Method and apparatus for controlling a sintering process of a soft magnetic core

Abstract

The application is suitable for the technical field of magnetic core sintering, and particularly relates to a soft magnetic core sintering process control method and equipment. The method comprises the following steps: obtaining historical temperature data; predicting first defect regions according to the historical temperature data; obtaining heating element parameters of each region in a sintering furnace according to the first defect regions; obtaining real-time temperature curves; determining first heating states of each region in the sintering furnace according to the heating element parameters after obtaining the real-time temperature curves for the first time; determining first switching times of switching to the first heating states according to the real-time temperature curves; obtaining second defect regions according to the real-time temperature curves; adjusting the corresponding heating element parameters according to the second defect regions; and determining time sequence heating states of each region in the sintering furnace according to the adjusted heating element parameters. Therefore, the method provided by the application can quickly and effectively control each stage of the soft magnetic core sintering.

Description

Technical Field

[0001] This application belongs to the field of magnetic core sintering technology, and particularly relates to a method and equipment for controlling the sintering process of soft magnetic cores. Background Technology

[0002] Magnetic core sintering technology is a key process that densifies magnetic powder particles through high-temperature treatment to form magnetic cores with stable magnetic properties and structural strength.

[0003] Existing technologies typically employ preset, fixed heating curves and rely on post-sintering inspections (such as X-ray or acoustic testing). These methods cannot intervene before defects form and are prone to uneven core shrinkage due to large temperature differences within the furnace, leading to defects such as cracking and deformation. Furthermore, they cannot correct parameters in real time. Therefore, existing core sintering technologies suffer from the difficulty of rapidly and effectively controlling each stage of soft magnetic core sintering. Summary of the Invention

[0004] This application provides a method and apparatus for controlling the sintering process of soft magnetic cores, which can solve the problem of difficulty in quickly and effectively controlling each stage of soft magnetic core sintering.

[0005] In a first aspect, embodiments of this application provide a method for controlling the sintering process of a soft magnetic core, applied to a control device. The control device is communicatively connected to a sintering furnace, which includes multiple heating elements. The method includes:

[0006] Historical temperature data of soft magnetic core green blanks in each region of the sintering furnace are obtained; wherein the sintering furnace includes a preheating region, a high-temperature region, a heat preservation region, and a cooling region;

[0007] Based on the historical temperature data, a first defect region of the soft magnetic core green billet in each region of the sintering furnace is predicted; wherein, the first defect region includes defect type and defect location;

[0008] The heating element parameters for each region within the sintering furnace are obtained based on each of the first defect regions; wherein, the heating element parameters include heating power and heating time;

[0009] When sintering is performed based on the parameters of each heating element, the real-time temperature curves of the soft magnetic core green blanks in each region of the sintering furnace are obtained.

[0010] After obtaining the real-time temperature curve for the first time, the first heating state of each region in the sintering furnace is determined according to the heating element parameters; wherein, the first heating state includes the state of multiple heating elements in each region of the sintering furnace;

[0011] The first switching time is determined based on the real-time temperature curve, and each of the heating elements is switched to the first heating state based on the first switching time.

[0012] The second defect region of the soft magnetic core green billet in each region of the sintering furnace is obtained based on the real-time temperature curves described above.

[0013] Adjust the corresponding heating element parameters according to each of the second defect areas;

[0014] The timing heating state of each region in the sintering furnace is determined based on the adjusted heating element parameters; wherein, the timing heating state is used to reflect the switching of the sintering state of multiple heating elements in each region of the sintering furnace.

[0015] The technical solutions described in this application embodiment have at least the following technical effects:

[0016] The soft magnetic core sintering process control method provided in this application embodiment acquires historical temperature data of soft magnetic core billets in each region of the sintering furnace; predicts a first defect region of the soft magnetic core billet in each region of the sintering furnace based on the historical temperature data; obtains heating element parameters for each region of the sintering furnace based on the first defect regions; acquires real-time temperature curves of the soft magnetic core billets in each region of the sintering furnace while sintering based on the heating element parameters; after obtaining the real-time temperature curves for the first time, determines a first heating state for each region of the sintering furnace based on the heating element parameters; determines a first switching time based on the real-time temperature curves, and switches each heating element to the first heating state based on the first switching time; obtains a second defect region of the soft magnetic core billet in each region of the sintering furnace based on the real-time temperature curves; adjusts the corresponding heating element parameters based on each second defect region; and determines the sequential heating state for each region of the sintering furnace based on the adjusted heating element parameters. Therefore, the soft magnetic core sintering process control method provided in this application embodiment can identify potential defects in the soft magnetic core blanks in each region, transforming defect identification from post-detection to pre-prevention and in-process control. Through real-time monitoring and dynamic adjustment, it reduces rework and scrap caused by defects, while avoiding energy waste caused by excessive heat preservation or cooling.

[0017] In one possible implementation of the first aspect, obtaining the heating element parameters for each region within the sintering furnace based on each of the first defective regions includes:

[0018] Based on each of the first defect regions, the corresponding first region that needs to optimize the sintering process is determined from the soft magnetic core green blank.

[0019] The corresponding heating element parameters are determined based on each of the first regions.

[0020] In one possible implementation of the first aspect, obtaining the second defect region of the soft magnetic core green billet in each region of the sintering furnace based on each of the real-time temperature curves includes:

[0021] Based on the real-time temperature curve of the preheating zone, the temperature field distribution inside the soft magnetic core blank is obtained through finite element analysis, and the temperature gradient is calculated.

[0022] The second defect region of the soft magnetic core blank in the preheating region is determined based on the temperature gradient;

[0023] Based on the real-time temperature curve of the high-temperature region, the region of the soft magnetic core blank whose temperature is outside the preset range and whose duration exceeds the preset threshold is determined as the second defect region of the soft magnetic core blank in the high-temperature region.

[0024] The grain growth prediction results are obtained by performing a phase transformation kinetic model corresponding to the real-time temperature curve of the insulation area; wherein, the prediction results include grain size distribution and porosity;

[0025] The second defect region of the soft magnetic core blank in the insulation region is determined based on the prediction results.

