Method and system for controlling preparation process of metal filtering material
By collecting sintering parameters in real time and adjusting dynamic thresholds, the problem of quality instability in the sintering process of metal filter materials was solved, achieving efficient and stable production control and improving product quality and production efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-18
- Publication Date
- 2026-03-27
AI Technical Summary
The sintering process of traditional metal filter materials is affected by fluctuations in raw material properties, equipment status, and environmental conditions, resulting in unstable quality and poor consistency, making it difficult to achieve efficient production.
By acquiring sintering parameters in real time, calculating dynamic thresholds, and periodically updating them based on historical comparison results, control mode signals are generated to adjust the control parameters of the sintering process, thereby achieving dynamic control.
It improves the sintering stability and product quality consistency of metal filter materials, reduces production costs, and increases production efficiency.
Smart Images
Figure CN121732802A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of filter material technology, and in particular to a method and system for controlling the preparation process of metal filter materials. Background Technology
[0002] In the preparation of metal filter materials, the sintering process is a crucial step, as it directly affects the final performance of the filter material, such as porosity, mechanical strength, and filtration efficiency.
[0003] Traditional sintering process control methods often rely on fixed process parameter settings, which are set before sintering begins and remain unchanged throughout the sintering cycle.
[0004] However, there are many variables in the actual sintering process, including but not limited to slight differences in the properties of raw materials, aging of equipment, and fluctuations in environmental conditions. These factors may lead to instability in the sintering effect, thereby affecting the quality consistency of the filter material.
[0005] Specifically, variations in raw material properties, such as uneven material thickness, fluctuations in target porosity, and differences in metal formulation, all affect heat conduction and mass migration during sintering, thereby altering the microstructure and macroscopic properties of the sintered body. Simultaneously, equipment aging, such as performance degradation of heating elements and decreased sensor accuracy, introduces additional errors, causing deviations between actual and preset sintering conditions. Furthermore, fluctuations in environmental conditions, such as temperature, pressure, and atmosphere concentration, also have a significant impact on the sintering process.
[0006] To overcome the above problems, it is necessary to provide a method and system for controlling the preparation process of metal filter materials. Summary of the Invention
[0007] To address the aforementioned technical problems, this invention provides a method and system for controlling the preparation process of metal filter materials. By dynamically adjusting the control parameters of the sintering process, the stability of the sintering of metal filter materials and the consistency of product quality are improved, production costs are reduced, and production efficiency is increased.
[0008] This invention provides a method for controlling the preparation process of metal filter materials, applied to the sintering process of metal filter materials. The control method includes the following steps: Based on the property parameters of the material to be sintered and the equipment status parameters, a dynamic threshold is calculated using a preset algorithm. The sintering parameter set during the sintering process is collected in real time, and the sintering parameter set is fused to obtain uniformity index and stability index. Based on the uniformity index and stability index, and in conjunction with historical comparison results, the dynamic threshold is periodically updated, wherein the historical comparison results are a set of comparison results records of historical uniformity index and historical dynamic threshold at corresponding times within multiple historical control periods. The uniformity index is compared with the dynamic threshold, and a control mode signal is generated based on the comparison result. The control parameters of the sintering process are adjusted based on the control mode signal and stability index, and the sintering process is controlled according to the adjusted control parameters.
[0009] Preferably, the calculation of the dynamic threshold based on the property parameters of the material to be sintered and the equipment status parameters using a preset algorithm includes: Based on the property parameters of the material to be sintered, a material reference coefficient is calculated, wherein the property parameters include material thickness, target porosity, and metal formulation type. Based on the equipment status parameters, a device correction factor is calculated, wherein the equipment status parameters include the heating element aging coefficient and the sensor confidence weight. Based on the material reference coefficient and the equipment correction factor, the dynamic threshold is generated by the preset algorithm, wherein the dynamic threshold includes a lower limit threshold and an upper limit threshold for the uniformity coefficient.
[0010] Preferably, the preset algorithm calculates the dynamic threshold using the following formula: in and As the baseline threshold, and For adjustment coefficients, For material reference coefficients, This is the equipment correction factor.
[0011] Preferably, the real-time acquisition of the sintering parameter set during the sintering process, and the fusion processing of the sintering parameter set to obtain uniformity and stability indices, includes: Temperature data, pressure data, and atmosphere concentration data from multiple temperature measuring points inside the sintering furnace are collected in real time and synchronously to form the sintering parameter set. Based on the temperature and pressure data, the uniformity index is calculated using the following formula: In the formula As a uniformity index, and The preset weighting coefficients, The variance of the temperature field is calculated based on the temperature values at each measurement point. The standard variance of the temperature field. The pressure field variance was calculated for each pressure value. The standard variance of the pressure field; Based on temperature, pressure, and atmosphere concentration data within a preset time window, the stability index is calculated using the following formula: In the formula As a stability indicator, , and The preset sensitivity coefficient, , and These are the variances calculated for temperature, pressure, and atmosphere concentration within the preset time window, respectively.
