Special sintering furnace for polytetrafluoroethylene and its verification method and system

Abstract

The present invention belongs to the field of plastics processing technology, specifically relating to a polytetrafluoroethylene (PTFE) sintering furnace and its calibration method and system. The sintering furnace includes a furnace body, a calibration device disposed within the furnace body, and a control device disposed on one side of the furnace body. The control device is provided with an alarm for use therewith. The control device is provided with a data processing module, a data transceiver module, a data acquisition module, and an acquisition control module. The method includes activating the calibration device via the acquisition control module, acquiring temperature and pressure data at different locations within the furnace body, establishing a fault type model based on a support vector machine, and controlling the alarm to sound an alarm when an anomaly occurs. The system includes a data acquisition subsystem, a fault type model establishment subsystem, an anomaly determination subsystem, and a product quality determination subsystem. The present invention helps accurately grasp the actual conditions within the sintering furnace and promptly detect temperature unevenness, thereby optimizing the sintering process and improving the sintering quality of polytetrafluoroethylene products.

Description

Technical Field

[0001] The invention belongs to the technical field of plastic processing, and in particular relates to a special sintering furnace for polytetrafluoroethylene and a verification method and system thereof. Background Art

[0002] Polytetrafluoroethylene (PTFE), a high-performance material with excellent chemical stability, high and low temperature resistance, low friction coefficient, and excellent electrical insulation, is widely used in numerous fields, including chemical engineering, electronics, and aerospace. Specialized sintering furnaces play a key role in the production of PTFE. After the billet is formed, it undergoes high-temperature sintering in the furnace. This promotes the movement and rearrangement of the PTFE particles through molecular chain movement, forming a dense and uniform structure, thereby achieving excellent physical and mechanical properties. However, traditional sintering furnaces have shortcomings in temperature uniformity and pressure control accuracy, which affect the stability and consistency of product quality and urgently require technical improvements.

[0003] The space inside the sintering furnace is large. During the heating process, the temperature at each point varies. Usually, the temperature can only be detected at a certain point, and the detection result is not accurate enough. If multiple temperature sensors are installed, the cost will increase. Therefore, a special sintering furnace for polytetrafluoroethylene and its calibration method and system are proposed to solve the above problems. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a special sintering furnace for polytetrafluoroethylene and its calibration method and system, which helps to accurately grasp the actual situation inside the sintering furnace and promptly detect temperature unevenness, so as to optimize the sintering process and improve the sintering quality of polytetrafluoroethylene products.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0006] A special sintering furnace for polytetrafluoroethylene includes a furnace body, a calibration device arranged inside the furnace body, and a control device arranged on one side of the furnace body, wherein:

[0007] The calibration device includes a calibrator and a temperature sensor and a pressure sensor provided on the calibrator. The temperature sensor is used to detect the temperature inside the furnace body, and the pressure sensor is used to detect the pressure inside the furnace body.

[0008] A frame for placing polytetrafluoroethylene is provided in the furnace body, the lower end of the frame extends to the outside of the furnace body, and a first drive motor is installed at the lower end of the frame, and the first drive motor is installed at the bottom of the furnace body;

[0009] A heating pipe is provided at the bottom of the furnace body, an isolation net is provided above the heating pipe, and a blower is provided at the top of the furnace body;

[0010] The control device is provided with an alarm for use therewith, and the control device is provided with a data processing module, a data transceiver module, a data acquisition module, and an acquisition control module, wherein:

[0011] The data acquisition module is used to detect and collect the operation data of the furnace body and send it to the data processing module through the data transceiver module;

[0012] The data processing module is used to process the operation data, temperature data and pressure data during the operation of the furnace body, and control the temperature and pressure during the operation of the furnace body;

[0013] The temperature sensor is used to send the temperature data inside the furnace body to the data processing module through the data transceiver module; the pressure sensor is used to send the pressure data inside the furnace body to the data processing module through the data transceiver module;

[0014] The acquisition control module is used to control the operation of the calibration device and collect temperature and pressure data at various points inside the furnace;

[0015] The data processing module is communicatively connected with the data transceiver module, the temperature sensor, the pressure sensor, the data acquisition module, and the acquisition control module.

[0016] Preferably, it further comprises a driving member, which is arranged on the back side of the detector and is used to drive the detector to move horizontally;

[0017] A screw rod and a bracket are provided below the driving member, which are used in conjunction with the driving member to move the calibrator up and down;

[0018] The calibration device further comprises a connecting member, which is arranged between the calibrator and the driving member and is used for limiting the connection between the calibrator and the lead screw.

[0019] Preferably, the driving member includes a driving box, a second driving motor, a hydraulic rod, a telescopic rod and a clamping member, and the second driving motor, the hydraulic rod, the telescopic rod and the clamping member are all arranged in the driving box;

[0020] The second drive motor is connected to the mounting plate by screws, and two meshing bevel gears are provided under the second drive motor, one bevel gear is provided at the output end of the second drive motor, and the other bevel gear is connected to the moving gear, which is provided outside the drive box;

[0021] The hydraulic rod and the telescopic rod are symmetrically arranged and are both installed between the bottom of the mounting plate and the bottom of the drive box by screws. The hydraulic rod is used to drive the second drive motor and the bevel gear connected thereto to move up and down;

[0022] The clamping member includes a clamping block and a connecting block. The clamping block is installed at the bottom of the bevel gear connected to the output end of the second drive motor through a rod. The connecting block is installed at the upper end of the screw rod, and a connecting groove adapted to the clamping block is provided inside the connecting block.

[0023] The connecting groove includes a connecting portion and a clamping portion, wherein the connecting portion is arranged below the clamping portion, and an inner wall of the clamping portion is provided with a groove adapted to the clamping block, and the clamping block does not contact the inner wall of the connecting portion;

[0024] When the second drive motor and the bevel gear connected thereto move upward, the clamping block moves upward and is assembled with the clamping portion to drive the screw rod to rotate. When the second drive motor and the bevel gear connected thereto move upward, the clamping block moves downward and is assembled with the connecting portion to drive the moving gear to rotate.

[0025] Sliders are symmetrically arranged on the back of the drive box, and the slides are slidably connected to the side panels. The side panels close to the drive box are fixed with the front panels by screws, and the side panels are fixed to the inner wall of the furnace body by screws.

[0026] A sliding groove adapted to the sliding block is provided on the side plate, a gear track is provided at the bottom inner side of the side plate, and a moving gear is meshed with the gear track.

[0027] Preferably, the connecting member includes a sliding seat, a connecting seat, a movable plate and a limit member, the sliding seat is L-shaped, the sliding seat is sleeved on the screw rod and the bracket, and is threadedly connected to the screw rod, the screw rod and the bracket are both installed at the bottom of the drive box, the bracket is L-shaped, and the lower end of the screw rod is installed at the bottom inside the bracket;

[0028] The connecting seat is arranged on the outer side of the sliding seat, and the movable plate is symmetrically arranged on the back of the detector and is slidably connected with the connecting seat;

[0029] The limiting parts are symmetrically arranged and arranged inside the connecting seat. The limiting parts include a cross part and a limiting part. One end of the cross part is connected to the limiting part, and the other end of the cross part extends to the outside of the connecting seat. The cross parts in the two limiting parts are rotatably connected and arranged crosswise. A torsion spring is installed on the outside of the connection between the two cross parts. The limiting part extends to the outside of the connecting seat and passes through the movable plate. A card slot adapted to the card connection part is provided on the movable plate.