[0026] In one possible implementation of the first aspect, adjusting the corresponding heating element parameters according to each of the second defect regions includes:

[0027] Based on the second defect region of the soft magnetic core blank in the high-temperature region, the heating element parameters of the corresponding heating element are adjusted by gradient heating;

[0028] In the second defect region of the soft magnetic core blank in the heat preservation area, the heating element parameters of the corresponding heating element are adjusted according to the porosity and grain size distribution.

[0029] In one possible implementation of the first aspect, adjusting the corresponding heating element parameters according to each of the second defect regions further includes:

[0030] A third defect region is obtained by comparing the second defect region in the current region with the second defect region in the previous region within the sintering furnace; wherein, the third defect region refers to the region of the soft magnetic core blank where the defect type or defect location differs from the second defect region in the current region or the second defect region in the previous region.

[0031] The parameters of the heating element are adjusted according to the third defect region to perform the next sintering in the current region and / or the previous region in the sintering furnace.

[0032] In one possible implementation of the first aspect, adjusting the heating element parameters for the next sintering in the current and / or previous region within the sintering furnace based on the third defect region includes:

[0033] The adjustment priority is determined based on the third defect area;

[0034] Adjust the heating element parameters in the current and / or previous regions of the sintering furnace according to the adjustment priority.

[0035] In one possible implementation of the first aspect, the control device includes multiple adjustment modules corresponding to each region within the sintering furnace, each adjustment module controlling one of the heating elements, and the method further includes:

[0036] If a first adjustment module exists that is in operation and has the same type of parameter that the first heating element needs to be adjusted, then the first adjustment module is used to adjust the heating element parameters of the first heating element; wherein, the first heating element refers to the heating element corresponding to the second defect area;

[0037] If the first adjustment module does not exist, then check if there is a second adjustment module that is in an idle state and has the same type of parameter that the first heating element needs to adjust.

[0038] If the second adjustment module exists, the first heating element is adjusted through the second adjustment module.

[0039] In one possible implementation of the first aspect, the method further includes:

[0040] The thermal stress distribution in the second defect region of the soft magnetic core blank in the cooling region is obtained;

[0041] Based on the thermal stress distribution, a rapid cooling method is used for low-stress areas, and a graded cooling method is used for high-stress areas.

[0042] In one possible implementation of the first aspect, the method further includes:

[0043] Obtain an image of the soft magnetic core obtained after sintering the soft magnetic core green blank;

[0044] The grain size is obtained based on the image analysis of the soft magnetic core, and the parameters of the phase transition kinetic model are adjusted accordingly.

[0045] Secondly, embodiments of this application provide a soft magnetic core sintering process control device, comprising:

[0046] The acquisition module is used to acquire historical temperature data of soft magnetic core green billets in various regions of the sintering furnace; wherein the sintering furnace includes a preheating region, a high-temperature region, a heat preservation region, and a cooling region;

[0047] The first defect region module is used to predict the first defect region of the soft magnetic core green billet in each region of the sintering furnace based on the historical temperature data; wherein, the first defect region includes defect type and defect location;

[0048] The heating element parameter module is used to obtain the heating element parameters of each region in the sintering furnace according to each of the first defect regions; wherein, the heating element parameters include heating power and heating time;

[0049] The real-time temperature curve module is used to obtain the real-time temperature curve of the soft magnetic core green blank in each region of the sintering furnace when sintering is performed based on the parameters of each heating element.

[0050] The first heating state module is used to determine the first heating state of each region in the sintering furnace according to the heating element parameters after the real-time temperature curve is obtained for the first time; wherein, the first heating state includes the state of multiple heating elements in each region of the sintering furnace.

[0051] The first switching time module is used to determine the first switching time based on the real-time temperature curve, and to switch each of the heating elements to the first heating state based on the first switching time.

[0052] The second defect region module is used to obtain the second defect region of the soft magnetic core green blank in each region of the sintering furnace according to the real-time temperature curves.

[0053] The adjustment module is used to adjust the corresponding heating element parameters according to each of the second defect areas;

[0054] The sequential heating state module is used to determine the sequential heating state of each region in the sintering furnace according to the adjusted heating element parameters; wherein, the sequential heating state is used to reflect the switching of the state of multiple heating elements in each region of the sintering furnace during sintering.

[0055] Thirdly, embodiments of this application provide a control device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method as described in any one of the first aspects above.

[0056] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in any of the first aspects above.

[0057] Fifthly, embodiments of this application provide a computer program product that, when run on a control device, causes the control device to perform the method described in any one of the first aspects above.

[0058] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 This is a schematic flowchart of a soft magnetic core sintering process control method provided in an embodiment of this application;

[0061] Figure 2 This is a schematic diagram of the implementation process of steps S300, S800 and S840 in the soft magnetic core sintering process control method provided in an embodiment of this application;

[0062] Figure 3 This is a schematic diagram of the implementation process of step S700 in the soft magnetic core sintering process control method provided in an embodiment of this application;

[0063] Figure 4 This is a schematic diagram of another implementation flow of the soft magnetic core sintering process control method provided in one embodiment of this application;

[0064] Figure 5 This is a schematic diagram of the structure of the soft magnetic core sintering process control device provided in the embodiments of this application;

[0065] Figure 6 This is a schematic diagram of the structure of the control device provided in the embodiments of this application. Detailed Implementation

[0066] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0067] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0068] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0069] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0070] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0071] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0072] In related technologies, a preset fixed heating curve is typically used, relying on post-sintering inspection (such as X-ray or acoustic inspection). This approach cannot intervene before defects form, and large temperature differences within the furnace can easily lead to uneven core shrinkage, resulting in defects such as cracking and deformation. Furthermore, parameters cannot be corrected in real time. Therefore, existing core sintering technologies suffer from the problem of difficulty in rapidly and effectively controlling each stage of soft magnetic core sintering.

