to predict the copper-based brake pad component combination that meets the specific goal.

[0044] In a specific embodiment, the component to be screened is a hard friction component, specifically iron. In step S1, the mass fraction of iron added to the copper-iron mixed powder is 25%, the particle size of the iron powder is 75μm, the mold rotation speed is 80r / min, and the mixing time is 1h. The powder is transferred to a pressing mold of the same shape and size for pressing. The pressing pressure is 400MPa and the holding time is 80s. The pressed body is transferred to an atmosphere hot pressing sintering furnace for sintering. The sintering temperature is 940°C, the sintering pressure is 2MPa, the holding sintering time is 2h, and the sintering atmosphere is H2. After sintering, the atmosphere is continuously introduced until the material cools to room temperature. The prepared sample is as follows Figure 4 .

[0045] In a specific embodiment, the component to be screened is a solid lubricating component, specifically molybdenum disulfide. In step S1, in the copper molybdenum disulfide mixed powder, the proportion of molybdenum disulfide added by mass fraction is 8%, the particle size of the iron powder is 18μm, the mold rotation speed is 50r / min, and the mixing time is 1.5h. The powder is transferred to a pressing mold of the same shape and size for pressing, the pressing pressure is 150 MPa, and the holding time is 70s. The pressed body is transferred to an atmosphere hot pressing sintering furnace for sintering, the sintering temperature is 800℃, the sintering pressure is 1MPa, the holding sintering time is 1h, and the sintering atmosphere is H2. After sintering is completed, the atmosphere is continuously introduced until the material cools to room temperature.

[0046] In a specific embodiment, the component to be screened is a hard friction component, specifically chromium. In step S1, in the copper-chromium mixed powder, the mass fraction of chromium added is 22%, the particle size of the chromium powder is 106μm, the mold rotation speed is 100r / min, and the mixing time is 0.5h. The powder is transferred to a pressing mold of the same shape and size for pressing, the pressing pressure is 500MPa, and the holding time is 120s. The pressed body is transferred to an atmosphere hot pressing sintering furnace for sintering, the sintering temperature is 920℃, the sintering pressure is 2.5MPa, the holding sintering time is 3h, and the sintering atmosphere is H2. After sintering is completed, the atmosphere is continuously introduced until the material cools to room temperature.

[0047] In a specific embodiment, the components to be screened are a uniform mixture of solid lubrication components and friction components, specifically a premixed powder of graphite and CrFe in a weight ratio of 1:1, wherein the particle size of flake graphite is 350 μm and the particle size of CrFe is 105 μm. In step S1, in the copper-premixed powder, the mass fraction is added to the mixed powder at a ratio of 30%, the mold rotation speed is 200 r / min, and the mixing time is 2 h. The powder is transferred to a pressing mold of the same shape and size for pressing, the pressing pressure is 320 MPa, and the holding time is 30 s. The pressed body is transferred to an atmosphere hot pressing sintering furnace for sintering, the sintering temperature is 980 ° C, the sintering pressure is 3 MPa, the holding sintering time is 2 h, and the sintering atmosphere is H2. After sintering is completed, the atmosphere is continuously introduced until the material cools to room temperature.

[0048] Step S2 includes: performing linear reciprocating friction perpendicular to the component gradient direction on the high-flux component gradient copper-based material to obtain the friction coefficient and stability data caused by the same material; performing linear reciprocating friction parallel to the component gradient direction on the high-flux component gradient copper-based material to obtain the friction coefficient change data caused by the component mass fraction change; and obtaining the component characteristic-friction performance data set from the friction coefficient and stability data caused by the same material and the friction coefficient change data caused by the component mass fraction change. Figure 3As shown by arrows 1 and 2. The material composition of path 1 is the same, so the friction coefficient and its stability caused by the same material can be obtained. The material composition of path 2 shows a continuous change, and the change in friction coefficient caused by the change in component mass fraction can be obtained at one time, thereby establishing a complete component feature-friction performance data set.