[0012] Preferably, the step of periodically updating the dynamic threshold based on the uniformity index and stability index, combined with historical comparison results, includes: The uniformity and stability indicators of the current control cycle are compared and analyzed with the historical data at the corresponding time in the comparison result record set to generate the deviation evaluation result between the current process state and the historical trend. The adjustment direction of the dynamic threshold is determined based on the deviation assessment results; Based on the adjustment direction, the adjustment amount of the dynamic threshold is quantitatively calculated and applied through a preset adjustment amount calculation model to generate the adjusted dynamic threshold.
[0013] Preferably, the step of comparing the uniformity index with the dynamic threshold and generating a control mode signal based on the comparison result includes: The uniformity index is compared with the lower and upper limits of the updated dynamic thresholds, and the process state is determined based on the comparison results. Preliminary control mode decisions are generated based on the process state, wherein the preliminary control mode decisions include conservative mode decisions, standard mode decisions, and optimized mode decisions. The initial control mode decision is revised based on the stability index to generate the final control mode decision. Based on the control mode decision, a corresponding control mode signal is generated, wherein the control mode signal includes a mode type code.
[0014] Preferably, adjusting the control parameters of the sintering process based on the control mode signal and stability index, and controlling the sintering process according to the adjusted control parameters, includes: The mode type code in the control mode signal is analyzed, and the PID control reference parameters, including the proportional coefficient reference value, integral time reference value and derivative time reference value, are determined according to the preset mode-parameter mapping relationship. Based on the stability index, the PID control baseline parameters are adaptively adjusted to generate the adjusted PID control parameters. Based on the adjusted PID control parameters, corresponding actuator control commands are generated and sent to the actuator of the sintering furnace to adjust the heating power, vacuum system pressure, and atmosphere control valve opening in real time.
[0015] This invention also provides a control system for a metal filter material preparation process, used to execute a control method for a metal filter material preparation process, and applied to the sintering process of the metal filter material. The control system includes: The dynamic threshold calculation module is used to calculate the dynamic threshold based on the property parameters of the material to be sintered and the equipment status parameters using a preset algorithm. The index generation module is used to collect the sintering parameter set of the sintering process in real time, and to perform fusion processing on the sintering parameter set to obtain uniformity index and stability index. The dynamic threshold update module is used to periodically update the dynamic threshold based on the uniformity index and the stability index, and in combination with the historical comparison results, wherein the historical comparison results are a set of comparison results records of historical uniformity index and historical dynamic threshold at the corresponding time within multiple historical control periods. The signal generation module is used to compare the uniformity index with the dynamic threshold and generate a control mode signal based on the comparison result. The real-time adjustment module is used to adjust the control parameters of the sintering process based on the control mode signal and stability index, and to control the sintering process according to the adjusted control parameters.
[0016] Compared with related technologies, the method and system for controlling the preparation process of metal filter materials provided by the present invention have the following beneficial effects: This invention significantly improves the stability of the sintering process and the consistency of product quality of metal filter materials by introducing a dynamic threshold calculation and real-time parameter adjustment mechanism.
[0017] Specifically, the present invention can calculate a dynamic threshold in real time based on the property parameters of the material to be sintered and the equipment status parameters through a preset algorithm. This threshold can reflect the optimal process range under the current sintering conditions.
[0018] Meanwhile, by collecting the sintering parameter set of the sintering process in real time and performing fusion processing to obtain uniformity and stability indicators, this scheme can accurately assess the deviation between the current sintering state and the optimal process range.
[0019] Based on these indicators and combined with historical comparison results, the dynamic thresholds are updated periodically, thereby ensuring the real-time performance and accuracy of the control parameters.
[0020] Ultimately, by generating control mode signals and adjusting the control parameters of the sintering process, this scheme achieves precise control of the sintering process, effectively improving the sintering quality and production efficiency of metal filter materials. Attached Figure Description
[0021] Figure 1 A flowchart of a method for controlling the preparation process of a metal filter material provided by the present invention; Figure 2 The present invention provides a modular structure diagram of a control system for the preparation process of a metal filter material. Detailed Implementation
[0022] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the drawings, not all structures. Moreover, unless otherwise specified, the embodiments and features described herein can be combined with each other.
[0023] It should also be noted that, for ease of description, the accompanying drawings show only the parts relevant to the invention and not all of them. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations (or steps) as sequential processes, many of the operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but it may also have additional steps not included in the drawings. The process may correspond to a method, function, procedure, subroutine, subroutine, etc.
