Preparation method of carbonized polydopamine / waste clay reinforced self-lubricating fabric composite material

Abstract

The present invention provides a preparation method of a carbonized polydopamine / waste clay reinforced self-lubricating fabric composite, belonging to the technical field of self-lubricating fabric composites. The waste clay is ball-milled in a ball mill to obtain waste clay powder; a mixed solution is stirred at room temperature to undergo a polymerization reaction to obtain a polydopamine / waste clay dispersion; the polydopamine / waste clay dispersion is centrifuged, washed, and dried to obtain polydopamine / waste clay powder; the polydopamine / waste clay powder is carbonized in a tubular furnace to obtain carbonized polydopamine / waste clay; the fiber fabric is subjected to plasma treatment to obtain an activated fiber fabric; and finally, heat curing is carried out to obtain a self-lubricating gasket fabric composite; the present invention can effectively reduce the shear force applied to the material surface and effectively prevent the fiber matrix from being damaged.

Description

Technical Field

[0001] The present invention relates to the technical field of self-lubricating gasket fabric composites, and particularly relates to a preparation method of a carbonized polydopamine / waste clay reinforced self-lubricating gasket fabric composite material. Background Art

[0002] As an important component in the machinery industry, the main function of a bearing is to support a mechanical rotating body, reduce the frictional force during the rotation of the rotating body, and at the same time ensure the rotational accuracy of the rotating body. As an important functional component with high load-bearing capacity, wear resistance, self-aligning, and rotation, self-lubricating spherical plain bearings have been widely used in the fields of aviation, aerospace, navigation, weapons, and high-end civilian instruments, and are indispensable components in parts such as aeroengines, landing gears, and rudders. The self-lubricating gasket is a very important part of the self-lubricating spherical plain bearing, which can play roles such as reducing and resisting wear, isolating metals, resisting impact, and extending the service life of the bearing. The self-lubricating gasket also has characteristics such as high strength and maintenance-free, and exhibits excellent self-lubricating performance and anti-wear performance in harsh service environments such as high temperature, heavy load, and high frequency. With the development of high-tech fields, the performance requirements for bearings by various machines are getting higher and higher, and at the same time, higher requirements are also put forward for self-lubricating gaskets.

[0003] For self-lubricating gasket fabric composites, the reinforcement is a two-dimensional composite fabric obtained by weaving reinforcing fibers and lubricating fibers. Among them, the interfacial bonding force between the fiber and the resin has a decisive influence on the performance of the composite material. When there is good bonding strength at the interface between the fiber and the resin, the internal stress of the material can be released through the continuous generation of microcracks at the interface; if there is not good bonding strength between the interfaces, the mechanical and anti-friction properties of the composite fabric will be greatly reduced. Therefore, it is necessary to construct a high-strength and high-toughness interfacial modification layer between the fiber and the resin, which can greatly improve the mechanical properties and tribological properties of the self-lubricating gasket fabric composite material.

[0004] At present, the strengthening of self-lubricating gasket fabric composites mainly includes methods such as chemical etching, surface coating, and grafting of polymer polymers. The above methods can achieve the purpose of improving the strength of the fibers in the self-lubricating gasket fabric, but the tribological properties of the self-lubricating gasket fabric material and the interfacial bonding effect between the fabric and the resin need to be improved. There is an urgent need for a self-lubricating gasket fabric material with high interfacial bonding effect, low friction coefficient, and high wear resistance. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technology, the purpose of the present invention is to provide a preparation method of a carbonized polydopamine / waste clay reinforced self-lubricating gasket fabric composite material, which can significantly improve the interfacial bonding effect, wear resistance of the self-lubricating gasket fabric material and reduce the friction coefficient.

[0006] In a first aspect, to achieve the above object, the present invention adopts the following technical solutions:

[0007] The present invention provides a preparation method for a carbonized polydopamine / waste clay enhanced self-lubricating liner fabric composite material;

[0008] Ball-mill the waste clay in a ball mill to obtain waste clay powder;

[0009] Furthermore, during the ball-milling process, the rotation speed is 400 r / min and the ball-milling time is 12 h;

[0010] Prepare a Tris solution, and add the waste clay powder and dopamine to the Tris solution;

[0011] Furthermore, the Tris solution is obtained by dissolving 3.128 g of Tris and 612 μL of hydrochloric acid and making up the volume to 500 mL;

[0012] Stir the mixed solution at room temperature to cause a polymerization reaction to obtain a polydopamine / waste clay dispersion;

[0013] Furthermore, the time for the polymerization reaction is 24 h, and the rotation speed of the stirrer is set to 850 r / min;