[0049] Step S2 is specifically performed on a pin-disc friction testing machine, wherein the material of the disk in the pin-disc friction testing machine is consistent with the material of the high-throughput component gradient-changing copper-based material, and the material of the pin is one of cast iron, cast steel and forged steel.

[0050] In a specific embodiment, in step S2, a reciprocating friction test is performed along the continuous change direction of copper-8 wt.% molybdenum disulfide, the mating part is a pin made of forged steel, and the time is 30 seconds. The change of friction coefficient with time is as follows Figure 5 The reciprocating friction process brings about a periodic change in the friction coefficient. The continuous increase in the content of molybdenum disulfide reduces the friction coefficient from about 0.24 to about 0.05.

[0051] In a specific embodiment, in step S2, a reciprocating friction test is performed along the continuous change direction of copper-25 wt.% iron, the mating piece is a pin made of forged steel, and the time is 30 seconds. The change of friction coefficient with time is shown in Figure 6 The reciprocating friction process brings about a periodic change in the friction coefficient. The continuous increase in iron content causes the friction coefficient to increase from about 0.1 to about 0.3-0.35. The fluctuation of the friction coefficient is larger than that of the lubricating component molybdenum disulfide.

[0052] In a specific embodiment, in step S2, a reciprocating friction test is performed along the continuous change direction of copper-22 wt.% chromium, the mating part is a pin made of forged steel, and the time is 30 seconds. The change of friction coefficient with time is as follows Figure 7 The reciprocating friction process brings about a periodic change in the friction coefficient. The continuous increase in chromium content causes the friction coefficient to increase from about 0.13 to about 0.5-0.55, causing large fluctuations in the friction coefficient.

[0053] In a specific embodiment, in step S2, a reciprocating friction test is performed along the direction of continuous change of the composition of the copper-30 wt.% (graphite and CrFe) premixed powder, the counterpiece is a forged steel pin, and the time is 30 seconds. The change of friction coefficient with time is shown in Figure 8 The reciprocating friction process brings about a periodic change in the friction coefficient. The continuous increase of the graphite and CrFe mixed powder increases the friction coefficient from about 0.03 to about 0.23, and the fluctuation of the friction coefficient is significantly reduced compared with when only the friction component is added.

[0054] In a specific embodiment, in step S3, the data preprocessing and feature selection process is: use high-throughput experimental data to divide the machine learning training data set and the test data set, and the ratio of the two is 7:3. Use the Pearson correlation coefficient to analyze the correlation between the feature data X (speed, pressure and temperature during the friction experiment, the type, mass fraction and particle size of the component), remove the redundant features with a correlation greater than 0.9, and use the standard score (Z-score) method to standardize the screened training data set. Select four models for modeling: random forest regression, artificial neural network, extreme gradient boosting (XGBoost) regression, and gradient boosting regression tree (GBRT). Use the grid search cross validation (GridSearchCV) automatic parameter adjustment tool to search and optimize the key parameters of each model, including the number of trees in the random forest, the hidden layer size and activation function of the artificial neural network, the learning rate of XGBoost, and the maximum depth of the tree. Use the training data set to train each model and adjust it to the optimal parameter configuration. Construct an evaluation standard for model prediction accuracy, using the root mean square error and R² score as evaluation indicators. The smaller the root mean square, the higher the R 2 The closer it is to 1, the higher the prediction accuracy of the model. Based on the evaluation results, the model with the highest prediction accuracy is selected as the final model. Enter the required condition parameters (speed, pressure), as well as the desired friction coefficient value or stability range. Use the model to predict the composition of copper-based brake pads that meet specific goals, including the type, mass fraction and particle size of the components.

[0055] The above method was used to screen out the components of the copper-based brake pad, as shown in Table 1.