[0073] To address the aforementioned problems, this application provides a method and apparatus for controlling the sintering process of soft magnetic cores. The method involves: acquiring historical temperature data of soft magnetic core billets in each region of the sintering furnace; predicting a first defect region of the soft magnetic core billet in each region of the sintering furnace based on the historical temperature data; obtaining heating element parameters for each region of the sintering furnace based on the first defect regions; acquiring real-time temperature curves of the soft magnetic core billets in each region of the sintering furnace while sintering based on the heating element parameters; determining a first heating state for each region of the sintering furnace based on the first real-time temperature curves after obtaining the first real-time temperature curves; determining a first switching time based on the real-time temperature curves and switching each heating element to the first heating state based on the first switching time; obtaining a second defect region of the soft magnetic core billet in each region of the sintering furnace based on the real-time temperature curves; adjusting the corresponding heating element parameters based on each second defect region; and determining the sequential heating state of each region of the sintering furnace based on the adjusted heating element parameters. Therefore, the soft magnetic core sintering process control method provided in this application embodiment can identify potential defects in the soft magnetic core blanks in each region, transforming defect identification from post-detection to pre-prevention and in-process control. Through real-time monitoring and dynamic adjustment, it reduces rework and scrap caused by defects, while avoiding energy waste caused by excessive heat preservation or cooling.

[0074] The soft magnetic core sintering process control method provided in this application embodiment can be applied to a control device. In this case, the control device is the execution subject of the soft magnetic core sintering process control method provided in this application embodiment. This application embodiment does not impose any restrictions on the specific type of control device.

[0075] For example, the control equipment is communicatively connected to the sintering furnace. The control equipment can be an industrial computer, a programmable logic controller, an embedded control system, or a distributed control system, but is not limited to these. The sintering furnace can be divided into a preheating zone, a high-temperature zone, a heat preservation zone, and a cooling zone, and includes multiple heating elements evenly distributed in each zone (each sub-zone obtained by dividing the soft magnetic core green billet corresponds to one or more heating elements).

[0076] To better understand the soft magnetic core sintering process control method provided in the embodiments of this application, the specific implementation process of the soft magnetic core sintering process control method provided in the embodiments of this application will be described by way of example below.

[0077] Figure 1 A schematic flowchart of a soft magnetic core sintering process control method provided in an embodiment of this application is shown. The soft magnetic core sintering process control method includes:

[0078] S100 acquires historical temperature data of soft magnetic core green billets in various regions within the sintering furnace. The sintering furnace includes a preheating zone, a high-temperature zone, a holding zone, and a cooling zone.

[0079] For example, temperature field data in the preheating zone (300-500℃), high-temperature zone (1200-1300℃), heat preservation zone (1100-1200℃), and cooling zone (room temperature gradient decrease) can be collected in real time using temperature sensors (such as thermocouples and resistance temperature detectors). This data can be transmitted to a cloud platform via 5G / 4G / Ethernet to obtain historical temperature data of the soft magnetic core green billets in each region of the sintering furnace. For instance, using a high-temperature thermocouple installed inside a specially designed high-temperature resistant ceramic protective tube and fixed to the furnace wall in the high-temperature zone via a flange, the temperature fluctuation range of a certain batch of green billets in the high-temperature zone was measured to be 1280-1320℃.

[0080] S200 predicts the first defect region of the soft magnetic core green billet in each region of the sintering furnace based on historical temperature data. The first defect region includes the defect type and defect location.

[0081] For example, multimodal data fusion technology can be used to combine temperature data, the current size and material properties of the soft magnetic core blank, and cost-sensitive support vector machine (CS-SVM) can be used to predict defects and obtain the first defect region. For example, it can be predicted that the soft magnetic core blank may produce crack defects in the high-temperature region, located in a sub-region at the edge of the soft magnetic core blank.

[0082] S300: Based on each first defect area, the heating element parameters for each region within the sintering furnace are obtained. These heating element parameters include heating power and heating time.

[0083] It is understood that each zone within the sintering furnace includes multiple heating elements, which are evenly distributed around the furnace chamber. Furthermore, the soft magnetic core green billet can be divided into multiple sub-zones, each corresponding to one or more heating elements. The heating element parameters for each zone within the sintering furnace can also include the frequency and duty cycle of each heating element.

[0084] For example, the particle swarm optimization (PSO) algorithm can be used to adjust the heating power and heating time of each zone in the sintering furnace. Taking the high-temperature zone as an example, the target temperature is 1300℃, the initial heating power is 1000W, and the heating time is 2 hours. After PSO optimization, the heating power is adjusted to 1200W, the time is extended to 2.5 hours, and the temperature stability is improved by 20%.

[0085] S400 acquires real-time temperature profiles of soft magnetic core green blanks in each region of the sintering furnace while sintering based on the parameters of each heating element.

[0086] For example, when sintering is performed based on the parameters of each heating element, the real-time temperature curves of the soft magnetic core green blank in each region of the sintering furnace can be obtained by temperature sensors (such as thermocouples and resistance thermometers) in multiple sub-regions divided by the soft magnetic core green blank.

[0087] After obtaining the real-time temperature curves for the first time, S500 determines the first heating state of each region within the sintering furnace based on the heating element parameters. The first heating state includes the state of multiple heating elements in each region within the sintering furnace.

[0088] For example, after the first real-time temperature curve is obtained—that is, after the soft magnetic core billet enters the preheating zone, high-temperature zone, holding zone, or cooling zone of the sintering furnace—the temperature of the soft magnetic core billet is measured for the first time. Based on the initial temperature obtained in each zone, it can be determined whether the preset target temperature range for the preheating, high-temperature, holding, or cooling zone has been reached. According to the heating element parameters and the real-time temperature curve, corresponding heating power and heating time are allocated to the heating elements in each zone of the sintering furnace to maintain or adjust to the target temperature range. A segmented PID algorithm can be used to switch to the first heating state.

[0089] S600 determines the first switching time based on each real-time temperature curve, and switches each heating element to the first heating state based on the first switching time.

[0090] For example, the first switching time for switching each heating element to the first heating state can be determined based on the difference between each real-time temperature curve and the corresponding target temperature range.

[0091] S700, based on the real-time temperature curves, obtains the second defect region of the soft magnetic core green billet in each region of the sintering furnace.

[0092] For example, the sub-regions of soft magnetic core green blanks in each region of the sintering furnace can be obtained by the difference between each real-time temperature curve and the corresponding target temperature range being greater than a preset threshold and the duration being greater than a time threshold.

[0093] S800, adjust the corresponding heating element parameters according to each second defect area.