[0024] Example 1 This invention provides a method for controlling the preparation process of metal filter materials, applied to the sintering process of metal filter materials, with reference to... Figure 1 As shown, the control method includes the following steps: S1: Based on the property parameters of the material to be sintered and the equipment status parameters, a dynamic threshold is calculated using a preset algorithm.
[0025] Specifically, step S1 includes the following steps: S11: Calculate the material reference coefficient based on the property parameters of the material to be sintered, wherein the property parameters include material thickness, target porosity and metal formulation type.
[0026] In this embodiment, the material reference system The calculation is based on a quantitative fusion process of the inherent property parameters of the material to be sintered. In practice, it is first necessary to accurately obtain three key property parameters: material thickness T (unit: mm), target porosity P (unit: %), and metal formulation type F (dimensionless classification coefficient). Material thickness is measured in real time during material transport using a laser thickness gauge, and the average value of at least 10 measurement points is taken to eliminate the influence of local fluctuations. The target porosity is determined according to the product specifications and is usually controlled within the range of 30%-70%. The metal formulation type is classified and coded according to the alloy composition, such as 316L stainless steel being coded as 1.0, Hastelloy as 1.2, and nickel-based alloys as 1.1, etc. The formula for calculating the material reference coefficient is: in It is 5mm thick, a standard thickness. The standard porosity is 50%. , , The formula, where is the weight and the sum of the three is 1, normalizes the parameters of three different dimensions into dimensionless reference coefficients through weighted averaging. Its effect is to make the material reference system... It can comprehensively reflect the overall influence of material properties on the sintering process. When the material thickness increases or the porosity increases, the material reference system... The corresponding increase indicates that the threshold needs to be adjusted to adapt to different heat conduction and diffusion conditions.
[0027] S12: Calculate the equipment correction factor based on the equipment status parameters, wherein the equipment status parameters include the heating element aging coefficient and the sensor confidence weight.
[0028] In this embodiment, the device correction factor The calculation requires a comprehensive assessment of the aging condition of the heating element and the degradation of sensor accuracy. In practice, the aging coefficient of the heating element... By measuring the current resistance value of the heating element With the initial resistance value The ratio is obtained as follows: The resistance value is measured monthly using a high-precision micro-ohmmeter. The sensor is judged to be significantly aged at that time. Sensor confidence weighting. Based on the sensor's calibration cycle and the error of the three most recent calibrations, sensors with a calibration cycle exceeding 6 months or an average error exceeding 2% have their weight reduced to 0.8. The formula for calculating the device correction factor is: in , As the weighting coefficient, this formula quantifies the impact of equipment performance degradation on process control accuracy. Its effect is that when heating element aging accelerates or sensor accuracy decreases, When the value decreases, the system will automatically relax the control threshold tolerance to avoid false alarms or control instability caused by equipment performance degradation.
[0029] S13: Based on the material reference coefficient and the equipment correction factor, the dynamic threshold is generated by the preset algorithm, wherein the dynamic threshold includes a lower limit threshold and an upper limit threshold of the uniformity coefficient.
[0030] In this embodiment, the dynamic threshold is generated by coordinating the modulation of the reference threshold using material reference coefficients and equipment correction factors.
[0031] In practical implementation, the benchmark threshold and Determined through extensive process experiments, it is usually set as follows: , These correspond to the lower and upper limits of the standards for qualified products, respectively. Adjustment coefficient. , The dynamic threshold, determined through analysis of variance, is calculated using the following formula: The dynamic threshold calculation formula enables the threshold to be adaptively adjusted according to material properties and equipment status, and its effect is reflected in three aspects: First, through... The multiplicative effect of this allows the threshold to match the material properties, resulting in a wider threshold range for thick or high-porosity materials; secondly, through... Additive modulation allows the threshold to be dynamically adjusted according to device performance, automatically widening the threshold range when performance degrades; finally, the adjustment coefficient... and The differential settings make the lower threshold more sensitive to device status than the upper threshold, ensuring a more stable quality baseline.
[0032] S2: Real-time acquisition of sintering parameter set during the sintering process, and fusion processing of the sintering parameter set to obtain uniformity index and stability index.
[0033] Specifically, step S2 includes the following steps: S21: Real-time synchronous acquisition of temperature data, pressure data, and atmosphere concentration data from multiple temperature measuring points inside the sintering furnace to form the sintering parameter set.
[0034] In this embodiment, the acquisition of sintering parameter sets is achieved through a distributed sensor network to ensure the spatiotemporal synchronization of temperature, pressure, and atmosphere concentration data.
