Metal filter screen machining method and device and medium

Abstract

The invention discloses a metal filter screen machining method and device and a medium, and relates to the technical field of filter screen machining, and the metal filter screen machining method comprises the steps that an upper-layer metal filter screen and a lower-layer supporting screen which are used for compounding are obtained, screen layer alignment is conducted, and a to-be-machined composite filter screen is obtained; acquiring a first hot-pressing related feature vector and a second hot-pressing related feature vector corresponding to the upper-layer metal filter screen and the lower-layer support screen, and inputting the first hot-pressing related feature vector and the second hot-pressing related feature vector into the hot-pressing balance control model to obtain a first rectangular pulse current signal and a second rectangular pulse current signal; and the pulse power supply control module is connected with the pulse hot-pressing equipment and controls an upper hot-pressing head and a lower hot-pressing head of the pulse hot-pressing equipment according to the first rectangular pulse current signal and the second rectangular pulse current signal to carry out hot-pressing on the to-be-processed composite filter screen, and the composite filter screen is obtained. The technical problems that in the prior art, due to the fact that the temperatures of the upper hot-pressing head and the lower hot-pressing head are inconsistent, interface thermal stress is uneven, compositing is not firm or warping occurs are solved, and the technical effect that the double-layer metal filter screen is evenly heated and composited is achieved.

Description

Technical Field

[0001] This invention relates to the field of filter screen processing technology, specifically to methods, apparatus and media for processing metal filter screens. Background Technology

[0002] In the composite processing of metal filter meshes, hot pressing equipment is typically used to heat and press different material layers together to achieve structural integration. However, due to differences in thermal conductivity, thickness, and mesh structure between the upper metal filter mesh and the lower support mesh, traditional hot pressing methods struggle to achieve precise and independent temperature control of the upper and lower hot pressing heads, resulting in uneven heating. During hot pressing, temperature differences cause inconsistent distribution of interfacial thermal stress, easily leading to localized delamination, weak bonding, and even overall warping and deformation, affecting the structural stability and performance of the composite filter mesh. Summary of the Invention

[0003] This application provides a method, apparatus, and medium for processing metal filter screens, which is used to address the technical problems in the prior art where inconsistent temperatures of the upper and lower hot press heads lead to uneven interface thermal stress, weak bonding, or warping.

[0004] In view of the above problems, this application provides a method, apparatus and medium for processing metal filter screens.

[0005] A first aspect of this application provides a method for processing metal filter screens, the method comprising: Obtain an upper metal filter and a lower support mesh for composite processing; align the upper metal filter and the lower support mesh to obtain a composite filter to be processed; collect the first and second thermo-pressing related feature vectors corresponding to the upper metal filter and the lower support mesh, respectively; input the first and second thermo-pressing related feature vectors into a thermo-pressing equalization control model to obtain a first rectangular pulse current signal and a second rectangular pulse current signal, wherein the thermo-pressing equalization control model is connected to a pulse power supply control module; connect a pulse thermo-pressing device, and the pulse power supply control module controls the upper and lower thermo-pressing heads of the pulse thermo-pressing device to perform thermo-pressing on the composite filter to be processed according to the first and second rectangular pulse current signals, to obtain a processed composite filter.

[0006] A second aspect of this application provides a metal filter processing apparatus, the apparatus comprising: The system comprises the following modules: a mesh acquisition module for acquiring the upper metal filter mesh and the lower support mesh for composite processing; an alignment module for aligning the upper metal filter mesh and the lower support mesh to obtain the composite filter mesh to be processed; a feature vector acquisition module for acquiring the first and second thermo-pressing related feature vectors corresponding to the upper metal filter mesh and the lower support mesh, respectively; a current signal acquisition module for inputting the first and second thermo-pressing related feature vectors into a thermo-pressing equalization control model to obtain a first rectangular pulse current signal and a second rectangular pulse current signal, wherein the thermo-pressing equalization control model is connected to a pulse power supply control module; and a thermo-pressing module for connecting to a pulse thermo-pressing device, wherein the pulse power supply control module controls the upper and lower thermo-pressing heads of the pulse thermo-pressing device to thermo-press the composite filter mesh to be processed according to the first and second rectangular pulse current signals, thereby obtaining the processed composite filter mesh.

[0007] A third aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the metal filter processing method provided in this application.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: This application obtains an upper metal filter and a lower support mesh for composite processing; aligns the upper metal filter and the lower support mesh between layers to obtain a composite filter to be processed; collects a first thermo-pressing related feature vector and a second thermo-pressing related feature vector corresponding to the upper metal filter and the lower support mesh, respectively; inputs the first thermo-pressing related feature vector and the second thermo-pressing related feature vector into a thermo-pressing equalization control model to obtain a first rectangular pulse current signal and a second rectangular pulse current signal, wherein the thermo-pressing equalization control model is connected to a pulse power supply control module; connects a pulse thermo-pressing device, and the pulse power supply control module controls the upper and lower thermo-pressing heads of the pulse thermo-pressing device to perform thermo-pressing on the composite filter to be processed according to the first rectangular pulse current signal and the second rectangular pulse current signal, to obtain a processed composite filter. This invention solves the technical problem in the prior art where inconsistent temperatures of the upper and lower hot press heads lead to uneven thermal stress at the interface, resulting in weak bonding or warping. It constructs a thermal pressure equalization control model based on the thermal pressure-related feature vectors of the upper and lower materials of the metal filter mesh, and uses the model to drive a pulse power supply module to generate two rectangular pulse current signals to independently control the upper and lower hot press heads, thereby ensuring uniform heating and bonding of the double-layer metal filter mesh. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a schematic diagram of the metal filter screen processing method provided in the embodiments of this application; Figure 2 This is a schematic diagram of the metal filter processing apparatus provided in the embodiments of this application; Figure 3 The pulse signal diagrams of the first rectangular pulse current signal and the second rectangular pulse current signal provided in the embodiments of this application are shown.

