Graphite forming process parameter optimization method and system

Abstract

The invention relates to the technical field of process parameter optimization, in particular to a graphite forming process parameter optimization method and system.The method comprises the following steps that mold pressing parameter time sequence data are obtained, a rate mapping structure is constructed, extreme values are extracted to calculate parameter offset, linkage priority pairs are recognized, a combination scheme is set to obtain a linkage path, and parameter statistics redundancy combination is adjusted. And performing disturbance test screening to feed back the minimum path to generate an optimal process path set. According to the method, the parameter rate extreme value is extracted, the time position is mapped, the dynamic change track is constructed, the perception capability of the process state is enhanced, the extreme value time offset is calculated to identify the stable linkage combination, the parameter cooperation path is revealed, the regulation and control precision is improved, the change direction is concluded to establish the response path, and the linkage relation under different performances is determined. And disturbance screening is executed to recognize and feed back the minimum path, the combination stability and the anti-interference capability are enhanced, modeling and path recognition are integrally driven by data, and the systematicness and robustness of process regulation and control are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of process parameter optimization, in particular to a graphite forming process parameter optimization method and system. BACKGROUND

[0002] The technical field of process parameter optimization includes systematic modeling, calculation and control of various process parameters involved in manufacturing or processing to improve processing quality and production efficiency. The core content of this technical field is to determine and adjust the value range and combination of key process parameters through data modeling, simulation analysis, parameter sensitivity research and mathematical optimization methods, so that the process achieves the target performance under the preset constraints. The entire process parameter optimization field mainly involves numerical calculation, experimental design methods, optimization theory and algorithms, response surface analysis and multi-factor coupling modeling techniques, and is widely used in industrial manufacturing processes such as mechanical processing, heat treatment, injection molding, and metallurgical forming.

[0003] Among them, the graphite forming process parameter optimization method refers to the method of using numerical simulation and optimization means to model and control parameters such as temperature, pressure, holding time, and charging amount that affect the forming quality in the forming process such as molding or extrusion of graphite materials. This method is usually based on the mathematical relationship between process influencing factors and forming performance, and through regression analysis or response surface model to extract parameter interaction, and then use multi-objective optimization algorithm to determine the parameter combination scheme, and verify the rationality and adaptability of parameter setting through simulation analysis.

[0004] The existing technology mainly relies on static parameter modeling and mathematical regression methods for process parameter analysis, which is difficult to capture the dynamic response characteristics in the process, especially in the case of severe parameter fluctuations or system disturbance, there are problems of lagging control and unstable response. In the existing response surface modeling, each variable often enters the model in the form of independent input, lacking systematic extraction of its time shift and linkage relationship, resulting in weak coupling identification ability between parameters, affecting the effectiveness of combination configuration. For example, in the graphite molding process, the heating rate has obvious stage response to the holding time, and it is difficult to accurately present its nonlinear linkage characteristics by simply using static parameter regression. In addition, most of the existing optimization paths focus on the improvement of the objective function value, ignoring the stable performance of the combination under different disturbance conditions, resulting in that part of the optimal configuration is easily affected by the working condition change in actual execution, reducing the forming consistency. Lack of path linkage direction identification and combination flexibility evaluation makes the processing process lack of stable alternative scheme reserve, and the control space is insufficient when facing batch production demand or working condition disturbance, affecting the stability and flexible response ability of the whole process. SUMMARY

[0005] The application aims to solve the problems in the prior art and provides a graphite forming process parameter optimization method and system.

[0006] To achieve the above-mentioned purpose, the application adopts the following technical scheme: a graphite forming process parameter optimization method, comprising the following steps: S1: obtaining time sequence data of mold pressing pressure, pressure holding time, temperature rising rate and particle size distribution in a graphite mold pressing equipment, calculating difference value of adjacent points to form a rate sequence, extracting rate extreme points and mapping time positions, constructing a rate change structure corresponding to parameters and a time axis, and generating a graphite process parameter rate mapping structure; S2: extracting extreme point information based on the graphite process parameter rate mapping structure, calculating time offset values between parameter combinations, screening combinations that repeatedly appear in a period and have stable offsets, judging response relationships, and obtaining a graphite process linkage priority parameter list; S3: setting a mold pressing pressure and temperature rising rate combination scheme according to the graphite process linkage priority parameter list, recording particle size distribution and pressure holding time response trends in a pressing period, identifying change paths and linkage directions between parameters, and obtaining a forming process linkage response path structure; S4: calling a combination setting in the forming process linkage response path structure, setting a target performance range, adjusting the temperature rising rate and recording pressure holding time change directions and amplitudes, counting consistent direction combination paths, classifying replaceable combinations under performance conditions, and generating a graphite parameter redundant combination group list.

[0007] As a further scheme of the application, the graphite process parameter rate mapping structure includes parameter time axis correspondence, rate change characteristic sequence, and extreme point time position mapping, the graphite process linkage priority parameter list includes time offset value stable combination, periodic repeated parameter group, and process flow response relationship judgment result, the forming process linkage response path structure includes parameter change path topology, linkage direction identification, and dynamic characteristic induction set, and the graphite parameter redundant combination group list includes target resistivity stable interval, volume density compaction standard path, and adjustment response record data set.

[0008] As a further scheme of the application, the specific steps of S1 are as follows: S101: obtaining time sequence data of mold pressing pressure, pressure holding time, temperature rising rate and particle size distribution, arranging according to parameter time sequence, calculating rate value by dividing difference value between adjacent time points by interval time, constructing rate sequence under unified time coordinates, and generating parameter rate sequence group; S102: Based on the parameter rate sequence group, the size of the value of the points in the rate sequence is compared, the time positions corresponding to the local maximum and minimum values are extracted, the corresponding relationship between the parameter rate value and the time point is recorded, the rate record sequence with extreme value identification is constructed, and the rate extreme value mapping coordinate set is generated S103: According to the rate extreme value mapping coordinate set, the parameter rate extreme value and the corresponding time point are paired, all parameter rate change structures with time are arranged, the rate time distribution map is constructed, and the graphite process parameter rate mapping structure is generated.

