Method and apparatus for optimizing the process of manufacturing diamond reinforced metal matrix composites

Abstract

The application discloses a preparation process optimization method and device of diamond reinforced metal matrix composite, and relates to the technical field of composite preparation.The method comprises the following steps: performing double-plating layer surface metallization modification treatment on diamond particles to form a composite plating layer; mixing to-be-mixed powder and to-be-mixed matrix powder according to a preset ratio to obtain a composite matrix powder, uniformly mixing the composite plating layer and the composite matrix powder to form mixed raw materials; performing real-time recording of pressureless sintering to generate a preparation record log, feeding back and optimizing sintering process parameters to generate sintering optimized process parameters; and reconfiguring alloying treatment parameters to adjust the sintering process parameters of subsequent batches of mixed raw materials, so that the diamond reinforced metal matrix composite is prepared.The technical problem that the material performance is unstable due to insufficient bonding strength of diamond and a metal matrix in the prior art is solved, and the technical effect of improving the performance of the diamond reinforced metal matrix composite is achieved.

Description

Technical Field

[0001] This invention relates to the field of composite material preparation technology, specifically to a method and apparatus for optimizing the preparation process of diamond-reinforced metal matrix composites. Background Technology

[0002] Diamond-reinforced metal matrix composites are a novel type of heat dissipation material, suitable for fabricating microchannel heat sinks for increasingly integrated microelectronic components. However, the fabrication of diamond-reinforced metal matrix composites still faces many challenges. On the one hand, the poor wettability between diamond and the metal matrix and the low interfacial bonding strength lead to debonding and cracking at the interface during stress or thermal cycling, severely affecting the overall performance of the composite material. On the other hand, the optimization of fabrication process parameters is still imperfect. Even slight fluctuations in key parameters such as sintering temperature, pressure, and time can significantly affect the microstructure and properties of the composite material, making it difficult to prepare composites with stable and excellent performance. Therefore, optimizing the fabrication process of diamond-reinforced metal matrix composites and improving the bonding strength between diamond and the metal matrix have become critical issues that urgently need to be addressed. Summary of the Invention

[0003] This application provides a method and apparatus for optimizing the preparation process of diamond-reinforced metal matrix composites, which solves the technical problem of unstable material properties caused by insufficient bonding strength between diamond and metal matrix in the prior art.

[0004] The first aspect of this application provides a method for optimizing the preparation process of diamond-reinforced metal matrix composites, the method comprising:

[0005] Diamond particles undergo a double-coating surface metallization modification treatment to form a composite coating, the composite coating containing bonding strength data. The powder to be mixed and the matrix powder to be mixed are mixed in a preset ratio to obtain a composite matrix powder. Based on the bonding strength data, the composite coating and the composite matrix powder are uniformly mixed to form a mixed raw material. Real-time recording of pressureless sintering of the mixed raw material is used to generate a preparation log. Based on the preparation log, sintering process parameters are optimized to generate optimized sintering process parameters. The alloying treatment parameters are reconfigured according to the optimized sintering process parameters, and the sintering process parameters for subsequent batches of mixed raw materials are adjusted to achieve the preparation of diamond-reinforced metal matrix composite materials.

[0006] A second aspect of this application provides an apparatus for optimizing the preparation process of diamond-reinforced metal matrix composites, the apparatus comprising:

[0007] The processing module is used to perform a double-coating surface metallization modification treatment on diamond particles to form a composite coating, wherein the composite coating contains bonding strength data; the mixing module is used to mix the powder to be mixed with the matrix powder to be mixed in a preset ratio to obtain a composite matrix powder, and to uniformly mix the composite coating and the composite matrix powder based on the bonding strength data to form a mixed raw material; the optimization module is used to record the pressureless sintering of the mixed raw material in real time, generate a preparation record log, and optimize the sintering process parameters based on the preparation record log to generate optimized sintering process parameters; the parameter adjustment module is used to reconfigure the alloying treatment parameters according to the optimized sintering process parameters to adjust the sintering process parameters of subsequent batches of mixed raw materials, thereby realizing the preparation of diamond-reinforced metal matrix composite materials.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] First, diamond particles undergo a double-coating surface metallization modification treatment to form a composite coating, which includes bonding strength data. Next, the powder to be mixed is mixed with the matrix powder to be mixed in a preset ratio to obtain a composite matrix powder. Based on the bonding strength data, the composite coating and the composite matrix powder are uniformly mixed to form a mixed raw material. Then, based on the real-time recording of pressureless sintering of the mixed raw material, a preparation log is generated. The sintering process parameters are then optimized based on the preparation log, generating optimized sintering process parameters. Finally, the alloying treatment parameters are reconfigured according to the optimized sintering process parameters, and the sintering process parameters for subsequent batches of mixed raw materials are adjusted to achieve the preparation of diamond-reinforced metal matrix composites. This solves the technical problem of insufficient bonding strength between diamond and the metal matrix in existing technologies, leading to unstable material properties, and achieves the technical effect of improving the performance of diamond-reinforced metal matrix composites. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A schematic diagram of the optimized preparation process of diamond-reinforced metal matrix composites provided in the embodiments of this application;

[0012] Figure 2 A schematic diagram of the device for optimizing the preparation process of diamond-reinforced metal matrix composites provided in this application embodiment.

[0013] Explanation of reference numerals in the attached diagram: Processing module 11, Mixing module 12, Optimization module 13, Parameter adjustment module 14. Detailed Implementation

[0014] This application provides an optimized method and apparatus for the preparation process of diamond-reinforced metal matrix composites, which solves the technical problem of unstable material properties caused by insufficient bonding strength between diamond and metal matrix in the prior art.