[0030] The verification method of the polytetrafluoroethylene special sintering furnace is based on the above-mentioned polytetrafluoroethylene special sintering furnace and includes the following steps:

[0031] The calibration device is started through the acquisition control module to obtain temperature and pressure data at different positions inside the furnace;

[0032] Based on support vector machine, a fault type model is established;

[0033] Obtain temperature-related data and pressure-related data inside the furnace body, establish temperature distribution models and pressure distribution models, obtain predicted temperature distribution and predicted pressure, respectively determine whether there are abnormal heat transfer conditions and pressure anomalies in the furnace body, and determine the abnormal heat transfer condition level and pressure anomaly level. The abnormal heat transfer condition level includes abnormal condition level 1 and abnormal condition level 2, and the pressure anomaly level includes abnormal condition level 1 and abnormal condition level 2:

[0034] If one of the abnormal condition level 2 and abnormal level 2 exists, the alarm will be controlled to sound an alarm;

[0035] Obtain the historical data set inside the furnace, establish a product quality prediction model, obtain the product quality prediction value, and determine whether the predicted product quality is qualified:

[0036] If it fails, determine the fault type and make adjustments;

[0037] If qualified, continue with the test.

[0038] Preferably, establishing a fault type model includes the following steps:

[0039] Obtain historical temperature anomaly data and historical pressure anomaly data as well as the corresponding fault types, obtain the anomaly data set, and establish the radial basis kernel function;

[0040] The abnormal data set is divided into a training set and a test set, the training set is imported into the support vector machine, and the support vector machine is trained;

[0041] Import the test set into the trained support vector machine, test the support vector machine, and obtain the fault type model;

[0042] The expression of the radial basis kernel function is:

[0043] ;

[0044] Where, for and The radial basis kernel function value between is the g-th input sample feature vector, is the hth input sample feature vector, is the kernel function parameter;

[0045] The expression is:

[0046] ;

[0047] In the formula, a is the dimension number, m is the dimension of the feature vector, is the g-th input sample feature vector in the a-th dimension, is the hth input sample feature vector of the ath dimension.

[0048] Preferably, judging whether there is an abnormal heat transfer condition and an abnormal pressure in the furnace body, and determining the level of the abnormal heat transfer condition and the level of the abnormal pressure, comprises the following steps:

[0049] Obtain the basic data of the furnace body, including the length, width and height of the furnace body;

[0050] Based on the basic data, the internal area of ​​the furnace is discretized into multiple interconnected units. Each unit has its own node. The temperature expression within the unit is obtained and substituted into the heat conduction equation to obtain the algebraic equation. The stiffness matrix and load vector of the unit are obtained by integration and derivation on the unit.

[0051] Assemble the algebraic equations of each unit according to the connection relationship of the nodes to form the overall algebraic equation group of the internal area of ​​the furnace body;

[0052] The iterative method is used to solve the overall algebraic equations to obtain the predicted temperature value of each node, that is, to obtain the predicted temperature distribution inside the furnace;

[0053] Compare the predicted temperature distribution inside the furnace body with the obtained temperature data at different locations inside the furnace body to obtain the absolute value of the temperature comparison difference. Compare the absolute value of the temperature comparison difference with the temperature deviation value range stored in the database:

[0054] If the absolute value of the temperature comparison difference falls within the temperature deviation range, there is an abnormal heat transfer condition in the furnace body, and it is recorded as a level one abnormal condition;

[0055] If the absolute value of the temperature comparison difference is less than the minimum value of the temperature deviation range, there is no abnormal heat transfer condition in the furnace body;

[0056] If the absolute value of the temperature comparison difference is greater than the maximum value of the temperature deviation value range, there is an abnormal heat transfer condition in the furnace body, and it is recorded as a second-level abnormal condition;

[0057] The heat conduction equation is:

[0058] ;

[0059] Where, is the material density of the furnace body, c is the specific heat capacity of the furnace body, is the gradient operator, T is the temperature, t is the time, is the rate of change of temperature with time, is the divergence of convective density, k is the thermal conductivity, is the temperature gradient, Q is the heat source intensity inside the furnace;

[0060] The temperature expression is:

[0061] ;

[0062] Where, For the space coordinates The temperature at is the shape function, is the temperature of unit node i, I is the number of unit nodes;

[0063] The overall algebraic equation system is:

[0064] ;

[0065] Where, is the overall stiffness matrix, is the node temperature vector, is the overall load vector;

[0066] Based on the ideal gas state, the ideal gas state equation is obtained and the theoretical pressure of the gas in the furnace is obtained by analysis;

[0067] Introduce leakage model to determine gas leakage;

[0068] According to the law of conservation of mass and the ideal gas state equation, the equation of pressure change with time considering leakage is derived, and the pressure correction value is obtained. Combined with the theoretical pressure, the predicted pressure is obtained;

[0069] Compare the predicted pressure inside the furnace body with the pressure data obtained at different positions inside the furnace body to obtain the absolute value of the pressure comparison difference, and compare the absolute value of the pressure comparison difference with the pressure deviation value range stored in the database:

[0070] If the absolute value of the pressure comparison difference falls within the pressure deviation value range, there is a pressure abnormality in the furnace body and it is recorded as abnormality level one;

[0071] If the absolute value of the pressure comparison difference is less than the minimum value of the pressure deviation value range, there is no pressure abnormality in the furnace body;

[0072] If the absolute value of the pressure comparison difference is greater than the maximum value of the pressure deviation value range, there is a pressure abnormality in the furnace body, and it is recorded as a level 2 abnormality;

[0073] The ideal gas state equation is:

[0074] ;

[0075] Where P is the internal pressure of the furnace, V is the volume occupied by the gas, n is the amount of substance, and R is the universal gas constant. is the internal temperature of the furnace;

[0076] The leakage model is:

[0077] ;

[0078] Where, is the leakage mass flow rate, is the flow coefficient, is the leakage area, is the gas density, is the pressure difference;

[0079] The equation for pressure variation with time considering leakage is:

[0080] ;

[0081] Where, is the pressure at time t, is the temperature of the gas, E is the leakage coefficient, is the integration constant, is the initial pressure, is the external environmental pressure, t is the time point number;

[0082] The calculation formula for the pressure correction value is:

[0083] ;

[0084] Where, is the pressure correction value, is the pressure under ideal conditions;

[0085] The formula for calculating the predicted pressure is:

[0086] ;

[0087] Where, To predict pressure.

[0088] Preferably, determining whether the predicted product quality is qualified comprises the following steps:

[0089] The historical data set includes historical temperature data, historical pressure data, and historical product quality data. Product quality data includes product density, product tensile strength, product melting point, and product dielectric constant.

[0090] Perform data cleaning on historical data sets to check whether there are missing values ​​in the historical data sets. If there are missing values, fill them with the median;

[0091] Check whether there are outliers, and if so, delete them;

[0092] Perform feature engineering on the historical dataset to obtain a historical feature dataset. The historical feature dataset includes average temperature, temperature standard deviation, temperature change rate, average pressure, pressure standard deviation, average product density, product density standard deviation, average product tensile strength, product tensile strength standard deviation, average product melting point, product melting point standard deviation, average product dielectric constant, and product dielectric constant standard deviation.