[0011] Explanation of reference numerals in the attached diagram: 11. Network acquisition module, 12. Alignment module, 13. Feature vector acquisition module, 14. Current signal acquisition module, 15. Hot pressing module. Detailed Implementation

[0012] This application provides a metal filter processing method, apparatus, and medium to address the technical problem in the prior art where inconsistent temperatures between the upper and lower hot press heads lead to uneven interface thermal stress, weak bonding, or warping. By constructing a thermal pressure equalization control model based on the thermal pressure-related feature vectors of the upper and lower layers of the metal filter, and using the model to drive a pulse power supply module to generate two rectangular pulse current signals to independently control the upper and lower hot press heads, the technical effect of ensuring uniform heating and bonding of the double-layer metal filter is achieved.

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0014] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, apparatus, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such process, method, product, or apparatus.

[0015] Example 1, as Figure 1 As shown, this application provides a method for processing metal filter screens, the method comprising: Step S100: Obtain the upper metal filter and the lower support mesh for composite.

[0016] In this embodiment, the upper metal filter and the lower support mesh are first taken from a pre-prepared material library according to specifications. The upper metal filter has been pre-set with dimensions, mesh structure, and material parameters according to the target product requirements, and has precision filtration performance; the lower support mesh is also pre-customized according to the support strength requirements and has matching dimensions.

[0017] Step S200: Align the upper metal filter screen and the lower support screen to obtain the composite filter screen to be processed.

[0018] In this embodiment, the acquired upper metal filter and lower support mesh are first placed on the upper and lower support platforms of the alignment station, respectively. The lower support mesh is laid flat as a base, and the upper metal filter mesh covers it. Then, using a mechanical alignment device or manual assistance, the boundaries of the two mesh layers are aligned along the longitudinal and transverse edges to ensure size overlap and corner alignment. After alignment, the two metal meshes are physically bonded, forming a preliminary composite structure, thus constituting the composite filter to be processed.

[0019] Step S300: Collect the first thermo-pressure related feature vector and the second thermo-pressure related feature vector corresponding to the upper metal filter and the lower support mesh, respectively.

[0020] In this embodiment, thermo-pressurized features are first defined, including thermal conductivity, thickness, mesh structure characteristics, and initial temperature. Then, feature data corresponding to the upper metal filter mesh and the lower support mesh are collected, and the collected data are normalized to obtain a first thermo-pressurized feature vector and a second thermo-pressurized feature vector used for thermo-pressurization modeling.

[0021] Furthermore, the method provided in the application embodiment, which involves collecting the first thermo-pressure related feature vector and the second thermo-pressure related feature vector corresponding to the upper metal filter and the lower support mesh, further includes: Define thermo-pressure related features, including thermal conductivity, thickness, mesh structure characteristics, and initial temperature; collect data from the upper metal filter mesh based on the thermo-pressure related features and perform normalization processing to obtain a first thermo-pressure related feature vector; collect data from the lower support mesh based on the thermo-pressure related features and perform normalization processing to obtain a second thermo-pressure related feature vector.

[0022] In this embodiment, technical experts first predefine hot-pressing related characteristics, including thermal conductivity, thickness, mesh structure characteristics, and initial temperature. Thermal conductivity reflects the material's ability to conduct heat, thickness characterizes the length of the heat diffusion path, and mesh structure characteristics include parameters such as pore size, porosity, and wire diameter. The initial temperature is collected in real-time by a temperature sensor before the hot-pressing process, reflecting the initial thermal state of the mesh.

[0023] Based on the defined thermo-pressure related characteristics, data were collected from both the upper metal filter mesh and the lower support mesh. Thermal conductivity, thickness, and mesh structure characteristics were preset design parameters, directly read from the production parameter table. The initial temperature was measured in real-time using temperature sensors deployed on the surface of the metal mesh to obtain the current temperature state of the mesh. After the above data collection, the data for each thermo-pressure related characteristic were numerically transformed using a min-max normalization method to ensure that all characteristic values ​​were mapped to a uniform 0-1 standard range, eliminating dimensional differences. Finally, the normalized upper metal filter mesh characteristic data constituted the first thermo-pressure related feature vector, and the lower support mesh characteristic data constituted the second thermo-pressure related feature vector.

[0024] Step S400: Input the first thermo-pressure related feature vector and the second thermo-pressure related feature vector into the thermo-pressure equalization control model to obtain the first rectangular pulse current signal and the second rectangular pulse current signal, wherein the thermo-pressure equalization control model is connected to the pulse power supply control module.