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0016] It should be noted that the terms "comprising" and "having" are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to these processes, methods, products, or devices.

[0017] Example 1, as Figure 1 As shown, this application provides an optimized method for the preparation process of diamond-reinforced metal matrix composites, wherein the method includes:

[0018] Diamond particles are subjected to a double-coating surface metallization modification treatment to form a composite coating, wherein the composite coating contains bonding strength data.

[0019] In this embodiment, a transition metal layer is deposited on the surface of diamond particles using chemical plating or physical vapor deposition (PVD). The transition metal layer provides good adhesion between the diamond and the metal substrate. Then, a bonding metal layer is deposited on the transition metal layer to improve the bonding force between the diamond particles and the metal substrate. By performing mechanical property tests such as tensile, shear, and peel tests on the composite coating, the bonding strength data between the composite coating and the diamond particles is obtained.

[0020] By forming a composite coating containing a transition metal layer and a bonding metal layer on the surface of diamond particles, the transition metal layer can form a good chemical bond with the diamond surface, while the bonding metal layer has good wettability and adhesion to the metal substrate, thereby effectively enhancing the bonding strength between the diamond and the metal substrate.

[0021] Furthermore, the diamond particles are subjected to a double-coating surface metallization modification treatment to form a composite coating. The methods include:

[0022] The diamond particles are subjected to surface roughening treatment to obtain pre-treated surface parameters. According to the pre-treated surface parameters, a first metal layer and a second metal layer are sequentially simulated and deposited onto the surface of the diamond particles according to the coating process parameters to form an initial composite coating. Based on the initial composite coating, the diamond particles are subjected to simulated annealing treatment, and the bonding strength data between the initial composite coating and the diamond particles is obtained through interfacial shear strength testing. The coating process parameters are dynamically optimized based on the bonding strength data, and a first metal layer and a second metal layer are deposited onto the surface of the diamond particles according to the optimized coating process parameters to form the composite coating.

[0023] Preferably, the diamond particles undergo surface roughening treatment using methods such as sandblasting, electrochemical treatment, or chemical etching to increase surface roughness, thereby improving the adhesion between the diamond particles and the metal layer. This yields pre-treated surface parameters for the diamond particles, including surface roughness and porosity. Based on these pre-treated surface parameters, and according to the coating process parameters, a first metal layer and a second metal layer are sequentially simulated for coating to form an initial composite coating. The first metal layer serves as a transition metal layer to enhance the bonding between the diamond particle surface and the metal substrate, while the second metal layer is a bonding metal layer used to further improve the bonding strength. After the initial composite coating is formed, the diamond particles undergo simulated annealing. The simulated annealing process heats the initial composite coating, simulating the high-temperature environment that may occur in actual use, promoting diffusion between the metal layers and forming a more robust bonding layer. After annealing, the bonding strength between the composite coating and the diamond particles is evaluated through interfacial shear strength testing to obtain accurate bonding strength data. Based on the obtained bonding strength data, the coating process parameters were dynamically optimized, adjusting parameters such as coating thickness, deposition rate, and annealing temperature to improve the bonding strength between the composite coating and the diamond particles. Following the optimized process parameters, the first and second metal layers were re-deposited to form the final composite coating, thereby ensuring excellent bonding between the diamond particles and the metal matrix and improving the overall performance of the diamond-reinforced metal matrix composite.

[0024] The powder to be mixed and the matrix powder to be mixed are mixed in a preset ratio to obtain a composite matrix powder. Based on the bonding strength data, the composite coating and the composite matrix powder are uniformly mixed to form a mixed raw material.

[0025] In this embodiment, the powder to be mixed and the matrix powder to be mixed are mixed in a preset ratio through mechanical alloying to obtain a composite matrix powder. The powder to be mixed is molybdenum powder, the matrix powder to be mixed is copper matrix powder, and the composite matrix powder is a Mo-Cu composite matrix powder.

[0026] Mechanical alloying is a solid-state reaction synthesis method that uses high-energy ball milling and other processes to grind molybdenum (Mo) powder and copper (Cu) powder with appropriate molar ratios together for a long time at high speed to achieve effective alloying between the metal powders. During this process, the powders rub and collide with each other, generating a uniform Mo-Cu composite matrix powder.

[0027] Based on the aforementioned bonding strength data, the metallized diamond particles of the composite coating are uniformly mixed with the prepared Mo-Cu composite matrix powder. During this mixing process, mechanical mixing equipment, such as a three-dimensional mixer or ball mill, can be used to ensure sufficient dispersion and uniform distribution of the diamond particles and the composite matrix powder, forming a mixed raw material with optimal bonding strength.

[0028] Furthermore, the method involves mixing the powder to be mixed with the matrix powder to be mixed in a preset ratio to obtain a composite matrix powder, and then uniformly mixing the composite coating with the composite matrix powder based on the bonding strength data to form a mixed raw material. The method includes:

[0029] The powder to be mixed and the matrix powder to be mixed are mechanically alloyed in a preset ratio to prepare a composite matrix powder; based on the bonding strength data of the composite coating, a mixing analysis is performed to determine the mixing ratio and mixing process parameters; the diamond particles of the composite coating and the composite matrix powder are added to a three-dimensional mixer in the specified mixing ratio, and mixed in stages according to the mixing process parameters to form a mixed raw material.