[0035] In practice, 21 temperature measurement points were deployed inside the sintering furnace, using a 3×7 grid array of K-type thermocouples (measuring range 0-800℃, accuracy ±1℃). Nine points were arranged in the central area of the furnace, and 12 points in the edge area, with a sampling frequency of 10Hz. Pressure monitoring employed six piezoresistive pressure sensors (range 0-1MPa, accuracy 0.5%FS), installed at two points each in the upper, middle, and lower parts of the furnace. Atmosphere concentration monitoring used a laser gas analyzer for real-time measurement. , Ar concentration (accuracy ±0.1%). All sensors are time-synchronized via the IEEE 1588 precision clock protocol, with a timestamp alignment error of less than 1ms. The data acquisition system adopts an industrial Ethernet architecture, using a PLC to package sensor data into a unified format sintering parameter set. Each data packet contains a timestamp, sensor ID, measured value, and quality identifier. This implementation ensures the acquisition of spatially distributed and time-synchronized process parameters, providing a reliable data foundation for subsequent performance calculations.
[0036] S22: Based on the temperature and pressure data, the uniformity index is calculated using the following formula: In the formula As a uniformity index, and The preset weighting coefficients, The variance of the temperature field is calculated based on the temperature values at each measurement point. The standard variance of the temperature field. The pressure field variance was calculated for each pressure value. This represents the baseline variance of the pressure field.
[0037] In this embodiment, the uniformity index The calculation is based on the spatial distribution characteristics of the temperature and pressure fields. In practice, the collected temperature data is first grouped according to time windows (usually 1 second), and the temperature variance of 21 measurement points within each time window is calculated. Pressure field variance The same method was used for calculation.
[0038] The function of this formula is to normalize the spatial fluctuations of the temperature and pressure fields. The dimensionless index S, within its range, indicates that uniformity is good when the value is close to 1 and poor when it is close to 0. In practical applications, an alarm is triggered when S is less than 0.8, indicating potential problems such as heating element malfunction or uneven airflow distribution. S23: Based on temperature data, pressure data, and atmosphere concentration data within a preset time window, the stability index is calculated using the following formula: In the formula As a stability indicator, , and The preset sensitivity coefficient, , and These are the variances calculated for temperature, pressure, and atmosphere concentration within the preset time window, respectively.
[0039] In this embodiment, stability index Process stability is assessed by analyzing the time-series fluctuation characteristics of process parameters.
[0040] In practice, a sliding time window of 5 minutes (containing 3000 sampling points) was set, and the sliding variances of temperature, pressure, and atmosphere concentration were calculated respectively. , and Temperature variance The calculation uses the Welford online algorithm to avoid storing large amounts of historical data.
[0041] , and The sensitivity coefficients were determined by principal component analysis and were 0.5, 0.3, and 0.2, respectively, reflecting the contribution of each parameter to stability.
[0042] The function of this formula is to synthesize and transform multi-parameter fluctuations into... The stability measure of an interval is that when the process is stable... When the value is close to 1 (small variance), the Q value approaches 0 when the fluctuation is large. In actual control, the setting is... A value less than 0.6 is the stability warning threshold, at which point the system automatically switches to conservative control mode.
[0043] S3: Based on the uniformity index and stability index, and in combination with historical comparison results, the dynamic threshold is periodically updated, wherein the historical comparison results are a set of comparison results records of historical uniformity index and historical dynamic threshold at corresponding times within multiple historical control cycles.
[0044] Specifically, step S3 includes the following steps: S31: Compare and analyze the uniformity and stability indicators of the current control cycle with the historical data at the corresponding time in the comparison result record set to generate a deviation evaluation result between the current process state and the historical trend.
[0045] In this embodiment, the deviation evaluation results are generated through intelligent comparative analysis of real-time data and historical databases.
[0046] In practice, the system establishes a circular buffer containing historical data from the most recent 1000 control cycles. Each data record includes a timestamp and a uniformity index. Stability indicators and the corresponding dynamic threshold and When new real-time data arrives, the corresponding data group for the same process stage (such as heating stage, heat preservation stage, and cooling stage) in the historical database is first found through a time series matching algorithm.
[0047] A similarity calculation method based on Euclidean distance was used, selecting the top 20 historical samples with the smallest distances as the reference set. The bias assessment results included three dimensions: Relative deviation rate of values (current) (Percentage of the difference from the historical mean) Trend deviation of the value (current) The degree of deviation from the historical trend line), and the threshold matching degree (the difference between the current threshold setting and the historical optimal setting).
[0048] These evaluation results are weighted and fused to generate a comprehensive deviation score, with a score range of... A higher score indicates a more severe deviation from the normal state. The system can automatically update the evaluation results every 5 minutes, providing a quantitative basis for threshold adjustment.