[0094] For example, the heating element parameters of the corresponding heating element can be adjusted according to the defect region characteristics (including defect type, location, defect degree, area, etc.) of each second defect region.

[0095] S900 determines the sequential heating state of each zone within the sintering furnace based on the adjusted heating element parameters. This sequential heating state reflects the switching of multiple sintering states performed by various heating elements in each zone of the sintering furnace.

[0096] For example, the sequential heating state of the heating elements in each area of ​​the sintering furnace can be determined based on the adjusted heating element parameters, that is, the on / off state and power changes at different time points (during multiple sintering processes), thus clarifying the heating operation at each time point. A state machine can be used to switch the sintering states of multiple heating elements in each area of ​​the sintering furnace multiple times.

[0097] Through the above steps S100 to S900, in actual production, the parameters of the overall heating element are first adjusted based on historical temperature data, and then the parameters of each heating element in each region of the sintering furnace are adjusted based on the first obtained real-time temperature, so as to make the control of the soft magnetic core sintering process faster; then, the parameters of the corresponding heating element are adjusted according to the second defect area of ​​the soft magnetic core blank in each region of the sintering furnace, so as to make the control of the soft magnetic core sintering process more specific and effective, and make the span of heating element parameter adjustment smaller, which is conducive to reducing the defect rate of the soft magnetic core sintering process; finally, the heating element parameters for the next continuous core sintering are determined according to the sequential heating state of each region in the sintering furnace.

[0098] In one possible implementation, please refer to Figure 2 S300, based on each first defect area, obtain the heating element parameters for each region within the sintering furnace, including:

[0099] S310, based on each first defect region, determines the first region from the soft magnetic core green blank that requires optimization of the sintering process.

[0100] For example, the first defect area can be divided into sub-regions corresponding to one or more heating elements to obtain the first region.

[0101] S320, determine the corresponding heating element parameters based on each first region.

[0102] For example, a parameter optimization algorithm combining particle swarm optimization (PSO) and orthogonal experiments can be used, with the temperature deviation and energy consumption of the sub-region as the objective function, to search for the optimal heating power and heating time through the PSO algorithm. For instance, in the preheating zone (300-500℃), the initial parameters are 800W / 1.5h, which are adjusted to 950W / 1.8h after PSO optimization, and the temperature standard deviation is reduced from 12℃ to 8℃.

[0103] Through steps S310 to S320, precise correspondence between defect locations and heating elements is achieved, providing a spatial reference for local parameter optimization and reducing energy waste caused by global adjustments. Local parameter optimization enables precise control of the temperature field during the sintering process, which helps reduce defect rates, improve production efficiency, and lower energy consumption.

[0104] In one possible implementation, please refer to Figure 3 S700, based on the real-time temperature curves, the second defect region of the soft magnetic core green billet in each region of the sintering furnace is obtained, including:

[0105] S710, based on the real-time temperature curve of the preheating zone, the temperature field distribution inside the soft magnetic core blank is obtained through finite element analysis, and the temperature gradient is calculated.

[0106] For example, a heat conduction model of the soft magnetic core green blank can be established using COMSOL Multiphysics, combined with the real-time temperature curve of the preheating region (300-500℃) as boundary conditions. The heat conduction model considers the temperature-dependent characteristics of the material's thermal conductivity (1.2-1.8 W / m·K), specific heat capacity (450-500 J / kg·K), and convective heat transfer coefficient (5-10 W / m²·K). Through the transient thermal analysis module, the real-time temperature data (updated every 10 seconds) is mapped to the boundary of the heat conduction model to calculate the internal temperature field distribution of the soft magnetic core green blank, and the temperature gradient is obtained by calculating the spatial derivative.

[0107] S720 determines the second defect region of the soft magnetic core blank in the preheating zone based on the temperature gradient.

[0108] For example, the second defect region can be identified using a threshold segmentation algorithm (gradient threshold set to 2.5℃ / mm) based on temperature gradient data. The distribution of the temperature gradient can be visualized using the MATLAB image processing toolbox. Figure 2 Value-based localization is used to locate secondary defect areas. For example, the edge of the green billet, where rapid heat dissipation results in a gradient of 3.2℃ / mm, is marked as a potential crack zone.

[0109] S730, based on the real-time temperature curve of the high-temperature region, identifies the region of the soft magnetic core blank where the temperature is outside the preset range and the duration exceeds the preset threshold as the second defect region of the soft magnetic core blank in the high-temperature region.

[0110] For example, the temperature range of the high-temperature zone (1200-1300℃) can be set to 1280-1320℃, the duration threshold is 5 minutes, the temperature curve is monitored in real time, and a timer is started when the temperature exceeds the range. If the temperature continues to exceed the limit, it is marked as a second defect area.

[0111] S740 uses a phase transformation kinetic model corresponding to the real-time temperature curve of the insulation area to predict grain growth and obtain the prediction results. These prediction results include grain size distribution and porosity.

[0112] For example, the grain growth process in the insulation zone can be simulated using a Phase Field model based on the real-time temperature curve of the insulation zone, and the phase transition kinetics can be predicted using the Johnson-Mehl-Avrami-Kolmogorov (JMAK) equation: X = 1 − exp(−kt n ), where X is the phase transition fraction, k is the rate constant, and n is the Avrami exponent. For example, grain size distribution (D50): D50 = 5 + 10X μm. At t = 3600 s (1 hour), D50 ≈ 15 μm. Porosity P: Assuming an initial porosity P0 = 3%, then P = 3% × (1 − X). At t = 3600 s, P ≈ 1.1%.

[0113] S750, based on the prediction results, determines the second defect area of ​​the soft magnetic core blank in the insulation area.

[0114] For example, based on the prediction results, a grain size threshold (D50 > 15 μm or < 5 μm) and a porosity threshold (> 2%) can be set, and the second defect region can be determined by comparing the thresholds. For instance, a green body's central region is predicted to have a D50 of 18 μm and a porosity of 2.5%, and is thus marked as a second defect region.

[0115] By combining temperature gradient, abnormal temperature duration, and phase transition prediction through the above steps S710 to S750, the accuracy of defect identification can be improved. The coupling of finite element analysis and phase transition model can reduce manual intervention, shorten process adjustment time, reduce core loss, and optimize energy consumption and cost.