[0056] Table 1 Predicted composition of copper-based brake pads

[0057] Element Cu Fe CrFe Cr Granular graphite Flake graphite <![CDATA[MoS2]]> <![CDATA[Al2O3 fiber]]> <![CDATA[SiO2]]> content 55 18 6 2 3 9 3 3 1

[0058] According to the composition designed by machine learning, the prepared samples were subjected to simulated braking experiments with a braking speed range of 50-350 km / h, such as Fig. 9 The dotted lines are the upper and lower limits of the average friction coefficient specified in the "Interim Technical Requirements for EMU Brake Pads" (TJ / CL 307-2019). The results show that the prepared brake pads have excellent friction coefficient size and stability under various braking conditions.

[0059] Those skilled in the art can understand that the above-mentioned embodiments are specific examples for implementing the present application, and in practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of the present application. Any person skilled in the art can make their own changes and modifications without departing from the spirit and scope of the present application, so the scope of protection of the present application shall be based on the scope defined in the claims.

Claims

1. A method for preparing a high-throughput component gradient-changing copper-based material, characterized in that: The preparation method comprises: powder mixing process, powder pressing process and sintering process; The step of powder mixing treatment includes: preparing a mixed powder in which the mass fraction of the components changes gradiently along the axis in a powder mixing device; The powder mixing device comprises a cube or rectangular parallelepiped mold, and a baffle arranged along the diagonal direction of the mold, and the baffle is detachable; the baffle divides the mold into two right triangular prism areas, one of the right triangular prism areas is used to load the matrix powder, and the other right triangular prism area is used to load a uniform mixture of one or more component powders that need to be screened; After the two straight triangular prism areas are loaded, the baffle is removed and the mold is horizontally rotated along the central axis parallel to the side length direction, so that the uniform mixture of the matrix powder and one or more component powders that need to be screened is evenly mixed only on the cross section of the mold to obtain a mixed powder with a component mass fraction gradiently changing along the axis.

2. The method for preparing a high-throughput component gradient-changing copper-based material according to claim 1, characterized in that: The matrix powder includes matrix pure copper powder; The uniform mixture of one or more component powders to be screened includes one or more of solid lubricating components and hard friction components; wherein the solid lubricating component range includes one or more of graphite, metal sulfide and metal fluoride; The hard friction component range includes one or more of hard metals and ceramics.

3. The method for preparing a high-throughput component gradient-changing copper-based material according to claim 2, characterized in that: In the mixed powder in which the mass fraction of the components changes along the axis gradient, the contents of the following components are all in mass fractions: The graphite is 5-20 parts, the metal sulfide is 1-10 parts, the metal fluoride is 1-10 parts, the hard metal is 20-35 parts, and the ceramic is 0-4 parts; Optionally, the graphite includes one or more of granular graphite, flake graphite and colloidal graphite; wherein the particle size of the granular graphite is 75 μm to 180 μm, the particle size of the flake graphite is 210 μm to 500 μm, and the particle size of the colloidal graphite is 1 μm to 10 μm; Optionally, the particle size of the metal sulfide is 10 μm to 25 μm; Optionally, the metal sulfide includes one or more of molybdenum disulfide, tungsten disulfide, ferrous sulfide, zinc sulfide and copper sulfide; Optionally, the particle size of the metal fluoride is 1 μm to 6.5 μm; Optionally, the metal fluoride includes one or more of calcium fluoride, barium fluoride, lanthanum trifluoride and cerium trifluoride; Optionally, the powder particle size of the hard metal is 53 μm to 150 μm; Optionally, the hard metal includes one or more of iron, chromium and ferrochrome; Optionally, the ceramic powder has a particle size of 0.5 μm to 2.6 μm; optionally, the ceramic includes one or more of silicon dioxide, silicon carbide, aluminum oxide and molybdenum trioxide.

