Neodymium-iron-boron magnet and method for regulating and controlling grain size and grain size distribution of coarse grain layer of neodymium-iron-boron magnet

Abstract

The invention discloses a neodymium-iron-boron magnet and a method for regulating and controlling the grain size and the grain size distribution of a coarse grain layer of the neodymium-iron-boron magnet, and particularly relates to the technical field of neodymium-iron-boron magnet material preparation. Collecting a local temperature change rate and a stress change rate in real time in a sintering temperature rise and heat preservation stage; triggering coarse grain layer structure evolution evaluation according to the monitoring data, and extracting a feature data set; a diffusion coupling non-uniformity index and a grain boundary migration tensor index are calculated based on the feature data set, a machine learning model is input, a coarse grain layer abnormal risk level is output, and sintering process parameters are dynamically adjusted according to the coarse grain layer abnormal risk level; according to the method, the abnormal evolution of the coarse grain layer is dynamically sensed by monitoring local temperature and stress changes in real time, the abnormal risk level of the grain size of the coarse grain layer is intelligently predicted based on the characteristic data set, abnormal growth of coarse grains is inhibited in time, and the microstructure consistency and service performance stability of the neodymium-iron-boron magnet are improved.

Description

Technical Field

[0001] The invention relates to the technical field of preparation of NdFeB magnet materials, and more specifically to a NdFeB magnet and a method for regulating the grain size and particle size distribution of a coarse-grained layer of the NdFeB magnet. Background Art

[0002] NdFeB magnets are widely used in electronics, automobiles, new energy and other fields due to their excellent magnetic properties. However, the microstructure of NdFeB magnets, especially the grain size and particle size distribution of the coarse-grained layer, has an important influence on the final performance stability and reliability of the magnets. In the traditional sintering preparation process, the grain size of the coarse-grained layer is easily affected by factors such as uneven local thermal field distribution, unreasonable sintering heating rate, and local stress concentration, resulting in local abnormal growth or uneven particle size distribution, which in turn causes fluctuations in magnetic properties and reduces the service reliability of the material.

[0003] In the prior art, the uniformity of the structure is usually improved by optimizing the initial forming pressure, adjusting the sintering curve or adding an external temperature control device, but these methods are mostly based on static process settings, and it is difficult to respond in time to the risk of mutation caused by local abnormal evolution during the sintering process. In addition, the abnormal evolution of the grain size of the coarse-grained layer often occurs on the sintering temperature rise platform or in the cooling process, and has the characteristics of spatial locality and temporal suddenness. Traditional methods are difficult to effectively detect and intervene in the early stages of evolution. Therefore, the present invention proposes a NdFeB magnet and a method for regulating the grain size and particle size distribution of the coarse-grained layer of the NdFeB magnet, in order to solve the above problems. Summary of the invention

[0004] To achieve the above object, the present invention provides the following technical solutions: A NdFeB magnet and a method for regulating the grain size and grain size distribution of a coarse-grained layer of a NdFeB magnet, comprising the following steps: In the first step, a powder preform with uniform density distribution and overall density within a preset range is prepared by using a multi-stage pressure forming and densification treatment method; The second step is to obtain the local temperature change data and local stress change data of each position in real time through multiple temperature sensors and strain sensors pre-arranged inside and on the surface of the preform during the sintering heating and insulation stage; The third step is to determine whether to trigger the evaluation of the evolution of the coarse-grained layer structure based on the local temperature change rate and local stress change rate monitored in real time. When the local temperature change rate or the local stress change rate exceeds the preset threshold condition, extract a feature data group including the local temperature change rate, the local stress change rate, the local diffusion rate estimate, and the local grain boundary migration rate estimate; The fourth step is to calculate the diffusion coupling unevenness index and grain boundary migration tensor index based on the feature data group, and send them together as input to the pre-trained machine learning model to output the risk level of abnormal grain size in the coarse-grained layer. The sintering process is further dynamically adjusted according to the output risk level.

[0005] In a preferred embodiment, the multi-stage pressure forming and densification treatment method includes the following steps: first, the NdFeB powder raw material is preliminarily pressed under low pressure conditions to form a preliminary preform with a continuous structure and an internal porosity lower than that of the initial powder state; then, on the basis of applying uniform pressurization as a whole, multiple pressure loading processes are continuously superimposed, and the amplitude and duration of each loading are gradually increased according to a preset pressure increase curve, and the direction of the pressure applied during the pressurization process is kept stable and consistent; during the entire pressurization process, high-frequency micro-vibrations are synchronously applied.

[0006] In a preferred embodiment, high-frequency micro-vibration is continuously applied throughout the multi-stage pressure forming and densification process, and the frequency of high-frequency micro-vibration is maintained in the range of 1,000 Hz to 100,000 Hz. The direction of applying high-frequency micro-vibration is set at a preset fixed angle with the pressurization direction. Continuous vibration energy input induces local microscopic displacement and rotation adjustment of powder particles, enhances the contact probability and bonding strength between particles, reduces local internal porosity, and prevents powder particles from agglomerating or forming density-uneven aggregation areas during unidirectional pressurization. While applying high-frequency micro-vibration, the pressure direction of the overall pressurization process is maintained stable to ensure that the direction of particle rearrangement is consistent with the pressing direction, thereby promoting the high uniformity of the internal microstructure of the pressed powder preform, thereby laying the foundation for subsequent temperature and stress monitoring and dynamic regulation of the grain size of the coarse-grained layer.

[0007] In a preferred embodiment, in the process of determining whether to trigger the coarse-grained layer microstructure evolution evaluation process based on the real-time monitoring of the local temperature change rate and the local stress change rate, the changing trends of the local temperature change rate and the local stress change rate are continuously monitored, and when any local temperature change rate or the local stress change rate exceeds the corresponding preset threshold condition, the coarse-grained layer microstructure evolution evaluation process is immediately triggered; The preset threshold conditions are based on process settings, and the static calibration values ​​are set in different ranges according to different sintering stages to ensure that potential abnormal changes are captured in real time in key process sections, avoid abnormal growth of grain size and particle size distribution in the coarse-grained layer due to hysteresis response, and improve the timeliness and accuracy of the trigger mechanism.

[0008] In a preferred embodiment, after triggering the coarse-grained layer microstructure evolution assessment, a feature data set is extracted, the feature data set consisting of a local temperature change rate, a local stress change rate, a local diffusion rate estimate, and a local grain boundary migration rate estimate; The local diffusion rate estimate is obtained by obtaining the local real-time temperature data and calculating it according to the diffusion rate calculation formula. The diffusion rate calculation formula is that the local diffusion rate is equal to the initial diffusion rate multiplied by the exponential function. The negative exponential term of the exponential function is obtained by dividing the diffusion activation energy by the product of the local real-time temperature and the gas constant. The local diffusion rate is obtained according to the following calculation formula: ; Indicates that the monitoring point is at position Place, time The diffusion rate at is the reference initial diffusion rate constant, is the material diffusion activation energy, is the gas constant, take 8.314, Indicates that the monitoring point is at position Place, time Real-time temperature at the time; The estimated value of the local grain boundary migration rate is obtained by multiplying the stress gradient measured by the local stress change rate with the linear response coefficient of the grain boundary migration driving force. The linear response coefficient of the grain boundary migration driving force is preset based on the internal grain boundary energy and grain size calibration data of the NdFeB magnet material; Each data item in the feature data group is synchronously paired according to the timestamp.

[0009] In a preferred embodiment, the diffusion coupling heterogeneity index is derived based on the discrete spatiotemporal distribution of the local diffusion rate estimate, specifically comprising the following steps: At each moment, the set of local diffusion rate estimates of all monitoring points in space is standardized. The standardization method is to subtract the global minimum diffusion rate from the local diffusion rate and divide it by the global diffusion rate range at the current moment to form a dimensionless normalized diffusion rate field. Based on the normalized diffusion rate field, the spatial statistical variance of the standardized diffusion rate at that moment is calculated to quantitatively characterize the degree of dispersion of the local diffusion rate distribution. The larger the spatial statistical variance, the more uneven the spatial distribution of the diffusion rate. The spatial statistical variance corresponding to each moment is cumulatively integrated or averaged along the time dimension to obtain the time-weighted spatial diffusion heterogeneity cumulative index; The diffusion coupling heterogeneity index is defined as the normalized value of the cumulative index of spatial diffusion heterogeneity. Finally, the diffusion coupling heterogeneity index quantifies the overall spatial discreteness level of the local diffusion process during the entire sintering cycle or cooling cycle. The higher the value, the worse the consistency of the diffusion behavior during the evolution of the coarse-grained layer organization, and the higher the risk of abnormal coarse-grained growth.