[0030] Composite matrix powder was prepared by mechanical alloying. Specifically, molybdenum powder and copper matrix powder were mixed at a ratio of 0.5-2.0 wt%, with a ball-to-powder ratio of 8:1 to 12:1. The mixture was ball-milled for 2-4 hours at a speed of 300-500 rpm under an argon protective atmosphere. After ball milling, the resulting powder had a particle size distribution between 10 and 50 μm, and the molybdenum particles were uniformly dispersed in the copper matrix powder, thus obtaining a uniform Mo-Cu composite matrix powder.

[0031] After obtaining the composite matrix powder, a mixing analysis was performed based on the bonding strength data of the composite coating to determine the mixing ratio and mixing process parameters. The interfacial shear strength of the composite coating (the coating after metallization treatment of the diamond particles) was tested to obtain the bonding strength data between the diamond particles and the coating. This bonding strength data reflects the strength of the bond between the diamond particles and the composite coating, directly affecting the dispersion and stability of the diamond particles during mixing. Different diamond volume fractions were set according to different bonding strengths. The optimal mixing ratio of diamond particles and the metal matrix was determined based on the magnitude of the interfacial shear strength. For example, if the interfacial shear strength is ≥150 MPa, the diamond volume fraction is set to 50%–60%; if the interfacial shear strength is between 100 and 150 MPa, the diamond volume fraction is set to 40%–50%; if the interfacial shear strength is <100 MPa, the diamond volume fraction needs to be reduced to 30%–40%, and the coating process needs to be re-optimized to ensure good bonding between the diamond particles and the metal matrix. Based on the bonding strength data, the volume fraction of diamond particles was determined, and the ratio of diamond particles to the composite matrix powder was calculated to form the optimal mixing ratio. Based on the determined mixing ratio and bonding strength data, the parameters of the mixing process were further optimized, including the selection of mixing equipment, mixing time, and mixing speed. This ensured that the diamond particles and composite matrix powder were uniformly dispersed during the mixing process, avoiding particle agglomeration or uneven distribution, thereby guaranteeing the homogeneity of the final mixed raw material and the stability of the material.

[0032] Based on the determined mixing ratio and process parameters, diamond particles for the composite coating and composite matrix powder are added to a three-dimensional mixer in a set proportion. The three-dimensional mixer uses multi-axis rotation to ensure uniform mixing of the diamond particles and composite matrix powder. A staged mixing process is employed, first a dry mixing stage, then a wet mixing stage, to ensure the uniformity of the mixed materials. Throughout the mixing process, based on real-time monitoring of the mixing uniformity and coefficient of variation, the mixing parameters are further adjusted to ensure that the diamond particles and composite matrix powder are uniformly dispersed, forming a highly uniform mixed material, providing a stable foundation for subsequent sintering or other molding processes.

[0033] Furthermore, the diamond particles of the composite coating and the composite matrix powder are added to a three-dimensional mixer according to the specified mixing ratio, and mixed in stages according to the mixing process parameters to form a mixed raw material. The method includes:

[0034] Dry mixing is performed in a three-dimensional mixer according to the mixing process parameters to generate dry mixing stage parameters; wet mixing is performed in a three-dimensional mixer according to the mixing process parameters to generate wet mixing stage parameters; real-time monitoring is performed based on the dry mixing stage parameters and the wet mixing stage parameters to obtain real-time mixing uniformity, the real-time mixing uniformity including mixing variation coefficient; when the mixing variation coefficient is less than a preset threshold, the mixed raw material is formed according to the real-time mixing uniformity.

[0035] Dry mixing is performed in a three-dimensional mixer according to the set mixing process parameters to generate dry mixing stage parameters, including the particle size distribution and bulk density of the powder. Specifically, the mixer speed is set to 20-30 rpm and the mixing time is set to 30-60 minutes to ensure that the diamond particles and Mo-Cu composite matrix powder can be initially dispersed and to avoid particle aggregation.

[0036] After dry mixing, wet mixing is performed in a three-dimensional mixer according to the mixing process parameters to generate wet mixing stage parameters, including particle morphology, moisture content, and adhesion strength after wet mixing. During this stage, 0.5–1.0 vol% zinc stearate is added to the mixer as a dispersant. Zinc stearate effectively reduces the adhesion between particles, further improving the dispersibility of diamond particles. During wet mixing, the mixer speed is increased to 50–80 rpm, and mixing continues for 20–40 minutes.

[0037] Throughout the mixing process, a laser particle size analyzer is used for real-time monitoring to analyze the mixing uniformity. The laser particle size analyzer, based on the principle of laser scattering, accurately measures the particle size distribution and dimensions during mixing, providing real-time information on the size, shape, and distribution of each particle. By monitoring the particle size distribution, the laser particle size analyzer provides detailed particle size distribution curves, reflecting the distribution of particles in the mixed raw materials and helping to determine if there are problems such as particle aggregation or uneven dispersion. Based on this data, the mixing coefficient of variation can be calculated. This coefficient quantifies the uniformity of the mixed raw materials; the mixing coefficient of variation is the ratio of the standard deviation of the particle size distribution to the average particle size. The smaller the value, the higher the uniformity of the mixing. Conversely, a large coefficient of variation indicates poor mixing, suggesting that the particles may not be sufficiently dispersed, requiring adjustment of the mixing process parameters. The mixing coefficient of variation = σ / μ × 100%, where σ is the standard deviation of the particle size, reflecting the dispersion of particle size, and μ is the average particle size, representing the central tendency of particle size.

[0038] When the real-time mixing uniformity meets the standard, that is, the mixing coefficient of variation is less than the preset threshold, it means that the mixing process has been completed and the expected uniformity has been achieved, forming the required mixed raw materials.