[0025] In this embodiment, a pre-built hot-pressure equalization control model is first invoked. This model, generated via a fuzzy neural network based on the target hot-pressure temperature, tolerance temperature difference threshold, and multiple sets of hot-pressure characteristic samples, is used to achieve adaptive adjustment of pulse control parameters. The hot-pressure equalization control model internally includes a multi-factor pulse parameter calculation function and a signal conversion unit. The calculation function uses a difference feature vector as input variables and pulse current amplitude, voltage, frequency, and duty cycle as response variables, outputting a constrained and corrected set of control parameters. This control parameter set is then converted into a rectangular pulse current signal by the signal conversion unit.

[0026] By inputting the first and second thermo-pressure related feature vectors into the thermo-pressure equalization control model, a first rectangular pulse current signal and a second rectangular pulse current signal are obtained. The thermo-pressure equalization control model is connected to the pulse power supply control module, and the generated first and second rectangular pulse current signals are transmitted to the pulse power supply control module respectively to drive the upper and lower thermo-pressure heads, thereby realizing independent control of the heat input of the upper and lower metal filter layers.

[0027] Furthermore, in the method provided in the application embodiments, inputting the first thermal pressure-related feature vector and the second thermal pressure-related feature vector into the thermal pressure equalization control model further includes: Initialize the target hot-pressing temperature and the tolerance temperature difference threshold, where the tolerance temperature difference threshold is the allowable temperature difference range between the upper and lower hot-pressing heads; initialize the control parameters of the pulse power supply control module, where the control parameters include pulse current amplitude, voltage, frequency, and duty cycle; acquire the first hot-pressing related feature vector sample and the second hot-pressing related feature vector sample, and construct a difference feature vector sample; using the target hot-pressing temperature as the solution target, the tolerance temperature difference threshold as the solution constraint, the difference feature vector as the input variable, and the control parameters as the response variable, obtain a multi-factor pulse parameter solution function; based on the multi-factor pulse parameter solution function and the signal conversion unit, obtain the hot-pressing equalization control model.

[0028] In this embodiment, the target hot-pressing temperature is first initialized, for example, set to 250°C. This temperature is determined by technical experts based on process requirements. Simultaneously, a tolerance temperature difference threshold is set, for example, ±5°C, as the maximum allowable temperature deviation range between the upper and lower hot-pressing heads, to ensure the balance of heat input from the upper and lower parts.

[0029] Next, the control parameters of the pulse power supply control module are initialized. These parameters include pulse current amplitude, voltage, frequency, and duty cycle. The pulse current amplitude is used to adjust the heating intensity per unit time, with a setting range of 10A to 100A; the voltage is used to match the resistance characteristics of the hot press head, and is set to 24V or 48V; the frequency controls the periodicity of the pulse occurrence, with a value range of 1Hz to 20Hz; the duty cycle defines the ratio of the pulse conduction time to the period time, determining the degree of heat accumulation, and is set to 30% to 70%.

[0030] Subsequently, the first and second hot-press-related feature vector samples were obtained. Specifically, the first and second hot-press-related feature vector samples were extracted from historical processing data in the historical sample database. These samples correspond to four thermal response attributes: thermal conductivity, thickness, mesh structure characteristics, and initial temperature of the upper metal filter and lower support mesh, respectively. All extracted data have been normalized. When constructing the difference feature vector samples, for each set of first and second hot-press-related feature vector samples, a dimension-wise difference method was used to calculate the difference between the two vectors in each corresponding feature dimension, thus obtaining the difference feature vector samples.

[0031] Next, using the target hot-pressing temperature as the solution objective, the tolerance temperature difference threshold as the solution constraint, the difference feature vector samples as the input variables, and the pulse current amplitude, voltage, frequency, and duty cycle as the response variables, a fuzzy neural network is employed for modeling. The fuzzy neural network structure includes an input layer, a fuzzification layer, a rule layer, a normalization layer, a defuzzification layer, and a constraint output layer. These layers work together to achieve a nonlinear mapping from features to control parameters. Through this step, a multi-factor pulse parameter solution function is obtained, capable of dynamically solving pulse control parameters based on the input difference features.

[0032] Finally, the multi-factor pulse parameter calculation function is connected to the signal conversion unit. The signal conversion unit, acting as a control signal interface module, receives the control parameters output by the multi-factor pulse parameter calculation function and converts them into standard-format drive electrical signals for execution by the downstream pulse power supply control module. Through this integration process, a thermal pressure equalization control model is obtained.

[0033] Furthermore, the method provided in the application embodiments also includes: The multi-factor pulse parameter calculation function is obtained through a fuzzy neural network. The architecture of the fuzzy neural network includes an input layer, a fuzzification layer, a rule layer, a normalization layer, a defuzzification layer, and a constraint output layer. A fuzzy set is defined for each differential feature vector using the differential feature vector samples as input variables, and multiple sets of fuzzy sets are output, wherein the number of fuzzy sets for each differential feature vector is 3. A fuzzy rule base is constructed using the multiple sets of fuzzy sets, wherein each fuzzy rule in the fuzzy rule base includes a corresponding activation intensity. The fuzzy neural network calculates control parameters based on the fuzzy rule base.

[0034] In this embodiment, the multi-factor pulse parameter solution function is obtained by a fuzzy neural network. The fuzzy neural network adopts a six-layer structure, including an input layer, a fuzzification layer, a rule layer, a normalization layer, a defuzzification layer, and a constraint output layer. First, the input layer receives normalized difference feature vector samples. The difference feature vector samples are composed of four attributes: the difference in thermal conductivity, thickness, mesh structure, and initial temperature between the upper metal filter and the lower support mesh. These attributes represent the asymmetry in the physical properties of the hot-pressed structure and are provided as input variables to the fuzzy neural network.