[0009] As a further scheme of the application, the specific steps of S2 are: S201: Based on the parameter rate mapping structure, the extreme point information of the parameter at the difference time is extracted, the time sequence between the parameter combinations is obtained according to the parameter name, the appearance time and the rate fluctuation interval of the extreme point, the time difference value of the parameter in the time sequence is called, the time offset value in the period is calculated, and the parameter combination time offset value is generated; S202: According to the parameter combination time offset value, the repeated appearance frequency of the parameter combination in the period is detected, the combination with an appearance frequency higher than the median value of the period combination is extracted, the time offset value sequence of the combination in the adjacent period is called, the parameter combination with an offset fluctuation amplitude less than the offset stability threshold is judged, and the repeated and offset stable parameters in the period are obtained. S203: Based on the repeated and offset stable parameters in the period, the sequence of the process stage where the parameters are located in the graphite process flow is combined, the arrangement position relationship of the parameters in the flow is called, the combination with a response relationship conforming to the upstream and downstream conduction characteristics is judged, and the graphite process linkage priority parameter list is screened.

[0010] As a further scheme of the application, the specific steps of S3 are: S301: Based on the graphite process linkage priority parameter list, the combination scheme of the mold pressure and the heating rate is set, the combination matrix is constructed, the mold equipment operating conditions are set, the particle size distribution and the pressure holding time in the pressing period are recorded, and the particle initial particle size and the time record value are generated. S302: According to the particle initial particle size and the time record value, the particle size change amplitude and the time point difference of the time period are extracted, the particle size change trend is extracted after combination comparison, and the particle size response trend value is obtained. S303: According to the particle size response trend value, the mold pressing parameter group and the trend direction are classified, the cooperative offset value is calculated, the parameter change path and the direction consistency are identified, and the forming process linkage response path structure is established.

[0011] As a further scheme of the application, the specific steps of S4 are: S401: Call the combination settings in the linkage response path structure of the molding process, set the resistivity range and the volume density range, obtain the heating rate and holding time records under the path, filter the path according to the resistivity range, and generate a resistivity range combination set. S402: Based on the resistivity range combination set, the bulk density value is compared with the compaction standard, and the path and parameter combination that simultaneously meets the resistivity and bulk density requirements is selected to generate a compaction compliance combination set. S403: Based on the compaction compliance combination set, statistically analyze the direction of change of heating rate and the magnitude of change of holding time, classify and record the combination path and parameter content, and generate a list of graphite parameter redundancy combination groups.

[0012] As a further aspect of the present invention, the method further includes: S5: Based on the path combinations in the graphite parameter redundancy combination group list, perform a heating rate disturbance test and monitor the change in holding time, screen the combination path with the smallest feedback change, sort and merge according to the disturbance control capability, and obtain the preferred process path set for graphite forming. The preferred process path set for graphite forming includes a disturbance feedback minimization path, a control capability ranking hierarchy, and a set of merged optimization criteria.

[0013] As a further aspect of the present invention, the specific steps of S5 are as follows: S501: Based on the path combination in the graphite parameter redundancy combination group list, extract the heating rate parameter, temperature zone setting and pressure holding period of the path, perform perturbation processing on the heating rate parameter and compare the difference with the original parameter, record the time change range of the pressure holding period after perturbation, and obtain the pressure holding time change range value. S502: Call the pressure holding time variation range value, calculate the offset value of the pressure holding time variation range before and after the path disturbance, filter the path set with the lowest variation range according to the set standard, and record the corresponding path number and parameter information to generate the path stability response range. S503: Based on the path number and change range in the path stability response interval, calculate the ratio between the pressure holding change and the disturbance intensity of the path, sort them according to the ratio, and filter the top-ranked paths to obtain the preferred path sequence set for graphite forming.

[0014] As a further aspect of the present invention, the extreme point refers to the local maximum or minimum value that appears in the parameter rate sequence, reflecting the key inflection point of parameter change; The disturbance test refers to the test method of observing the parameter response after subjecting a specific process parameter to a slight disturbance; The disturbance control capability refers to the response magnitude of the parameter path to external disturbances under disturbance testing. The smaller the magnitude, the stronger the control capability and the better the path selection.

[0015] A graphite forming process parameter optimization system, comprising: The rate feature construction module acquires time-series data of molding pressure, holding time, heating rate, and particle size distribution in the graphite molding equipment, calculates the difference between parameters at consecutive time points to form a rate sequence, extracts the extreme points of each rate sequence and marks the corresponding time positions, constructs the rate change structure of parameters on the time axis, and generates a rate mapping structure of graphite process parameters. The parameter offset extraction module calls the extreme point time distribution information in the graphite process parameter rate mapping structure, calculates the extreme point time offset value between molding pressure, heating rate, and holding time, filters parameter combinations that repeat in multiple cycles and have consistent offsets, and judges the response relationship in combination with the operation order in the graphite molding process to obtain a list of priority parameters for graphite process linkage. The linkage path identification module sets the combination path of molding pressure and heating rate on the list according to the graphite process linkage priority parameters. During the pressing cycle, it records the response curve of particle size distribution and holding time, identifies the corresponding path of the change direction of the median particle size and the change of holding time, summarizes the linkage trend of heating rate and molding pressure, constructs a dynamic path structure, and generates the linkage response path structure of molding process. The performance path screening module calls the combination settings in the linkage response path structure of the molding process, sets the range of target resistivity and bulk density, adjusts the heating rate and records the direction and magnitude of the change in holding time, counts the path combinations where resistivity remains stable and bulk density reaches the compaction standard, and generates a list of redundant graphite parameter combinations. The disturbance capability sorting module performs a heating rate disturbance operation and monitors the change in holding time based on the combination paths in the graphite parameter redundancy combination group list. It then filters out the combination paths with the smallest feedback changes and sorts and classifies them according to their disturbance control capabilities to obtain the preferred process path set for graphite forming.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by extracting the extreme values ​​of parameter rates and mapping them to time positions, a dynamic change trajectory is constructed to enhance the perception of process states. The extreme value time offset is calculated to identify stable linkage combinations, revealing parameter coordination paths and improving control accuracy. The change direction is summarized to establish response paths, clarifying the linkage relationship under different performance conditions, supporting combination configuration, statistically analyzing parameter combinations that meet performance conditions, constructing a set of alternative configurations to improve control flexibility, performing disturbance screening to identify the feedback minimum path, and enhancing combination stability and anti-disturbance capability. Overall, data-driven modeling and path identification improve the systematicness and robustness of process control. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a system module diagram of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0019] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0020] Please see Figure 1 A method for optimizing graphite forming process parameters includes the following steps: S1: Obtain time-series data of molding pressure, holding time, heating rate, and particle size distribution in the graphite molding equipment; calculate the difference between adjacent points to form a rate sequence; extract rate extreme points and map them to time positions; construct a rate change structure corresponding to the parameters and time axis; and generate a rate mapping structure for graphite process parameters. S2: Extract extreme point information based on the graphite process parameter rate mapping structure, calculate the time offset between parameter combinations, screen the combinations that recur within the cycle and have stable offsets, and combine the process flow to determine the response relationship to obtain the graphite process linkage priority parameter list. S3: Set the combination scheme of molding pressure and heating rate according to the graphite process linkage priority parameter list, record the response trend of particle size distribution and holding time during the pressing cycle, identify the change path and linkage direction between parameters, summarize the dynamic characteristics between groups, and obtain the linkage response path structure of molding process. S4: Call the combination settings in the linkage response path structure of the molding process, set the target resistivity and bulk density range, adjust the heating rate and record the direction and magnitude of the change in the holding time, count the combination paths where the resistivity is in the stable range and the bulk density reaches the compaction standard, and generate a list of redundant graphite parameter combination groups. S5: Based on the path combination in the graphite parameter redundancy combination group list, perform heating rate disturbance test and monitor the change of holding time, screen the combination path with the smallest feedback change, sort and merge according to disturbance control capability, and obtain the graphite forming preferred process path set; Extreme points refer to local maximum or minimum values ​​that appear in a parameter rate sequence, reflecting key inflection points in parameter changes; Disturbance testing refers to the experimental method of observing the parameter response after subjecting a specific process parameter to a minor disturbance; Disturbance control capability represents the magnitude of the response of the parameter path to external disturbances under disturbance testing. The smaller the magnitude, the stronger the control capability and the better the path selection.