[0014] Centrifuge, wash, and dry the polydopamine / waste clay dispersion to obtain polydopamine / waste clay powder;

[0015] Furthermore, in the centrifugation process, the rotation speed of the centrifuge is 8000 r / min, the centrifugation time is 5 min, and it is cycled with the washing operation, and the cycle needs to be carried out 3 - 5 times;

[0016] Carry out carbonization treatment on the polydopamine / waste clay powder in a tubular furnace to obtain carbonized polydopamine / waste clay;

[0017] Furthermore, for the carbonization treatment of the polydopamine / waste clay, the temperature of the tubular furnace is set to 600 °C, the time is 5 h, and argon is introduced into the tubular furnace.

[0018] Carry out plasma treatment on the fiber fabric to obtain an activated fiber fabric;

[0019] Furthermore, the power of the plasma treatment is 50 - 150 W and the time is 30 - 35 min.

[0020] Immerse the activated fiber fabric in an impregnating solution containing a mixture of carbonized polydopamine / waste clay powder and a resin solution, repeat impregnation-drying, and finally heat and cure to obtain a self-lubricating liner fabric composite material;

[0021] Further, the resin includes one or more of epoxy resin, polyimide resin, polyamideimide or phenolic resin; the fiber fabric includes one or more of glass fiber fabric, carbon fiber fabric, aramid fiber / polytetrafluoroethylene fiber blended fabric, polyimide fiber / polytetrafluoroethylene fiber blended fabric or polyetheretherketone fiber / polytetrafluoroethylene fiber blended fabric;

[0022] Further, the ratio of the carbonized polydopamine / waste clay powder to the resin solution is 1 g: 40 mL;

[0023] Further, the mass fraction of the resin in the self-lubricating liner fabric composite is 30% - 40%; the curing temperature is 100 - 200 °C, the pressure is 0.5 - 1 MPa, and the curing time is 1 - 2 h;

[0024] Further, an intelligent optimization algorithm for polymerization reaction conditions is introduced, and the specific steps are as follows:

[0025] Step 1, installation of sensors and description of parameter variables;

[0026] The installation and data acquisition of sensors are the basis of the intelligent optimization algorithm. The following is a detailed description of the installation positions, functions and related parameters of various sensors:

[0027] Temperature sensor:

[0028] Installation position: The temperature sensor is installed inside the reactor near the stirrer to ensure that the measured temperature represents the actual temperature of the entire reaction system;

[0029] Function: Temperature is a key parameter affecting the polymerization reaction rate and effect of dopamine. The temperature sensor is used to monitor the temperature change in real time during the reaction and provide important input data for the optimization model;

[0030] Accuracy: The accuracy of the temperature sensor is within ±0.1 °C to ensure the reliability of the data;

[0031] Formula:

[0032] ;

[0033] Among them, is the average temperature over a period of time, is the number of measurements, is the temperature value of each measurement, represents the index of the current time point, represents the i-th time point;

[0034] PH sensor:

[0035] Installation location: The pH sensor should be installed in the solution part of the reactor, close to the reactant area, in order to accurately measure the acidity and alkalinity of the solution;

[0036] Function: The pH value affects the polymerization rate and reaction degree of dopamine. Real-time monitoring of the change in pH value helps to judge the reaction progress and adjust the reaction conditions in a timely manner;

[0037] Measurement range: Concentrated between 6 and 8 in this reaction system;

[0038] Formula:

[0039] ;

[0040] Among them, is the average pH value within a certain period of time, is the pH value of each measurement;

[0041] Viscosity sensor:

[0042] Installation location: The viscosity sensor should be installed in the area where the liquid flow in the reactor is the most uniform, in order to obtain accurate viscosity readings;

[0043] Function: Viscosity is an important characterization of the polymer formed during the reaction process. The change in viscosity reflects the growth and cross-linking of polymer chains and directly affects the final properties of the material;

[0044] Measurement range: 0.1 Pa·s to 10 Pa·s;

[0045] Formula:

[0046] ;

[0047] Among them, is the average viscosity within a certain period of time, is the viscosity value of each measurement;

[0048] Agitator speed sensor:

[0049] Installation location: The agitator speed sensor is directly installed on the agitator shaft for real-time monitoring of the agitator speed;

[0050] Function: The stirring speed is an important parameter for controlling the uniformity of the reaction mixture. By real-time monitoring of the stirring speed, the system can adjust the agitator speed according to needs to optimize the reaction conditions;

[0051] Speed range: Set between 600 r / min and 1000 r / min;

[0052] Formula:

[0053] ;