[0035] After entering the fuzzification layer, based on the characteristics of each input dimension, three fuzzy sets are preset for each differential feature vector dimension, named low, medium, and high. Each fuzzy set is defined by a set of trapezoidal or triangular membership functions, specifying its value range and fuzziness level. Taking the triangular membership function as an example, assuming the value range of a certain input variable is [0, 1], the peak value of the low fuzzy set can be set at 0, covering a range of [0, 0.5]; the peak value of the medium fuzzy set is at 0.5, covering a range of [0, 1]; and the peak value of the high fuzzy set is at 1, also covering a range of [0.5, 1]. When the input value is 0.4, it is first identified that it falls into the right descending interval of the low fuzzy set and the left ascending interval of the medium fuzzy set. At this time, the corresponding membership values ​​are calculated for the low and medium fuzzy sets respectively. Membership degrees are assigned using linear interpolation. If the decreasing interval of the low fuzzy set is [0.3, 0.5], and 0.4 is in the middle, then its membership degree can be calculated as (0.5-0.4) / (0.5-0.3)=0.5. Similarly, in the increasing interval of the medium fuzzy set [0.3, 0.5], the membership degree of 0.4 is (0.4-0.3) / (0.5-0.3)=0.5. Therefore, the final membership degree of the input value 0.4 is 0.5 in both the low and medium fuzzy sets. This process achieves fuzzification through the membership function, thus realizing a soft partitioning of the uncertainty of the numerical input, providing a foundation for fuzzy inference.

[0036] In the rule layer, the fuzzification results of all input dimensions are combined to form multiple precondition combinations, and a fuzzy rule base is constructed based on these combinations. Each fuzzy rule describes the causal relationship between the input fuzzy state and the output control parameters, in the form of "if input dimension 1 is low, input dimension 2 is medium, ..., then the pulse current amplitude is high, the voltage is medium, the frequency is low, and the duty cycle is medium." Each fuzzy rule is bound to an activation strength, which is determined by the membership value of the corresponding input condition. The activation strength of the rule is obtained by taking the minimum value or product of all membership values ​​that make up the rule, thereby quantifying the confidence of the rule under the current input conditions.

[0037] In the normalization layer, the activation intensities of all activated fuzzy rules are normalized to a sum of 1, ensuring a reasonable proportion of each rule in the subsequent output. The defuzzification layer performs a weighted average of the output suggestions for each rule based on the normalized activation intensities, yielding continuous control parameter results, including four indicators: pulse current amplitude, voltage, frequency, and duty cycle. Finally, in the constraint output layer, boundary constraints are applied to the defuzzification results to ensure they meet the preset target hot-pressing temperature and tolerance temperature difference threshold requirements, and to keep the pulse control parameters within the equipment's allowable execution range.

[0038] In summary, the fuzzy neural network calculates control parameters based on the fuzzy rule base. That is, through steps such as fuzzy set definition, fuzzy rule construction, membership degree calculation and fuzzy inference output, it transforms the differential feature vector samples into pulse current control parameters that meet the process requirements, providing a key dynamic adjustment basis for the thermal pressure equalization control model.

[0039] Furthermore, in the method provided in the application embodiment, the first thermo-pressure related feature vector and the second thermo-pressure related feature vector are input into the thermo-pressure equalization control model to obtain the first rectangular pulse current signal and the second rectangular pulse current signal, and the method further includes: A difference feature vector is constructed between the first hot-press related feature vector and the second hot-press related feature vector. The hot-press equalization control model performs fuzzification processing on each difference feature vector in the difference feature vector according to the fuzzy neural network, and outputs the fuzzy membership degree of each difference feature vector. The activation intensity of each fuzzy rule in the fuzzy rule base is calculated according to the fuzzy membership degree of each difference feature vector to determine the first fuzzy rule. With the target hot-press temperature as the solution target, the first rectangular pulse current signal and the second rectangular pulse current signal output under the first fuzzy rule are obtained.

[0040] In this embodiment, a difference feature vector is first constructed between a first thermo-pressure related feature vector and a second thermo-pressure related feature vector. Specifically, in this process, the attribute value corresponding to the first thermo-pressure related feature vector is subtracted from the attribute value corresponding to the second thermo-pressure related feature vector to obtain the difference feature vector.

[0041] Subsequently, the thermal pressure equalization control model invokes the embedded fuzzy neural network to fuzzify each of the aforementioned differential feature vectors, mapping continuous input values ​​to corresponding fuzzy membership outputs. This fuzzy neural network structure includes an input layer, a fuzzification layer, a rule layer, a normalization layer, a defuzzification layer, and a constraint output layer. In the fuzzification layer, three fuzzy linguistic variables (e.g., low, medium, high) are set for each differential feature dimension, and a triangular or trapezoidal membership function is used to perform a fuzzification transformation on each input value. For example, when an input differential feature value is 0.4, and the low fuzzy set is defined in the interval [0, 0.5], and the medium fuzzy set is defined in [0.3, 0.7], linear interpolation can be used to calculate its membership degree in both the low and medium fuzzy sets, both being 0.5. The same operation is performed on each input variable dimension, ultimately outputting the membership degree result of each differential feature vector in its respective fuzzy set. Assume the differential feature vector contains four dimensions, denoted as... , , , This will output four sets of fuzzy membership vectors, respectively. This represents the membership degree of the first-dimensional difference feature (such as the difference in thermal conductivity) in low, medium, and high fuzzy sets; This indicates the membership degree of the second-dimensional difference feature (such as thickness difference) in low, medium, and high fuzzy sets; This represents the membership degree of the third-dimensional difference feature (mesh density difference) in low, medium, and high fuzzy sets; This represents the membership degree of the fourth dimension difference feature (such as the initial temperature difference) in the low, medium, and high fuzzy sets.