[0093] Divide the historical feature dataset into a training set and a test set, and import the training set into the random forest regression model;

[0094] Set the parameters of the random forest regression model, including determining the number of trees, controlling the maximum depth of the decision tree, and determining the minimum number of samples required for a node to split;

[0095] Randomly extract sample data with replacement from the training set to build multiple decision trees for training, obtain the product quality prediction model, and test the product quality prediction model using the test set;

[0096] Input the acquired temperature and pressure data at different locations inside the furnace into the product quality prediction model to obtain predicted product quality data, and conduct comprehensive analysis to obtain the product quality prediction value. The predicted product quality data includes predicted product density, predicted product tensile strength, predicted product melting point, and predicted product dielectric constant;

[0097] Compare the product quality prediction value with the qualified threshold value of the product quality. If the product quality prediction value is higher than the qualified threshold value of the product quality, the product is qualified.

[0098] If the predicted product quality value is lower than the qualified threshold of product quality, it is unqualified;

[0099] The calculation formula for the product quality prediction value is:

[0100] ;

[0101] Where, is the product quality prediction value, To predict product density, To predict the tensile strength of the product, To predict the melting point of the product, To predict the dielectric constant of the product, Md is the product density, Ls is the product tensile strength, Rd is the product melting point, Jd is the product dielectric constant, for The weight factor, for The weight factor, for The weight factor, for The weight factor of , e is a natural constant.

[0102] Preferably, determining the fault type includes the following steps:

[0103] Input the acquired temperature data and pressure data at different positions inside the furnace into the fault type model;

[0104] If there is a first level of abnormal condition, but there is no first level of abnormal condition, the temperature data is sequentially compared with each abnormal temperature data stored in the fault type model, and the fault type corresponding to the closest abnormal temperature data is obtained and output;

[0105] If there is an abnormal level 1, but there is no abnormal condition level 1, the pressure data is sequentially compared with each abnormal pressure data stored in the fault type model, and the fault type corresponding to the closest abnormal pressure data is obtained and output;

[0106] If both abnormal condition level 1 and abnormality level 1 do not exist, perform maintenance.

[0107] The PTFE sintering furnace verification system is used to implement the above-mentioned PTFE sintering furnace verification method, including a data acquisition subsystem, a fault type model establishment subsystem, an abnormality judgment subsystem and a product quality qualification judgment subsystem, wherein:

[0108] The data acquisition subsystem is used to start the operation of the calibration device through the acquisition control module to obtain temperature and pressure data at different positions inside the furnace;

[0109] A fault type model establishment subsystem is used to establish a fault type model based on a support vector machine model;

[0110] The abnormality judgment subsystem is used to obtain temperature-related data and pressure-related data inside the furnace body, establish temperature distribution models and pressure distribution models, obtain predicted temperature distribution and predicted pressure, respectively judge whether there are abnormal heat transfer conditions and pressure anomalies in the furnace body, and determine the abnormal heat transfer condition level and pressure anomaly level. The abnormal heat transfer condition level includes abnormal condition level 1 and abnormal condition level 2, and the pressure anomaly level includes abnormal condition level 1 and abnormal condition level 2:

[0111] If one of the abnormal condition level 2 and abnormal level 2 exists, the alarm will be controlled to sound an alarm;

[0112] The product quality qualification judgment subsystem is used to obtain the historical data set inside the furnace body, establish a product quality prediction model, obtain the product quality prediction value, and judge whether the predicted product quality is qualified:

[0113] If it fails, determine the fault type and make adjustments;

[0114] If qualified, continue with the test.

[0115] The present invention has the following beneficial effects:

[0116] The sintering furnace of the present invention is used in conjunction with a driving part, a screw rod, a bracket, a movable gear, a rack, a side plate and a connecting part. During the calibration process, the calibrator can be moved horizontally and up and down, and temperature and pressure detection can be performed on multiple points inside the furnace body to obtain more comprehensive and accurate parameter distribution information, which helps to accurately grasp the actual situation in the sintering furnace and promptly discover temperature unevenness, so as to optimize the sintering process and improve the sintering quality of polytetrafluoroethylene products.

[0117] The sintering furnace of the present invention can connect the detector and the sliding seat by using the provided connecting seat, movable plate and limiter, so as to facilitate the disassembly, repair or replacement of the detector when it fails. The invention is convenient to use and easy to operate.

[0118] The calibration method and system of the present invention obtain the temperature and pressure data inside the furnace body, providing a basis for subsequent analysis, which can predict faults in advance, improve equipment reliability, accurately judge abnormal conditions and levels, and ensure safety and stability through an alarm mechanism. At the same time, the ideal gas state equation and leakage model are combined to refine the judgment of pressure anomalies, monitor quality, judge whether it is qualified, and adjust and optimize if it is unqualified, so as to ensure that the sintering process is controlled, reduce the production of defective products, improve product quality, reduce losses, ensure that products meet standards, realize refined and intelligent calibration and process optimization, and improve economic benefits and product competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0119] Figure 1 It is a schematic diagram of the structure of the present invention;

[0120] Figure 2 This is a schematic diagram of the structure of the calibration device of the present invention inside the furnace;

[0121] Figure 3 Schematic diagram of the structure of the verification device of the present invention;

[0122] Figure 4 This is a schematic diagram of the structure inside the drive box of the present invention;

[0123] Figure 5 This is a schematic diagram of the structure of the slider and the side plate being separated according to the present invention;

[0124] Figure 6 This is a schematic structural diagram of the connection between the movable plate and the limiting member of the present invention;

[0125] Figure 7 This is a structural diagram of the connecting block and the clamping block of the present invention;

[0126] Figure 8This is a structural diagram of the separation of the movable plate and the connecting base of the present invention;

[0127] Figure 9 This is a schematic diagram of the structure inside the connecting seat of the present invention;

[0128] Figure 10 This is a flow chart of the method for testing a polytetrafluoroethylene-specific sintering furnace according to the present invention;

[0129] Figure 11 This is a module diagram of the calibration system of the polytetrafluoroethylene special sintering furnace of the present invention.

[0130] In the figure, 1. furnace body; 2. control device; 3. alarm; 4. blower; 5. frame; 6. first drive motor; 7. heating tube; 8. isolation net; 9. calibrator; 10. temperature sensor; 11. pressure sensor; 12. sliding seat; 13. screw rod; 14. bracket; 15. drive box; 16. connecting block; 17. connecting groove; 18. clamping block; 19. bevel gear; 20. second drive motor; 21. mounting plate; 22. hydraulic rod; 23. telescopic rod; 24. moving gear; 25. slider; 26. rack; 27. side panel; 28. front panel; 29. ​​moving plate; 30. connecting seat; 31. limiter; 32. torsion spring; 33. clamping slot. DETAILED DESCRIPTION

[0131] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present invention.