[0049] In the weighted fusion to generate the comprehensive deviation score, the system assigns different weight coefficients to each dimension index based on their importance in representing process anomalies. Among these, the uniformity index... The deviation of the value is given the highest weight because this indicator is directly related to the final quality of the product, and is set to 0.5. Stability Index The trend deviation of the value receives the second highest weight, reflecting the stability of the production process, and is set to 0.3. The threshold matching degree has the lowest weight, but its role cannot be ignored; it reflects the rationality of the control parameter settings, and is set to 0.2. The final comprehensive deviation score is obtained by adding the three weighted index values.
[0050] S32: Determine the adjustment direction of the dynamic threshold based on the deviation evaluation results.
[0051] In this embodiment, the decision on the direction of threshold adjustment is based on a multi-level judgment strategy using the deviation assessment results. Specifically, three decision thresholds are first set: a slight deviation threshold of 0.3, a moderate deviation threshold of 0.6, and a severe deviation threshold of 0.8.
[0052] When the overall deviation score is below 0.3, it is determined to maintain the current threshold; when the score is between 0.3 and 0.6, stability indicators are considered. Perform a second-level judgment: If If the value is greater than 0.7 and the S value has a positive deviation, then the threshold should be relaxed. If the score is less than 0.5 and the S value has a negative deviation, the tightening threshold is selected; if the score exceeds 0.6, the tightening threshold strategy is triggered directly.
[0053] S33: Based on the adjustment direction, the adjustment amount of the dynamic threshold is quantitatively calculated and applied through a preset adjustment amount calculation model to generate the adjusted dynamic threshold.
[0054] In this embodiment, the threshold adjustment amount is calculated using an adaptive algorithm based on model prediction. Specifically, a multiple regression model is first established, relating the threshold adjustment amount to the deviation score and the stability index: Where a, b, and c are weight coefficients obtained through training with historical data. The deviation score, It is the moving average of the three most recent adjustments.
[0055] After the adjustment amount is calculated, the new threshold must be verified through the security check module to ensure that it is met. (Quality baseline) (Up to the limit of the process), and (Minimum operating range). When applying a new threshold, a smooth transition strategy is adopted, with a 10-minute transition period. During this period, the threshold is gradually switched using linear interpolation to avoid drastic fluctuations in the control system. The system also records the effect data of each adjustment. If the deviation score increases after three consecutive adjustments, the expert diagnosis mode is automatically triggered, indicating that manual intervention may be needed to check the equipment status or process parameter settings.
[0056] S4: Compare the uniformity index with the dynamic threshold, and generate a control mode signal based on the comparison result.
[0057] Specifically, step S4 includes the following steps: S41: Compare the uniformity index with the lower and upper limits of the updated dynamic thresholds, and determine the process state based on the comparison results.
[0058] In this embodiment, process status identification is achieved through multi-level comparison between the real-time uniformity index S and the dynamic threshold.
[0059] In practice, the system first obtains the updated dynamic thresholds, including the lower limit threshold. and upper limit threshold The identification process employs a three-level judgment mechanism: when... When, it is determined to be below the lower limit; when When, it is judged as a normal state; when When the time is exceeded, it is determined to be in a state above the upper limit.
[0060] To avoid frequent state switching near the threshold boundary, the system sets a hysteresis interval of 0.02. For example, when S drops from below... Rise to Only when the state is below the lower limit will it switch to the normal state.
[0061] Status recognition is performed every 10 seconds, and the duration of each status is recorded. When the same status lasts for more than 5 minutes, the reliability level of that status is increased. During implementation, the system displays the current process status in real time and provides intuitive status indications through color coding (red - abnormal, yellow - critical, green - normal).
[0062] S42: Generate preliminary control mode decisions based on the process state, wherein the preliminary control mode decisions include conservative mode decisions, standard mode decisions, and optimized mode decisions.
[0063] In this embodiment, the initial control mode decision is based on a rule-mapping mechanism according to the process state. Specifically, the system presets three basic decision modes: When the system is below the lower limit, a conservative mode decision is triggered, in which the system prioritizes improving stability. When in a normal state, a standard mode decision is triggered, in which the system maintains the current control strategy. When the system is above the limit, an optimization mode decision is triggered, in which the system allows for performance optimization exploration.
[0064] Each decision-making mode includes a complete parameter setting scheme. The conservative mode uses a larger proportional gain and a longer integration time, while the optimization mode appropriately reduces the proportional gain and shortens the integration time to improve the response speed.
[0065] S43: Based on the stability index, revise the preliminary control mode decision to generate the final control mode decision.
[0066] In this embodiment, decision correction is achieved through stability indices. Adaptive adjustments are made to the initial decisions.