[0021] The graphite process parameter rate mapping structure includes the parameter time axis correspondence, rate change characteristic sequence, and extreme point time position mapping. The graphite process linkage priority parameter list includes stable combinations of time offset values, periodically repeating parameter groups, and process flow response relationship judgment results. The molding process linkage response path structure includes parameter change path topology, linkage direction identifier, and dynamic feature induction set. The graphite parameter redundancy combination group list includes the target resistivity stable range, volume density compaction standard path, and adjustment response record dataset. The graphite molding preferred process path set includes disturbance feedback minimization path, control capability ranking hierarchy, and merging optimization standard set.

[0022] Please see Figure 1 The specific steps of S1 are as follows: S101: Acquire time-series data of molding pressure, holding time, heating rate and particle size distribution, arrange them in the order of parameter time, calculate the difference between adjacent time points and divide by the interval time to obtain the rate value, construct the rate sequence under a unified time coordinate, and generate a parameter rate sequence group. Time-series data such as molding pressure, holding time, heating rate, and particle size distribution are recorded by a real-time acquisition system at the equipment end. The sampling frequency for each type of data is set to be consistent or uniform to a 1-second cycle in the corresponding process step. For example, the output pressure data of the molding equipment is recorded once per second in MPa. The holding time is directly read by the program timer. The heating rate is obtained by the temperature curve fed back by the temperature control system and differential processing, expressed in °C per second. The particle size distribution data comes from the particle size detection system and reads the particle size change by sampling once every 10 seconds. For example, D50 represents the median particle size. Unified processing requires the timestamps to be standardized and sorted. Taking the molding pressure as an example, which is 10.0 MPa at 0 seconds and 13.0 MPa at 3 seconds, the average pressure change per second can be calculated as 1.0 MPa / s through rate changes. If the cumulative value of the holding time from the beginning to the end of the pressing stage is 90 seconds, then no rate change will occur in this segment. If the heating rate rises from 20°C to 80°C in 300 seconds, the average rate is 0.2°C / s. If the particle size parameter, such as D50, changes from 12μm to 14μm, time interpolation mapping needs to be performed to make it uniformly represented on the second-level time axis. For example, in the change from 0 seconds to 10 seconds, the rate at the 5-second point is linearly interpolated to be 0.2μm / s. The rate values ​​of all parameters are established according to the second-level time axis to create a data structure. The current change rate value of each parameter is recorded every second, which finally constitutes the rate sequence group of four types of process parameters in the molding process, providing a basis for subsequent time series analysis.

[0023] S102: Based on the parameter rate sequence group, compare the magnitude of the values ​​of the points before and after in the rate sequence, extract the time positions corresponding to the local maximum and minimum values, record the correspondence between the parameter rate values ​​and the time points, construct a rate record sequence with extreme value identifiers, and generate a rate extreme value mapping coordinate set; For each time point in the rate sequence, a sequential traversal is performed. The relationship between the rate values ​​of adjacent points is used to determine if it is a local extremum. Specifically, if the rate at the current point is greater than the two adjacent points, it is a local maximum; if it is less than the two adjacent points, it is a local minimum. For example, if the molding pressure rate is 0.5, 1.2, 0.7, 1.6, 0.9, and the rate at the 2nd second is 1.2 (preceded by 0.5, followed by 0.7), it constitutes a local maximum. If the rate at the 3rd second is 0.7, which is less than the adjacent points, it is a local minimum. Continuous judgment is achieved through a sliding window method, with a window width of 3 points, moving 1 point at a time to form a local extremum judgment sequence. For example, if the heating rate is 0.3, 0.4, 0.1, 0.5, 0.2, and the value at the 3rd second is 0.1, it is a local minimum. Each time an extremum is identified, its time position and corresponding rate value are recorded. The extremum type is then identified in the record, categorized as maximum or minimum. The record structure can be a combination of three items: time, rate value, and extremum label. When the particle size distribution rate, such as the D50 rate, changes to 0.15, 0.28, 0.12, 0.32, and 0.18, the value of 0.28 in the second second is the maximum, and the value of 0.12 in the third second is the minimum. In this way, corresponding extreme point data are generated for all four types of parameters, forming a rate record sequence with extreme value identification. Each point in this data structure reflects the abrupt change behavior of the parameter at a certain time, laying the foundation for subsequent behavior pattern combinations.