[0132] Example 1:

[0133] like Figures 1-9 As shown, this embodiment provides a special sintering furnace for polytetrafluoroethylene, including a furnace body 1, a calibration device arranged inside the furnace body 1, and a control device 2 arranged on one side of the furnace body 1, wherein: the calibration device includes a calibrator 9 and a temperature sensor 10 and a pressure sensor 11 arranged on the calibrator 9, the temperature sensor 10 is used to detect the temperature inside the furnace body 1, and the pressure sensor 11 is used to detect the pressure inside the furnace body 1; a driving member, the driving member is arranged on the back side of the calibrator 9, and is used to drive the calibrator 9 to move horizontally; a screw rod 13 and a bracket 14 are provided below the driving member, which are used in conjunction with the driving member to move the calibrator 9 up and down; the calibration device also includes a connecting member, which is arranged between the calibrator 9 and the driving member, and is used to limit the connection between the calibrator 9 and the screw rod 13; the temperature sensor 10 is used to obtain temperature data inside the furnace body 1 and send it to the data processing module through the data transceiver module;

[0134] The pressure sensor 11 is used to obtain the pressure data inside the furnace body 1 and send it to the data processing module through the data transceiver module; the control device 2 is provided with an alarm 3 used in conjunction with it, and the control device 2 is provided with a data processing module, a data transceiver module, a data acquisition module, and an acquisition control module, wherein: the data acquisition module is used to detect and collect the operation data of the furnace body 1, and send it to the data processing module through the data transceiver module; the data processing module is used to process the operation data, temperature data and pressure data during the operation of the furnace body 1, and control the temperature and pressure during the operation of the furnace body 1; the acquisition control module is used to control the operation of the calibration device and collect temperature data and pressure data of various points inside the furnace body 1; the data processing module is communicatively connected with the data transceiver module, the temperature sensor, the pressure sensor, the data acquisition module, and the acquisition control module.

[0135] A frame 5 for placing polytetrafluoroethylene is provided in the furnace body 1, the lower end of the frame 5 extends to the outside of the furnace body 1, and a first drive motor 6 is installed at the lower end of the frame 5, and the first drive motor 6 is installed at the bottom of the furnace body 1; a heating tube 7 is provided at the bottom of the furnace body 1, an isolation net 8 is provided above the heating tube 7, and a blower 4 is provided at the top of the furnace body 1.

[0136] The frame 5 is used to hold polytetrafluoroethylene. The first drive motor 6 is used in conjunction with the frame 5 to ensure that the polytetrafluoroethylene is heated evenly. The blower 4 is used to promote the circulation of air in the furnace to ensure that the polytetrafluoroethylene is heated evenly throughout the sintering process, thereby improving the sintering quality. The blower 4 can also be used to cool down when the internal temperature is too high.

[0137] The driving part includes a driving box 15, a second driving motor 20, a hydraulic rod 22, a telescopic rod 23 and a clamping part. The second driving motor 20, the hydraulic rod 22, the telescopic rod 23 and the clamping part are all arranged in the driving box 15; the second driving motor 20 is connected to the mounting plate 21 by screws, and two meshing bevel gears 19 are arranged under the second driving motor 20, one bevel gear 19 is arranged at the output end of the second driving motor 20, and the other bevel gear 19 is connected to the moving gear 24, and the moving gear 24 is arranged outside the driving box 15; the hydraulic rod 22 and the telescopic rod 23 are symmetrically arranged and are both mounted on the mounting plate 21 by screws. 1 between the bottom and the bottom of the drive box 15, the hydraulic rod 22 is used to drive the second drive motor 20 and the bevel gear 19 connected thereto to move up and down. The clamping member includes a clamping block 18 and a connecting block 16. The clamping block 18 is installed at the bottom of the bevel gear 19 connected to the output end of the second drive motor 20 through a rod. The connecting block 16 is installed at the upper end of the screw rod 13, and a connecting groove 17 adapted to the clamping block 18 is provided inside the connecting block 16. The connecting groove 17 includes a connecting portion and a clamping portion. The connecting portion is provided below the clamping portion. The inner wall of the clamping portion is provided with a groove adapted to the clamping block 18. The clamping block 18 does not contact the inner wall of the connecting portion.

[0138] When the second drive motor 20 and the bevel gear 19 connected thereto move upward, the clamping block 18 moves upward and is assembled with the clamping part to drive the screw rod 13 to rotate. When the second drive motor 20 and the bevel gear 19 connected thereto move upward, the clamping block 18 moves downward and is assembled with the connecting part to drive the moving gear 24 to rotate. Slide blocks 25 are symmetrically provided on the back of the drive box 15. The slide blocks 25 are slidably connected to the side plates 27. The side plates 27 are close to the drive box 15 and are mounted with a front panel 28 by screws. The side plates 27 are mounted on the inner wall of the furnace body 1 by screws. A sliding groove matching the slide block 25 is provided on the side plate 27. A rack 26 is provided on the inner bottom of the side plate 27, and the moving gear 24 is meshed with the rack 26.

[0139] The drive box 15 is welded to the slider 25, and the second drive motor 20 operates to drive a bevel gear 19 connected thereto to rotate, meshing and driving another bevel gear 19 to rotate, thereby driving the moving gear 24 to rotate and connected to the rack 26. Under the sliding limit of the slider 25 and the side plate 27, the drive box 15 drives the screw rod 13, the bracket 14, the sliding seat 12 and the tester 9 to move horizontally in the furnace body 1. After moving to the position, the second drive motor 20 stops operating, and the hydraulic rod 22 operates to push the second drive motor 20 and the bevel gear 19 connected thereto to move upward. The telescopic rod 23 limits and guides it, so that the clamping block 18 moves upward, separates from the connecting part, and engages with the clamping part. The second drive motor 20 operates, through the clamping block 18 and the connecting block 16, to drive the screw rod 13 to rotate. Under the limit guidance of the bracket 14, the sliding seat 12 drives the tester 9 to move up and down. The temperature and pressure inside the furnace body 1 can be detected from multiple points to ensure the accuracy of the detection, thereby making an accurate judgment.

[0140] The connecting member includes a sliding seat 12, a connecting seat 30, a movable plate 29 and a limit member 31. The sliding seat 12 is L-shaped. The sliding seat 12 is sleeved on the screw rod 13 and the bracket 14 and is threadedly connected to the screw rod 13. The screw rod 13 and the bracket 14 are both installed at the bottom of the drive box 15. The bracket 14 is L-shaped. The lower end of the screw rod 13 is installed at the bottom inside the bracket 14; the connecting seat 30 is arranged on the outside of the sliding seat 12, and the movable plate 29 is symmetrically arranged on the back of the detector 9 and slides with the connecting seat 30. Dynamic connection; the limiting members 31 are symmetrically arranged and arranged inside the connecting seat 30. The limiting members 31 include a cross portion and a limiting portion. One end of the cross portion is connected to the limiting portion, and the other end of the cross portion extends to the outside of the connecting seat 30. The cross portions in the two limiting members 31 are rotatably connected and are cross-arranged. A torsion spring 32 is installed on the outside of the connection between the two cross portions. The limiting portion extends to the outside of the connecting seat 30 and passes through the movable plate 29. A card slot 33 adapted to the card connection portion is provided on the movable plate 29.

[0141] The movable plate 29 is welded to the back of the tester 9, and the connecting seat 30 and the sliding seat 12 are an integrated structure. Through the setting of the torsion spring 32, after squeezing the two intersections, the intersections are reset by the torsion spring 32. When the tester 9 needs to be disassembled, hold the two intersections extending outside the connecting seat 30 and squeeze them so that the end moves closer to one side. The limiting part is separated from the slot 33, and the limit on the movable plate 29 is released, thereby releasing the limit on the tester 9. Move the tester 9 and the movable plate 29, so that the movable plate 29 is separated from the connecting seat 30, and the disassembly of the tester 9 is completed.