[0039] Based on the real-time recording of pressureless sintering of the mixed raw materials, a preparation record log is generated. Based on the preparation record log, the sintering process parameters are optimized by feedback to generate optimized sintering process parameters.

[0040] Real-time recording of pressureless sintering based on mixed raw materials involves detailed data acquisition during the sintering process, generating a preparation log. This log records key process parameters such as sintering temperature, holding time, atmosphere pressure, and temperature rise / fall rates.

[0041] After generating the preparation log, the sintering process parameters are optimized based on this log. By comparing the actual process data in the log with the preset ideal process parameters, the advantages and disadvantages of the current sintering process can be analyzed, and potential areas for improvement can be identified. Through the feedback optimization process, optimized sintering process parameters are generated.

[0042] Furthermore, based on the real-time recording of pressureless sintering of the mixed raw materials, a preparation record log is generated. Based on the preparation record log, sintering process parameters are optimized through feedback to generate optimized sintering process parameters. The method includes:

[0043] The pressureless sintering process of the mixed raw materials is collected in real time by multiple sensors to generate a dynamic sintering parameter dataset. A preparation record log is constructed based on the dynamic sintering parameter dataset. The correlation analysis between the sintering process parameters and the composite material properties of the mixed raw materials is performed based on the preparation record log to determine the process-performance mapping network. An optimization objective is set according to the process-performance mapping network, and the sintering process parameters are optimized based on the optimization objective to generate the optimized sintering process parameters.

[0044] The pressureless sintering process of mixed raw materials is monitored in real time using multiple sensors, primarily focusing on key process parameters such as sintering temperature, holding time, atmosphere pressure, and interfacial reaction data. This data constitutes a dynamic sintering parameter dataset. Sensors include temperature sensors, atmosphere pressure sensors, and gas composition analyzers, which continuously monitor environmental changes during sintering and transmit data in real time. This real-time data is stored and organized into the dynamic sintering parameter dataset, providing a complete dynamic record for process analysis and optimization.

[0045] Based on the collected dynamic sintering parameter dataset, a preparation log was constructed. The preparation log contains all the key data in the sintering process, including the rise and fall of sintering temperature, the control of holding time, the change of atmosphere pressure, and the interfacial reaction data in the sintering process.

[0046] Based on data from the preparation log, a correlation analysis was conducted between sintering process parameters and the properties of composite materials from mixed raw materials to determine a process-performance mapping network. This network illustrates the specific impact of different sintering process parameters on material properties. Optionally, a machine learning model was used to correlate sintering process parameters with composite material performance indicators. First, key process parameters during sintering (such as sintering temperature, holding time, and atmosphere pressure) and composite material performance indicators (such as hardness, compressive strength, wear resistance, and thermal stability) were collected, and the data were preprocessed, including outlier removal, missing value imputation, and standardization. Next, feature selection methods were used to extract features with significant influence from process parameters and performance indicators, generating feature engineering to improve the model's predictive ability. Then, a suitable machine learning model (such as regression model, neural network, random forest, etc.) was selected for training, and cross-validation was used to optimize the model parameters to ensure that the model can accurately predict the performance of composite materials. In the model evaluation stage, the predictive ability of the model was verified using indicators such as mean squared error and coefficient of determination, and the model was optimized to improve accuracy. The trained model can predict the performance of composite materials based on the input sintering process parameters.

[0047] Based on the established process-performance mapping network, optimization objectives are set, such as improving the material's hardness, compressive strength, or thermal stability. By setting these objectives, the sintering process parameters are optimized using the process-performance mapping network. Optimization algorithms (such as particle swarm optimization and genetic algorithms) can be used to adjust parameters such as sintering temperature, holding time, and atmosphere pressure to obtain optimized sintering process parameters.

[0048] Furthermore, the method for constructing a preparation record log based on the dynamic sintering parameter dataset includes:

[0049] The dynamic sintering parameter dataset is decomposed to obtain sintering temperature data, holding time data, atmosphere pressure data, and interface reaction data. The sintering temperature data, holding time data, and atmosphere pressure data are stored as a structured data table in time series. Feature extraction is performed on the interface reaction data, and the interface reaction data is associated with timestamps based on the interface reaction characteristics to obtain reaction time association parameters. Anomaly analysis is performed based on the reaction time association parameters and the structured data table, and anomaly results are automatically marked to identify abnormal data segments. The abnormal data segments, reaction time association parameters, and structured data table are integrated to construct the preparation record log.

[0050] Based on the dynamic sintering parameter dataset, core parameters of the sintering process were extracted, including sintering temperature data, holding time data, atmosphere pressure data, and interfacial reaction data. The sintering temperature, holding time, and atmosphere pressure data reflect the influence of temperature control, time management, and atmosphere environment on the material reaction during sintering, respectively, while the interfacial reaction data reflects the interfacial reaction between diamond particles and the metal matrix. Next, the sintering temperature, holding time, and atmosphere pressure data were stored as a structured data table in time series to accurately record the timing of the sintering process.

[0051] Based on the interfacial reaction data, feature extraction was performed, primarily extracting features related to the interfacial reaction, such as the thickness of the Mo2C layer, the carbon diffusion depth at the diamond edge, and porosity. These features are important indicators of the reaction between diamond particles and the metal matrix during sintering, directly affecting the performance of the composite material. Based on the extracted interfacial reaction features, the interfacial reaction data was correlated with timestamps to obtain reaction time-related parameters.

[0052] Anomaly analysis was performed on the obtained reaction time correlation parameters and structured data tables to identify abnormal fluctuations or inconsistencies in the data, such as excessive temperature fluctuations or abnormal atmosphere pressure. Based on the anomaly analysis results, abnormal data segments were automatically marked, which may represent unstable factors or process errors in the sintering process. Finally, the abnormal data segments, reaction time correlation parameters, and structured data tables were integrated to generate a complete preparation log.