[0024] S103: Based on the rate extremum mapping coordinate set, establish a pairing relationship between the parameter rate extremum and the corresponding time point, organize the change structure of all parameter rates over time, form a complete rate time distribution map, and generate the graphite process parameter rate mapping structure. The specific calculation formulas for the changes in all parameter rates over time are as follows: ; Calculate the characteristic values ​​of parameter rate time fluctuation, construct a complete rate time distribution map, and generate a rate mapping structure for graphite process parameters; in, Representing the The process parameters are in the first The characteristic value of rate temporal fluctuation within a time interval Representing the The process parameters are in the first Within the time interval, the first The parameter rate value at each sampling point Representing the The process parameters are in the first The arithmetic mean of the parameter rate values ​​of all sampling points within a time interval Representing the The process parameters are in the first The maximum parameter rate of all sampling points within a time interval Representing the Within the time interval, the first The time value corresponding to each sampling point Representing the The arithmetic mean of the time values ​​corresponding to all sampling points within a time interval. In Representing the The sampling point sequence number within each time interval Representing the The total number of sampling points within each time period, indicated by the superscript. Represents the first The process parameters are in the first The identifier of the rate-time fluctuation characteristic value within a time interval, subscript Represents the process parameter number, subscript Represents the time segment number, subscript This represents the maximum value within the corresponding time period. The examples are taken from the same section Five consecutive monitoring records are recorded. The monitored temperature data comes from the thermocouple and data acquisition card data of the graphitization furnace. The time interval is derived from the timestamp on the data acquisition card. The records are as follows: , , , , , The interval within the range is obtained by difference calculation. One rate value: ; ; ; ; ; The time point corresponding to the rate in the segment time series is: ; The mean and extreme values ​​are calculated from the sequence within the segment: ; ; ; Molecules expanded by summation: ; ; ; ; Expand the first part of the denominator by taking the square root of the sum of squares: ; ; ; ; The second part of the denominator contains the extreme value deviation term: ; Denominator synthesis: ; Substituting into the principal formula, we get: ; The result indicates the segment The numerical level of the coupling fluctuation intensity of the intrinsic parameter rate series relative to the time series. Corresponding to the parameter rate change over time structure in step S103, by segment Formed by segment-by-segment calculation across the entire time domain The sequence, together with the time point pairing relationship in the rate extremum mapping coordinate set, enters the process of organizing the rate time distribution map, and then merges into the graphite process parameter rate mapping structure.

[0025] Real-world source of interval constraints: Common overlays for graphitization temperatures. to The heating rate is commonly covered to The temperature capability of graphitization equipment can reach to .

[0026] Please see Figure 1 The specific steps of S2 are as follows: S201: Based on the graphite process parameter rate mapping structure, extract the extreme point information of parameters at the time of difference, obtain the time series between parameter combinations according to the parameter name, occurrence time and rate fluctuation range corresponding to the extreme point, call the time difference value of the parameters in the time series, calculate the time offset value within the period segment, and generate the time offset value of parameter combination. In the graphite process parameter rate mapping structure, key parameters involved in the process, such as temperature, pressure, gas flow rate, and current density, need to be acquired first. These parameters are then labeled according to process stages; for example, temperatures T1, T2, and T3 correspond to different heating units. The sampling frequency is set to once per second, and the data range covers the entire process cycle, for example, from 0 to 300 seconds. After collecting the time series data, the rate of change of each parameter over time is calculated by dividing the difference between two consecutive time points by the time interval, forming a parameter rate curve. For example, if the rate of change of temperature T1 reaches 2.3 units per second at the 87th second, it is identified as a point of maximum change, called an extreme point, determined by the abrupt change in the direction of rate change. Each extreme point needs to record its occurrence time, the corresponding parameter name, and the rate fluctuation range within 20 seconds before and after it. This range can be represented as the difference between the maximum and minimum rates, for example, a fluctuation range of 1.8 units per second. These extreme points are grouped into an ordered parameter combination sequence based on their occurrence time; for example, T1 at 87 seconds, P1 at 90 seconds, and F2 at 95 seconds form a group. The time difference between adjacent parameters in a given group is calculated. For example, if the time difference between T1 and P1 is 3 seconds, and the time difference between P1 and F2 is 5 seconds, then the time offset values ​​for this combination are 3 and 5. All combinations are grouped into a set period, for example, every 300 seconds. The time offset value of each parameter combination is counted sequentially, ultimately forming a set of offset data for all combinations within the period. This process is suitable for identifying rapid changes in various parameter fluctuations during actual graphite production. It facilitates tracking the sequence and response delays of parameter changes between different equipment or processes, thereby reflecting the actual coordination rhythm and potential control opportunities between equipment.

[0027] S202: Based on the time offset value of the parameter combination, detect the frequency of recurrence of the parameter combination within the period segment, extract the combination whose frequency of occurrence is higher than the midpoint of the period combination, call the time offset value sequence of the combination in adjacent periods, determine the parameter combination whose offset fluctuation amplitude is less than the offset stability threshold, and obtain the parameters that are recurring and have stable offset within the period. After the time offset values ​​of parameter combinations within a period are generated, the frequency of occurrence of the combinations within each period is statistically analyzed. For example, in a 300-second period, a combination such as T1-P1-F2 appears 8 times, while the median of all combinations is 5 times. This combination meets the condition of having a frequency higher than the median and is listed as a key combination. Then, its time offset value sequence in adjacent periods is checked. If the combination T1-P1-F2 has offsets of 3 and 5 in period 1 and offsets of 4 and 5 in period 2, the maximum magnitude of the offset change in the two periods is calculated, which is the absolute value of the difference between the offset values ​​at the same position. If the difference is 1 or 0, the maximum value is 1. Then, it is compared with the set offset stability threshold, for example, if the threshold is 2 seconds, then the offset fluctuation of the combination is determined to be stable, and it is included in the set of periodically repeating and offset-stable parameters.

[0028] S203: Based on parameters that are repetitive and have stable offsets within the cycle, combined with the order of the parameters in the process stage in the graphite process flow, the arrangement relationship of parameter pairs in the process is called, and the combination of response relationship that conforms to the upstream and downstream transmission characteristics is judged to form a priority parameter list for graphite process linkage. Among the identified periodically repetitive and offset-stable parameter combinations, the sequence information of graphite process stages, such as heating, holding, and cooling, is introduced to clarify the stage to which each parameter belongs. For example, T1 belongs to heating, P1 to holding, and F2 to cooling. If the parameter sequence in the combination is T1→P1→F2, and the process sequence is also heating→holding→cooling, then it is confirmed that it meets the logical sequence of upstream and downstream transmission in the process flow, and is considered to have a stable process transmission chain. Finally, combinations that meet this sequence requirement are selected and summarized into a priority parameter list for graphite process linkage, which is used for subsequent process control and parameter synchronization identification reference.