[0010] In a preferred embodiment, the grain boundary migration tensor index is used to quantify the anisotropic distribution characteristics of the grain boundary migration rate inside the coarse-grained layer of the NdFeB magnet on a spatial scale to evaluate the consistency level of grain boundary movement during the evolution of the coarse-grained layer structure. The calculation process includes the following steps: Based on the local grain boundary migration rate estimates collected at different monitoring positions, the local grain boundary migration velocity vector field is constructed, and the grain boundary migration velocity vector of each monitoring point is defined. for: ; They represent the instantaneous migration components of the grain boundary along the X, Y, and Z directions, respectively; Based on the local grain boundary migration velocity vector, the local grain boundary mobility tensor is constructed , defined as: ; This tensor reflects the gradient change of local grain boundary migration velocity in different spatial directions; Calculate the symmetry deviation index of the local mobility tensor for the entire preform area , defined as the ratio of the sum of squares of the off-diagonal elements of the tensor to the sum of squares of the diagonal elements at each point, calculated as: ; They are all direction indexes, taken from the X, Y, and Z directions respectively, representing the three coordinate directions of space. is the partial derivative of the grain boundary migration velocity in the i-th direction with respect to the j-th direction, indicating the gradient change of the grain boundary migration velocity with the spatial position. It represents the sum of squares of all non-diagonal elements, reflecting the degree of change of grain boundary migration rate in non-orientation space and the discreteness of migration direction. It represents the sum of squares of all diagonal elements, reflecting the intensity of change in the grain boundary migration rate along its own direction and the degree of migration direction. The symmetry deviation index is used to reflect the consistency of the local grain boundary migration direction. The larger the value, the more chaotic the migration direction, and the smaller the value, the more isotropic the migration tends to be. In the entire monitoring area and time period, the symmetry deviation index of all local points is spatially and temporally weighted averaged, and the grain boundary migration tensor index GBMTI is finally defined as: ; Represents the volume of the entire monitoring area, It represents the total length of observation time. The larger the GBMTI value, the more uneven the grain boundary migration process is, the stronger the anisotropy is, and the higher the risk of abnormal growth of the grain size and particle size distribution of the coarse-grained layer is.

[0011] In a preferred embodiment, after completing the calculation of the diffusion coupling unevenness index and the grain boundary migration tensor index, the diffusion coupling unevenness index and the grain boundary migration tensor index are synchronously input into the pre-trained machine learning model in the form of feature input vectors. The machine learning model is trained based on the historical coarse-grained layer tissue evolution data set, and can infer the risk level of the coarse-grained layer grain size anomaly under the current preparation state according to the input diffusion behavior spatial discreteness characteristics and grain boundary migration directional discreteness characteristics. The output coarse-grained layer grain size anomaly risk level is a multi-level classification result or a continuous numerical regression result. The higher the coarse-grained layer grain size anomaly risk level, the greater the potential probability of abnormal growth or uneven growth of the coarse-grained layer tissue. The machine learning model is obtained by training based on regression analysis, classification tree, support vector machine or deep neural network.

[0012] In a preferred embodiment, according to the abnormal risk level of the coarse-grained layer grain size output by the machine learning model, the abnormal risk level is divided into three risk levels: low, medium and high, and the corresponding preset control operations are triggered according to different risk levels. When the abnormal risk level is low, the basic fine-tuning operation is performed, and the basic fine-tuning operation is used to adjust the sintering heating rate curve, the cooling curve change rate and the local heating power output parameters to achieve a slight optimization of the local thermal field; when the abnormal risk level is medium, the strategy switching operation is performed, and the strategy switching operation is used to dynamically replace the current process control strategy, introduce a slow rise section processing in the heating stage and a multi-stage buffer strategy in the cooling stage to weaken the stress concentration of the organization; when the abnormal risk level is high, a nonlinear correction operation is performed, and the nonlinear correction operation is used to adaptively adjust the cooling rate curve according to the local abnormal expansion trend and extend the constant temperature time of the highest temperature platform section, so as to maximize the suppression of the abnormal evolution trend of the grain size and particle size distribution of the coarse-grained layer, and improve the stability and consistency of the microstructure of the coarse-grained layer of the NdFeB magnet.

[0013] Technical effects and advantages of the present invention: The present invention adopts a multi-stage pressure forming and densification treatment method, combined with the synergistic effect of low-pressure pre-pressing, continuous pressurization loading and high-frequency micro-vibration, to effectively improve the overall density and density uniformity of the NdFeB powder preform before sintering, significantly reduce the local porosity and particle agglomeration phenomenon, and inhibit the abnormal growth trend of the coarse-grained layer caused by uneven heat diffusion and stress concentration in the subsequent sintering process from the source, improve the consistency of the evolution of the coarse-grained layer organization, and provide a highly homogeneous physical basis for dynamic monitoring and precise regulation during the sintering process.

[0014] During the sintering heating and insulation stages, the present invention presets multi-point temperature sensors and strain sensors to continuously collect local temperature change rate and local stress change rate data in real time, and extracts characteristic data groups including local diffusion rate estimates and local grain boundary migration rate estimates based on the super-threshold trigger mechanism. It can realize dynamic perception and high-timeliness early warning in the early stage of abnormal evolution of the coarse-grained layer, establish a real-time monitoring and anomaly capture mechanism for the evolution process of the coarse-grained layer organization, and significantly improve the response speed and prediction accuracy to the abnormal growth trend of the grain size of the coarse-grained layer.

[0015] The present invention calculates the diffusion coupling unevenness index and the grain boundary migration tensor index based on the characteristic data group, and feeds them as input to the pre-trained machine learning model synchronously, so as to infer the risk level of abnormal grain size in the coarse-grained layer in real time, and further triggers basic fine-tuning operation, strategy switching operation or nonlinear correction operation according to different risk levels. It can realize dynamic adjustment of sintering heating rate, cooling curve change rate and local heating power output parameters, effectively smooth out local thermodynamic field anomalies, accurately suppress abnormal growth of coarse grains, and improve the consistency of the final microstructure of NdFeB magnets and the stability of service performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 It is a schematic diagram of the NdFeB magnet and the method for regulating the grain size and particle size distribution of the coarse-grained layer of the NdFeB magnet in the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Reference Figure 1 The following embodiments are obtained: Example 1: The present invention aims at the problem of difficulty in controlling the grain size and particle size distribution of the coarse-grained layer of NdFeB magnets during the sintering preparation process, and proposes a dynamic control method based on intelligent predictive control. First, by adopting a multi-stage pressure forming and densification treatment method, combined with the synergistic effect of initial low-pressure pressing, continuous multiple pressurization and high-frequency micro-vibration, a powder preform with uniform density distribution and overall density within a preset range is prepared. In the subsequent sintering heating and insulation stages, the local temperature change data and local stress change data at each position are obtained in real time through multiple temperature sensors and strain sensors arranged inside and on the surface of the preform, and the local temperature change rate and the local stress change rate are used to determine whether to trigger the evaluation of the evolution of the coarse-grained layer organization, to ensure that potential anomalies can be captured in time in the key process section, and to ensure the closed-loop integrity of the initial forming and sintering dynamic monitoring.