[0053] Furthermore, according to the process-performance mapping network, an optimization objective is set, and the sintering process parameters are optimized and solved based on the optimization objective to generate optimized sintering process parameters. The method includes:

[0054] Based on the optimization objective, the sintering process parameters are solved using a multi-objective method to determine an initial particle swarm, where each particle contains a set of sintering process parameters. An adaptive inertia weight is introduced to calculate the fitness values ​​of multiple particles. The particle positions of the initial particle swarm are iteratively updated according to the fitness values ​​of the multiple particles until the convergence condition is met, thereby obtaining the optimized sintering process parameters.

[0055] Based on the established optimization objectives, multi-objective solutions are applied to the sintering process parameters. The multi-objective optimization aims to simultaneously maximize thermal conductivity and interfacial shear strength while minimizing porosity, and a comprehensive optimization objective is obtained through weighted combination.

[0056] An initial particle swarm is defined, where each particle contains a set of sintering process parameters, including sintering temperature, holding time, and atmosphere pressure. Each particle in the initial particle swarm represents a possible solution. These particles are then searched in the solution space using an optimization algorithm, progressively adjusting the sintering process parameters to meet the optimization objective.

[0057] In particle swarm optimization, adaptive inertia weights are used to initialize the particle swarm. These weights dynamically adjust the search pace based on the current state of the particle swarm, allowing particles to find a balance between global and local searches, avoiding getting trapped in local optima. Typically, during optimization, the adaptive inertia weights gradually decrease with increasing iterations to encourage particles to engage in more local searches as they approach the optimal solution. Adaptive inertia weights: ;in, Let the inertia weight be the weight for the k-th iteration. and These are the initial and final inertia weight values, respectively. k is the current iteration number, and K is the maximum iteration number.

[0058] Based on the sintering process parameters represented by each particle, its fitness value is calculated. The fitness value measures the degree to which the set of process parameters meets the optimization objectives; a higher fitness value indicates that the set of process parameters performs better across multiple optimization objectives. For example, the performance of each objective is calculated using the sintering process parameters corresponding to the particles. For instance, thermal conductivity is calculated using a thermal conductivity model, interfacial shear strength is determined experimentally or through simulation, and porosity is calculated based on the sintering process. Then, the performance indicators of each objective are normalized. Next, different weights are assigned to each objective based on its importance; for example, thermal conductivity and interfacial shear strength have a weight of 0.4, while porosity has a weight of 0.2. The normalized objective values ​​are multiplied by their respective weights and weighted to obtain a comprehensive fitness value, representing the overall performance of the set of sintering process parameters.

[0059] The particle positions of the particle swarm are iteratively updated based on the fitness values ​​of multiple particles. In each iteration, the particles update their positions based on inertia, individual experience, and global optimum experience; the particle positions represent the new sintering process parameters. This process iterates until the convergence condition is met, i.e., the fitness change is less than 1% for 10 consecutive generations. At this point, the search results of the particle swarm tend to stabilize, and the obtained sintering process parameters are the final optimization results.

[0060] The alloying treatment parameters were reconfigured according to the sintering optimization process parameters, and the sintering process parameters of subsequent batches of mixed raw materials were adjusted to achieve the preparation of diamond-reinforced metal matrix composites.

[0061] Based on the optimized sintering process parameters, the alloying treatment parameters were reconfigured, and the sintering process parameters for subsequent batches of mixed raw materials were adjusted to achieve the preparation of diamond-reinforced metal matrix composites. Specifically, target parameters were determined according to the optimized sintering process parameters, and then the alloying treatment conditions were adjusted to ensure that each batch of mixed raw materials could achieve optimal performance during the sintering process. Subsequently, the adjusted alloying treatment parameters were mixed with the diamond particles of the composite coating to form a new homogeneous mixed raw material, and pressureless sintering was performed according to the optimized sintering process parameters.

[0062] Furthermore, adjusting the sintering process parameters of subsequent batches of mixed raw materials by reconfiguring the alloying treatment parameters according to the aforementioned optimized sintering process parameters includes:

[0063] Based on the optimized sintering process parameters, the target parameters are determined by reverse derivation. The mechanical alloying conditions of subsequent batches are adjusted according to the target parameters to prepare optimized parameters for the composite matrix powder. The optimized parameters for the composite matrix powder are mixed with diamond particles of the composite coating to form a new batch of mixed raw materials. The new batch of mixed raw materials is pressurelessly sintered according to the optimized sintering process parameters to realize the preparation of diamond-reinforced metal matrix composite materials.

[0064] Preferably, target parameters are determined by reverse engineering based on the obtained optimized sintering process parameters. These target parameters include key process conditions such as sintering temperature, holding time, and atmosphere pressure, aiming to ensure that each batch of sintering processes achieves the expected performance targets. Based on these target parameters, the mechanical alloying treatment conditions for subsequent batches are adjusted to optimize the preparation process of the composite matrix powder. The adjustment of mechanical alloying treatment parameters includes changing ball milling time, rotation speed, and ball-to-powder ratio, thereby preparing composite matrix powder that meets the optimization targets. Next, the obtained optimized parameters of the composite matrix powder are mixed with diamond particles of the composite coating to ensure that the diamond particles are uniformly distributed in the metal matrix, forming a new batch of mixed raw materials. Finally, the new batch of mixed raw materials is pressurelessly sintered according to the optimized sintering process parameters to ensure the preparation of diamond-reinforced metal matrix composites under optimized sintering conditions. During the sintering process, parameters such as temperature, time, and atmosphere pressure are precisely controlled to ensure that the bonding strength between the diamond particles and the metal matrix reaches the optimal level, ultimately obtaining a diamond-reinforced metal matrix composite material with excellent thermal conductivity, interfacial shear strength, and low porosity.