[0042] Next, the thermal pressure equalization control model, based on the aforementioned fuzzy membership results, calls the fuzzy rule base to calculate the rule activation strength. Each rule in the fuzzy rule base consists of input conditions and output actions, where the input conditions are fuzzy combinations of differential features (e.g., "low-medium-high-medium"), and the output is a set of control command templates. Activation strength calculation is performed for all fuzzy rules, using the fuzzy product method (i.e., taking the product of input membership degrees) to determine the activation value. For example, if a rule condition is... For China For low, For high, For the middle, and the corresponding membership degrees are respectively =0.5、 =0.6、 =0.8、 =0.4, then the activation strength of this rule is 0.5×0.6×0.8×0.4=0.096. By traversing all rules, the fuzzy rule with the highest activation strength is selected as the first fuzzy rule in the current state.

[0043] Finally, using the target hot-pressing temperature as the solution objective, a set of control parameter calculation results is first generated, including a first set of control parameters and a second set of control parameters for the upper and lower hot-pressing electrodes. These two sets of control parameters are then corrected using a tolerance temperature difference threshold as a constraint. Finally, a signal conversion unit is called to convert the corrected control parameter set into a standard format, and the output is as follows: Figure 3 The first rectangular pulse current signal and the second rectangular pulse current signal are shown.

[0044] Furthermore, in the method provided in the application embodiment, obtaining the first rectangular pulse current signal and the second rectangular pulse current signal output under the first fuzzy rule further includes: Using the target hot-pressing temperature as the solution target, the control parameter solution results under the first fuzzy rule are obtained. The control parameter solution results include a first set of control parameters and a second set of control parameters. The first set of control parameters and the second set of control parameters are corrected using the tolerance temperature difference threshold as the solution constraint condition, and the corrected first set of control parameters and the second set of control parameters are output. The corrected first set of control parameters and the second set of control parameters are converted by the signal conversion unit to obtain a first rectangular pulse current signal and a second rectangular pulse current signal.

[0045] In this embodiment, the target hot-pressing temperature is first used as the calculation objective, and the corresponding control parameter calculation results are derived through a first fuzzy rule. Based on the target hot-pressing temperature, preliminary control parameter calculation results are generated under the action of the fuzzy rule, including a first set of control parameters and a second set of control parameters. The first set of control parameters corresponds to the control of the upper hot-pressing head, and the second set of control parameters corresponds to the control of the lower hot-pressing head. These control parameters include indicators such as pulse current amplitude, voltage, frequency, and duty cycle. The pulse current amplitude adjusts the heating intensity during the hot-pressing process, the voltage adjusts the electrical characteristics of the device and the hot-pressing head, the frequency controls the periodic change of the pulse signal, and the duty cycle determines the ratio of the pulse signal's conduction time to the periodic time. The control parameter calculation results are derived through the first fuzzy rule in the fuzzy rule base of the fuzzy neural network, where the fuzzy rule base is constructed by technical experts based on historical data.

[0046] Next, the first and second sets of control parameters are corrected using the tolerance temperature difference threshold as the solution constraint. The tolerance temperature difference threshold sets the allowable error range of temperature between the upper and lower hot press heads; this value is set to ±5℃ to ensure that the temperature difference between the upper and lower hot press heads does not exceed a reasonable tolerance range. If the temperature difference during the hot pressing process exceeds this threshold, it may lead to uneven hot pressing, thereby affecting product quality or causing equipment damage. At this point, the first and second sets of control parameters obtained from the initial calculation are corrected to ensure that the corrected control parameters meet the temperature difference constraint, thus ensuring that the temperature difference between the hot press heads remains within the tolerance temperature difference threshold.

[0047] Finally, the signal conversion unit converts the corrected first set of control parameters and the second set of control parameters respectively. In this process, the first set of control parameters is first converted into a first rectangular pulse current signal, that is, the pulse period, pulse width, and pulse amplitude are calculated based on the control parameter set. Then, the same calculation is performed based on the second set of control parameters to generate a second rectangular pulse current signal.

[0048] Furthermore, in the method provided in the application embodiment, the first set of control parameters and the second set of control parameters are converted by the signal conversion unit to obtain the first rectangular pulse current signal and the second rectangular pulse current signal, and the method further includes: The pulse period, pulse width, and pulse amplitude are calculated based on the first set of control parameters to generate a first rectangular pulse current signal; the pulse period, pulse width, and pulse amplitude are calculated based on the output of the second set of control parameters to generate a second rectangular pulse current signal; the first rectangular pulse current signal and the second rectangular pulse current signal are processed and output simultaneously.