[0019] NdFeB magnets are rare earth permanent magnet materials with neodymium (Nd), iron (Fe) and boron (B) as the main components. They have extremely high magnetic energy product and are widely used in motors, wind power generation, magnetic resonance imaging (MRI), acoustic equipment and other fields. NdFeB magnets are known as the "king of magnets" for their excellent magnetic properties (such as high coercivity, high remanence and high energy product). After triggering the evaluation of the evolution of the coarse-grained layer structure, the diffusion coupling inhomogeneity index and the grain boundary migration tensor index are calculated by extracting characteristic data groups including the local temperature change rate, the local stress change rate, the local diffusion rate estimate and the local grain boundary migration rate estimate. The diffusion coupling inhomogeneity index is derived based on the local diffusion rate field normalization and spatial statistical variance integral, and the grain boundary migration tensor index is calculated based on the cumulative calculation of the symmetry deviation index of the local mobility tensor. These two indices are synchronously input as feature input vectors into the pre-trained machine learning model. The machine learning model is trained based on historical coarse-grained layer organizational evolution data. It can infer the risk level of abnormal grain size in the coarse-grained layer under the current preparation state based on the spatial discreteness characteristics of the diffusion behavior and the directional discreteness characteristics of the grain boundary migration, thereby establishing a dynamic evaluation and prediction feedback mechanism.

[0020] According to the abnormal risk level of the coarse-grained layer grain size output by the machine learning model, the abnormal risk level is divided into three risk levels: low, medium and high, and the corresponding preset control operations are triggered according to different risk levels. When the abnormal risk level is low, basic fine-tuning operations are performed to make fine adjustments within the proportional range for the heating rate curve, cooling change rate and local heating power output parameters; when the abnormal risk level is medium, a strategy switching operation is performed to introduce a slow-rise stage processing in the heating stage and a multi-stage buffer strategy in the cooling stage to dynamically optimize the process path; when the abnormal risk level is high, a nonlinear correction operation is performed to adaptively adjust the cooling rate curve based on the local abnormal expansion trend and extend the constant temperature time of the highest temperature platform section, so as to minimize the abnormal growth of the coarse-grained layer grains and the discrete expansion of the grain size, and ensure the overall stability and consistency of the microstructure of the coarse-grained layer of the NdFeB magnet. Specifically including the following steps: The first step is to use multi-stage pressure forming and densification treatment to prepare a powder preform with uniform density distribution and overall density within a preset range; the purpose of using multi-stage pressure forming and densification treatment is to optimize the arrangement and contact state between powder particles before sintering, significantly improve the internal density and density uniformity of the powder preform, and reduce the internal porosity, thereby laying a homogeneous foundation for the organizational evolution of the coarse-grained layer during the subsequent sintering process. The initial continuous structure is formed by low-pressure pre-pressing, and then after multiple incremental pressure loading and synchronous application of high-frequency micro-vibration, the powder can be effectively rearranged at the microscopic level, fill small pores, avoid the formation of local high-porosity areas or particle agglomeration areas, and make the powder preform as a whole reach the set density range. This stage ensures that the internal diffusion process and grain boundary migration process of the material under the subsequent temperature field and stress field have high consistency and controllability, which directly affects the organizational stability and magnetic performance consistency of the finished magnet after sintering.

[0021] In the second step, during the sintering heating and insulation stage, multiple temperature sensors and strain sensors pre-arranged inside and on the surface of the preform are used to obtain local temperature change data and local stress change data at each position in real time; during the sintering heating and insulation stage, multiple temperature sensors and strain sensors are arranged inside and on the surface of the powder preform to obtain local temperature change data and local stress change data in real time, aiming to build a dynamic monitoring network for the internal thermodynamic state of the material. By continuously collecting the changing trends of local temperature and stress in real time, it is possible to timely capture potential uneven diffusion phenomena, local thermal gradient accumulation effects, and tissue stress concentration areas during the sintering process, providing a direct quantitative basis for the subsequent risk assessment of the evolution of the coarse-grained layer. The significance of this step is to transform the traditional passive post-detection method into active real-time monitoring, thereby improving the predictability and intervention of the process to the change trend of the coarse-grained layer organization, ensuring that the response mechanism can be activated in time at key process nodes to avoid the irreversible phenomenon of abnormal coarse-grained growth.

[0022] The third step is to determine whether to trigger the evaluation of the evolution of the coarse-grained layer organization based on the real-time monitored local temperature change rate and local stress change rate. When the local temperature change rate or the local stress change rate exceeds the preset threshold condition, extract a feature data group including the local temperature change rate, the local stress change rate, the local diffusion rate estimate, and the local grain boundary migration rate estimate. Determine whether to trigger the evaluation of the evolution of the coarse-grained layer organization based on the real-time monitored local temperature change rate and the local stress change rate, and extract a feature data group including the local temperature change rate, the local stress change rate, the local diffusion rate estimate, and the local grain boundary migration rate estimate when the abnormal threshold is triggered. The purpose is to accurately lock in the precursor characteristics of the coarse-grained layer organization change in a data-driven manner in the early stage of potential abnormal development. Through the over-limit detection of temperature change rate and stress change rate, risk sources such as uneven thermal diffusion and local stress anomaly during sintering can be effectively identified. Subsequently, key features that comprehensively reflect diffusion dynamics and grain boundary migration behavior can be extracted to provide complete and timely data input for subsequent intelligent prediction models, ensuring that the coarse-grained layer evolution assessment is not only real-time, but also relevant to physical mechanisms, thereby improving the scientificity and accuracy of overall prediction and regulation.

[0023] The fourth step is to calculate the diffusion coupling uneven index and grain boundary migration tensor index based on the feature data set, and send them as input to the pre-trained machine learning model to output the risk level of abnormal grain size in the coarse grain layer, and further dynamically adjust the sintering process according to the output risk level. The diffusion coupling uneven index and grain boundary migration tensor index are calculated based on the feature data set, and the two indices are sent as input to the pre-trained machine learning model to output the risk level of abnormal grain size in the coarse grain layer, and further dynamically adjust the sintering process according to the risk level. Its significance lies in building a real-time adaptive, data-driven dynamic optimization control closed loop. The diffusion coupling uneven index reflects the spatial discreteness of the local diffusion process, and the grain boundary migration tensor index quantitatively characterizes the directional unevenness of grain boundary migration. The two comprehensively reflect the microscopic dynamic state of the evolution of the coarse grain layer. Through the machine learning model to predict the risk level of complex nonlinear behavior, the dynamic optimization adjustment of the sintering heating curve, local heating power and cooling curve can be realized at the early stage of the abnormal evolution trend of the coarse grain layer, so as to suppress the abnormal growth of the grain size of the coarse grain layer to the greatest extent and improve the consistency and reliability of the final magnet performance.

[0024] The multi-stage pressure forming and densification treatment method includes the following steps: first, the NdFeB powder raw material is preliminarily pressed under low pressure conditions to form a preliminary preform with a continuous structure and an internal porosity lower than that of the initial powder state; then, on the basis of applying uniform pressurization as a whole, multiple pressure loading processes are continuously superimposed, and the amplitude and duration of each loading are gradually increased according to a preset pressure increase curve, and the direction of the pressure applied during the pressurization process is kept stable and consistent; during the entire pressurization process, high-frequency micro-vibrations are synchronously applied.

[0025] High-frequency micro-vibration is continuously applied throughout the entire multi-stage pressure forming and densification process. The frequency of high-frequency micro-vibration is maintained in the range of 1,000 Hz to 100,000 Hz, and the direction of applying high-frequency micro-vibration is set at a preset fixed angle with the pressurization direction. Continuous vibration energy input induces local microscopic displacement and rotation adjustment of powder particles, enhances the contact probability and bonding strength between particles, reduces local internal porosity, and prevents powder particles from agglomerating or forming density-uneven aggregation areas during unidirectional pressurization. While applying high-frequency micro-vibration, the pressure direction of the overall pressurization process is maintained stable to ensure that the direction of particle rearrangement is consistent with the pressing direction, thereby promoting a high degree of uniformity in the internal microstructure of the pressed powder preform, thereby laying the foundation for subsequent temperature and stress monitoring and dynamic regulation of the grain size of the coarse-grained layer.

[0026] The multi-stage pressure forming and densification treatment method first includes the step of implementing preliminary pressing of the NdFeB powder raw material under low pressure conditions, wherein the low pressure condition refers to the pressing pressure applied being less than 20% to 50% of the yield strength of the powder particles, usually controlled in the range of 50 MPa to 120 MPa, to avoid the crushing of the powder particles and to promote the initial formation of a continuous connection structure of the powder. NdFeB powder raw material refers to a composite metal magnetic powder material with a specific particle size distribution, containing neodymium, iron, boron and rare earth elements. The purpose of preliminary pressing is to promote initial contact and overlap between powder particles through externally applied uniform pressure without destroying the integrity of the particles, thereby forming a continuous three-dimensional skeleton structure, and making the internal porosity of the preliminary preform significantly lower than the initial loose powder state, usually reduced by more than 10%, providing a good foundation for subsequent densification treatment.