[0067] In practical implementation, the system establishes a stability-decision correction mapping table: when If the system is deemed to have excellent stability, positive corrections are allowed based on the initial decision, such as adjusting the conservative mode to the standard mode, or adjusting the standard mode to the optimized mode. when If the system is deemed to be in good stability, the initial decision is maintained. when If the system is deemed unstable, a negative correction is made, adjusting all modes towards a more conservative approach.
[0068] Correction range and The degree of deviation is proportional to the value; a linear correction algorithm is used, and the correction coefficient is ( -0.7) × 2. The correction process also considers... The trend of value change, if If the value is trending downwards, the correction range should be increased appropriately; if If the value is on an upward trend, the correction range should be reduced appropriately.
[0069] During the correction, the system first needs to... The trend of the value is accurately identified. This process is based on a 10-minute time window, which contains 60 samples sampled at 10-second intervals. The data points were analyzed using linear regression with the least squares method to fit the slope parameter of the trend line. This slope value is the key basis for trend determination: when the slope is less than -0.001, it indicates... The value is considered to be in a downward trend, meaning the decrease exceeds 0.06 per minute; when the slope is greater than 0.001, it is considered to be in an upward trend, corresponding to an increase exceeding 0.06 per minute; and when the absolute value of the slope does not exceed 0.001, it is considered to be... The value remains stable.
[0070] Regarding the calculation of the basic correction factor, the system adopts ( The initial correction is calculated by multiplying -0.7 by 2. This base value is then adjusted accordingly based on the trend type. When a downward trend is detected, the system will appropriately increase the correction magnitude, with the adjustment factor being the sum of 1 and 50 times the absolute value of the slope.
[0071] For example, when a clear downward trend is detected (slope of -0.002), the correction magnitude will increase by 10%. Conversely, when an upward trend occurs, the correction magnitude will decrease accordingly, with the adjustment factor being the difference between 1 and 50 times the slope. If an upward trend with a slope of 0.0015 is detected, the correction magnitude will decrease by 7.5%. For a stable trend, the system maintains the base correction amount unchanged.
[0072] In addition, the trend analysis results are updated every 10 seconds, and the current slope value is calculated in real time. After determining the trend type based on the slope range, the basic correction coefficient and trend adjustment amount are calculated sequentially to obtain the correction coefficient for practical application. To ensure system stability, adjustment range limits are also set: the maximum increase is no more than 30%, and the maximum decrease is no less than -20%. When the value exceeds the normal range (below 0.4 or above 0.9), the effect of trend adjustment will gradually weaken, and instead, it will become more... The absolute level of the value is the primary basis.
[0073] S44: Generate a corresponding control mode signal based on the control mode decision, wherein the control mode signal includes a mode type code.
[0074] In this embodiment, the control mode signal generation converts the final decision into standardized control commands.
[0075] In practice, the system uses a three-digit code to represent the control mode: 001 represents conservative mode, 010 represents standard mode, and 100 represents optimized mode.
[0076] The signal contains the following fields: mode type code (3 bits), control parameter set identifier (8 bits), effective timestamp (16 bits), and checksum (4 bits).
[0077] After signal generation, it is transmitted to the control system via industrial Ethernet using Modbus TCP to ensure reliable signal transmission. The system also includes a signal acknowledgment mechanism; the receiving end must return an acknowledgment signal upon receiving it. If no acknowledgment is received within 2 seconds, the sending end will automatically retransmit. All signal exchange records are stored in a log file for troubleshooting and performance analysis. Furthermore, the system provides signal simulation testing capabilities, allowing verification of the correctness of signal generation and processing in an offline environment.
[0078] S5: Adjust the control parameters of the sintering process based on the control mode signal and stability index, and control the sintering process according to the adjusted control parameters.
[0079] Specifically, step S5 includes the following steps: S51: Analyze the mode type code in the control mode signal, and determine the PID control reference parameters according to the preset mode-parameter mapping relationship, including the proportional coefficient reference value, integral time reference value and derivative time reference value.
[0080] In this embodiment, control mode signal parsing and PID parameter mapping are completed collaboratively by a signal decoder and a parameter database.
[0081] In practice, the system first receives the control mode signal from step S44 and uses a Modbus TCP protocol parser to extract the mode type code (3-bit binary number) from the signal. The system maintains a parameter mapping database, which contains the baseline PID parameters corresponding to three control modes: conservative mode (code 001) corresponds to proportional coefficient P0=2.5, integral time I0=180 seconds, and derivative time D0=30 seconds; standard mode (code 010) corresponds to P0=2.0, I0=120 seconds, and D0=20 seconds; and optimized mode (code 100) corresponds to P0=1.5, I0=90 seconds, and D0=15 seconds.