[0049] In this embodiment, based on the first set of control parameters, the pulse period, pulse width, and pulse amplitude are first calculated to generate a first rectangular pulse current signal. The pulse period is controlled by the frequency, and the calculation formula is pulse period T = 1 / f, where f is the frequency, representing the number of pulses per second. The pulse width is determined by both the duty cycle and the pulse period, and the calculation formula is pulse width W = D × T, where D is the duty cycle, representing the proportion of the pulse signal's conduction time to the entire cycle. The pulse amplitude is specified by the pulse current amplitude, directly determining the maximum current value of the pulse signal. For example, if the frequency is 5Hz, the duty cycle is 40%, and the pulse current amplitude is 60A, then the calculated pulse period is 0.2 seconds, the pulse width is 0.08 seconds, and the pulse amplitude is 60A, generating the first rectangular pulse current signal.

[0050] Similarly, following the same steps described above, the pulse period, pulse width, and pulse amplitude are calculated based on the output of the second set of control parameters to generate a second rectangular pulse current signal.

[0051] Finally, the first and second rectangular pulse current signals are synchronized and output in a coordinated manner. This synchronization ensures that the two pulse signals maintain a consistent heating mode within the same timeframe, thereby preserving temperature equilibrium during the hot pressing process.

[0052] Furthermore, the method provided in the application embodiments also includes: Detect whether the upper metal filter and the lower support mesh include a coating. If so, obtain the coating information. Add the coating information to the first thermo-pressure related feature vector or the second thermo-pressure related feature vector to update the first rectangular pulse current signal and the second rectangular pulse current signal.

[0053] In this embodiment of the application, it is first detected whether the upper metal filter and the lower support mesh include a coating. That is, the material information of the upper metal filter and the lower support mesh is obtained through a preset database, and it is determined whether the information contains a coating and its type. If a coating is included, the corresponding coating information, such as the coating type, thickness and thermal conductivity, is obtained from the preset database.

[0054] Next, the coating information corresponding to the upper metal filter and the lower support mesh is added to the corresponding first hot-press related feature vector and second hot-press related feature vector.

[0055] Finally, the updated first and second thermo-pressure related feature vectors are input into the thermo-pressure equalization control model to obtain the updated first and second rectangular pulse current signals.

[0056] Step S500: Connect the pulse hot pressing device. The pulse power control module controls the upper and lower hot pressing heads of the pulse hot pressing device to hot press the composite filter screen to be processed according to the first rectangular pulse current signal and the second rectangular pulse current signal, so as to obtain the processed composite filter screen.

[0057] In this embodiment, a pulse power control module is first connected to a pulse hot pressing device, which is used to hot press the composite filter screen to be processed. The pulse power control module controls the heating process of the upper and lower hot pressing heads in the pulse hot pressing device based on the generated first rectangular pulse current signal and second rectangular pulse current signal.

[0058] The pulse power control module receives and processes these signals to control the heating intensity and duration of the upper and lower heating heads, respectively. The upper and lower heating heads apply uniform heat, ensuring uniform heating between the two layers of the composite filter screen and preventing deformation or uneven heating caused by excessive temperature differences.

[0059] Finally, under the precise control of the pulse current signal, the upper and lower hot pressing heads of the pulse hot pressing equipment perform hot pressing operations on the composite filter screen to ensure the structural strength and temperature stability of the composite filter screen, and finally obtain the processed composite filter screen.

[0060] In summary, the embodiments of this application have at least the following technical effects: This application obtains an upper metal filter and a lower support mesh for composite processing; aligns the upper metal filter and the lower support mesh between layers to obtain a composite filter to be processed; collects a first thermo-pressing related feature vector and a second thermo-pressing related feature vector corresponding to the upper metal filter and the lower support mesh, respectively; inputs the first thermo-pressing related feature vector and the second thermo-pressing related feature vector into a thermo-pressing equalization control model to obtain a first rectangular pulse current signal and a second rectangular pulse current signal, wherein the thermo-pressing equalization control model is connected to a pulse power supply control module; connects a pulse thermo-pressing device, and the pulse power supply control module controls the upper and lower thermo-pressing heads of the pulse thermo-pressing device to perform thermo-pressing on the composite filter to be processed according to the first rectangular pulse current signal and the second rectangular pulse current signal, to obtain a processed composite filter. This invention solves the technical problem in the prior art where inconsistent temperatures of the upper and lower hot press heads lead to uneven thermal stress at the interface, resulting in weak bonding or warping. It constructs a thermal pressure equalization control model based on the thermal pressure-related feature vectors of the upper and lower materials of the metal filter mesh, and uses the model to drive a pulse power supply module to generate two rectangular pulse current signals to independently control the upper and lower hot press heads, thereby ensuring uniform heating and bonding of the double-layer metal filter mesh.

[0061] Example 2, based on the same inventive concept as the metal filter screen processing method in the foregoing examples, such as... Figure 2 As shown, this application provides a metal filter processing apparatus. The apparatus and method embodiments in this application are based on the same inventive concept. The apparatus includes: The system comprises the following modules: a mesh acquisition module 11, for acquiring the upper metal filter mesh and the lower support mesh for composite processing; an alignment module 12, for aligning the upper metal filter mesh and the lower support mesh between layers to obtain the composite filter mesh to be processed; a feature vector acquisition module 13, for acquiring the first and second hot-pressing related feature vectors corresponding to the upper metal filter mesh and the lower support mesh, respectively; a current signal acquisition module 14, for inputting the first and second hot-pressing related feature vectors into a hot-pressing equalization control model to obtain a first rectangular pulse current signal and a second rectangular pulse current signal, wherein the hot-pressing equalization control model is connected to a pulse power supply control module; and a hot-pressing module 15, for connecting to a pulse hot-pressing device, wherein the pulse power supply control module controls the upper and lower hot-pressing heads of the pulse hot-pressing device to hot-press the composite filter mesh to be processed according to the first and second rectangular pulse current signals, thereby obtaining the processed composite filter mesh.