[0029] Please see Figure 1 The specific steps of S3 are as follows: S301: Based on the priority parameter list of graphite process linkage, set the combination scheme of molding pressure and heating rate, construct the combination matrix, set the operating conditions of molding equipment, record the particle size distribution and holding time in the pressing cycle, and generate the initial particle size and time record values. Based on the collected initial particle size and time records, the entire pressing cycle was divided into multiple time intervals, each lasting 10 minutes. The degree of particle size change between adjacent time points was compared to obtain the particle size variation amplitude for each time period. For example, from 10 minutes to 20 minutes, the average particle size decreased from 85 μm to 74 μm, a change of 11 μm. These variation amplitudes were arranged in chronological order. Subsequently, the variation amplitudes within the time intervals were compared under different combinations of molding parameters. If one combination showed a continuous decrease in particle size in the first 30 minutes, with an average decrease greater than 10 μm, while another combination showed a variation amplitude concentrated between 5 and 7 μm, the former was determined to have a more significant particle size reduction trend. Furthermore, the particle size variation curves over time corresponding to each parameter combination were fitted to obtain the trend direction for each group. By comparing the slope values ​​of the trends corresponding to different combinations, the speed of the particle size reduction trend was determined. In this way, the particle size response trend value over time under each molding condition is extracted. For example, the particle size of one combination decreases by about 1.2 μm per minute on average, and the particle size of another combination decreases by about 0.6 μm per minute on average. The extracted result is the particle size response trend value.

[0030] S302: Based on the initial particle size and time record values, extract the particle size change range and time point differences over a time period, combine and compare them to extract the particle size change trend, and obtain the particle size response trend value. The specific formula for calculating the granularity change range and time point difference within the extracted time period is as follows: ; Calculate the comprehensive characteristic value of particle size change, perform combination comparison and extract the particle size change trend to obtain the particle size response trend value; in, This represents an intermediate composite value calculated based on the granularity variation over a time period and the differences at specific points in time, with a superscript indicating that... This represents the first comprehensive quantity identifier within step S302. Representing the The initial particle size values ​​corresponding to each time recording point, and their units of measurement are... Consistent, Representing the The particle size record values ​​corresponding to each time point and their units of measurement are... Consistent, Representing the The and the first The time interval difference between each time recording point and its unit of measurement is the same as all time records. Items consistent, This represents the total number of time records used in the calculation within the specified time period and is a positive integer. The value represents the arithmetic mean of the particle size records corresponding to all time points within the stated time period, and the unit of measurement is the same as... Consistent, Represents all within the stated time period The maximum granularity recorded value in the data. Represents all within the stated time period The smallest granularity of the recorded values, summation symbol subscript The index represents the sequence number of the time record point and superscript Represents the initial state identifier, superscript Represents the time record status indicator; Granularity recording values Data was collected at various time points using an online laser diffraction particle size analyzer. Its applicable particle size range covers approximately 0.1 micrometers to 3 millimeters and can be extended under specialized conditions, meeting the requirements for powder particle size monitoring. The update frequency of the time recording points is obtained by setting the online particle size data refresh cycle. Industry data indicates that online particle size data can be output at an update frequency of approximately 5 seconds, with the time interval difference... The initial granularity value is obtained by subtracting the timestamps of two consecutive data collections. The samples were collected from the same material source using the same equipment at the start of the operating condition and used as reference inputs for subsequent time recording points. The total number of time recording points is... The maximum granularity of the recorded value is obtained by counting time points. Taken from the whole The maximum value and the smallest granularity of the recorded value. Taken from the whole The minimum value in the average granularity record. By all Summation and division As can be seen, the weight parameters and adjustment coefficients do not exist in this formula system and therefore do not need to be assigned values ​​or have fluctuation basis. Before entering the calculation, non-numerical data needs to be quantized into numerical granularity and timestamp data. The quantization process is to generate serial numbers from sampling points. The sampling time is read from the timestamp, and the sampling particle size is output as a particle size value with the unit uniformly set to micrometers. The data source for the case study is four consecutive outputs from online laser diffraction particle size monitoring and the initial baseline output, maintaining unit consistency in micrometers and seconds. The collected timestamp sequences are 0 seconds, 5 seconds, 10 seconds, and 15 seconds, therefore Seconds and Seconds and Seconds and seconds, the initial baseline granularity sequence record is as follows micrometers and micrometers and micrometers and Micrometers, time-recorded granularity sequence is recorded as micrometers and micrometers and micrometers and Micrometers, maximum, minimum, and average values ​​are calculated based on the monitoring sequence: ; The sum of squares of the time interval is calculated from the timestamp differences: ; The dimensionless term in the denominator is calculated from the particle size range and the average particle size: ; The first part of the molecule is expanded item by item by substituting the point-by-point collected values: ; ; ; Substitute the second part of the molecule into the average definition and expand: ; ; Substitute the main expression and expand the operations: ; The result indicates This is a dimensionless comprehensive value representing the magnitude of particle size change over a time period and the difference at specific time points. The value is determined by the relative change in monitored particle size and the normalization term of the monitoring time interval. The numerical result corresponds to the comprehensive characteristic value of particle size change in step S302 and serves as input for combined comparison in subsequent trend extraction and processing. Trend extraction and processing can identify the corresponding values ​​for different time periods or batches. The sequences are summarized according to their size relationship and direction of change to form the input sequence of particle size response trend values.

[0031] S303: Based on the particle size response trend value, classify the molding parameter group and trend direction, calculate the collaborative offset value, identify the parameter change path and direction consistency, and establish the molding process linkage response path structure. For the extracted particle size response trend values, the molding parameter combinations to which they belong need to be grouped and categorized. Based on the magnitude of the trend direction, stratification is performed. For example, a reduction of more than 0.8 μm per minute is defined as a rapid trend group, a reduction between 0.5 and 0.8 μm as a medium trend group, and a reduction below 0.5 μm as a slow trend group. The molding pressure and heating rate parameters corresponding to each combination are associated and grouped into the corresponding trend group. A difference analysis is performed between the response trend value of each combination in the same group and the average trend value of that group, defined as the co-offset value. If the offset value is within 0.2 μm, it indicates that the change direction of the combination is highly consistent. Further analysis of the correlation paths between different combination parameters is conducted to assess whether an increase in molding pressure is always accompanied by an increase in heating rate, resulting in a consistent trend direction. Parameter groups with this characteristic are connected through nodes as shown in the diagram to form a path structure. For example, if the trend value of the combination P=40MPa and R=8°C / min decreases by 1.3μm per minute, and the trend value of P=30MPa and R=5°C / min decreases by 1.0μm per minute, both belong to the rapid trend group and the offset is less than 0.2, it can be determined that the path is related. Thus, the linkage response path structure between molding process parameters is established to present the common trend correlation between parameters.