[0142] When in use, the second drive motor 20 operates, driving a bevel gear 19 connected to it to rotate, meshing and driving another bevel gear 19 to rotate, thereby driving the moving gear 24 to rotate, and connected to the rack 26. Under the sliding limit of the slider 25 and the side plate 27, the drive box 15 drives the screw rod 13, the bracket 14, the sliding seat 12 and the tester 9 to move horizontally in the furnace body 1. After moving to the position, the second drive motor 20 stops operating, and the hydraulic rod 22 operates to push the second drive motor 20 and the bevel gear 19 connected to it to move upward. The telescopic rod 23 limits and guides it, so that the clamping block 18 moves upward, separates from the connecting part, and engages with the clamping part. The second drive motor 20 operates, and drives the screw rod 13 to rotate through the clamping block 18 and the connecting block 16. Under the limit guidance of the bracket 14, the sliding seat 12 drives the tester 9 to move up and down, and the temperature and pressure inside the furnace body 1 can be detected from multiple points.

[0143] When it is necessary to disassemble the tester 9, hold the two intersection parts extending outside the connecting seat 30 and squeeze them so that the end moves closer to one side. The limiting part is separated from the slot 33, and the limiting of the movable plate 29 is released, thereby releasing the limiting of the tester 9. Move the tester 9 and the movable plate 29 so that the movable plate 29 is separated from the connecting seat 30, and the disassembly of the tester 9 is completed.

[0144] Example 2:

[0145] like Figure 10 As shown, the calibration method of the polytetrafluoroethylene dedicated sintering furnace based on Example 1 includes the following steps: starting the calibration device operation through the acquisition control module to obtain temperature data and pressure data at different positions inside the furnace body 1; and establishing a fault type model based on the support vector machine model.

[0146] Obtain historical temperature anomaly data and historical pressure anomaly data as well as the corresponding fault types to obtain an abnormal data set and establish a radial basis kernel function; divide the abnormal data set into a training set and a test set, import the training set into a support vector machine, and train the support vector machine; import the test set into the trained support vector machine, and test the support vector machine to obtain a fault type model.

[0147] The expression of the radial basis kernel function is:

[0148] ;

[0149] Where, for and The radial basis kernel function value between is the g-th input sample feature vector, is the hth input sample feature vector, is the kernel function parameter.

[0150] The expression is:

[0151] ;

[0152] In the formula, a is the dimension number, m is the dimension of the feature vector, is the g-th input sample feature vector in the a-th dimension, is the hth input sample feature vector of the ath dimension.

[0153] The obtained temperature data and pressure data at different positions inside the furnace body 1 are input into the fault type model; if there is a level one abnormal condition but no level one abnormal condition, the temperature data are compared with each abnormal temperature data stored in the fault type model in sequence, and the fault type corresponding to the closest abnormal temperature data is obtained and output; if there is a level one abnormal condition but no level one abnormal condition, the pressure data are compared with each abnormal pressure data stored in the fault type model in sequence, and the fault type corresponding to the closest abnormal pressure data is obtained and output; if neither the level one abnormal condition nor the level one abnormal condition exists, maintenance is performed.

[0154] Training a support vector machine model using anomaly datasets can uncover complex relationships between data and establish reliable fault type models. This allows for accurate fault diagnosis and prediction, enabling early detection of failures in heating or pressure control systems, preventing substandard products and ensuring production continuity and stable product quality. This facilitates process optimization by identifying potential failure triggers and providing a basis for optimizing operating parameters (such as heating power and pressure control), thereby improving product quality and production efficiency. This ensures product quality, reduces fluctuations in product quality caused by failures, and ensures the consistency and reliability of various product performance indicators. Real-time monitoring and alarms trigger an immediate alarm upon predicted failures, enabling operators to quickly inspect and address them, minimizing losses.

[0155] The temperature-related data and pressure-related data inside the furnace body 1 are obtained, and a temperature distribution model and a pressure distribution model are established to obtain a predicted temperature distribution and a predicted pressure. It is judged whether there is an abnormal heat transfer condition and a pressure abnormality in the furnace body 1, and the abnormal heat transfer condition level and the pressure abnormality level are determined. The abnormal heat transfer condition level includes abnormal condition level 1 and abnormal condition level 2, and the pressure abnormality level includes abnormal condition level 1 and abnormal condition level 2: if one of the abnormal condition level 2 and abnormal condition level 2 exists, the alarm device 3 is controlled to alarm.

[0156] Obtain basic data of the furnace body 1, including the length, width, and height of the furnace body 1; based on the basic data, discretize the internal area of ​​the furnace body 1 into multiple interconnected units, each unit having its own node, obtain the temperature expression within the unit, substitute it into the heat conduction equation to obtain an algebraic equation, and integrate and deduce on the unit to obtain the stiffness matrix and load vector of the unit; assemble the algebraic equations of each unit according to the connection relationship of the nodes to form an overall algebraic equation group for the internal area of ​​the furnace body 1; use the iterative method to solve the overall algebraic equation group to obtain the predicted temperature value of each node, that is, to obtain the predicted temperature distribution inside the furnace body 1.

[0157] The predicted temperature distribution inside the furnace body 1 is compared with the temperature data obtained at different positions inside the furnace body 1 to obtain the absolute value of the temperature comparison difference, and the absolute value of the temperature comparison difference is compared with the temperature deviation value range stored in the database: if the absolute value of the temperature comparison difference falls within the temperature deviation value range, an abnormal heat transfer condition exists in the furnace body 1, and it is recorded as a first-level abnormal condition; if the absolute value of the temperature comparison difference is less than the minimum value of the temperature deviation value range, there is no abnormal heat transfer condition in the furnace body 1; if the absolute value of the temperature comparison difference is greater than the maximum value of the temperature deviation value range, there is an abnormal heat transfer condition in the furnace body 1, and it is recorded as a second-level abnormal condition.

[0158] The heat conduction equation is:

[0159] ;

[0160] Where, is the material density of the furnace body (1), c is the specific heat capacity of the furnace body (1), is the gradient operator, T is the temperature, t is the time, is the rate of change of temperature with time, is the divergence of convective density, k is the thermal conductivity, is the temperature gradient, and Q is the heat source intensity inside the furnace (1).

[0161] The temperature expression is:

[0162] ;

[0163] Where, For the space coordinates The temperature at is the shape function, is the temperature of unit node i, and I is the number of unit nodes.

[0164] The overall algebraic equation system is:

[0165] ;

[0166] Where, is the overall stiffness matrix, is the node temperature vector, is the overall load vector;

[0167] Based on the ideal gas state, the ideal gas state equation is obtained, and the theoretical pressure of the gas in the furnace body 1 is obtained by analysis.

[0168] A leakage model is introduced to determine the gas leakage situation; based on the law of conservation of mass and combined with the ideal gas state equation, an equation is derived to consider the change of pressure with time under leakage conditions, and the pressure correction value is obtained. Combined with the theoretical pressure, the predicted pressure is obtained.

[0169] The predicted pressure inside the furnace body 1 is compared with the pressure data obtained at different positions inside the furnace body 1 to obtain the absolute value of the pressure comparison difference, and the absolute value of the pressure comparison difference is compared with the pressure deviation value range stored in the database: if the absolute value of the pressure comparison difference falls within the pressure deviation value range, there is a pressure anomaly in the furnace body 1, and it is recorded as a first-level anomaly; if the absolute value of the pressure comparison difference is less than the minimum value of the pressure deviation value range, there is no pressure anomaly in the furnace body 1; if the absolute value of the pressure comparison difference is greater than the maximum value of the pressure deviation value range, there is a pressure anomaly in the furnace body 1, and it is recorded as a second-level anomaly.