[0062] Furthermore, the device is also used to perform the following functions: Define thermo-pressure related features, including thermal conductivity, thickness, mesh structure characteristics, and initial temperature; collect data from the upper metal filter mesh based on the thermo-pressure related features and perform normalization processing to obtain a first thermo-pressure related feature vector; collect data from the lower support mesh based on the thermo-pressure related features and perform normalization processing to obtain a second thermo-pressure related feature vector.

[0063] Furthermore, the device is also used to perform the following functions: Initialize the target hot-pressing temperature and the tolerance temperature difference threshold, where the tolerance temperature difference threshold is the allowable temperature difference range between the upper and lower hot-pressing heads; initialize the control parameters of the pulse power supply control module, where the control parameters include pulse current amplitude, voltage, frequency, and duty cycle; acquire the first hot-pressing related feature vector sample and the second hot-pressing related feature vector sample, and construct a difference feature vector sample; using the target hot-pressing temperature as the solution target, the tolerance temperature difference threshold as the solution constraint, the difference feature vector as the input variable, and the control parameters as the response variable, obtain a multi-factor pulse parameter solution function; based on the multi-factor pulse parameter solution function and the signal conversion unit, obtain the hot-pressing equalization control model.

[0064] Furthermore, the device is also used to perform the following functions: A fuzzy set is defined for each differential feature vector using the differential feature vector samples as input variables, and multiple sets of fuzzy sets are output, wherein the number of fuzzy sets for each differential feature vector is 3; a fuzzy rule base is constructed using the multiple sets of fuzzy sets, wherein each fuzzy rule in the fuzzy rule base includes a corresponding activation intensity; the fuzzy neural network calculates control parameters based on the fuzzy rule base.

[0065] Furthermore, the device is also used to perform the following functions: A difference feature vector is constructed between the first hot-press related feature vector and the second hot-press related feature vector. The hot-press equalization control model performs fuzzification processing on each difference feature vector in the difference feature vector according to the fuzzy neural network, and outputs the fuzzy membership degree of each difference feature vector. The activation intensity of each fuzzy rule in the fuzzy rule base is calculated according to the fuzzy membership degree of each difference feature vector to determine the first fuzzy rule. With the target hot-press temperature as the solution target, the first rectangular pulse current signal and the second rectangular pulse current signal output under the first fuzzy rule are obtained.

[0066] Furthermore, the device is also used to perform the following functions: Using the target hot-pressing temperature as the solution target, the control parameter solution results under the first fuzzy rule are obtained. The control parameter solution results include a first set of control parameters and a second set of control parameters. The first set of control parameters and the second set of control parameters are corrected using the tolerance temperature difference threshold as the solution constraint condition, and the corrected first set of control parameters and the second set of control parameters are output. The corrected first set of control parameters and the second set of control parameters are converted by the signal conversion unit to obtain a first rectangular pulse current signal and a second rectangular pulse current signal.

[0067] Furthermore, the device is also used to perform the following functions: The pulse period, pulse width, and pulse amplitude are calculated based on the first set of control parameters to generate a first rectangular pulse current signal; the pulse period, pulse width, and pulse amplitude are calculated based on the output of the second set of control parameters to generate a second rectangular pulse current signal; the first rectangular pulse current signal and the second rectangular pulse current signal are processed and output simultaneously.

[0068] Furthermore, the device is also used to perform the following functions: Detect whether the upper metal filter and the lower support mesh include a coating. If so, obtain the coating information. Add the coating information to the first thermo-pressure related feature vector or the second thermo-pressure related feature vector to update the first rectangular pulse current signal and the second rectangular pulse current signal.

[0069] In Embodiment 3, based on the same inventive concept as the metal filter processing method in the foregoing embodiments, this application also provides a computer-readable storage medium storing a computer program, which, when executed, implements the steps of any one of the methods described in Embodiment 1.

[0070] It should be noted that the order of the embodiments described above is for descriptive purposes only and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0071] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0072] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method of processing a metal filter screen, characterized by, The method comprises: acquiring an upper layer metal filter screen and a lower layer support screen for compounding; aligning the upper layer metal filter screen and the lower layer support screen layer by layer to obtain a to-be-processed composite filter screen; collecting a first hot pressing related feature vector and a second hot pressing related feature vector corresponding to the upper layer metal filter screen and the lower layer support screen, respectively; inputting the first hot pressing related feature vector and the second hot pressing related feature vector into a hot pressing balance control model to obtain a first rectangular pulse current signal and a second rectangular pulse current signal, wherein the hot pressing balance control model is connected with a pulse power supply control module; connecting a pulse hot pressing device, and the pulse power supply control module controls an upper hot pressing head and a lower hot pressing head of the pulse hot pressing device to perform hot pressing on the to-be-processed composite filter screen according to the first rectangular pulse current signal and the second rectangular pulse current signal, to obtain a processed composite filter screen; wherein inputting the first hot pressing related feature vector and the second hot pressing related feature vector into the hot pressing balance control model comprises: initializing a target hot pressing temperature and a tolerance temperature difference threshold value, the tolerance temperature difference threshold value being a temperature difference interval of the tolerance between the upper hot pressing head and the lower hot pressing head; initializing control parameters of the pulse power supply control module, the control parameters including pulse current amplitude, voltage, frequency and duty cycle; acquiring first hot pressing related feature vector samples and second hot pressing related feature vector samples to construct difference feature vector samples; taking the target hot pressing temperature as a calculation target, the tolerance temperature difference threshold value as a calculation constraint condition, the difference feature vector as an input variable, and the control parameters as a response variable to obtain a multi-factor pulse parameter calculation function; connecting the multi-factor pulse parameter calculation function and a signal conversion unit to obtain a hot pressing balance control model.