[0065] Furthermore, the new batch of mixed raw materials is subjected to pressureless sintering according to the optimized sintering process parameters to prepare diamond-reinforced metal matrix composites. The method includes:

[0066] Based on the new batch of mixed raw materials, diamond-reinforced metal matrix composites are prepared by continuous batch-by-batch pressureless sintering according to the optimized sintering process parameters. The performance of the diamond-reinforced metal matrix composites is tested to obtain the performance test results. If the performance test results do not reach the preset threshold, the weights of the process-performance mapping network are adjusted, and iterative optimization is performed until the performance test results reach the preset threshold. The adjustment of the sintering process parameters is completed, and the preparation of diamond-reinforced metal matrix composites is realized.

[0067] Based on the new batch of mixed raw materials, continuous batch-by-batch pressureless sintering was carried out according to the optimized sintering process parameters to ensure optimal bonding between diamond particles and the metal matrix during sintering. Performance tests were conducted on the prepared diamond-reinforced metal matrix composites, including tests on thermal conductivity, interfacial shear strength, and porosity. Based on the performance test results, the prepared material was evaluated to determine whether it met the design requirements and preset standards. If the performance test results did not reach the preset threshold, the sintering process needed to be adjusted. By correcting the weights of the process-performance mapping network, the sintering process parameters were updated to optimize the relationship between process and performance. Through continuous iterative optimization of the sintering process parameters, the sintering temperature, time, and other process conditions were gradually adjusted until the performance test results reached the preset threshold requirements.

[0068] In summary, the embodiments of this application have at least the following technical effects:

[0069] First, diamond particles undergo a double-coating surface metallization modification treatment to form a composite coating, which includes bonding strength data. Next, the powder to be mixed is mixed with the matrix powder to be mixed in a preset ratio to obtain a composite matrix powder. Based on the bonding strength data, the composite coating and the composite matrix powder are uniformly mixed to form a mixed raw material. Then, based on the real-time recording of pressureless sintering of the mixed raw material, a preparation log is generated. The sintering process parameters are then optimized based on the preparation log, generating optimized sintering process parameters. Finally, the alloying treatment parameters are reconfigured according to the optimized sintering process parameters, and the sintering process parameters for subsequent batches of mixed raw materials are adjusted to achieve the preparation of diamond-reinforced metal matrix composites. This solves the technical problem of insufficient bonding strength between diamond and the metal matrix in existing technologies, leading to unstable material properties, and achieves the technical effect of improving the performance of diamond-reinforced metal matrix composites.

[0070] Example 2, based on the same inventive concept as the optimization method for the preparation process of diamond-reinforced metal matrix composites in the foregoing examples, such as... Figure 2 As shown, this application provides an apparatus for optimizing the preparation process of diamond-reinforced metal matrix composites, wherein the apparatus includes:

[0071] Processing module 11 is used to perform double-coating surface metallization modification treatment on diamond particles to form a composite coating, wherein the composite coating contains bonding strength data; mixing module 12 is used to mix the powder to be mixed with the matrix powder to be mixed in a preset ratio to obtain a composite matrix powder, and uniformly mix the composite coating and the composite matrix powder based on the bonding strength data to form a mixed raw material; optimization module 13 is used to record the pressureless sintering of the mixed raw material in real time, generate a preparation record log, and optimize the sintering process parameters based on the preparation record log to generate optimized sintering process parameters; parameter adjustment module 14 is used to reconfigure the alloying treatment parameters according to the optimized sintering process parameters to adjust the sintering process parameters of subsequent batches of mixed raw materials to realize the preparation of diamond-reinforced metal matrix composite material.

[0072] Furthermore, the processing module 11 is configured to perform the following methods:

[0073] The diamond particles are subjected to surface roughening treatment to obtain pre-treated surface parameters. According to the pre-treated surface parameters, a first metal layer and a second metal layer are sequentially simulated and deposited onto the surface of the diamond particles according to the coating process parameters to form an initial composite coating. Based on the initial composite coating, the diamond particles are subjected to simulated annealing treatment, and the bonding strength data between the initial composite coating and the diamond particles is obtained through interfacial shear strength testing. The coating process parameters are dynamically optimized based on the bonding strength data, and a first metal layer and a second metal layer are deposited onto the surface of the diamond particles according to the optimized coating process parameters to form the composite coating.

[0074] Furthermore, the mixing module 12 is used to perform the following methods:

[0075] The powder to be mixed and the matrix powder to be mixed are mechanically alloyed in a preset ratio to prepare a composite matrix powder; based on the bonding strength data of the composite coating, a mixing analysis is performed to determine the mixing ratio and mixing process parameters; the diamond particles of the composite coating and the composite matrix powder are added to a three-dimensional mixer in the specified mixing ratio, and mixed in stages according to the mixing process parameters to form a mixed raw material.

[0076] Furthermore, the mixing module 12 is used to perform the following methods:

[0077] Dry mixing is performed in a three-dimensional mixer according to the mixing process parameters to generate dry mixing stage parameters; wet mixing is performed in a three-dimensional mixer according to the mixing process parameters to generate wet mixing stage parameters; real-time monitoring is performed based on the dry mixing stage parameters and the wet mixing stage parameters to obtain real-time mixing uniformity, the real-time mixing uniformity including mixing variation coefficient; when the mixing variation coefficient is less than a preset threshold, the mixed raw material is formed according to the real-time mixing uniformity.