[0116] In one possible implementation, please refer to Figure 2 S800, adjusts the corresponding heating element parameters according to each second defect area, including:

[0117] S810 uses gradient heating to adjust the heating element parameters of the corresponding heating element based on the second defect area of ​​the soft magnetic core blank in the high-temperature region.

[0118] For example, the temperature distribution of the defect area can be obtained based on the second defect area (such as a temperature exceeding the limit or an abnormal fluctuation area) in the high-temperature region and the thermocouple array. The temperature gradient is calculated using the finite difference method based on the temperature distribution of the defect area, and the gradient abrupt change point (such as a region with a gradient > 5℃ / cm) is located. The heating power of the heating element adjacent to the gradient abrupt change point is dynamically adjusted according to the direction and magnitude of the temperature gradient. For instance, if the temperature is low on the left side and high on the right side of the defect area, the heating power of the heating element on the left side is increased (ΔP = +10%), and the heating power on the right side is decreased (ΔP = -8%), forming gradient compensation from high to low temperature. A fuzzy PID control algorithm can be used, with temperature uniformity (standard deviation < 2℃) and gradient compression rate (gradient reduction > 30%) as objectives, to adjust the duty cycle (0-100%) and frequency (1-10kHz) of the heating element in real time. If the temperature gradient of a sub-region of the soft magnetic core blank is >5℃ / cm and lasts for 30 seconds, start high-frequency pulse heating (frequency = 8kHz, duty cycle = 70%); if the temperature gradient is <2℃ / cm and remains stable for 10 minutes, switch to low-frequency steady-state heating (frequency = 2kHz, duty cycle = 50%).

[0119] S820, in the second defect area of ​​the soft magnetic core blank in the heat preservation area, adjusts the heating element parameters of the corresponding heating element according to the porosity and grain size distribution.

[0120] For example, based on the second defect area of ​​the insulation region (such as a region with porosity >2% or grain size D50 >15μm), if the porosity exceeds the standard (>2%), increase the heating power of the heating element corresponding to the second defect area (ΔP = +5-15%), and extend the insulation (heating) time (Δt = +10-20 minutes); if the grain size is too large (D50 > 15μm), reduce the heating power (ΔP = -8-12%) and shorten the insulation (heating) time (Δt = -15-25%). A relationship between porosity (P), grain size (G), and heating parameters (power P) can be established based on the porosity and grain size distribution. heat The regression model for time t is: P = 0.8 − 0.03P heat +0.002t, G=10+0.05P heat -0.01t, the optimal parameter combination is solved by genetic algorithm so that P≤2% and 10μm≤G≤12μm.

[0121] Through the above steps S810 to S820, combined with gradient heating and microstructure control, it is beneficial to reduce the overall defect rate of the product; dynamic parameter adjustment makes the process highly adaptable and can handle different batches and different sizes of magnetic core blanks; it is beneficial to reduce the batch standard deviation of magnetic core performance parameters (permeability, iron loss), improve quality consistency, and reduce production costs.

[0122] In one possible implementation, please refer to Figure 2 S800, which adjusts the corresponding heating element parameters according to each second defect area, also includes:

[0123] S830 compares the second defect region in the current region with the second defect region in the previous region within the sintering furnace to obtain the third defect region. The third defect region refers to the region of the soft magnetic core green blank where the defect type or location differs from the second defect region in the current region or the second defect region in the previous region.

[0124] For example, the coordinates of the second defect region of the soft magnetic core green billet in the current region (e.g., the insulation zone) and the previous region (e.g., the high-temperature zone) can be mapped to a unified coordinate system. Then, the second defect region in the current region is compared with the second defect region in the previous region within the sintering furnace to obtain the third defect region. For instance, if the center coordinates of the second defect region in the high-temperature zone are (100mm, 50mm) and the center coordinates of the corresponding region in the insulation zone are (102mm, 48mm), spatial alignment is achieved through coordinate transformation (translation + rotation), with the error controlled within ±0.5mm. The defect type of the second defect region is encoded as a digital label (e.g., temperature exceeding limit = 1, porosity exceeding standard = 2). Using the geometric center of the defect region as a reference, the relative distance (d) and angle (θ) between it and the center point of the soft magnetic core green billet are calculated to form a position feature vector [d, θ]. A cosine similarity algorithm is used to compare the type label and position vector of the defect region in the current region with those in the previous region. If the type label is different or the angle between the position vectors is >15°, it is determined to be a difference region. For example, if the defect type in the current region is "temperature exceeds the limit" (label=1) and the previous region is "porosity exceeds the standard" (label=2), then it is marked as the third defect region; or if the cosine similarity between the defect position vector [d=20mm, θ=30°] in the current region and the previous region [d=25mm, θ=35°] is <0.9 (i.e. the included angle is >15°), it is also marked as the third defect region.

[0125] S840, adjusts the heating element parameters for the next sintering in the current and / or previous region of the sintering furnace based on the third defect region.

[0126] For example, a rule base for associating the characteristics (type, location, and degree of defect) of the third defect region with the parameters of the heating element can be established based on historical data. For instance, if the third defect region is of type "temperature exceeding limit" and is located at the edge of the magnetic core, the heating power of the heating element at the edge is adjusted (ΔP = +10%). If the third defect region is correlated with the defects in the previous region (such as the high-temperature region) (e.g., temperature fluctuations in the high-temperature region lead to increased porosity in the insulation region), the parameters of the previous region are adjusted. For example, the power of the heating element on the left side of the high-temperature region is reduced (ΔP = -8%). By monitoring the temperature of the adjusted third defect region in real time, the parameter combination is iteratively optimized using the gradient descent method. For example, if the standard deviation of the insulation region temperature decreases from ±3℃ to ±2℃ after the initial adjustment, failing to reach the target (±1.5℃), the corresponding heating element power is further increased to 690W (ΔP = +15%). The adjustment is repeated until the parameters converge, the optimal parameter combination is recorded, and the rule base is updated.