[0054] Among them, is the average stirring speed within a certain period of time, is the rotational speed value measured each time;

[0055] Step 2: Data acquisition and preliminary processing

[0056] During the reaction process, the reaction data of various sensors installed (temperature sensor, pH sensor, viscosity sensor, stirrer rotational speed sensor) are collected in real time. These sensors are connected to the data acquisition system through the Internet of Things (IoT) interface to ensure the efficient transmission of real-time data;

[0057] Data acquisition frequency:

[0058] Temperature data: collected once per minute, record the temperature value ;

[0059] pH data: collected once per minute, record the pH value ;

[0060] Viscosity data: collected once every 5 minutes, record the viscosity value ;

[0061] Stirrer rotational speed data: collected once per second, record the rotational speed value ;

[0062] Preliminary data processing:

[0063] Moving average filtering: used to eliminate random noise, smooth the data by calculating the average value of multiple consecutive data points;

[0064] ;

[0065] Among them, is the data value after moving average filtering processing, represents the original data collected at the time point , is the window size of the moving average filtering, is the total width of the window, represents the offset, and the value range is from -k to k;

[0066] Median filtering: eliminate outliers by replacing the outliers with the median of the nearby data;

[0067] ;

[0068] Among them, is the denoised data value, represents the median operation, represents at the time point The original data value collected at

[0069] Exponential Weighted Moving Average (EWMA): Assigns higher weights to new data and lower weights to old data, making the latest data have a greater impact on the smoothed result;

[0070] ;

[0071] Among them, is the smoothed data, is the smoothing coefficient, is the smoothed data value after exponential weighted moving average processing, is the time point of the original data value;

[0072] Step 3, Application of the ISOA-DELM model

[0073] ISOA-DELM (Deep Extreme Learning Machine Optimized by Improved Seagull Algorithm) is an advanced machine learning model that combines the Improved Seagull Optimization Algorithm (ISOA) and the Deep Extreme Learning Machine (DELM) for the prediction and optimization of complex systems; in the patented technology of this article, the ISOA-DELM model will be used to optimize the key parameters in the polymerization reaction process;

[0074] Input variables:

[0075] ;

[0076] ;

[0077] ;

[0078] ;

[0079] ;

[0080] ;

[0081] DELM structure:

[0082] Input layer: Receives the normalized input data;

[0083] Hidden layer: Multiple hidden neurons that capture complex non-linear relationships in the input data;

[0084] Output layer: Outputs the predicted target variable ;

[0085] ISOA Optimization:

[0086] Optimize the weights and biases of the DELM model using the improved Seagull Optimization Algorithm (ISOA) to minimize the prediction error;

[0087] ;

[0088] Among them, is the weight matrix, is the bias vector;

[0089] Weight Update:

[0090] ;

[0091] Among them, is the generalized inverse matrix of the hidden layer output matrix, is the target output matrix, is the weight matrix;

[0092] Seagull Optimization Update Formula:

[0093] ;

[0094] Among them, is the updated parameter position, is the global optimal position, is the convergence factor, used to control the convergence speed of the algorithm, is the current parameter position, is the position of the current seagull;

[0095] Optimization Process:

[0096] Initialization: First, randomly generate the positions of the seagull population, and each position represents a possible solution (i.e., a combination of a set of weights and biases);

[0097] Fitness Calculation: For each seagull, calculate its fitness in the current environment, that is, calculate the corresponding objective function value; the seagull with higher fitness (smaller objective function value) represents a better solution;

[0098] Update Position: According to the fitness value and the foraging behavior of the seagulls (including exploration and exploitation), update the position of each seagull, and the new position should be closer to the global optimal solution;

[0099] Exploration Phase: Seagulls explore new areas by flying randomly to avoid falling into local optima;

[0100] Exploitation Phase: Seagulls adjust their flying directions and speeds according to the known optimal solution and gradually converge to the global optimal solution;

[0101] Final goal: Through multiple iterations, make the seagull population gradually approach the global optimal solution, that is, find the set of weights and biases that minimize the prediction error;

[0102] Real-time prediction and optimization:

[0103] Real-time input:

[0104] : Temperature data collected in real time;

[0105] : pH value data collected in real time;

[0106] : Viscosity data collected in real time;

[0107] : Stirring speed data collected in real time;

[0108] Real-time output:

[0109] : Polymer coverage effect predicted in real time;

[0110] : Optimal stirring speed;

[0111] : Optimal reaction temperature;

[0112] : Optimal viscosity data collected;

[0113] : Optimal reaction time adjustment value;

[0114] Implement the prediction formula:

[0115] ;

[0116] Among them, represents the trained ISOA-DELM model function;