[0078] Furthermore, the optimization module 13 is used to perform the following method:

[0079] The pressureless sintering process of the mixed raw materials is collected in real time by multiple sensors to generate a dynamic sintering parameter dataset. A preparation record log is constructed based on the dynamic sintering parameter dataset. The correlation analysis between the sintering process parameters and the composite material properties of the mixed raw materials is performed based on the preparation record log to determine the process-performance mapping network. An optimization objective is set according to the process-performance mapping network, and the sintering process parameters are optimized based on the optimization objective to generate the optimized sintering process parameters.

[0080] Furthermore, the optimization module 13 is used to perform the following method:

[0081] The dynamic sintering parameter dataset is decomposed to obtain sintering temperature data, holding time data, atmosphere pressure data, and interface reaction data. The sintering temperature data, holding time data, and atmosphere pressure data are stored as a structured data table in time series. Feature extraction is performed on the interface reaction data, and the interface reaction data is associated with timestamps based on the interface reaction characteristics to obtain reaction time association parameters. Anomaly analysis is performed based on the reaction time association parameters and the structured data table, and anomaly results are automatically marked to identify abnormal data segments. The abnormal data segments, reaction time association parameters, and structured data table are integrated to construct the preparation record log.

[0082] Furthermore, the optimization module 13 is used to perform the following method:

[0083] Based on the optimization objective, the sintering process parameters are solved using a multi-objective method to determine an initial particle swarm, where each particle contains a set of sintering process parameters. An adaptive inertia weight is introduced to calculate the fitness values ​​of multiple particles. The particle positions of the initial particle swarm are iteratively updated according to the fitness values ​​of the multiple particles until the convergence condition is met, thereby obtaining the optimized sintering process parameters.

[0084] Furthermore, the parameter adjustment module 14 is used to perform the following method:

[0085] Based on the optimized sintering process parameters, the target parameters are determined by reverse derivation. The mechanical alloying conditions of subsequent batches are adjusted according to the target parameters to prepare optimized parameters for the composite matrix powder. The optimized parameters for the composite matrix powder are mixed with diamond particles of the composite coating to form a new batch of mixed raw materials. The new batch of mixed raw materials is pressurelessly sintered according to the optimized sintering process parameters to realize the preparation of diamond-reinforced metal matrix composite materials.

[0086] Furthermore, the parameter adjustment module 14 is used to perform the following method:

[0087] Based on the new batch of mixed raw materials, diamond-reinforced metal matrix composites are prepared by continuous batch-by-batch pressureless sintering according to the optimized sintering process parameters. The performance of the diamond-reinforced metal matrix composites is tested to obtain the performance test results. If the performance test results do not reach the preset threshold, the weights of the process-performance mapping network are adjusted, and iterative optimization is performed until the performance test results reach the preset threshold. The adjustment of the sintering process parameters is completed, and the preparation of diamond-reinforced metal matrix composites is realized.

[0088] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0089] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0090] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. An optimized method for the preparation process of diamond-reinforced metal matrix composites, characterized in that, The method includes: Diamond particles are subjected to a double-coating surface metallization modification treatment to form diamond particles with a composite coating, wherein the composite coating contains bonding strength data. The powder to be mixed and the matrix powder to be mixed are mixed in a preset ratio and mechanically alloyed to obtain a composite matrix powder. Based on the bonding strength data, the diamond particles that form the composite coating are uniformly mixed with the composite matrix powder to form a mixed raw material. Based on the real-time recording of pressureless sintering of the mixed raw materials, a preparation record log is generated. Based on the preparation record log, the sintering process parameters are optimized by feedback to generate optimized sintering process parameters. The alloying treatment parameters were reconfigured according to the sintering optimization process parameters, and the sintering process parameters of subsequent batches of mixed raw materials were adjusted to achieve the preparation of diamond-reinforced metal matrix composites. The method for performing a double-layer surface metallization modification treatment on diamond particles to form a composite coating includes: The diamond particles are subjected to surface roughening treatment to obtain the pre-treated surface parameters of the diamond particles. According to the pretreatment surface parameters, the first metal layer and the second metal layer are sequentially simulated and coated on the surface of the diamond particles according to the coating process parameters to form an initial composite coating. Based on the initial composite coating, the diamond particles were subjected to simulated annealing treatment, and the bonding strength data between the initial composite coating and the diamond particles were obtained by interfacial shear strength test. The coating process parameters are dynamically optimized based on the bonding strength data, and a first metal layer and a second metal layer are deposited on the surface of the diamond particles according to the optimized coating process parameters to form the composite coating.

2. The optimized preparation process method for diamond-reinforced metal matrix composites as described in claim 1, characterized in that, The powder to be mixed and the matrix powder to be mixed are mixed in a preset ratio and mechanically alloyed to obtain a composite matrix powder. Based on the bonding strength data, diamond particles to form a composite coating are uniformly mixed with the composite matrix powder to form a mixed raw material. The method includes: The powder to be mixed and the matrix powder to be mixed are mechanically alloyed in a preset ratio to prepare a composite matrix powder. Based on the bonding strength data of the composite coating, a mixing analysis is performed to determine the mixing ratio and mixing process parameters; Diamond particles with composite coating and composite matrix powder are added to a three-dimensional mixer according to the specified mixing ratio, and mixed in stages according to the mixing process parameters to form a mixed raw material.