[0170] The ideal gas state equation is:

[0171] ;

[0172] Where P is the internal pressure of the furnace 1, V is the volume occupied by the gas, n is the amount of substance, and R is the universal gas constant. is the internal temperature of the furnace body 1.

[0173] The leakage model is:

[0174] ;

[0175] Where, is the leakage mass flow rate, is the flow coefficient, is the leakage area, is the gas density, is the pressure difference.

[0176] The equation for pressure variation with time considering leakage is:

[0177] ;

[0178] Where, is the pressure at time t, is the temperature of the gas, E is the leakage coefficient, is the integration constant, is the initial pressure, is the external environmental pressure, and t is the time point number.

[0179] The calculation formula for the pressure correction value is:

[0180] ;

[0181] Where, is the pressure correction value, The pressure under ideal conditions.

[0182] The formula for calculating the predicted pressure is:

[0183] ;

[0184] Where, To predict pressure.

[0185] By comparing the predicted temperature distribution inside the furnace body 1 with the actual temperature data obtained at different locations, based on the set temperature deviation range, it is possible to accurately determine whether there is an abnormal heat transfer condition and its level, and to discover potential heating system problems in advance, such as uneven heating, local overheating or overcooling, to avoid affecting the sintering quality of polytetrafluoroethylene products due to abnormal heat transfer.

[0186] Regarding pressure, based on the ideal gas state equation and introducing a leakage model, an equation for how pressure changes over time in the event of a leak is derived, and the predicted pressure is calculated. This is then compared with the actual pressure data, and the pressure deviation range is used to determine pressure anomalies and their levels, enabling early detection of problems such as gas leaks and pressure instability. Real-time monitoring and assessment of heat transfer and pressure conditions within the furnace are performed, and alarm 3 is activated promptly in the event of a Level 2 abnormality or an abnormal Level 2 pressure anomaly, preventing malfunctions.

[0187] During the calibration of the PTFE-specific sintering furnace, by establishing temperature and pressure distribution models, the working conditions inside the furnace body 1 can be carefully evaluated, which is conducive to optimizing the production process, extending the service life of the equipment, and reducing the risk of production interruptions due to failures, thereby improving the reliability of the entire PTFE production process.

[0188] Obtain the historical data set inside the furnace body 1, establish a product quality prediction model, obtain the product quality prediction value, and judge whether the predicted product quality is qualified: if unqualified, determine the fault type and make adjustments; if qualified, continue to test.

[0189] The historical data set includes historical temperature data, historical pressure data, and historical product quality data. The product quality data includes product density, product tensile strength, product melting point, and product dielectric constant. The historical data set is cleaned to check whether there are missing values ​​in the historical data set. If there are missing values, the median is filled. The outliers are checked and deleted if they exist. The historical data set is feature engineered to obtain a historical feature data set. The historical feature data set includes the average temperature, standard deviation of temperature, rate of temperature change, average pressure, standard deviation of pressure, average product density, standard deviation of product density, average product tensile strength, standard deviation of product tensile strength, average product melting point, standard deviation of product melting point, average product dielectric constant, and standard deviation of product dielectric constant.

[0190] The historical feature dataset is divided into a training set and a test set, and the training set is imported into the random forest regression model; the parameters of the random forest regression model are set, including determining the number of trees, controlling the maximum depth of the decision tree, and determining the minimum number of samples required for node splitting; sample data is randomly extracted with replacement from the training set to construct multiple decision trees for training to obtain a product quality prediction model, which is then tested using the test set.

[0191] The temperature data and pressure data currently acquired at different positions inside the furnace body 1 are input into the product quality prediction model to obtain predicted product quality data, and a comprehensive analysis is performed to obtain a product quality prediction value, where the predicted product quality data includes predicted product density, predicted product tensile strength, predicted product melting point, and predicted product dielectric constant; the product quality prediction value is compared with a qualified threshold value for product quality; if the product quality prediction value is higher than the qualified threshold value for product quality, the product is qualified; if the product quality prediction value is lower than the qualified threshold value for product quality, the product is unqualified.

[0192] The calculation formula for the product quality prediction value is:

[0193] ;

[0194] Where, is the product quality prediction value, To predict product density, To predict the tensile strength of the product, To predict the melting point of the product, To predict the dielectric constant of the product, Md is the product density, Ls is the product tensile strength, Rd is the product melting point, Jd is the product dielectric constant, for The weight factor, for The weight factor, for The weight factor, for The weight factor of , e is a natural constant.

[0195] Each weight factor is obtained from the database. Based on historical data, a mapping set of historically calculated relevant data and its weight factors is established to obtain the current weight factor. It can also be set based on experience.

[0196] Using a specialized product quality prediction formula, we can quantitatively assess product quality. By comparing this value against the acceptable quality threshold, we can determine whether a product is acceptable. This allows us to predict product quality in advance, ensuring consistency and stability, avoiding substandard products, and improving production efficiency.

[0197] At the same time, the effective use of historical data and the dynamic prediction of current data can expose potential problems in the production process in advance, and optimize and adjust the process parameters such as heating and pressure control of the sintering furnace in a targeted manner, thereby reducing the cost increase and resource waste caused by product quality problems and ensuring the quality of polytetrafluoroethylene products.

[0198] Example 3:

[0199] The verification system for implementing the verification method of Example 2 is as follows: Figure 11 As shown, it includes a data acquisition subsystem, a fault type model building subsystem, an abnormality judgment subsystem and a product quality qualification judgment subsystem, among which:

[0200] The data acquisition subsystem is used to start the operation of the calibration device through the acquisition control module and obtain temperature data and pressure data at different positions inside the furnace body 1; the fault type model establishment subsystem is used to establish a fault type model based on the support vector machine model; the abnormality judgment subsystem is used to obtain temperature-related data and pressure-related data inside the furnace body 1, establish a temperature distribution model and a pressure distribution model, obtain a predicted temperature distribution and a predicted pressure, respectively judge whether there is an abnormal heat transfer condition and a pressure abnormality in the furnace body 1, and determine the abnormal heat transfer condition level and the pressure abnormality level. The abnormal heat transfer condition level includes abnormal condition level 1 and abnormal condition level 2, and the pressure abnormality level includes abnormal condition level 1 and abnormal condition level 2:

[0201] If one of the abnormal condition level 2 and the abnormal condition level 2 exists, the control alarm 3 will sound an alarm; the product quality qualification judgment subsystem is used to obtain the historical data set inside the furnace body 1, establish a product quality prediction model, obtain the product quality prediction value, and judge whether the predicted product quality is qualified: if it is unqualified, the fault type is judged and adjustments are made; if it is qualified, the detection continues.