[0117] Dynamic adjustment:

[0118] Stirring speed adjustment:

[0119] ;

[0120] Temperature adjustment:

[0121] ;

[0122] Reaction time adjustment:

[0123] ;

[0124] Among them, is the adjusted stirring speed, is the optimal value recommended by the model, is the adjusted reaction temperature, is the optimal value recommended by the model, is the adjusted reaction time, is the original reaction time, is the time adjustment value recommended by the model;

[0125] Step 4, Actuator operation

[0126] Intelligent speed control controller: The stirrer is equipped with an intelligent speed control controller, which automatically adjusts the stirring speed through the output signal of the ISOA-DELM model; the stirring speed is crucial for the uniformity of the reaction solution and the formation of polymers;

[0127] Control formula:

[0128] ;

[0129] Among them, is the speed adjustment coefficient, is the time point when the optimal stirring speed recommended by the ISOA-DELM model, is the time point when the actual rotation speed of the current stirrer, is the time point when the adjusted new rotation speed of the stirrer;

[0130] Operation process:

[0131] The intelligent speed control controller receives the optimal stirring speed output by the ISOA-DELM model in real time ; According to the current stirring speed , the intelligent speed control controller calculates and adjusts to the new stirring speed , ensuring the uniform mixing of the reaction solution and preventing local overheating or incomplete polymerization;

[0132] Temperature regulation system: The heating system in the reaction kettle is closely connected to the temperature sensor, and dynamically adjusts the temperature of the reaction kettle according to the temperature adjustment recommendation of the ISOA-DELM model to ensure that the reaction temperature always remains within the optimal range;

[0133] Control formula:

[0134] ;

[0135] Among them, is the temperature regulation coefficient, is the time point When, the optimal temperature recommended by the ISOA-DELM model, is the time point when, the current reactor temperature, time point when, the new adjusted reactor temperature;

[0136] Operation process:

[0137] The temperature control system monitors the reaction temperature in real time through a temperature sensor and receives the optimal temperature recommendation of the ISOA-DELM model; according to the new temperature calculated by the formula , adjust the heating power or cooling system, dynamically adjust the reactor temperature, and prevent incomplete polymerization reactions caused by overheating or insufficient temperature;

[0138] Time control system: The reaction time control system relies on the ISOA-DELM model's prediction of the optimal reaction time; the central control system automatically controls the reaction time according to the model's output to ensure that the reaction ends at the optimal time point;

[0139] Control formula:

[0140] ;

[0141] Wherein, is the currently consumed reaction time, is the time adjustment amount recommended by the model, is the new adjusted reaction time;

[0142] Operation process:

[0143] According to the reaction completion time predicted by the model, the central control system will end the reaction process at the optimal time point; when the reaction is approaching the end, the system will issue an alarm and automatically turn off the heating and stirring devices to prevent overreaction or underreaction;

[0144] Step Five, feedback and adaptive learning;

[0145] Feedback process:

[0146] Data collection: After each polymerization reaction is completed, the system will collect actual data on the polymer coverage effect, including coverage rate, uniformity, and thickness;

[0147] Feedback to the model: These actually measured data will be input into the ISOA-DELM model as feedback signals and compared with the results predicted by the model;

[0148] Error calculation:

[0149] ;

[0150] Among them, is the prediction error at the time point ;

[0151] Based on the error data, the model will automatically adjust its internal parameters, the weight W and the bias b, to reduce future prediction errors;

[0152] Step Six, Human-Machine Interface Setting

[0153] Real-time Monitoring: The operator can monitor the entire reaction process in real time through the human-machine interface, including sensor data, model prediction results, and the operating status of the actuator; the interface can visually display the current reaction conditions, including the temperature curve, pH change, and viscosity change;

[0154] Data Visualization: The human-machine interface provides a data visualization function, and the operator can view and analyze the change trends of key parameters in the form of charts, curves, etc.; data visualization can help the operator better understand and judge the changes in the reaction process;

[0155] Manual Intervention and Parameter Adjustment: In special cases, the operator can manually intervene through the human-machine interface to adjust key parameters (such as temperature, stirring speed), and the system will be updated in real time according to the operator's adjustment and recalculated through the ISOA-DELM model to ensure that the adjusted conditions still meet the best reaction requirements.

[0156] In the second aspect, the present invention provides a self-lubricating gasket fabric composite material with carbonized polydopamine / waste clay as reinforcing fillers.