[0027] Subsequently, on the basis of uniform pressurization as a whole, multiple pressure loading processes are continuously superimposed, and the amplitude and duration of each loading are gradually increased according to the preset pressure increase curve. Uniform pressurization means that the pressing force applied to the entire surface of the preform is evenly distributed in all directions to avoid local high pressure or low pressure areas; continuous superposition of multiple pressure loading processes means that after the initial pressing is completed, the pressure is gradually increased in stages by controlling the press or pressure device, for example, starting from the initial pressing pressure, the pressure is increased by 10% to 20% each time, and the pressure is continuously increased two to four times, and the duration of each pressure increase is controlled between ten seconds and thirty seconds. The preset pressure increase curve is a reference trajectory of the pressure change over time pre-set according to the material forming characteristics, which usually shows an increasing trend to achieve progressive densification and avoid particle crushing or structural unevenness caused by one-time high pressure. During the entire multiple pressurization process, the direction of the applied pressure remains stable and consistent, that is, the direction of the pressing force is kept constant in the spatial coordinate system without deviation, so as to ensure that the powder particles are rearranged and deformed in a fixed direction during the pressing process, thereby promoting the overall uniformity of the density distribution.

[0028] During the entire pressurization process, high-frequency micro-vibration is applied synchronously. High-frequency micro-vibration refers to vibration frequencies controlled within the range of 1,000 Hz to 100,000 Hz, and the vibration amplitude is small, usually in the micron range. High-frequency micro-vibration is applied by transferring high-frequency vibration energy to the interior of the powder preform through a mechanical vibration source while pressurizing. The direction of the high-frequency micro-vibration is set at a preset fixed angle with the pressurization direction. The angle is generally controlled within the range of 15 to 45 degrees to induce local microscopic displacement, rotation and rearrangement of the powder particles. In this way, the contact probability and interface reconstruction ability between the powder particles are significantly improved, while the local internal porosity can be reduced, effectively preventing the powder from agglomerating particles or local density unevenness during the unidirectional pressurization process, thereby further improving the consistency of the overall density.

[0029] For example, in the specific implementation, the following process parameters can be used: first, NdFeB powder with a particle size ranging from 3 to 50 microns is preliminarily pressed at a pressure of 50 MPa to form a preliminary preform; then, the pressure is increased by 15% each time, and the loading time is 20 seconds each time, and the overall pressure is pressed to a final pressure of about 180 MPa. During the pressurization process, a high-frequency micro-vibration source with a vibration frequency of about 5 kHz and an angle of 30 degrees is arranged to continuously act on the preform. After this series of treatments, a powder preform with a density distribution standard deviation of less than 3% and an overall density of more than 95% is obtained, which provides a homogeneous and reliable physical basis for monitoring and regulating the evolution of the coarse-grained layer structure during the subsequent sintering heating, insulation and cooling processes.

[0030] In the process of judging whether to trigger the coarse-grained layer microstructure evolution evaluation based on the real-time monitoring of the local temperature change rate and the local stress change rate, the changing trends of the local temperature change rate and the local stress change rate are continuously monitored. When any local temperature change rate or local stress change rate exceeds the corresponding preset threshold condition, the coarse-grained layer microstructure evolution evaluation process is immediately triggered; The preset threshold conditions are based on process settings, and the static calibration values ​​are set in different ranges according to different sintering stages to ensure that potential abnormal changes are captured in real time in key process sections, avoid abnormal growth of grain size and particle size distribution in the coarse-grained layer due to hysteresis response, and improve the timeliness and accuracy of the trigger mechanism.

[0031] In the process of judging whether the evolution of the coarse-grained layer organization is triggered based on the real-time monitoring of the local temperature change rate and the local stress change rate, the changing trends of the local temperature change rate and the local stress change rate are continuously monitored. The local temperature change rate refers to the rate of change of temperature per unit time at a designated monitoring position inside or on the surface of the NdFeB powder preform, usually measured in degrees Celsius per second. The local stress change rate refers to the rate of change of the internal stress of the material per unit time at the corresponding monitoring position, usually measured in megapascals per second. Continuous monitoring refers to the real-time recording of the dynamic changes of the local temperature change rate and the local stress change rate through high-frequency, uninterrupted data acquisition during the entire sintering heating stage and the insulation stage, so as to ensure that the slight fluctuations or abnormal trends in the internal thermodynamic state of the material can be captured in time.

[0032] When any local temperature change rate or local stress change rate exceeds the corresponding preset threshold conditions, the coarse-grained layer microstructure evolution assessment process is immediately triggered. The preset threshold conditions refer to the critical change rate values ​​set for different monitoring positions and different stages according to the predetermined process flow and material properties, which serve as the trigger threshold for judging whether there is a potential abnormal evolution trend of the coarse-grained layer. Exceeding the preset threshold conditions means that abnormal diffusion rate, abnormal grain boundary migration rate or thermal stress concentration effect may occur in the local area of ​​the material, which requires immediate subsequent coarse-grained layer microstructure evolution risk assessment.

[0033] The preset threshold conditions are based on process settings, specifically based on process parameters such as the sintering heating rate, insulation temperature platform, cooling rate curve of the NdFeB magnet, combined with powder particle size, initial density distribution, and material thermophysical properties (such as thermal diffusivity, thermal expansion coefficient, yield strength, etc.). The static calibration value refers to the reasonable range of local temperature change rate and local stress change rate set at different sintering stages through small batch experimental verification before sintering. For example, the temperature change rate threshold set at the initial stage of heating is higher to meet the overall rapid heating requirements, while the temperature change rate threshold set at the high temperature platform stage is lower to accurately monitor local thermal stability.

[0034] By setting preset threshold conditions in different intervals at different sintering stages, the sensitivity of abnormal capture can be dynamically adjusted according to the different organizational evolution characteristics at each stage of the sintering process. For example, in practical applications, the local temperature change rate threshold can be set to five degrees Celsius per second and the local stress change rate threshold can be set to one megapascal per second in the initial stage of sintering heating; and in the highest temperature platform stage, the local temperature change rate threshold is lowered to one degree Celsius per second, and the local stress change rate threshold is lowered to 0.5 megapascals per second. This setting ensures that potential abnormal changes are captured in real time in key process sections, avoiding irreversible abnormal growth of grain size and particle size distribution in the coarse-grained layer due to response lag.

[0035] Through the combination of the above-mentioned continuous monitoring, dynamic judgment and staged threshold setting, the present invention can greatly improve the timeliness and accuracy of the trigger mechanism, realize accurate early warning and intervention control of the evolution of the coarse-grained layer organization during the sintering process, and ensure that the final NdFeB magnet product has higher consistency and reliability.

[0036] In the specific implementation process, for example, when preparing a batch of NdFeB powder preforms with a particle size distribution range of three microns to fifty microns, they are first preliminarily pressed under a low pressure of fifty MPa to form a preliminary continuous structure, and the porosity is reduced by about fifteen percent compared with the original loose powder. Subsequently, under the guidance of the preset pressure increase curve, three consecutive pressurizations are applied in a manner of increasing the pressure amplitude by twenty percent each time, and each pressurization lasts for twenty seconds, eventually reaching a final pressure value of one hundred and eighty MPa. During this pressurization process, the direction of applied pressure is kept stable, and high-frequency micro-vibration with a frequency of about five kilohertz and an angle of thirty degrees with the pressurization direction is applied simultaneously, and the vibration amplitude is controlled within a few microns. Through this multi-stage pressure forming and densification treatment method, a high-uniformity powder preform with a standard deviation of density distribution less than three percent and an overall density exceeding ninety-five percent is obtained.