3. The optimized preparation process method for diamond-reinforced metal matrix composites as described in claim 2, characterized in that, Diamond particles with a composite coating and the composite matrix powder are added to a three-dimensional mixer according to the specified mixing ratio. The mixture is then mixed in stages according to the specified mixing process parameters to form a mixed raw material. The method includes: Dry mixing is performed in a three-dimensional mixer according to the mixing process parameters to generate dry mixing stage parameters; Wet mixing is performed in a three-dimensional mixer according to the mixing process parameters to generate wet mixing stage parameters; Real-time monitoring is performed based on the dry mixing stage parameters and the wet mixing stage parameters to obtain real-time mixing uniformity, which includes the mixing variation coefficient. When the mixing variation coefficient is less than a preset threshold, the mixed raw materials are formed according to the real-time mixing uniformity.

4. The optimized preparation process method for diamond-reinforced metal matrix composites as described in claim 1, characterized in that, Based on real-time recording of pressureless sintering of the mixed raw materials, a preparation log is generated. Based on the preparation log, sintering process parameters are optimized to generate optimized sintering process parameters. The method includes: The pressureless sintering process of the mixed raw materials is collected in real time by multiple sensors to generate a dynamic sintering parameter dataset. Based on the dynamic sintering parameter dataset, a preparation record log is constructed. Based on the preparation record log, a correlation analysis is performed on the composite material properties of the sintering process parameters and the mixed raw materials to determine the process-performance mapping network. The optimization objective is set according to the process-performance mapping network, and the sintering process parameters are optimized and solved based on the optimization objective to generate the optimized sintering process parameters.

5. The optimized preparation process method for diamond-reinforced metal matrix composites as described in claim 4, characterized in that, The method for constructing a preparation record log based on the dynamic sintering parameter dataset includes: The dynamic sintering parameter dataset is decomposed to obtain sintering temperature data, holding time data, atmosphere pressure data, and interface reaction data. The sintering temperature data, the holding time data, and the atmosphere pressure data are stored as a structured data table according to the time series. Feature extraction is performed based on the interface reaction data, and the interface reaction data is associated with timestamps according to the interface reaction features to obtain reaction time association parameters. Anomaly analysis is performed based on the reaction time correlation parameters and the structured data table. Anomaly results are automatically marked to identify anomalous data segments. The abnormal data segments, the reaction time-related parameters, and the structured data table are integrated to construct the preparation record log.

6. The optimized preparation process method for diamond-reinforced metal matrix composites as described in claim 4, characterized in that, The optimization objective is set according to the process-performance mapping network, and the sintering process parameters are optimized and solved based on the optimization objective to generate optimized sintering process parameters. The method includes: Based on the optimization objective, the sintering process parameters are solved in multiple objectives to determine the initial particle swarm, where each particle in the initial particle swarm contains a set of sintering process parameters. An adaptive inertia weight is introduced to calculate the fitness value of the initial particle swarm, thereby determining the fitness value of multiple particles; The particle positions of the initial particle swarm are iteratively updated according to the fitness values ​​of the multiple particles until the convergence condition is met, thereby obtaining the sintering optimization process parameters.

7. The optimized preparation process method for diamond-reinforced metal matrix composites as described in claim 4, characterized in that, The method for adjusting the sintering process parameters of subsequent batches of mixed raw materials by reconfiguring the alloying treatment parameters according to the optimized sintering process parameters includes: Based on the optimized sintering process parameters, the target parameters are determined by reverse derivation. The mechanical alloying conditions of subsequent batches are then adjusted according to the target parameters to prepare composite matrix powder. The composite matrix powder is mixed with the diamond particles of the composite coating to form a new batch of mixed raw materials; The new batch of mixed raw materials was subjected to pressureless sintering according to the optimized sintering process parameters to prepare diamond-reinforced metal matrix composites.

8. The optimized preparation process method for diamond-reinforced metal matrix composites as described in claim 7, characterized in that, The new batch of mixed raw materials is subjected to pressureless sintering according to the optimized sintering process parameters to prepare diamond-reinforced metal matrix composites. The method includes: Diamond-reinforced metal matrix composites are prepared by continuous batch-by-batch pressureless sintering of the new batch of mixed raw materials according to the optimized sintering process parameters. The diamond-reinforced metal matrix composite material was subjected to performance testing, and the performance test results were obtained. If the performance test result does not reach the preset threshold, the weights of the process-performance mapping network are corrected, and iterative optimization is performed until the performance test result reaches the preset threshold, thereby completing the adjustment of the sintering process parameters and realizing the preparation of diamond-reinforced metal matrix composites.

9. A device for optimizing the preparation process of diamond-reinforced metal matrix composites, characterized in that, The apparatus for optimizing the preparation process of diamond-reinforced metal matrix composites according to any one of claims 1-8 comprises: The processing module is used to perform a double-coating surface metallization modification treatment on diamond particles to form diamond particles with a composite coating, wherein the composite coating contains bonding strength data. The mixing module is used to mix the powder to be mixed with the matrix powder to be mixed in a preset ratio, perform mechanical alloying treatment to obtain composite matrix powder, and uniformly mix the diamond particles that form the composite coating with the composite matrix powder based on the bonding strength data to form a mixed raw material. The optimization module is used to record the pressureless sintering based on the mixed raw materials in real time, generate a preparation record log, and optimize the sintering process parameters based on the preparation record log to generate optimized sintering process parameters. The parameter adjustment module is used to reconfigure the alloying treatment parameters according to the sintering optimization process parameters to adjust the sintering process parameters of subsequent batches of mixed raw materials, thereby realizing the preparation of diamond-reinforced metal matrix composite materials.

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