[0127] Through the above steps S830 to S840, the source of defects is located by identifying the third defect area, providing direction for process improvement; the parameter linkage adjustment from the high temperature zone to the heat preservation zone realizes full-process quality control, ensuring that the magnetic core blank is in the optimal thermal environment throughout the sintering cycle, which is conducive to improving the product qualification rate; dynamically adapting to the sintering requirements of different batches and different sizes of magnetic cores helps to reduce manual intervention and improve process stability.

[0128] Optionally, please refer to Figure 2 S840, adjusting the heating element parameters for the next sintering in the current and / or previous region within the sintering furnace based on the third defect region, including:

[0129] S841, determine the adjustment priority based on the third defect area.

[0130] For example, weights can be assigned to different types of defects based on historical data and process requirements. For instance: temperature exceeding limits: weight = 0.5; porosity exceeding limits: weight = 0.3; grain abnormalities: weight = 0.2. A position coefficient is determined based on the defect's location on the core blank. For example: central region defects: coefficient = 1.2; edge region defects: coefficient = 0.8; surface defects: coefficient = 1.0; internal defects: coefficient = 1.5. The comprehensive score S for the third defect region is calculated. The formula is: Weight × Location Coefficient × Defect Severity, where the defect severity parameter is obtained by standardizing real-time detection data (e.g., temperature exceeding limits / maximum allowable temperature difference, porosity / standard porosity, etc.). Historical data can be used to determine whether the third defect area was caused by defect propagation from the previous area (e.g., temperature fluctuations in the high-temperature zone may lead to an increase in porosity in the insulation zone). If it is determined that it was caused by defect propagation from the previous area, the priority of the previous area is dynamically weighted (e.g., S × 1.5), and the priority is adjusted by sorting the areas from high to low according to the comprehensive score S.

[0131] S842, adjust the heating element parameters in the current and / or previous zones of the sintering furnace according to the adjustment priority.

[0132] For example, high-priority defects (S≥1.0) can be quickly corrected (e.g., power adjustment ±15%, frequency adjustment ±20%); medium-priority defects (0.5≤S<1.0) can be gradually adjusted (e.g., power adjustment ±8%, frequency adjustment ±10%); low-priority defects (S<0.5) can be monitored without adjustment or finely adjusted (e.g., power ±3%). The parameters of the heating element corresponding to the third defect area can be modified (e.g., if the center temperature of the high-temperature zone exceeds the limit → increase the power of the center heating element); compensation adjustment for the previous area: if the existence of a third defect area is caused by the transmission of defects from the previous area, the parameters of the previous area can be adjusted (e.g., if the temperature of the high-temperature zone exceeds the limit, causing an increase in the porosity of the insulation zone → reduce the power of the heating element at the edge of the high-temperature zone to reduce thermal stress transmission), and the direction and step size of parameter adjustment can be iteratively calculated using the defect parameters (e.g., temperature, porosity) as the objective function.

[0133] Through the above steps S841 to S842, invalid operations are reduced, downtime is reduced, resource utilization is made more efficient and product performance is stabilized: it is conducive to reducing the fluctuation range of key parameters and improving the consistency of magnetic core permeability; the process adaptability is enhanced, supporting multi-variety, small-batch production.

[0134] In one possible implementation, please refer to Figure 2 The control equipment includes multiple adjustment modules corresponding to each area within the sintering furnace, with each adjustment module controlling a heating element. The method also includes:

[0135] 001. If a first adjustment module exists that is in operation and has the same type of parameter that needs to be adjusted as the first heating element, then the first adjustment module is used to adjust the heating element parameters of the first heating element. Here, the first heating element refers to the heating element corresponding to the second defect area.

[0136] For example, heating element parameters include power (P), frequency (F), and duty cycle (D). Each adjustment module can be labeled with its adjustable parameter type (e.g., "P+F" indicates that power and frequency can be adjusted simultaneously). All adjustment modules are iterated through, and modules currently in operation (i.e., controlling other heating elements) with parameter types matching the requirements of the first heating element are selected. For example, if multiple first adjustment modules are in operation and have the same parameter type as the first heating element, the module with the lowest load rate is selected as the first adjustment module. After the first adjustment module adjusts other heating elements, it immediately adjusts the heating element parameters of the first heating element. For example, power adjustment of H1: increase by 20W per second (900W-800W=100W, completed in 5 steps); frequency adjustment of H1: increase by 0.1kHz per second (3.5kHz-3kHz=0.5kHz, completed in 5 steps).

[0137] 002. If the first adjustment module does not exist, then check if there is a second adjustment module that is in an idle state and has the same type of parameter as the first heating element that needs to be adjusted.

[0138] For example, the module status (working / idle / faulty) can be collected periodically by adjusting the module communication interface (such as Modbus, CAN bus) and updated to the central database. After troubleshooting faulty modules (such as communication interruption, overload alarm), the second adjustment module that is idle and whose parameter type matches the requirements of the first heating element can be selected.

[0139] 003. If a second adjustment module exists, the first heating element is adjusted through the second adjustment module.

[0140] For example, if a second adjustment module is present, the first heating element can be adjusted by the second adjustment module.

[0141] Through the above steps S001 to S003, the working state module is reused and the idle module is activated, reducing the number of times new modules are started and maximizing resource utilization; priority is given to selecting close-range, low-load modules, which is conducive to improving response speed and adapting to the needs of rapid sintering.

[0142] In one possible implementation, please refer to Figure 3 The methods also include:

[0143] 004, Obtain the thermal stress distribution in the second defect region of the soft magnetic core blank in the cooling zone.

[0144] For example, during the cooling process, the surface temperature distribution of the green blank can be monitored in real time using an infrared thermal imager, and the thermal stress distribution of the second defect region of the soft magnetic core green blank can be obtained by measuring the local strain using strain gauges or digital image correlation (DIC).

[0145] 005. Based on the thermal stress distribution, a rapid cooling method is used for low-stress areas, and a graded cooling method is used for high-stress areas.

[0146] For example, forced air cooling or water cooling can be used for low-stress areas (such as sub-regions of soft magnetic core blanks where thermal stress is less than 50% of the material's yield strength), for example, increasing the cooling rate to 100°C / min. For high-stress areas (such as sub-regions of soft magnetic core blanks where thermal stress is close to or exceeds the yield strength), staged cooling is used, for example: first stage: cooling to 400°C at 20°C / min; second stage: holding at that temperature for 30 minutes; third stage: cooling to room temperature at 10°C / min. During the cooling process, the flow rate or temperature of the cooling medium is dynamically adjusted by monitoring temperature and strain data in real time.