[0037] During the sintering heating and insulation stages, a total of 20 temperature sensors and strain sensors are set up inside and on the surface to collect continuous data once a second. The local temperature change rate threshold is set at five degrees Celsius per second in the initial stage of heating, and the local stress change rate threshold is set at one megapascal per second; the local temperature change rate threshold is set at one degree Celsius per second in the high-temperature platform stage, and the local stress change rate threshold is set at 0.5 megapascals per second. When the actual monitoring shows that the local temperature change rate of a certain area exceeds one degree Celsius per second in the high-temperature platform section, or the local stress change rate exceeds 0.5 megapascals per second, the coarse-grained layer organization evolution assessment process is immediately triggered, and the real-time feature data group of the position and the neighborhood is extracted to provide basic data support for the subsequent prediction of coarse-grained layer abnormal risk and dynamic regulation.

[0038] After triggering the evaluation of the evolution of the coarse-grained layer structure, a characteristic data set is extracted, which consists of a local temperature change rate, a local stress change rate, an estimated value of a local diffusion rate, and an estimated value of a local grain boundary migration rate; The local diffusion rate estimate is obtained by obtaining the local real-time temperature data and calculating it according to the diffusion rate calculation formula. The diffusion rate calculation formula is that the local diffusion rate is equal to the initial diffusion rate multiplied by the exponential function. The negative exponential term of the exponential function is obtained by dividing the diffusion activation energy by the product of the local real-time temperature and the gas constant. The local diffusion rate is obtained according to the following calculation formula: ; Indicates that the monitoring point is at position Place, time The diffusion rate at is the reference initial diffusion rate constant, is the material diffusion activation energy, is the gas constant, take 8.314, Indicates that the monitoring point is at position Place, time Real-time temperature at the time; The estimated value of the local grain boundary migration rate is obtained by multiplying the stress gradient measured by the local stress change rate with the linear response coefficient of the grain boundary migration driving force. The linear response coefficient of the grain boundary migration driving force is preset based on the internal grain boundary energy and grain size calibration data of the NdFeB magnet material; Each data item in the feature data group is synchronously paired according to the timestamp.

[0039] After triggering the evaluation of the evolution of the coarse-grained layer structure, a characteristic data set is extracted, which consists of the local temperature change rate, the local stress change rate, the local diffusion rate estimate and the local grain boundary migration rate estimate. The local temperature change rate refers to the rate of actual temperature change per unit time at a specified monitoring position, usually measured in degrees Celsius per second, and is used to characterize the local heating or cooling trend during the sintering process. The local stress change rate refers to the rate of actual stress change per unit time at the same position, usually measured in megapascals per second, and is used to reflect the evolution of local mechanical strain during the sintering process.

[0040] The local diffusion rate estimate is obtained by obtaining local real-time temperature data and calculating it according to the diffusion rate calculation formula. Local real-time temperature data refers to the actual temperature value of the current monitoring point collected in real time by the temperature sensor, in Kelvin. The diffusion rate calculation formula is that the local diffusion rate is equal to the reference initial diffusion rate constant multiplied by an exponential function, and the negative exponential term of the exponential function is obtained by dividing the material diffusion activation energy by the product of the local real-time temperature and the gas constant. The reference initial diffusion rate constant is the standard diffusion rate of the material under reference conditions, usually expressed in square meters per second; the material diffusion activation energy refers to the energy required for particles to overcome energy barriers during the diffusion process, in joules per mole; the gas constant is taken as 8.314 joules per mole per Kelvin, which is a universal natural constant.

[0041] The local diffusion rate is derived according to the following calculation formula: The local diffusion rate is equal to the reference initial diffusion rate constant multiplied by an exponential function, and the negative exponential term of the exponential function is the material diffusion activation energy divided by the product of the gas constant and the local real-time temperature. This formula reflects the exponential relationship that the diffusion process is highly sensitive to temperature, that is, the higher the temperature, the faster the diffusion rate.

[0042] In the specific implementation process, for example, for a batch of preforms prepared from NdFeB powder with a particle size of three to fifty microns, temperature sensors are arranged inside to collect temperature data at different spatial positions once per second in real time. By reading the real-time temperature of the monitoring point, substituting the known reference initial diffusion rate constant of one times ten to the negative eighth power of square meters per second, the material diffusion activation energy of one hundred and eighty kilojoules per mole, and the gas constant of eight point three one four joules per mole per Kelvin, the local diffusion rate estimate of each monitoring point at the current moment is calculated in real time according to the above derivation formula. As an important part of the characteristic data set, this estimate provides basic physical quantity support for the subsequent calculation of the diffusion coupling unevenness index and the prediction of the risk level of the coarse grain layer evolution.

[0043] The diffusion coupling heterogeneity index is derived based on the discrete spatiotemporal distribution of the local diffusion rate estimate, which includes the following steps: At each moment, the set of local diffusion rate estimates of all monitoring points in space is standardized. The local diffusion rate estimate refers to the estimate of the diffusion capacity of material particles at different positions in the sintering process, which is inferred from the temperature monitoring data, usually in square meters per second. The standardization method is to subtract the smallest diffusion rate value among all monitoring points at the same moment from each local diffusion rate, and then divide it by the diffusion rate range of all monitoring points at that moment, that is, the difference between the maximum diffusion rate value and the minimum diffusion rate value, thereby forming a dimensionless normalized diffusion rate field. The dimensionless normalized diffusion rate field refers to the diffusion rate data of all monitoring positions being normalized to a unified interval of zero to one, eliminating the influence of different absolute numerical magnitudes, so that subsequent statistical analysis only focuses on the spatial distribution characteristics of the diffusion behavior, without being affected by the absolute value.

[0044] Based on the normalized diffusion rate field, the spatial statistical variance of the standardized diffusion rate at that moment is calculated. The spatial statistical variance refers to the quantitative calculation of the degree of dispersion of the normalized diffusion rate in the spatial distribution, which is usually obtained by summing the square difference between the normalized diffusion rate value of all monitoring points and its average value and then taking the average. The larger the spatial statistical variance value, the more violent the fluctuation of the local diffusion rate in space, that is, the more uneven the distribution of the diffusion rate, reflecting the intensified inconsistency of the diffusion activity at different positions during the sintering process.

[0045] Along the time dimension, the spatial statistical variance corresponding to each moment is cumulatively integrated or averaged in discrete time. Cumulative integration refers to the continuous integration of the spatial statistical variance values ​​at all time points in the entire sintering process or selected stage; discrete time averaging refers to the average value of the spatial statistical variance at each discrete sampling moment. In this way, a time-weighted cumulative index of spatial diffusion heterogeneity is obtained, which not only reflects the spatial discreteness, but also comprehensively considers the changing trend of discreteness in the time dimension, forming a comprehensive evaluation of the consistency of diffusion behavior throughout the sintering or cooling cycle.

[0046] The diffusion coupling heterogeneity index is defined as the normalized value of the cumulative index of spatial diffusion heterogeneity. The normalization process is to ensure that the index is comparable and uniform under different sintering batches and different process parameters. Ultimately, the diffusion coupling heterogeneity index quantifies the overall spatial discreteness level of the local diffusion process during the entire sintering cycle or cooling cycle. The higher the value of the diffusion coupling heterogeneity index, the worse the consistency of the local diffusion behavior during the evolution of the coarse-grained layer organization, which means that it is more likely to have abnormal growth of the coarse-grained layer, uneven particle size distribution and other undesirable phenomena, which suggests that more stringent dynamic control strategies need to be implemented in the future.

[0047] In the specific implementation process, for example, after a batch of preliminary preforms are prepared, the sintering heating stage is carried out. It is assumed that at a certain moment, the local real-time temperature data of twenty monitoring points are collected by sensors, and the corresponding local diffusion rate estimate is obtained by calculation. After standardization, the normalized diffusion rate value of each monitoring point is obtained, and its spatial statistical variance is further calculated. By accumulating and integrating the spatial statistical variance of the sampling points once per second during the entire sintering heating stage, the spatial diffusion heterogeneity cumulative index is finally obtained, and normalized to obtain the diffusion coupling unevenness index. If the diffusion coupling unevenness index reaches above the preset high-risk threshold, it indicates that the coarse-grained layer structure has a large heterogeneity risk during the sintering process, and it is necessary to start the subsequent dynamic sintering temperature control adjustment mechanism for compensation optimization.