Claims

1. The verification method of polytetrafluoroethylene special sintering furnace is characterized by: A special sintering furnace for polytetrafluoroethylene comprises a furnace body (1), a calibration device arranged inside the furnace body (1), and a control device (2) arranged on one side of the furnace body (1), wherein: The calibration device includes a calibrator (9) and a temperature sensor (10) and a pressure sensor (11) provided on the calibrator (9), wherein the temperature sensor (10) is used to detect the internal temperature of the furnace body (1), and the pressure sensor (11) is used to detect the internal pressure of the furnace body (1); A frame (5) for placing polytetrafluoroethylene is provided in the furnace body (1), the lower end of the frame (5) extends to the outside of the furnace body (1), and a first drive motor (6) is installed at the lower end of the frame (5), and the first drive motor (6) is installed at the bottom of the furnace body (1); A heating tube (7) is provided at the bottom of the furnace body (1), an isolation net (8) is provided above the heating tube (7), and a blower (4) is provided at the top of the furnace body (1); The control device (2) is provided with an alarm (3) for use therewith. The control device (2) is provided with a data processing module, a data transceiver module, a data acquisition module, and an acquisition control module, wherein: The data acquisition module is used to detect and collect the operation data of the furnace body (1), and send it to the data processing module through the data transceiver module; The data processing module is used to process the operation data, temperature data and pressure data of the furnace body (1) during operation, and to control the temperature and pressure of the furnace body (1) during operation; The temperature sensor (10) is used to send temperature data inside the furnace body (1) to the data processing module through the data transceiver module; the pressure sensor (11) is used to send pressure data inside the furnace body (1) to the data processing module through the data transceiver module; The acquisition control module is used to control the operation of the calibration device and collect temperature data and pressure data from various points inside the furnace body (1); The data processing module is in communication connection with the data transceiver module, the temperature sensor (10), the pressure sensor (11), the data acquisition module, and the acquisition control module; It also includes a driving member, which is arranged on the back side of the tester (9) and is used to drive the tester (9) to move horizontally; A screw rod (13) and a bracket (14) are provided below the driving member and are used in conjunction with the driving member to move the tester (9) up and down; The calibration device further comprises a connecting member, which is arranged between the calibrator (9) and the driving member and is used to connect the calibrator (9) and the screw rod (13) in a limiting manner; The driving member comprises a driving box (15), a second driving motor (20), a hydraulic rod (22), a telescopic rod (23) and a clamping member, wherein the second driving motor (20), the hydraulic rod (22), the telescopic rod (23) and the clamping member are all arranged in the driving box (15); The second drive motor (20) is connected to the mounting plate (21) by screws. Two meshing bevel gears (19) are provided below the second drive motor (20). One bevel gear (19) is provided at the output end of the second drive motor (20). The other bevel gear (19) is connected to a moving gear (24). The moving gear (24) is provided outside the drive box (15). The hydraulic rod (22) and the telescopic rod (23) are symmetrically arranged and are both mounted between the bottom of the mounting plate (21) and the bottom of the drive box (15) by screws. The hydraulic rod (22) is used to drive the second drive motor (20) and the bevel gear (19) connected thereto to move up and down; The clamping member includes a clamping block (18) and a connecting block (16), wherein the clamping block (18) is mounted on the bottom of a bevel gear (19) connected to the output end of the second drive motor (20) through a rod, and the connecting block (16) is mounted on the upper end of the screw rod (13), and a connecting groove (17) adapted to the clamping block (18) is provided inside the connecting block (16); The connecting groove (17) includes a connecting portion and a clamping portion, the connecting portion is arranged below the clamping portion, the inner wall of the clamping portion is provided with a groove adapted to the clamping block (18), and the clamping block (18) does not contact the inner wall of the connecting portion; When the second drive motor (20) and the bevel gear (19) connected thereto move upward, the clamping block (18) moves upward and is assembled with the clamping portion, thereby driving the screw rod (13) to rotate. When the second drive motor (20) and the bevel gear (19) connected thereto move upward, the clamping block (18) moves downward and is assembled with the connecting portion, thereby driving the moving gear (24) to rotate. The back of the driving box (15) is symmetrically provided with a slider (25), the slider (25) is slidably connected to a side plate (27), a front panel (28) is mounted on the side of the side plate (27) close to the driving box (15) by screws, and the side plate (27) is mounted on the inner wall of the furnace body (1) by screws; A slide groove adapted to the slider (25) is provided on the side plate (27), a rack (26) is provided on the inner bottom of the side plate (27), and the moving gear (24) is meshed with the rack (26); The connecting member includes a sliding seat (12), a connecting seat (30), a movable plate (29) and a limiting member (31), the sliding seat (12) is L-shaped, the sliding seat (12) is sleeved on the screw rod (13) and the bracket (14), and is threadedly connected to the screw rod (13), the screw rod (13) and the bracket (14) are both installed at the bottom of the drive box (15), the bracket (14) is L-shaped, and the lower end of the screw rod (13) is installed at the bottom of the inner side of the bracket (14); The connecting seat (30) is arranged on the outer side of the sliding seat (12), and the movable plate (29) is symmetrically arranged on the back of the tester (9) and is slidably connected to the connecting seat (30); The limiting member (31) is symmetrically arranged and arranged inside the connecting seat (30). The limiting member (31) includes a cross portion and a limiting portion. One end of the cross portion is connected to the limiting portion, and the other end of the cross portion extends to the outside of the connecting seat (30). The cross portions in the two limiting members (31) are rotatably connected and arranged in a cross-shaped manner. A torsion spring (32) is installed outside the connection of the two cross portions. The limiting portion extends to the outside of the connecting seat (30) and passes through the movable plate (29). The movable plate (29) is provided with a card slot (33) adapted to the card connection portion. The verification method of the above-mentioned polytetrafluoroethylene special sintering furnace includes the following steps: The calibration device is started to operate through the acquisition control module to obtain temperature data and pressure data at different positions inside the furnace body (1); Based on support vector machine, a fault type model is established; Obtain temperature-related data and pressure-related data inside the furnace body (1), establish a temperature distribution model and a pressure distribution model, obtain a predicted temperature distribution and a predicted pressure, respectively judge whether there is an abnormal heat transfer condition and a pressure abnormality in the furnace body (1), and determine the abnormal heat transfer condition level and the pressure abnormality level. The abnormal heat transfer condition level includes abnormal condition level 1 and abnormal condition level 2, and the pressure abnormality level includes abnormal condition level 1 and abnormal condition level 2: If one of the abnormal condition level 2 and abnormal level 2 exists, the control alarm (3) sounds an alarm; Obtain the historical data set inside the furnace body (1), establish a product quality prediction model, obtain the product quality prediction value, and judge whether the predicted product quality is qualified: If it fails, determine the fault type and make adjustments; If qualified, continue with the test; Establishing a fault type model includes the following steps: Obtain historical temperature anomaly data and historical pressure anomaly data as well as the corresponding fault types, obtain the anomaly data set, and establish the radial basis kernel function; The abnormal data set is divided into a training set and a test set, the training set is imported into the support vector machine, and the support vector machine is trained; Import the test set into the trained support vector machine, test the support vector machine, and obtain the fault type model; The expression of the radial basis kernel function is: ； Where, for and The radial basis kernel function value between is the g-th input sample feature vector, is the hth input sample feature vector, is the kernel function parameter; The expression is: ； In the formula, a is the dimension number, m is the dimension of the feature vector, is the g-th input sample feature vector in the a-th dimension, is the hth input sample feature vector of the ath dimension; Determining whether there is an abnormal heat transfer condition and an abnormal pressure in the furnace body (1), and determining the level of the abnormal heat transfer condition and the level of the abnormal pressure, includes the following steps: Obtaining basic data of the furnace body (1), the basic data including the length of the furnace body (1), the width of the furnace body (1), and the height of the furnace body (1); Based on the basic data, the internal area of ​​the furnace body (1) is discretized into multiple interconnected units, each unit has its own node, and the temperature expression within the unit is obtained. Substitute it into the heat conduction equation to obtain an algebraic equation, and integrate and deduce it on the unit to obtain the unit's stiffness matrix and load vector; Assemble the algebraic equations of each unit according to the connection relationship of the nodes to form the overall algebraic equation group of the internal area of ​​the furnace body (1); The overall algebraic equations are solved by iterative method to obtain the predicted temperature value of each node, that is, to obtain the predicted temperature distribution inside the furnace body (1); The predicted temperature distribution inside the furnace body (1) is compared with the obtained temperature data at different positions inside the furnace body (1), and the absolute value of the temperature comparison difference is obtained. The absolute value of the temperature comparison difference is compared with the temperature deviation value range stored in the database: If the absolute value of the temperature comparison difference falls within the temperature deviation value range, then there is an abnormal heat transfer condition in the furnace body (1), and it is recorded as a first-level abnormal condition; If the absolute value of the temperature comparison difference is less than the minimum value of the temperature deviation value range, then there is no abnormal heat transfer condition in the furnace body (1); If the absolute value of the temperature comparison difference is greater than the maximum value of the temperature deviation value range, then an abnormal heat transfer condition exists in the furnace body (1), and it is recorded as a second-level abnormal condition; The heat conduction equation is: ； Where, is the material density of the furnace body (1), c is the specific heat capacity of the furnace body (1), is the gradient operator, T is the temperature, t is the time, is the rate of change of temperature with time, is the divergence of convective density, k is the thermal conductivity, is the temperature gradient, Q is the internal heat source intensity of the furnace (1); The temperature expression is: ； Where, For the space coordinates The temperature at is the shape function, is the temperature of unit node i, I is the number of unit nodes; The overall algebraic equation system is: ； Where, is the overall stiffness matrix, is the node temperature vector, is the overall load vector; Based on the ideal gas state, the ideal gas state equation is obtained, and the theoretical pressure of the gas in the furnace body (1) is obtained by analysis; Introduce leakage model to determine gas leakage; According to the law of conservation of mass and the ideal gas state equation, the equation of pressure change with time considering leakage is derived, and the pressure correction value is obtained. Combined with the theoretical pressure, the predicted pressure is obtained; The predicted pressure inside the furnace body (1) is compared with the obtained pressure data at different positions inside the furnace body (1), and the absolute value of the pressure comparison difference is obtained. The absolute value of the pressure comparison difference is compared with the pressure deviation value range stored in the database: If the absolute value of the pressure comparison difference falls within the pressure deviation value range, then there is a pressure anomaly in the furnace body (1), and it is recorded as a level one anomaly; If the absolute value of the pressure comparison difference is less than the minimum value of the pressure deviation value range, then there is no pressure abnormality in the furnace body (1); If the absolute value of the pressure comparison difference is greater than the maximum value of the pressure deviation value range, then there is a pressure abnormality in the furnace body (1), and it is recorded as a second-level abnormal condition; The ideal gas state equation is: ； Where P is the internal pressure of the furnace (1), V is the volume occupied by the gas, n is the amount of substance, and R is the universal gas constant. is the internal temperature of the furnace (1); The leakage model is: ； Where, is the leakage mass flow rate, is the flow coefficient, is the leakage area, is the gas density, is the pressure difference; The equation for pressure variation with time considering leakage is: ； Where, is the pressure at time t, is the temperature of the gas, E is the leakage coefficient, is the integration constant, is the pressure at the initial moment, is the external environmental pressure, t is the time point number; The calculation formula for the pressure correction value is: ； Where, is the pressure correction value, is the pressure under ideal conditions; The formula for calculating the predicted pressure is: ； Where, To predict stress; Judging whether the predicted product quality is qualified includes the following steps: The historical data set includes historical temperature data, historical pressure data, and historical product quality data. Product quality data includes product density, product tensile strength, product melting point, and product dielectric constant. Perform data cleaning on historical data sets to check whether there are missing values ​​in the historical data sets. If there are missing values, fill them with the median; Check whether there are outliers, and if so, delete them; Perform feature engineering on the historical dataset to obtain a historical feature dataset. The historical feature dataset includes average temperature, temperature standard deviation, temperature change rate, average pressure, pressure standard deviation, average product density, product density standard deviation, average product tensile strength, product tensile strength standard deviation, average product melting point, product melting point standard deviation, average product dielectric constant, and product dielectric constant standard deviation. Divide the historical feature dataset into a training set and a test set, and import the training set into the random forest regression model; Set the parameters of the random forest regression model, including determining the number of trees, controlling the maximum depth of the decision tree, and determining the minimum number of samples required for a node to split; Randomly extract sample data with replacement from the training set to build multiple decision trees for training, obtain the product quality prediction model, and test the product quality prediction model using the test set; Input the acquired temperature data and pressure data of different positions inside the furnace body (1) into the product quality prediction model to obtain predicted product quality data, and obtain the product quality prediction value through comprehensive analysis. The predicted product quality data includes predicted product density, predicted product tensile strength, predicted product melting point, and predicted product dielectric constant; Compare the product quality prediction value with the qualified threshold value of the product quality. If the product quality prediction value is higher than the qualified threshold value of the product quality, the product is qualified. If the predicted product quality value is lower than the qualified threshold of product quality, it is unqualified; The calculation formula for the product quality prediction value is: ； Where, is the product quality prediction value, To predict product density, To predict the tensile strength of the product, To predict the melting point of the product, To predict the dielectric constant of the product, Md is the product density, Ls is the product tensile strength, Rd is the product melting point, Jd is the product dielectric constant, for The weight factor, for The weight factor, for The weight factor, for The weight factor, e is a natural constant; Determining the fault type includes the following steps: Input the acquired temperature data and pressure data of different positions inside the furnace body (1) into the fault type model; If there is a first level of abnormal condition, but there is no first level of abnormal condition, the temperature data is sequentially compared with each abnormal temperature data stored in the fault type model, and the fault type corresponding to the closest abnormal temperature data is obtained and output; If there is a first-level abnormality but no first-level abnormal condition, the pressure data is sequentially compared with each abnormal pressure data stored in the fault type model to obtain the fault type corresponding to the closest abnormal pressure data and output it; If both abnormal condition level 1 and abnormality level 1 do not exist, perform maintenance.