[0082] The parameter mapping relationship is established based on a large amount of process test data to ensure that each mode can achieve the optimal control effect under specific operating conditions. After parsing the code, the system retrieves the corresponding baseline parameter set through the database query interface, and records the parameter call timestamp and the reason for mode switching for subsequent performance analysis.
[0083] S52: Adaptively adjust the PID control baseline parameters based on the stability index to generate adjusted PID control parameters.
[0084] In this embodiment, the adaptive adjustment of PID parameters adopts a multi-level correction strategy based on the stability index Q.
[0085] In practice, the system first establishes The correspondence between the value and the parameter adjustment coefficient: when When, adjust the coefficient 1.2 (allowing parameter optimization); when When, adjust the coefficient Set to 1.0 (maintain baseline parameters); when When, adjust the coefficient It is 0.8 (conservative adjustment).
[0086] The adjustment calculation uses a component processing method: proportional coefficient ; Points Time ; Differential time .
[0087] This adjustment mechanism ensures enhanced system response speed when stability is good and improved system robustness when stability is insufficient. The system updates the adjustment parameters every 30 seconds, while setting a limit on the parameter change rate, with a single adjustment not exceeding 20% to prevent drastic parameter fluctuations.
[0088] S53: Based on the adjusted PID control parameters, generate corresponding actuator control commands and send them to the actuator of the sintering furnace to adjust the heating power, vacuum system pressure and atmosphere control valve opening in real time.
[0089] In this embodiment, the generation and execution of actuator control commands are achieved through a distributed control system.
[0090] In practice, the system first converts the adjusted PID parameters into control commands: the heating power command generates a duty cycle signal through the PWM modulator (0-100% corresponds to 0-100kW); the vacuum system pressure command is output through the PID controller (0-10V corresponds to 0-0.1MPa); and the atmosphere valve opening command is set through the position controller (0-100% corresponds to fully closed-fully open).
[0091] Command transmission employs a priority scheduling mechanism, with temperature control commands having the highest priority, followed by pressure control, and atmosphere control commands having the lowest priority. The system transmits commands to each actuator via a PROFIBUS-DP fieldbus, with a transmission cycle of 100ms. Each actuator is equipped with a local feedback system to monitor command execution status in real time, automatically triggering a calibration procedure when an execution deviation exceeds 5%. All control process data is stored in the database in real time for generating process reports and performing performance analysis.
[0092] Example 2 This invention also provides a control system for a metal filter material preparation process, used to execute a control method for a metal filter material preparation process, and applied to the sintering process of metal filter materials, see reference. Figure 2 As shown, the control system includes: The dynamic threshold calculation module 100 is used to calculate the dynamic threshold based on the property parameters of the material to be sintered and the equipment status parameters through a preset algorithm.
[0093] The index generation module 200 is used to collect the sintering parameter set of the sintering process in real time, and to perform fusion processing on the sintering parameter set to obtain uniformity index and stability index.
[0094] The dynamic threshold update module 300 is used to periodically update the dynamic threshold based on the uniformity index and the stability index, and in combination with the historical comparison results, wherein the historical comparison results are a set of comparison results records of historical uniformity index and historical dynamic threshold at corresponding times within multiple historical control periods.
[0095] The signal generation module 400 is used to compare the uniformity index with the dynamic threshold and generate a control mode signal based on the comparison result.
[0096] The real-time adjustment module 500 is used to adjust the control parameters of the sintering process based on the control mode signal and stability index, and to control the sintering process according to the adjusted control parameters.
[0097] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0098] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.
[0099] It should also be noted that the term "include" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Without further limitations, the inclusion of an element by a statement that defines "..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.
Claims
1. A method for controlling the preparation process of metal filter materials, applied to the sintering process of metal filter materials, characterized in that, The control method includes the following steps: Based on the property parameters of the material to be sintered and the equipment status parameters, a dynamic threshold is calculated using a preset algorithm. The sintering parameter set during the sintering process is collected in real time, and the sintering parameter set is fused to obtain uniformity index and stability index. Based on the uniformity index and stability index, and in conjunction with historical comparison results, the dynamic threshold is periodically updated, wherein the historical comparison results are a set of comparison results records of historical uniformity index and historical dynamic threshold at corresponding times within multiple historical control periods. The uniformity index is compared with the dynamic threshold, and a control mode signal is generated based on the comparison result. The control parameters of the sintering process are adjusted based on the control mode signal and stability index, and the sintering process is controlled according to the adjusted control parameters.