4. The method for preparing a high-throughput component gradient-changing copper-based material according to claim 1, characterized in that: The powder mixing process is performed at a rotation speed of 50 r / min to 200 r / min for a time of 0.5 h to 2 h; Optionally, the powder pressing process includes placing the mixed powder whose mass fraction of the components changes gradually along the axis into a pressing mold of the same shape and size for pressing to obtain a green body; the pressing pressure is 150MPa~500MPa, and the holding time is 30s~120s; Optionally, the sintering treatment includes sintering the green body in an atmosphere hot pressing sintering furnace, with a sintering temperature of 800° C. to 980° C., a sintering pressure of 1 MPa to 3 MPa, a pressure holding sintering time of 1 h to 3 h, and a sintering atmosphere of H2.

5. A high-throughput component gradient-changing copper-based material, characterized in that: The high-flux component gradient-changing copper-based material is prepared according to the preparation method of the high-flux component gradient-changing copper-based material as described in any one of claims 1 to 4; the composition of the high-flux component gradient-changing copper-based material changes gradiently along the axis.

6. Application of the high-flux component gradient-changing copper-based material as claimed in claim 5 in the field of preparing brake pads and designing brake pad components.

7. The use according to claim 6, characterized in that: The method for designing the brake pad composition includes: Step 1: testing the friction performance of the high-throughput component gradient-changing copper-based material to obtain a component characteristic-friction performance data set; Step 2: Perform machine learning on the component feature-friction performance data set, generate a model, and train it to obtain a brake pad component combination; Wherein, the step 1 comprises: The high-flux component gradient-changing copper-based material is subjected to linear reciprocating friction perpendicular to the component gradient direction to obtain friction coefficient and stability data caused by the same material; The high-flux component gradient-changing copper-based material is subjected to linear reciprocating friction parallel to the component gradient direction to obtain friction coefficient change data caused by component mass fraction change; The component characteristic-friction performance data set is obtained from the friction coefficient and stability data caused by the same material and the friction coefficient change data caused by the change in the mass fraction of the component.

8. The use according to claim 7, characterized in that: The step 1 is specifically carried out on a pin-disc friction testing machine, wherein the material of the disk in the pin-disc friction testing machine is consistent with the material of the high-throughput component gradient-changing copper-based material, and the material of the pin is one of cast iron, cast steel and forged steel.

9. The use according to claim 7, characterized in that: The second step comprises: Preprocessing and feature selecting the component feature-friction performance data set to obtain a screened training data set; Standardizing the screened training data set to obtain a standardized training data set; Establishing and evaluating a model using the standardized training data set to obtain a trained standard prediction model; The required condition parameters and the expected friction coefficient value or stability range are input into the trained standard prediction model to obtain a brake pad component combination; wherein the condition parameters include one or more of speed, pressure and temperature; and the brake pad component combination includes the type, mass fraction and particle size of the components.

10. The use according to claim 9, characterized in that: The step 2 specifically includes: The component feature-friction performance data set is used to divide the machine learning training data set and the test data set, and the Pearson correlation coefficient is used to analyze the correlation between the feature data X, and the redundant features with a correlation greater than 0.9 are removed; the ReliefF algorithm is used to further screen the features that have an important impact on the target attribute Y to obtain the screened training data set; wherein the feature data X includes the speed, pressure and temperature during the friction test, the type, mass fraction and particle size of the component, and the target attribute Y includes the friction coefficient value and stability; The screened training data set is standardized using a Z-score method to obtain a standardized training data set; Four models, random forest regression, artificial neural network, XGBoost regression, and gradient boosting regression tree, were selected for modeling. The key parameters of each model were searched and optimized using the GridSearchCV automatic parameter adjustment tool, including the number of trees in the random forest, the size of the hidden layer and activation function of the artificial neural network, the learning rate of XGBoost, and the maximum depth of the tree. Each model was trained using the standardized training data set and adjusted to the optimal parameter configuration. An evaluation standard for model prediction accuracy was constructed, using mean square error, root mean square error, and R² score as evaluation indicators. Based on the evaluation results, a trained standard prediction model was obtained. After the trained standard prediction model is input with the required condition parameters and the expected friction coefficient value or stability range, the model is used to predict the copper-based brake pad component combination that meets specific goals; wherein the specific goals include, for example, optimal friction performance.

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