[0048] The grain boundary migration tensor index is used to quantify the anisotropic distribution characteristics of the grain boundary migration rate inside the coarse-grained layer of NdFeB magnets on a spatial scale, so as to evaluate the consistency level of grain boundary movement during the evolution of the coarse-grained layer structure. The calculation process includes the following steps: Based on the local grain boundary migration rate estimates collected at different monitoring positions, the local grain boundary migration velocity vector field is constructed, and the grain boundary migration velocity vector of each monitoring point is defined. for: ; They represent the instantaneous migration components of the grain boundary along the X, Y, and Z directions, respectively; Based on the local grain boundary migration velocity vector, the local grain boundary mobility tensor is constructed , defined as: ; This tensor reflects the gradient change of local grain boundary migration velocity in different spatial directions; Calculate the symmetry deviation index of the local mobility tensor for the entire preform area , defined as the ratio of the sum of squares of the off-diagonal elements of the tensor to the sum of squares of the diagonal elements at each point, calculated as: ; They are all direction indexes, taken from the X, Y, and Z directions respectively, representing the three coordinate directions of space. is the partial derivative of the grain boundary migration velocity in the i-th direction with respect to the j-th direction, indicating the gradient change of the grain boundary migration velocity with the spatial position. It represents the sum of squares of all non-diagonal elements, reflecting the degree of change of grain boundary migration rate in non-orientation space and the discreteness of migration direction. It represents the sum of squares of all diagonal elements, reflecting the intensity of change in the grain boundary migration rate along its own direction and the degree of migration direction. The symmetry deviation index is used to reflect the consistency of the local grain boundary migration direction. The larger the value, the more chaotic the migration direction, and the smaller the value, the more isotropic the migration tends to be. In the entire monitoring area and time period, the symmetry deviation index of all local points is spatially and temporally weighted averaged, and the grain boundary migration tensor index GBMTI is finally defined as: ; Represents the volume of the entire monitoring area, It represents the total length of observation time. The larger the GBMTI value, the more uneven the grain boundary migration process is, the stronger the anisotropy is, and the higher the risk of abnormal growth of the grain size and particle size distribution of the coarse-grained layer is.

[0049] The grain boundary migration tensor index is used to quantify the anisotropic distribution characteristics of the grain boundary migration rate in the coarse-grained layer of NdFeB magnets on a spatial scale, so as to evaluate the consistency level of grain boundary movement during the evolution of the coarse-grained layer structure. The calculation process of the grain boundary migration tensor index includes the following steps: Based on the local grain boundary migration rate estimates collected at different monitoring positions, a local grain boundary migration rate vector field is constructed. The local grain boundary migration rate estimate refers to the instantaneous movement rate of the grain boundary in all directions obtained by calculating the local stress change rate during sintering or cooling, usually measured in meters per second. The local grain boundary migration rate vector field refers to the definition of the grain boundary migration rate vector at each monitoring point in space, indicating the instantaneous direction and amplitude changes of the grain boundary movement. The grain boundary migration rate vector of each monitoring point is defined as a combination of components along the three coordinate directions of the grain boundary, including the component along the X direction of space, the component along the Y direction of space, and the component along the Z direction of space. Each component uses the position coordinate and time as a function, indicating the instantaneous movement rate of the grain boundary at different spatial positions and different time points.

[0050] Based on the local grain boundary migration velocity vector, the local grain boundary mobility tensor is constructed. The local grain boundary mobility tensor refers to the gradient change of the grain boundary migration velocity in different spatial directions, which is expressed in the form of a tensor matrix. The specific definition is: in the three spatial directions of X, Y, and Z, the components of the grain boundary migration velocity are partially differentiated with respect to each spatial coordinate, and arranged to form a three-by-three matrix. This tensor reflects the gradient degree of the local grain boundary migration rate in different directions in space, reflecting the anisotropic characteristics of the local grain boundary migration process.

[0051] The symmetry deviation index of the local mobility tensor is calculated for the entire preform area. The symmetry deviation index refers to the ratio of the sum of squares of non-diagonal elements to the sum of squares of diagonal elements in the mobility tensor at each monitoring point, which is used to quantify the degree of deviation in the consistency of motion between directions during grain boundary migration. The specific calculation method is: calculate the sum of squares of all non-diagonal partial derivatives separately, then calculate the sum of squares of all diagonal partial derivatives, and take the ratio of the two. The direction indexes i, j, and k are taken from the X, Y, and Z directions, respectively, representing the three orthogonal coordinate axes of the three-dimensional space.

[0052] In the above definition, the partial derivative of the grain boundary migration velocity in the i-th direction with respect to the j-th direction spatial coordinate represents the gradient change of the grain boundary migration velocity with the spatial position. The sum of squares of all non-diagonal terms reflects the degree of change of the grain boundary migration velocity in the non-local space, reflecting the high dispersion of the migration direction; while the sum of squares of all diagonal terms reflects the intensity of the change of the grain boundary migration velocity along the local direction, reflecting the degree of isotropy of the migration direction. The symmetry deviation index is used to reflect the consistency of the direction of local grain boundary migration. The larger the value, the more chaotic the grain boundary migration direction, and the smaller the value, the more isotropic the migration tends to be.

[0053] In the entire monitoring area and time period, the symmetry deviation indicators of all local points are spatially and temporally weighted averaged to finally define the grain boundary migration tensor index. Spatial weighting refers to the integration or summation of local indicators at different spatial positions, and temporal weighting refers to the integration or averaging of indicators at each moment in the entire observation time period. The final grain boundary migration tensor index is defined as the weighted integral average of local symmetry deviation indicators per unit observation volume and observation time length. The larger the value of the grain boundary migration tensor index, the more uneven the changes in all directions during the grain boundary migration process, the stronger the anisotropy of the local microstructure evolution, and the higher the risk of abnormal growth of the grain size and particle size distribution of the coarse-grained layer.

[0054] In the specific implementation process, for example, twenty spatial monitoring points are set during the sintering process, and the local stress change rate is measured in real time to infer the local grain boundary migration rate. The local mobility tensor matrix is ​​further constructed according to the gradient changes of the velocity components in different directions. Samples are taken once every second, and the local symmetry deviation index of each monitoring point is calculated. Then, the weighted average is performed along the time axis and the spatial region to finally obtain the grain boundary migration tensor index value. If the grain boundary migration tensor index reaches a preset high-risk threshold value or above in the middle and late stages of sintering, it indicates that the directionality of the grain boundary migration process inside the coarse-grained layer is out of control, and dynamic temperature control and cooling adjustment measures need to be initiated to prevent abnormal coarse grain growth and uneven expansion of grain size.

[0055] After completing the calculation of the diffusion coupling unevenness index and the grain boundary migration tensor index, the diffusion coupling unevenness index and the grain boundary migration tensor index are synchronously input into the pre-trained machine learning model in the form of feature input vectors. The machine learning model is trained based on the historical coarse-grained layer tissue evolution data set, and can infer the risk level of the coarse-grained layer grain size anomaly under the current preparation state according to the input diffusion behavior spatial discreteness characteristics and grain boundary migration directional discreteness characteristics. The output coarse-grained layer grain size anomaly risk level is a multi-level classification result or a continuous numerical regression result. The higher the risk level of the coarse-grained layer grain size anomaly, the greater the potential probability of abnormal growth or uneven growth of the coarse-grained layer tissue. The machine learning model is obtained by training based on regression analysis, classification tree, support vector machine or deep neural network.

[0056] After the calculation of the diffusion coupling inhomogeneity index and the grain boundary migration tensor index, the diffusion coupling inhomogeneity index and the grain boundary migration tensor index are synchronously input into the pre-trained machine learning model in the form of feature input vectors. The diffusion coupling inhomogeneity index is an indicator that quantifies the spatial discreteness of the local diffusion process inside the coarse-grained layer, and the grain boundary migration tensor index is an indicator that quantifies the anisotropy of grain boundary migration inside the coarse-grained layer. The feature input vector refers to combining these two quantitative indicators with different physical meanings into a unified data input format for processing and inference by the machine learning model.

[0057] The machine learning model is trained based on the historical coarse-grained layer microstructure evolution dataset. The historical coarse-grained layer microstructure evolution dataset refers to the monitoring data related to the evolution of the coarse-grained layer microstructure under different batches and different process conditions collected over a long period of time, including local diffusion rate, local grain boundary migration rate, sintering process temperature curve, cooling process temperature change, and final coarse-grained layer grain size and particle size distribution results. By utilizing the existing rich data samples, the machine learning model can learn the nonlinear mapping relationship between input features and output results.