2. A calibration system for a polytetrafluoroethylene sintering furnace, used to implement the calibration method for a polytetrafluoroethylene sintering furnace according to claim 1, characterized in that: It includes data acquisition subsystem, fault type model establishment subsystem, abnormality judgment subsystem and product quality qualification judgment subsystem, among which: A data acquisition subsystem is used to start the operation of the calibration device through the acquisition control module to obtain temperature data and pressure data at different positions inside the furnace body (1); A fault type model establishment subsystem is used to establish a fault type model based on a support vector machine model; The abnormality judgment subsystem is used to obtain temperature-related data and pressure-related data inside the furnace body (1), establish a temperature distribution model and a pressure distribution model, obtain a predicted temperature distribution and a predicted pressure, respectively judge whether there is an abnormal heat transfer condition and a pressure abnormality in the furnace body (1), and determine the abnormal heat transfer condition level and the pressure abnormality level. The abnormal heat transfer condition level includes abnormal condition level 1 and abnormal condition level 2, and the pressure abnormality level includes abnormal condition level 1 and abnormal condition level 2: If one of the abnormal condition level 2 and abnormal level 2 exists, the control alarm (3) sounds an alarm; The product quality qualification judgment subsystem is used to obtain the historical data set inside the furnace body (1), establish a product quality prediction model, obtain the product quality prediction value, and judge whether the predicted product quality is qualified: If it fails, determine the fault type and make adjustments; If qualified, continue with the test.

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