[0147] Through the above steps S004 to S005, precise control of the cooling process is achieved: reducing the cracking rate in areas of concentrated thermal stress; reducing the scrap rate caused by improper cooling and lowering costs; and improving the consistency of magnetic core permeability.

[0148] In one possible implementation, please refer to Figure 4 The methods also include:

[0149] 006, Obtain an image of the soft magnetic core obtained after sintering the soft magnetic core green blank.

[0150] For example, images of soft magnetic cores obtained after sintering of the green compact can be acquired using optical microscopy (OM), scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), etc., and then subjected to grayscale correction, noise reduction (median filtering), and contrast enhancement (histogram equalization). For instance, Gaussian filtering (σ=1.5) can be applied to the SEM image to remove noise, and then the grain boundaries can be automatically segmented using the Otsu algorithm.

[0151] 007. The grain size is obtained by analyzing the soft magnetic core image, and the parameters of the phase transition kinetic model are adjusted accordingly.

[0152] For example, random straight lines can be drawn in an image of a soft magnetic core, and the number of intersections N between the lines and grain boundaries can be counted. The average grain size D = L / (N·M) can then be calculated, where L is the length of the line and M is the image magnification. For instance, if 10 lines of length L = 100 μm are drawn in a 50 μm × 50 μm area, the total number of intersections N = 120, and the magnification M = 2000, then D = 100 / (120 / 2000) ≈ 1.67 μm. Alternatively, the area A of each grain can be calculated through image segmentation (such as the watershed algorithm), and the grain size can be obtained by using the diameter of a circle with equal area D = 2√(A / π). For example, if the area A of a grain is 2.5 μm², then D = 2√(2.5 / π) ≈ 1.78 μm. The measured grain size data can then be substituted into the phase transition kinetic model (X = 1 − exp(−kt). n In the equation, X is the phase transition fraction, t is time, K = K0exp(−Q / (RT)) (K0 is the frequency factor, Q is the activation energy, R is the gas constant, T is the temperature), and n is the Avrami exponent), K0, Q, and n are optimized using the least squares method. For example, if the grain size is 1.5 μm after sintering for 4 hours, while the model predicts 1.2 μm, then K0 needs to be adjusted (e.g., from 10¹). 0 Increase Q to 10¹¹ or decrease Q (e.g., from 400 kJ / mol to 380 kJ / mol). Alternatively, neural networks (such as BP networks) can be used to establish a nonlinear mapping relationship between grain size and process parameters (temperature, time), and the parameters of the phase transition kinetic model can be automatically adjusted through training data.

[0153] Through steps S006 to S007 above, closed-loop optimization of the sintering process is achieved, resulting in a more uniform grain size distribution, which helps reduce core loss (Pcv) and improve performance stability. Combining image analysis and machine learning, an "image-model-process" linked optimization system is formed, providing a standardized solution for the research and development of soft magnetic materials.

[0154] It should be understood that the sequence number of each step in the above embodiments does not imply 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 this application.

[0155] Corresponding to the soft magnetic core sintering process control method described in the above embodiments, this application also provides a soft magnetic core sintering process control device, the various modules of which can realize the various steps of the soft magnetic core sintering process control method. Figure 5 A structural block diagram of the soft magnetic core sintering process control device provided in the embodiments of this application is shown. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0156] Reference Figure 5 The device includes:

[0157] The acquisition module is used to acquire historical temperature data of soft magnetic core green billets in various regions of the sintering furnace; wherein the sintering furnace includes a preheating region, a high-temperature region, a heat preservation region, and a cooling region;

[0158] The first defect region module is used to predict the first defect region of the soft magnetic core green billet in each region of the sintering furnace based on the historical temperature data; wherein, the first defect region includes defect type and defect location;

[0159] The heating element parameter module is used to obtain the heating element parameters of each region in the sintering furnace according to each of the first defect regions; wherein, the heating element parameters include heating power and heating time;

[0160] The real-time temperature curve module is used to obtain the real-time temperature curve of the soft magnetic core green blank in each region of the sintering furnace when sintering is performed based on the parameters of each heating element.

[0161] The first heating state module is used to determine the first heating state of each region in the sintering furnace according to the heating element parameters after the real-time temperature curve is obtained for the first time; wherein, the first heating state includes the state of multiple heating elements in each region of the sintering furnace.

[0162] The first switching time module is used to determine the first switching time based on the real-time temperature curve, and to switch each of the heating elements to the first heating state based on the first switching time.

[0163] The second defect region module is used to obtain the second defect region of the soft magnetic core green blank in each region of the sintering furnace according to the real-time temperature curves.

[0164] The adjustment module is used to adjust the corresponding heating element parameters according to each of the second defect areas;

[0165] The sequential heating state module is used to determine the sequential heating state of each region in the sintering furnace according to the adjusted heating element parameters; wherein, the sequential heating state is used to reflect the switching of the state of multiple heating elements in each region of the sintering furnace during sintering.

[0166] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0167] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above device can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0168] This application also provides a control device. Figure 6 This is a schematic diagram of the structure of a control device provided in one embodiment of this application. Figure 6 As shown, the control device 6 in this embodiment includes: at least one processor 60 ( Figure 6 Only one is shown in the image), at least one memory 61 ( Figure 6 (Only one is shown in the image) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, it causes the control device 6 to perform the steps in any of the above-described soft magnetic core sintering process control method embodiments, or causes the control device 6 to perform the functions of each module / unit in the above-described device embodiments.

[0169] For example, the computer program 62 may be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to complete this application. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 62 in the control device 6.

[0170] The control device 6 may be an industrial computer, a programmable logic controller, an embedded control system, or a distributed control system, etc. This control device may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art will understand that... Figure 6 This is merely an example of control device 6 and does not constitute a limitation on control device 6. It may include more or fewer components than shown, or combine certain components, or different components, such as input / output devices, network access devices, buses, etc.