[0058] The machine learning model can infer the risk level of abnormal grain size in the coarse-grained layer under the current preparation state based on the input diffusion behavior spatial discreteness characteristics and grain boundary migration directional discreteness characteristics. The risk level of abnormal grain size in the coarse-grained layer refers to the quantitative scoring of whether there is an abnormal growth risk in the evolution of the coarse-grained layer organization during the current sintering process, which is usually divided into multi-level classification results or continuous numerical regression results. The higher the output abnormal grain size risk level of the coarse-grained layer, the greater the potential probability of abnormal growth of the coarse-grained layer organization or uneven growth of the grain size distribution, indicating that the process parameters need to be adjusted in a timely manner.

[0059] Machine learning models can be trained using different types of methods: Regression analysis refers to establishing a mathematical regression relationship between input variables and output continuous numerical values ​​by minimizing the sum of squared errors between input features and output risk levels. It is suitable for scenarios where the output is a continuous risk score.

[0060] Classification tree refers to a tree structure model formed by recursively dividing the feature space into several sub-regions and outputting a category label in each sub-region. It is suitable for dividing the risk level of coarse-grained layer anomalies into discrete levels. The classification tree training process is achieved by minimizing the impurity index (such as Gini index or information entropy) after each node is divided.

[0061] Support vector machine refers to finding the optimal hyperplane in the feature space to separate samples of different risk levels with the maximum interval. It is suitable for training input data with small samples, high dimensions and certain nonlinear characteristics. The training process maximizes the classification boundary interval and introduces kernel function for feature mapping.

[0062] A deep neural network refers to a multi-layer nonlinear transformation structure that establishes a highly nonlinear relationship between complex inputs and outputs through weighted connections between a large number of neuron nodes. It is suitable for processing large-scale data sets and can explore the deep impact of the complex interaction between diffusion behavior and grain boundary migration on the abnormal evolution of the coarse-grained layer. The training process usually uses a back-propagation algorithm combined with a gradient descent to optimize the loss function.

[0063] In the specific implementation process, for example, a deep neural network can be used as the machine learning model structure, the input nodes correspond to the diffusion coupling uneven index and the grain boundary migration tensor index, the number of hidden layers is set to three layers, the number of nodes in each layer is sixty-four, thirty-two and sixteen respectively, the activation function uses the modified linear unit function, and the loss function uses the mean square error loss. By training one thousand sets of historical sintering sample data, it is finally possible to accurately predict the risk level of abnormal grain size in the coarse grain layer, dynamically adjust the sintering parameters according to the prediction results in the actual production process, and realize the consistency control of the evolution of the coarse grain layer organization.

[0064] According to the abnormal risk level of the coarse-grained layer grain size output by the machine learning model, the abnormal risk level is divided into three risk levels: low, medium and high, and the corresponding preset control operations are triggered according to different risk levels. Among them, when the abnormal risk level is low, the basic fine-tuning operation is performed, and the basic fine-tuning operation is used to adjust the sintering heating rate curve, the cooling curve change rate and the local heating power output parameters to achieve a slight optimization of the local thermal field; when the abnormal risk level is medium, the strategy switching operation is performed, and the strategy switching operation is used to dynamically replace the current process control strategy, introduce the slow rise stage processing in the heating stage and the multi-stage buffer strategy in the cooling stage to weaken the stress concentration of the organization; when the abnormal risk level is high, the nonlinear correction operation is performed, and the nonlinear correction operation is used to adaptively adjust the cooling rate curve according to the local abnormal expansion trend and extend the constant temperature time of the highest temperature platform section, so as to maximize the suppression of the abnormal evolution trend of the grain size and particle size distribution of the coarse-grained layer, and improve the stability and consistency of the microstructure of the coarse-grained layer of the NdFeB magnet.

[0065] According to the abnormal risk level of grain size in the coarse-grained layer output by the machine learning model, the abnormal risk level is divided into three risk levels: low, medium and high, and the corresponding preset control operations are triggered according to different risk levels.

[0066] When the abnormal risk level is low, perform basic fine-tuning operations. Basic fine-tuning operations refer to the dynamic adjustment of the heating rate curve, cooling curve change rate and local heating power output parameters in a subtle range during the sintering process. The sintering heating rate curve refers to the set temperature increase rate trajectory that changes with time, the cooling curve change rate refers to the temperature change rate during the cooling process after sintering is completed, and the local heating power output parameter refers to the power intensity applied by the heater in different areas. The specific usage of the basic fine-tuning operation is: when the abnormal risk level is detected to be low, the sintering heating rate is proportionally reduced, for example, by less than 5%, and the cooling curve change rate is fine-tuned at the same time, the cooling rate is adjusted to increase or decrease by no more than 5%, and the heating power output is reduced or increased by 3% to 5% in the local area, so as to slightly optimize the local thermal field uniformity, suppress the subtle uneven diffusion trend, and ensure the smooth evolution of the overall organization.

[0067] When the abnormal risk level is medium, the strategy switching operation is performed. The strategy switching operation refers to dynamically replacing the current process control strategy according to the real-time detection results during the sintering process, introducing the slow rise section processing in the heating stage and the multi-stage buffer strategy in the cooling stage. The slow rise section processing in the heating stage refers to inserting a transition section with a lower heating rate in the heating curve, such as reducing the original heating rate to 70% to 80% of the original rate, and slowly heating up to reduce the risk of local thermal stress concentration. The multi-stage buffer strategy in the cooling stage refers to dividing the original continuous cooling curve into multiple small sections with different cooling rates, and the cooling rate between each section is smoothly transitioned. For example, slow cooling is used in the high temperature to medium temperature section, and moderately accelerated cooling is used in the medium temperature to low temperature section, forming a staged progressive cooling mode as a whole. Through the strategy switching operation, the internal stress concentration and uneven diffusion of the organization during the sintering process are effectively weakened, thereby reducing the moderate risk of abnormal evolution of the coarse grain layer.

[0068] When the abnormal risk level is high, a nonlinear correction operation is performed. The nonlinear correction operation refers to adaptively adjusting the cooling rate curve and extending the constant temperature time of the highest temperature platform segment according to the local abnormal expansion trend detected in real time, so as to minimize the abnormal evolution of the grain size and particle size distribution of the coarse grain layer. The adaptive adjustment of the cooling rate curve means that after the high-risk area is monitored, the original rapid cooling rate is adjusted to a gradual slow decline curve. The cooling rate reduction can reach 30% to 50% of the original set value, and the cooling speed is dynamically fine-tuned according to the local thermal field state. Extending the constant temperature time of the highest temperature platform segment means setting an additional holding time in the highest temperature segment, such as increasing the constant temperature time by 20% to 50% on the basis of the original platform holding, so as to fully release the local thermal stress and diffusion driving force, smooth the evolution process of the coarse grain boundary, and suppress the abnormal growth trend of the coarse grains.

[0069] In the specific implementation process, for example, in the preparation process of a batch of NdFeB magnets, the machine learning model outputs the risk level of abnormal grain size in the coarse-grained layer in real time. If it is detected that the risk level at a certain stage is at a low level, the sintering heating curve is automatically adjusted, the heating rate is reduced by three percent, and the heating power output is fine-tuned in the local area; if the risk level rises to a medium level, the strategy is triggered to switch, and a slow-rise segment is inserted when the temperature rises to one thousand degrees Celsius, the heating rate is reduced to seventy-five percent of the original planned rate, and multi-stage buffer cooling is started when the temperature cools to eight hundred degrees Celsius; if the risk level further rises to a high level, the maximum sintering temperature platform holding time is extended by thirty percent, and the cooling curve is changed to a three-stage gradual decline, with the cooling rate in each stage gradually decreasing, effectively suppressing the abnormal growth of the coarse-grained layer and ensuring that the final magnet structure is uniform and stable.