[0032] Please see Figure 1 The specific steps of S4 are as follows: S401: Call the combination settings in the molding process linkage response path structure, set the resistivity range and bulk density range, obtain the heating rate and holding time records under the path, filter the path according to the resistivity range, and generate a resistivity range combination set. Calling the combined settings in the linkage response path structure of the molding process requires retrieving multiple historical path structures from the graphite material molding process database. These path structures typically contain parameter information such as heating rate, holding time, resistivity range, and bulk density range. For example, in path A, the heating rate is 10℃ / min, the holding time is 120min, the resistivity range is 50 to 60 μΩ·cm, and the bulk density is 1.80 g / cm³. Given a resistivity range of 50 to 60 μΩ·cm and a bulk density range of 1.75 to 1.85 g / cm³, all paths are traversed and searched to obtain the heating rate and holding time data for matching paths. The measured resistivity needs to be based on... The samples are subjected to electrical performance tests under standard compaction conditions, generally using a four-point probe testing device. The resistivity value is derived from the conversion between the measured resistance and the sample structure dimensions. If the resistance measured in a certain path is 0.005Ω, the sample area is 2cm², and the probe spacing is 1cm, the calculated resistivity is 100μΩ·cm. This value is not within the preset range, so the path is excluded. This process is repeated for all paths, leaving only all path combinations that meet the resistivity range. For example, the resistivities in path B and path C are 52μΩ·cm and 59μΩ·cm, respectively, which meet the set conditions. Finally, a combination set containing multiple path numbers is generated and marked as the initial resistivity combination set for subsequent compaction compliance screening.

[0033] S402: Based on the resistivity range combination set, the bulk density value is compared with the compaction standard, and the path and parameter combination that simultaneously meets the resistivity and bulk density requirements is selected to generate a compaction compliance combination set; After obtaining the resistivity combination set, it is necessary to further verify the bulk density in each path. The compaction standard range is set from 1.75 to 1.85 g / cm³. Search paths B and C. If the recorded bulk density of path B is 1.78 g / cm³ and that of path C is 1.80 g / cm³, both within the set range, they are determined to be compliant paths. If the bulk density of path D is 1.72 g / cm³, it does not meet the requirements and is excluded. The calculation of the bulk density of a path is usually based on the measured results of the sample mass and volume. For example, if the mass of a sample in a path is 12 g and the volume is 6.7 cm³, the calculated bulk density is 1.79 g / cm³, which is determined to be qualified. During the screening process, the reference density value is set at 1.75 g / cm³, and the allowable deviation range is ±0.05 g / cm³, forming a compliance boundary interval. All paths that meet this interval form a new compaction compliance combination set. Each path records two parameter data, resistivity and bulk density, and establishes a mapping relationship with the path number. For example, path B has 52 μΩ·cm and 1.78 g / cm³, and path C has 59 μΩ·cm and 1.80 g / cm³, identifying the effective combinations of the path under multiple compliance parameter dimensions, ensuring the stability of path parameter input for subsequent trend classification and analysis.

[0034] S403: According to the compaction compliance combination set, count the change direction of the heating rate and the change amplitude of the holding pressure time, classify and record the combined paths and parameter contents, and generate a list of graphite parameter redundancy combination groups; Based on paths B and C in the compaction compliance combination set, record their heating rate and holding pressure time data respectively. The heating rate of path B is 10 °C / min and the holding pressure time is 120 min. Path C has a heating rate of 12 °C / min and a holding pressure time of 90 min. Analyzing the change trends between the two paths shows that the heating rate increases from 10 to 12, which is an upward trend, and the holding pressure time decreases from 120 to 90, which is a downward trend. Classify and label the changes in the two groups of parameters respectively. The heating rate is marked as rising, and the holding pressure time is marked as falling, thus forming a parameter trend label of "rising and falling type". Quantify the change amplitude. The change in the heating rate is 2 °C / min, and the change in the holding pressure time is 30 min. To ensure that the path combination is within the parameter floating range of the redundant structure, the tolerance of the heating rate is set at ±3 °C / min, and the tolerance of the holding pressure time is set at ±40 min. Judge the parameter changes in the path combination. If the difference is within the tolerance, they are combined into a group of redundant combinations, record the path number and parameter values, such as path B: 10 °C / min, 120 min, path C: 12 °C / min, 90 min, and classify them into the combined group number G1. Also, note in the record that the heating rate trend is rising and the holding pressure time trend is falling. The redundant combination group G1 includes such trends and parameter fluctuations. Finally, summarize to obtain a redundant combination list containing multiple path and parameter variation records, providing a reusable reference set for subsequent molding parameter matching or path optimization.

[0035] Please see Figure 1 The specific steps of S5 are as follows: S501: Based on the path combination in the graphite parameter redundancy combination group list, extract the heating rate parameter, temperature zone setting and pressure holding period of the path, perturb the heating rate parameter and compare the difference with the original parameter, record the time change range of the pressure holding period after perturbation, and obtain the pressure holding time change range value. The graphite parameter redundancy combination list contains multiple path combinations. Each path is associated with a set of heating rate parameters, temperature zone settings, and holding time parameters. First, the parameter data must be extracted from this path combination list. For example, path number P001 contains a heating rate of 5°C / min, a temperature zone divided into three segments corresponding to temperatures of 500°C, 800°C, and 1200°C, and holding times of 60min, 90min, and 120min respectively. The heating rate parameter is perturbed, generating a new rate with a perturbation range of ±10%. For example, if the original rate is 5°C / min, the perturbed rate covers 4.5°C / min to 5.5°C / min. For each perturbed rate, the heating time for each temperature segment of the path is recalculated. For instance, when heating to 500°C, if the perturbed rate is 5.5°C / min, the heating time is approximately 91min, compared to the original 100min, a difference of approximately 9min. Calculate the heating time and pressure holding start point for each temperature range after the disturbance, and compare the pressure holding time settings before and after the disturbance, recording the differences. For example, after the disturbance, the pressure holding time of path P001 in the three temperature ranges changes to 58 min, 92 min, and 122 min, respectively. Comparing this to the original parameters, the corresponding changes are -2 min, +2 min, and +2 min, with the recorded time change range being [-2 min, +2 min]. Summarize the time change results for all paths to form a dataset of the pressure holding stage time change range.