2. The method for controlling the preparation process of a metal filter material according to claim 1, characterized in that, The dynamic threshold calculated using a preset algorithm based on the property parameters of the material to be sintered and the equipment status parameters includes: Based on the property parameters of the material to be sintered, a material reference coefficient is calculated, wherein the property parameters include material thickness, target porosity, and metal formulation type. Based on the equipment status parameters, a device correction factor is calculated, wherein the equipment status parameters include the heating element aging coefficient and the sensor confidence weight. Based on the material reference coefficient and the equipment correction factor, the dynamic threshold is generated by the preset algorithm, wherein the dynamic threshold includes a lower limit threshold and an upper limit threshold for the uniformity coefficient.
3. The method for controlling the preparation process of a metal filter material according to claim 2, characterized in that, The preset algorithm calculates the dynamic threshold using the following formula: in and As the baseline threshold, and For adjustment coefficients, For material reference coefficients, This is the equipment correction factor.
4. The method for controlling the preparation process of a metal filter material according to claim 3, characterized in that, The real-time acquisition of sintering parameter sets during the sintering process, and the fusion processing of these parameter sets to obtain uniformity and stability indices, include: Temperature data, pressure data, and atmosphere concentration data from multiple temperature measuring points inside the sintering furnace are collected in real time and synchronously to form the sintering parameter set. Based on the temperature and pressure data, the uniformity index is calculated using the following formula: In the formula As a uniformity index, and The preset weighting coefficients, The variance of the temperature field is calculated based on the temperature values at each measurement point. The standard variance of the temperature field. The pressure field variance was calculated for each pressure value. The standard variance of the pressure field; Based on temperature, pressure, and atmosphere concentration data within a preset time window, the stability index is calculated using the following formula: In the formula As a stability indicator, , and The preset sensitivity coefficient, , and These are the variances calculated for temperature, pressure, and atmosphere concentration within the preset time window, respectively.
5. The method for controlling the preparation process of a metal filter material according to claim 4, characterized in that, The step of periodically updating the dynamic threshold based on the uniformity and stability indices, combined with historical comparison results, includes: The uniformity and stability indicators of the current control cycle are compared and analyzed with the historical data at the corresponding time in the comparison result record set to generate the deviation evaluation result between the current process state and the historical trend. The adjustment direction of the dynamic threshold is determined based on the deviation assessment results; Based on the adjustment direction, the adjustment amount of the dynamic threshold is quantitatively calculated and applied through a preset adjustment amount calculation model to generate the adjusted dynamic threshold.
6. The method for controlling the preparation process of a metal filter material according to claim 5, characterized in that, The step of comparing the uniformity index with the dynamic threshold and generating a control mode signal based on the comparison result includes: The uniformity index is compared with the lower and upper limits of the updated dynamic thresholds, and the process state is determined based on the comparison results. Preliminary control mode decisions are generated based on the process state, wherein the preliminary control mode decisions include conservative mode decisions, standard mode decisions, and optimized mode decisions. The initial control mode decision is revised based on the stability index to generate the final control mode decision. Based on the control mode decision, a corresponding control mode signal is generated, wherein the control mode signal includes a mode type code.
7. The method for controlling the preparation process of a metal filter material according to claim 6, characterized in that, The step of adjusting the control parameters of the sintering process based on the control mode signal and stability index, and controlling the sintering process according to the adjusted control parameters, includes: The mode type code in the control mode signal is analyzed, and the PID control reference parameters, including the proportional coefficient reference value, integral time reference value and derivative time reference value, are determined according to the preset mode-parameter mapping relationship. Based on the stability index, the PID control baseline parameters are adaptively adjusted to generate the adjusted PID control parameters. Based on the adjusted PID control parameters, corresponding actuator control commands are generated and sent to the actuator of the sintering furnace to adjust the heating power, vacuum system pressure, and atmosphere control valve opening in real time.
8. A control system for a metal filter material preparation process, used to execute a control method for a metal filter material preparation process as described in any one of claims 1 to 7, and applied to the sintering process of the metal filter material, characterized in that, The control system includes: The dynamic threshold calculation module is used to calculate the dynamic threshold based on the property parameters of the material to be sintered and the equipment status parameters using a preset algorithm. The index generation module is used to collect the sintering parameter set of the sintering process in real time, and to perform fusion processing on the sintering parameter set to obtain uniformity index and stability index. The dynamic threshold update module is used to periodically update the dynamic threshold based on the uniformity index and the stability index, and in combination with the historical comparison results, wherein the historical comparison results are a set of comparison results records of historical uniformity index and historical dynamic threshold at the corresponding time within multiple historical control periods. The signal generation module is used to compare the uniformity index with the dynamic threshold and generate a control mode signal based on the comparison result. The real-time adjustment module is used to adjust the control parameters of the sintering process based on the control mode signal and stability index, and to control the sintering process according to the adjusted control parameters.