[0171] The processor 60 can be a Central Processing Unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0172] In some embodiments, the memory 61 may be an internal storage unit of the control device 6, such as a hard disk or memory of the control device 6. In other embodiments, the memory 61 may be an external storage device of the control device 6, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the control device 6. Furthermore, the memory 61 may include both internal and external storage units of the control device 6. The memory 61 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 61 can also be used to temporarily store data that has been output or will be output.

[0173] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0174] This application provides a computer program product that, when run on a control device, causes the control device to perform the steps in any of the above method embodiments.

[0175] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to a control device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0176] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0177] Those skilled in the art will recognize that the units and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0178] In the embodiments provided in this application, it should be understood that the disclosed control devices and methods can be implemented in other ways. For example, the control device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0179] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0180] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for controlling the sintering process of a soft magnetic core, characterized in that, The method is applied to a control device that is communicatively connected to a sintering furnace, the sintering furnace including multiple heating elements, and includes: Historical temperature data of soft magnetic core green blanks in each region of the sintering furnace are obtained; wherein the sintering furnace includes a preheating region, a high-temperature region, a heat preservation region, and a cooling region; The first defect region of the soft magnetic core green billet in each region of the sintering furnace is predicted based on the historical temperature data; wherein, the first defect region includes defect type and defect location; The heating element parameters for each region within the sintering furnace are obtained based on each of the first defect regions; wherein, the heating element parameters include heating power and heating time; When sintering is performed based on the parameters of each heating element, the real-time temperature curves of the soft magnetic core green blanks in each region of the sintering furnace are obtained. After obtaining the real-time temperature curves for the first time, the first heating state of each region in the sintering furnace is determined according to the heating element parameters; wherein, the first heating state includes the state of multiple heating elements in each region of the sintering furnace; The first switching time is determined based on the real-time temperature curves, and the heating elements are switched to the first heating state based on the first switching time. The second defect region of the soft magnetic core green billet in each region of the sintering furnace is obtained based on the real-time temperature curves described above. Adjust the corresponding heating element parameters according to each of the second defect areas; The timing heating state of each region in the sintering furnace is determined based on the adjusted heating element parameters; wherein, the timing heating state is used to reflect the switching of the sintering state of multiple heating elements in each region of the sintering furnace.

2. The method for controlling the sintering process of soft magnetic cores as described in claim 1, characterized in that, The step of obtaining the heating element parameters for each region within the sintering furnace based on each of the first defect regions includes: Based on each of the first defect regions, the corresponding first region that needs to optimize the sintering process is determined from the soft magnetic core green blank. The corresponding heating element parameters are determined based on each of the first regions.

3. The method for controlling the sintering process of soft magnetic cores as described in claim 1, characterized in that, The method of obtaining the second defect region of the soft magnetic core green blank in each region of the sintering furnace based on the real-time temperature curves includes: Based on the real-time temperature curve of the preheating zone, the temperature field distribution inside the soft magnetic core blank is obtained through finite element analysis, and the temperature gradient is calculated. The second defect region of the soft magnetic core blank in the preheating region is determined based on the temperature gradient; Based on the real-time temperature curve of the high-temperature region, the region of the soft magnetic core blank whose temperature is outside the preset range and whose duration exceeds the preset threshold is determined as the second defect region of the soft magnetic core blank in the high-temperature region. The grain growth prediction results are obtained by performing a phase transformation kinetic model corresponding to the real-time temperature curve of the insulation area; wherein, the prediction results include grain size distribution and porosity; The second defect region of the soft magnetic core blank in the insulation region is determined based on the prediction results.

4. The method for controlling the sintering process of soft magnetic cores as described in claim 1, characterized in that, The step of adjusting the heating element parameters according to each of the second defect regions includes: Based on the second defect region of the soft magnetic core blank in the high-temperature region, the heating element parameters of the corresponding heating element are adjusted by gradient heating; In the second defect region of the soft magnetic core blank in the heat preservation area, the heating element parameters of the corresponding heating element are adjusted according to the porosity and grain size distribution.

5. The method for controlling the sintering process of soft magnetic cores as described in claim 1, characterized in that, The step of adjusting the heating element parameters according to each of the second defect regions further includes: A third defect region is obtained by comparing the second defect region in the current region with the second defect region in the previous region within the sintering furnace; wherein, the third defect region refers to the region of the soft magnetic core blank where the defect type or defect location differs from the second defect region in the current region or the second defect region in the previous region. The parameters of the heating element are adjusted according to the third defect region to perform the next sintering in the current region and / or the previous region in the sintering furnace.

6. The method for controlling the sintering process of soft magnetic cores as described in claim 5, characterized in that, The step of adjusting the heating element parameters for the next sintering cycle in the current region and / or previous region within the sintering furnace based on the third defect region includes: The adjustment priority is determined based on the third defect area; Adjust the heating element parameters in the current and / or previous regions of the sintering furnace according to the adjustment priority.

7. The method for controlling the sintering process of soft magnetic cores as described in claim 1, characterized in that, The control device includes multiple adjustment modules corresponding to each region within the sintering furnace, each adjustment module controlling one of the heating elements; the method further includes: If a first adjustment module exists that is in operation and has the same type of parameter that the first heating element needs to be adjusted, then the first adjustment module is used to adjust the heating element parameters of the first heating element; wherein, the first heating element refers to the heating element corresponding to the second defect area; If the first adjustment module does not exist, then check if there is a second adjustment module that is in an idle state and has the same type of parameter that the first heating element needs to adjust. If the second adjustment module exists, the first heating element is adjusted through the second adjustment module.

8. The method for controlling the sintering process of soft magnetic cores as described in claim 1, characterized in that, The method further includes: The thermal stress distribution in the second defect region of the soft magnetic core blank in the cooling region is obtained; Based on the thermal stress distribution, a rapid cooling method is used for low-stress areas, and a graded cooling method is used for high-stress areas.

9. The method for controlling the sintering process of soft magnetic cores as described in claim 3, characterized in that, The method further includes: Obtain an image of the soft magnetic core obtained after sintering the soft magnetic core green blank; The grain size is obtained based on the image analysis of the soft magnetic core, and the parameters of the phase transition kinetic model are adjusted accordingly.

10. A control device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 9.

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