[0036] S502: Call the range of pressure holding time variation, calculate the offset value of the pressure holding time variation before and after the path disturbance, filter the set of paths with the lowest variation according to the set standard, and record the corresponding path number and parameter information to generate the path stability response range. The data on the variation range of holding time is retrieved, and the path parameters before and after the disturbance are compared. The holding time offset value is calculated segment by segment. For example, if the original path is set to 60min, 90min, and 120min, and the corresponding values ​​after the disturbance are 62min, 95min, and 118min, then the first segment offset is +2min, the second segment is +5min, and the third segment is -2min, with the sum of the offset values ​​for each segment being 9min. After calculating the total offset value of each path, they are sorted, and the set of paths with the lowest variation range of holding time is selected according to preset criteria, such as a total offset not exceeding 10min. Paths that meet the criteria, such as path number P002, are selected into the set with a total offset of 9min, and the path number and its process parameters after the disturbance are recorded, such as a heating rate of 5.2°C / min and holding times of 62min, 95min, and 118min respectively. Finally, a path stability response range is formed, which consists of all paths and their parameters that meet the holding time variation range criteria, serving as the basis for subsequent selection of optimal paths.

[0037] S503: Based on the path number and change range in the path stability response interval, calculate the ratio between the pressure holding change and the disturbance intensity of the path, sort them according to the ratio, and filter the top-ranked paths to obtain the preferred path sequence set for graphite forming. Based on the path numbers and parameters recorded in the path stability response interval, the ratio between the pressure holding change and the heating rate disturbance intensity is calculated for each path. The disturbance intensity refers to the percentage change in the disturbance rate relative to the original rate. For example, if the rate increases from 5°C / min to 5.5°C / min, the disturbance intensity is 10%. If the total change in pressure holding time for this path is 8 minutes, then the ratio is 1.25. All path ratios are sorted, and several paths with smaller ratios are selected as the preferred path sequence for graphite forming. The sorting criteria are based on ascending numerical values, and the top 5 paths with the highest ratios are selected. For example, paths numbered P002, P007, P010, P015, and P021 constitute the preferred path sequence set, suitable for subsequent path configuration and parameter optimization processes in the forming scheme.

[0038] Please see Figure 2 A graphite forming process parameter optimization system, comprising: The rate feature construction module acquires time-series data of molding pressure, holding time, heating rate, and particle size distribution in the graphite molding equipment, calculates the difference between parameters at consecutive time points to form a rate sequence, extracts the extreme points of each rate sequence and marks the corresponding time positions, constructs the rate change structure of parameters on the time axis, and generates a rate mapping structure of graphite process parameters. The parameter offset extraction module calls the extreme point time distribution information in the graphite process parameter rate mapping structure, calculates the extreme point time offset value between molding pressure, heating rate, and holding time, filters parameter combinations that appear repeatedly in multiple cycles and have consistent offsets, and judges the response relationship in combination with the operation order in the graphite molding process to obtain a list of priority parameters for graphite process linkage. The linkage path identification module sets the combination path of molding pressure and heating rate in the list according to the linkage priority parameters of graphite process. It records the response curve of particle size distribution and holding time in the pressing cycle, identifies the corresponding path of the change direction of median particle size and the change of holding time, summarizes the linkage trend of heating rate and molding pressure and constructs dynamic path structure to generate the linkage response path structure of molding process. The performance path screening module calls the combination settings in the molding process linkage response path structure, sets the range of target resistivity and bulk density, adjusts the heating rate and records the direction and magnitude of the change in holding time, counts the path combinations where resistivity remains stable and bulk density reaches the compaction standard, and generates a list of redundant graphite parameter combinations. The disturbance capability ranking module performs heating rate disturbance operation and monitors the change in holding time based on the combination paths in the graphite parameter redundancy combination group list. It then filters the combination paths with the smallest feedback changes and sorts and classifies them according to their disturbance control capabilities to obtain the preferred process path set for graphite forming.

[0039] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for optimizing graphite forming process parameters, characterized in that, Includes the following steps: S1: Obtain time-series data of molding pressure, holding time, heating rate, and particle size distribution in the graphite molding equipment; calculate the difference between adjacent points to form a rate sequence; extract rate extreme points and map them to time positions; construct a rate change structure corresponding to the parameters and time axis; and generate a rate mapping structure for graphite process parameters. S2: Extract extreme point information based on the graphite process parameter rate mapping structure, calculate the time offset value between parameter combinations, filter combinations that recur within the cycle and have stable offsets, determine the response relationship, and obtain a list of priority parameters for graphite process linkage. S3: Set the combination scheme of molding pressure and heating rate according to the graphite process linkage priority parameter list, record the particle size distribution and holding time response trend during the pressing cycle, identify the change path and linkage direction between parameters, and obtain the linkage response path structure of the molding process. S4: Call the combination settings in the linkage response path structure of the molding process, set the target performance range, adjust the heating rate and record the direction and magnitude of the pressure holding time change, count the combination paths with consistent directions, classify the alternative combinations that meet the performance conditions, and generate a list of redundant graphite parameter combination groups.

2. The method for optimizing graphite forming process parameters according to claim 1, characterized in that, The graphite process parameter rate mapping structure includes parameter time axis correspondence, rate change feature sequence, and extreme point time position mapping. The graphite process linkage priority parameter list includes stable combinations of time offset values, periodically repeating parameter groups, and process flow response relationship determination results. The molding process linkage response path structure includes parameter change path topology, linkage direction identifier, and dynamic feature induction set. The graphite parameter redundancy combination group list includes target resistivity stable range, volume density compaction standard path, and adjustment response record dataset.

3. The method for optimizing graphite forming process parameters according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Acquire time-series data of molding pressure, holding time, heating rate and particle size distribution, arrange them in the order of parameter time, calculate the difference between adjacent time points and divide by the interval time to obtain the rate value, construct the rate sequence under a unified time coordinate, and generate a parameter rate sequence group. S102: Based on the parameter rate sequence group, compare the magnitude of the values ​​of the preceding and following points in the rate sequence, extract the time positions corresponding to the local maximum and minimum values, record the correspondence between the parameter rate values ​​and the time points, construct a rate record sequence with extreme value identifiers, and generate a rate extreme value mapping coordinate set; S103: Based on the set of rate extrema mapping coordinates, establish a pairing relationship between the parameter rate extrema and the corresponding time points, organize the change structure of all parameter rates over time, form a rate time distribution map, and generate a graphite process parameter rate mapping structure.