[0070] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0071] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0072] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0073] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0074] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A NdFeB magnet and a method for regulating the grain size and grain size distribution of a coarse-grained layer of a NdFeB magnet, characterized in that: The following steps are involved: In the first step, a powder preform with uniform density distribution and overall density within a preset range is prepared by using a multi-stage pressure forming and densification treatment method; The second step is to obtain the local temperature change data and local stress change data of each position in real time through multiple temperature sensors and strain sensors pre-arranged inside and on the surface of the preform during the sintering heating and insulation stage; The third step is to determine whether to trigger the evaluation of the evolution of the coarse-grained layer structure based on the local temperature change rate and local stress change rate monitored in real time. When the local temperature change rate or the local stress change rate exceeds the preset threshold condition, extract a feature data group including the local temperature change rate, the local stress change rate, the local diffusion rate estimate, and the local grain boundary migration rate estimate; The fourth step is to calculate the diffusion coupling unevenness index and grain boundary migration tensor index based on the feature data group, and send them together as input to the pre-trained machine learning model to output the risk level of abnormal grain size in the coarse-grained layer. The sintering process is further dynamically adjusted according to the output risk level.

2. The NdFeB magnet and the method for regulating the grain size and particle size distribution of the coarse grain layer of the NdFeB magnet according to claim 1, characterized in that: The multi-stage pressure forming and densification treatment method includes the following steps: first, the NdFeB powder raw material is preliminarily pressed under preset low pressure conditions to form a preliminary preform with a continuous structure and an internal porosity lower than that of the initial powder state; then, on the basis of applying uniform pressurization as a whole, multiple pressure loading processes are continuously superimposed, and the amplitude and duration of each loading are gradually increased according to a preset pressure increase curve, and the direction of the pressure applied during the pressurization process is kept stable and consistent, and high-frequency micro-vibrations are synchronously applied during the entire pressurization process.

3. The NdFeB magnet and the method for regulating the grain size and grain size distribution of the coarse grain layer of the NdFeB magnet according to claim 2, characterized in that: High-frequency micro-vibration is continuously applied throughout the entire multi-stage pressure forming and densification process, the frequency of the high-frequency micro-vibration is maintained in the range of 1,000 Hz to 100,000 Hz, and the direction of applying the high-frequency micro-vibration is set at a preset fixed angle with the direction of pressurization.

4. The NdFeB magnet and the method for regulating the grain size and grain size distribution of the coarse grain layer of the NdFeB magnet according to claim 3, characterized in that: In the process of judging whether to trigger the coarse-grained layer microstructure evolution evaluation based on the real-time monitoring of the local temperature change rate and the local stress change rate, the changing trends of the local temperature change rate and the local stress change rate are continuously monitored. When any local temperature change rate or local stress change rate exceeds the corresponding preset threshold condition, the coarse-grained layer microstructure evolution evaluation process is immediately triggered; The preset threshold conditions are based on process settings, and the static calibration values ​​are set in different intervals according to different sintering stages.

5. The NdFeB magnet and the method for regulating the grain size and grain size distribution of the coarse-grained layer of the NdFeB magnet according to claim 4, characterized in that: After triggering the evaluation of the evolution of the coarse-grained layer structure, a characteristic data set is extracted, which consists of a local temperature change rate, a local stress change rate, an estimated value of a local diffusion rate, and an estimated value of a local grain boundary migration rate; The local diffusion rate estimate is obtained by obtaining the local real-time temperature data and calculating it according to the diffusion rate calculation formula. The diffusion rate calculation formula is that the local diffusion rate is equal to the initial diffusion rate multiplied by the exponential function. The negative exponential term of the exponential function is obtained by dividing the diffusion activation energy by the product of the local real-time temperature and the gas constant. The local diffusion rate is obtained according to the following calculation formula: ； Indicates that the monitoring point is at position Place, time The diffusion rate at is the reference initial diffusion rate constant, is the material diffusion activation energy, is the gas constant, take 8.314, Indicates that the monitoring point is at position Place, time Real-time temperature at The estimated value of the local grain boundary migration rate is obtained by multiplying the stress gradient measured by the local stress change rate with the linear response coefficient of the grain boundary migration driving force. The linear response coefficient of the grain boundary migration driving force is preset based on the internal grain boundary energy and grain size calibration data of the NdFeB magnet material; Each data item in the feature data group is synchronously paired according to the timestamp.

6. The NdFeB magnet and the method for regulating the grain size and grain size distribution of the coarse grain layer of the NdFeB magnet according to claim 5, characterized in that: The diffusion coupling heterogeneity index is derived based on the discrete spatiotemporal distribution of the local diffusion rate estimate, which includes the following steps: At each moment, the set of local diffusion rate estimates of all monitoring points in space is standardized. The standardization method is to subtract the global minimum diffusion rate from the local diffusion rate and divide it by the global diffusion rate range at the current moment to form a dimensionless normalized diffusion rate field. Based on the normalized diffusion rate field, the spatial statistical variance of the normalized diffusion rate at that moment is calculated to quantitatively characterize the degree of dispersion of the local diffusion rate distribution; The spatial statistical variance corresponding to each moment is cumulatively integrated or averaged along the time dimension to obtain the time-weighted cumulative index of spatial diffusion heterogeneity; The diffusion coupling heterogeneity index is defined as the normalized value of the cumulative index of spatial diffusion heterogeneity.

7. The NdFeB magnet and the method for regulating the grain size and grain size distribution of the coarse-grained layer of the NdFeB magnet according to claim 6, characterized in that: The grain boundary migration tensor index is used to quantify the anisotropic distribution characteristics of the grain boundary migration rate inside the coarse-grained layer of NdFeB magnets on a spatial scale. The calculation process includes the following steps: Based on the local grain boundary migration rate estimates collected at different monitoring positions, the local grain boundary migration velocity vector field is constructed, and the grain boundary migration velocity vector of each monitoring point is defined. for: ； They represent the instantaneous migration components of the grain boundary along the X, Y, and Z directions, respectively; Based on the local grain boundary migration velocity vector, the local grain boundary mobility tensor is constructed , defined as: ; Calculate the symmetry deviation index of the local mobility tensor for the entire preform area , defined as the ratio of the sum of squares of the off-diagonal elements of the tensor to the sum of squares of the diagonal elements at each point, calculated as: ; They are all direction indexes, taken from the X, Y, and Z directions respectively, representing the three coordinate directions of space. is the partial derivative of the grain boundary migration velocity in the i-th direction with respect to the j-th direction, indicating the gradient change of the grain boundary migration velocity with the spatial position. It represents the sum of squares of all non-diagonal elements, reflecting the degree of change of grain boundary migration rate in non-orientation space and the discreteness of migration direction. It represents the sum of squares of all diagonal elements, reflecting the intensity of change in the grain boundary migration rate along its own direction and reflecting the degree of migration directionality; In the entire monitoring area and time period, the symmetry deviation index of all local points is spatially and temporally weighted averaged, and the grain boundary migration tensor index GBMTI is finally defined as: ; Represents the volume of the entire monitoring area, Indicates the total length of observation time.

8. The NdFeB magnet and the method for regulating the grain size and grain size distribution of the coarse grain layer of the NdFeB magnet according to claim 7, characterized in that: After completing the calculation of the diffusion coupling unevenness index and the grain boundary migration tensor index, the diffusion coupling unevenness index and the grain boundary migration tensor index are synchronously input into the pre-trained machine learning model in the form of feature input vectors. The machine learning model is trained based on the historical coarse-grained layer tissue evolution data set, and can infer the risk level of the coarse-grained layer grain size anomaly under the current preparation state according to the input diffusion behavior spatial discreteness characteristics and grain boundary migration directional discreteness characteristics. The output coarse-grained layer grain size anomaly risk level is a multi-level classification result or a continuous numerical regression result. The higher the risk level of the coarse-grained layer grain size anomaly, the greater the potential probability of abnormal growth or uneven growth of the coarse-grained layer tissue. The machine learning model is obtained by training based on regression analysis, classification tree, support vector machine or deep neural network.

9. The NdFeB magnet and the method for regulating the grain size and grain size distribution of the coarse-grained layer of the NdFeB magnet according to claim 8, characterized in that: According to the abnormal risk level of the coarse-grained layer grain size output by the machine learning model, the abnormal risk level is divided into three risk levels: low, medium and high. The corresponding preset control operations are triggered according to different risk levels. Among them, when the abnormal risk level is low, the basic fine-tuning operation is performed; when the abnormal risk level is medium, the strategy switching operation is performed; when the abnormal risk level is high, the nonlinear correction operation is performed.

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