2. The method of claim 1, wherein, collecting a first hot pressing related feature vector and a second hot pressing related feature vector corresponding to the upper layer metal filter screen and the lower layer support screen, respectively, comprises: defining hot pressing related features, the hot pressing related features including thermal conductivity, thickness, mesh structure features and initial temperature; normalizing data of the upper layer metal filter screen based on the hot pressing related features to obtain a first hot pressing related feature vector; normalizing data of the lower layer support screen based on the hot pressing related features to obtain a second hot pressing related feature vector.

3. The method of claim 1, wherein, The multi-factor pulse parameter calculation function is obtained through a fuzzy neural network, and an architecture of the fuzzy neural network comprises an input layer, a fuzzification layer, a rule layer, a normalization layer, a de-fuzzification layer and a constraint output layer; defining a fuzzy set of each difference feature vector with the difference feature vector samples as input variables, and outputting multiple groups of fuzzy sets, wherein the number of fuzzy sets of each difference feature vector is 3; constructing a fuzzy rule base using the multiple groups of fuzzy sets, wherein each fuzzy rule in the fuzzy rule base includes a corresponding activation intensity; The fuzzy neural network performs control parameter calculation based on the fuzzy rule base.

4. The method of claim 3, wherein, inputting the first hot-pressing related feature vector and the second hot-pressing related feature vector into a hot-pressing balancing control model to obtain a first rectangular pulse current signal and a second rectangular pulse current signal, and the method comprises: constructing a difference feature vector of the first hot-pressing related feature vector and the second hot-pressing related feature vector; the hot-pressing balancing control model performs fuzzy processing on each difference feature vector in the difference feature vector according to the fuzzy neural network, and outputs the fuzzy membership degree of each difference feature vector; calculate the activation strength of each fuzzy rule in the fuzzy rule base according to the fuzzy membership degree of each difference feature vector, and determine a first fuzzy rule; take the target hot-pressing temperature as a calculation target to obtain the first rectangular pulse current signal and the second rectangular pulse current signal output under the first fuzzy rule.

5. The method of claim 4, wherein, obtain the first rectangular pulse current signal and the second rectangular pulse current signal output under the first fuzzy rule, and the method comprises: take the target hot-pressing temperature as a calculation target to obtain a control parameter calculation result under the first fuzzy rule, and the control parameter calculation result comprises a first set of control parameters and a second set of control parameters; modify the first set of control parameters and the second set of control parameters under the tolerance temperature difference threshold as a calculation constraint, and output the modified first set of control parameters and the second set of control parameters; convert the modified first set of control parameters and the second set of control parameters through the signal conversion unit respectively to obtain the first rectangular pulse current signal and the second rectangular pulse current signal.

6. The method of claim 5, wherein, convert the first set of control parameters and the second set of control parameters through the signal conversion unit respectively to obtain the first rectangular pulse current signal and the second rectangular pulse current signal, and the method comprises: calculate the pulse period, pulse width and pulse amplitude according to the first set of control parameters to generate the first rectangular pulse current signal; calculate the pulse period, pulse width and pulse amplitude according to the second set of control parameters to generate the second rectangular pulse current signal; synchronously process the first rectangular pulse current signal and the second rectangular pulse current signal output.

7. The method of claim 1, wherein, The method comprises: detecting whether the upper metal filter screen and the lower support screen comprise a plating layer, and if so, acquiring plating layer information; adding the plating layer information to the first hot-pressing related feature vector or the second hot-pressing related feature vector to update the first rectangular pulse current signal and the second rectangular pulse current signal.

8. A metal screen processing apparatus characterized by comprising: The device is used to perform the metal filter screen processing method as claimed in any one of claims 1-7, and the device comprises: a screen acquisition module configured to acquire an upper metal filter screen and a lower support screen for compounding; an alignment module configured to align the upper metal filter screen and the lower support screen to obtain a compound filter screen to be processed; a feature vector acquisition module configured to acquire a first hot-pressing related feature vector and a second hot-pressing related feature vector corresponding to the upper metal filter screen and the lower support screen respectively. The current signal acquisition module is configured to input the first hot pressing related feature vector and the second hot pressing related feature vector into a hot pressing equalization control model to obtain a first rectangular pulse current signal and a second rectangular pulse current signal, wherein the hot pressing equalization control model is connected with a pulse power supply control module. The hot pressing module is configured to be connected with a pulse hot pressing device, and the pulse power supply control module controls the upper hot pressing head and the lower hot pressing head of the pulse hot pressing device to perform hot pressing on the to-be-processed composite filter screen according to the first rectangular pulse current signal and the second rectangular pulse current signal, so as to obtain the processed composite filter screen.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the metal filter screen processing method in any one of claims 1-7.

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