4. The method for optimizing graphite forming process parameters according to claim 3, characterized in that, The specific steps of S2 are as follows: S201: Based on the graphite process parameter rate mapping structure, extract the extreme point information of the parameters at the time of difference, obtain the time series between parameter combinations according to the parameter name, occurrence time and rate fluctuation range corresponding to the extreme point, call the time difference value of the parameters in the time series, calculate the time offset value within the period segment, and generate the time offset value of the parameter combination. S202: Based on the time offset value of the parameter combination, detect the frequency of recurrence of the parameter combination within the period segment, extract the combination whose frequency of occurrence is higher than the midpoint of the period combination, call the time offset value sequence of the combination in adjacent periods, determine the parameter combination whose offset fluctuation amplitude is less than the offset stability threshold, and obtain the parameters that are recurring and have stable offset within the period. S203: Based on the repeated and offset stable parameters within the cycle, and combined with the order of the parameters in the process stage in the graphite process flow, the arrangement relationship of the parameter pairs in the process is called, and the combination of response relationship conforming to the upstream and downstream transmission characteristics is judged to form a priority parameter list for graphite process linkage.

5. The method for optimizing graphite forming process parameters according to claim 4, characterized in that, The specific steps for S3 are as follows: S301: Based on the graphite process linkage priority parameter list, set the molding pressure and heating rate combination scheme, construct the combination matrix, set the molding equipment operating conditions, record the particle size distribution and holding time in the pressing cycle, and generate the initial particle size and time record values. S302: Based on the initial particle size and time record value, extract the particle size change range and time point difference over a time period, perform a combination comparison, and extract the particle size change trend to obtain the particle size response trend value. S303: For the particle size response trend value, classify the molding parameter group and trend direction, calculate the collaborative offset value, identify the consistency of parameter change path and direction, and establish the linkage response path structure of molding process.

6. The method for optimizing graphite forming process parameters according to claim 5, characterized in that, The specific steps of S4 are as follows: S401: Call the combination settings in the linkage response path structure of the molding process, set the resistivity range and the volume density range, obtain the heating rate and holding time records under the path, filter the path according to the resistivity range, and generate a resistivity range combination set. S402: Based on the resistivity range combination set, the bulk density value is compared with the compaction standard, and the path and parameter combination that simultaneously meets the resistivity and bulk density requirements is selected to generate a compaction compliance combination set. S403: Based on the compaction compliance combination set, statistically analyze the direction of change of heating rate and the magnitude of change of holding time, classify and record the combination path and parameter content, and generate a list of graphite parameter redundancy combination groups.

7. The method for optimizing graphite forming process parameters according to claim 1, characterized in that, The method further includes: S5: Based on the path combinations in the graphite parameter redundancy combination group list, perform a heating rate disturbance test and monitor the change in holding time, screen the combination path with the smallest feedback change, sort and merge according to the disturbance control capability, and obtain the preferred process path set for graphite forming. The preferred process path set for graphite forming includes a disturbance feedback minimization path, a control capability ranking hierarchy, and a set of merged optimization criteria.

8. The method for optimizing graphite forming process parameters according to claim 7, characterized in that, The specific steps of S5 are as follows: S501: Based on the path combination in the graphite parameter redundancy combination group list, extract the heating rate parameter, temperature zone setting and pressure holding period of the path, perform perturbation processing on the heating rate parameter and compare the difference with the original parameter, record the time change range of the pressure holding period after perturbation, and obtain the pressure holding time change range value. S502: Call the pressure holding time variation range value, calculate the offset value of the pressure holding time variation range before and after the path disturbance, filter the path set with the lowest variation range according to the set standard, and record the corresponding path number and parameter information to generate the path stability response range. S503: Based on the path number and change range in the path stability response interval, calculate the ratio between the pressure holding change and the disturbance intensity of the path, sort them according to the ratio, and filter the top-ranked paths to obtain the preferred path sequence set for graphite forming.

9. The method for optimizing graphite forming process parameters according to claim 1, characterized in that, The extreme points refer to the local maximum or minimum values ​​that appear in the parameter rate sequence, reflecting the key inflection points of parameter changes; The disturbance test refers to the test method of observing the parameter response after subjecting a specific process parameter to a slight disturbance; The disturbance control capability refers to the response magnitude of the parameter path to external disturbances under disturbance testing. The smaller the magnitude, the stronger the control capability and the better the path selection.

10. A graphite forming process parameter optimization system, characterized in that, A method for optimizing graphite forming process parameters according to any one of claims 1-9, wherein the system comprises: The rate feature construction module acquires time-series data of molding pressure, holding time, heating rate, and particle size distribution in the graphite molding equipment, calculates the difference between parameters at consecutive time points to form a rate sequence, extracts the extreme points of each rate sequence and marks the corresponding time positions, constructs the rate change structure of parameters on the time axis, and generates a rate mapping structure of graphite process parameters. The parameter offset extraction module calls the extreme point time distribution information in the graphite process parameter rate mapping structure, calculates the extreme point time offset value between molding pressure, heating rate, and holding time, filters parameter combinations that repeat in multiple cycles and have consistent offsets, and judges the response relationship in combination with the operation order in the graphite molding process to obtain a list of priority parameters for graphite process linkage. The linkage path identification module sets the combination path of molding pressure and heating rate on the list according to the graphite process linkage priority parameters. During the pressing cycle, it records the response curve of particle size distribution and holding time, identifies the corresponding path of particle size median change direction and holding time change, summarizes the linkage trend of heating rate and molding pressure and constructs dynamic path structure to generate molding process linkage response path structure. The performance path screening module calls the combination settings in the linkage response path structure of the molding process, sets the range of target resistivity and bulk density, adjusts the heating rate and records the direction and magnitude of the change in holding time, counts the path combinations where resistivity remains stable and bulk density reaches the compaction standard, and generates a list of redundant graphite parameter combinations. The disturbance capability sorting module performs a heating rate disturbance operation and monitors the change in holding time based on the combination paths in the graphite parameter redundancy combination group list. It then filters out the combination paths with the smallest feedback changes and sorts and classifies them according to their disturbance control capabilities to obtain the preferred process path set for graphite forming.

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