[0157] The present invention provides a self-lubricating gasket fabric composite material with carbonized polydopamine / waste clay as reinforcing fillers; among them, carbonized modified polydopamine / waste clay can, on the one hand, promote the uniform distribution of waste clay in the resin matrix of the filler and improve its dispersion performance in the self-lubricating gasket fabric, and on the other hand, the carbonization treatment can weaken the interlayer force of waste clay and further enhance the lubrication performance of waste clay; and carbonized polydopamine / waste clay has excellent mechanical properties and a large specific surface area, which helps to form a synergistic enhancement effect on the friction and wear performance of the self-lubricating gasket fabric;

[0158] The present invention introduces the reinforcing filler carbonized polydopamine / waste clay reinforcing particles, which can enhance the interfacial bonding strength between the fabric and the resin, and then ensure that the stress during the tensile process in all directions can be evenly transmitted to the fiber fabric, so as to improve the mechanical properties of the material. The introduction of carbonized polydopamine / waste clay in the self-lubricating gasket fabric material can effectively reduce the shear force applied to the material surface and thus effectively prevent the fiber matrix from being damaged, thereby significantly improving the lubrication performance and friction and wear performance of the gasket fabric composite material.

[0159] The present invention introduces an intelligent optimization algorithm for polymerization reaction conditions. By integrating the improved seagull optimization algorithm and the deep extreme learning machine (ISOA-DELM), it realizes the real-time prediction and dynamic optimization of the polymerization reaction process. The system can automatically adjust the stirring speed, temperature, and reaction time according to the sensor data to ensure that the reaction conditions are always in the optimal state. At the same time, through the feedback mechanism and adaptive learning, the model is continuously optimized after each reaction, improving the prediction accuracy and production efficiency. The seamless integration of the central control system and the human-machine interface enables operators to monitor and adjust key parameters in real time, ensuring the stability of the reaction process and the product quality. This intelligent optimization algorithm significantly improves the automation level and reliability of the polymerization reaction. Description of the Drawings

[0160] Figure 1 Scanning electron microscope images of polydopamine / waste clay before and after carbonization modification; (a) is before modification; (b) is after modification;

[0161] Figure 2 Scanning image of carbonized polydopamine / waste clay-reinforced carbon fiber self-lubricating gasket fabric; (a) is the scanning image of the fabric surface; (b) is the high-magnification cross-section scanning image; (c) is the low-magnification cross-section scanning image;

[0162] Figure 3 Comparison of friction coefficients between three examples of carbonized polydopamine / waste clay-reinforced self-lubricating gasket fabrics and three comparative examples of non-reinforced self-lubricating gasket fabrics;

[0163] Figure 4 Comparison of tensile strength and elastic modulus between three examples of carbonized polydopamine / waste clay-reinforced self-lubricating gasket fabrics and non-reinforced self-lubricating gasket fabrics;

[0164] Figure 5 Physical map and scanning electron microscope image of the friction and wear surface of carbonized polydopamine / waste clay-reinforced carbon fiber self-lubricating gasket fabric; The upper part is the physical map of the self-lubricating gasket fabric after friction testing under different loads, (a) is a load of 200 g and a rotation speed of 300 r / min; (b) is a load of 500 g and a rotation speed of 300 r / min; (c) is a load of 800 g and a rotation speed of 300 r / min; (d) is a load of 1000 g and a rotation speed of 300 r / min; The lower part is the electron scanning microscope image of the wear scar of the self-lubricating gasket fabric under different experimental conditions, where (e) corresponds to (a); (f) corresponds to (b); (g) corresponds to (c); (h) corresponds to (d);

[0165] Figure 6 Flow chart of an intelligent optimization algorithm for polymerization reaction conditions. Detailed Implementation Modes

[0166] The specific content of the present invention will be further explained below in conjunction with embodiments.

[0167] Example 1

[0168] 1. Grind 10 g of waste clay in a ball mill, set the rotation speed of the ball mill to 400 r / min, and the ball milling time to 12 h;

[0169] 2. Dissolve 3.128 g of Tris and 612 μL of hydrochloric acid and make up to 500 mL to obtain a Tris solution. Add the above-mentioned waste clay powder and dopamine into the Tris solution.

[0170] 3. Stir the mixed solution, set the rotation speed to 850 r / min, and stir for 24 h to cause a polymerization reaction to obtain a polydopamine / waste clay dispersion.

[0171] 4. Centrifuge and wash the polydopamine / waste clay dispersion three times, and then put it in an oven to dry to obtain polydopamine / waste clay powder.

[0172] 5. Place the polydopamine / waste clay powder in a crucible and carry out carbonization treatment in a tube furnace. Set the reaction temperature to 600 °C and the reaction time to 5 h. After the reaction is completed, carbonized polydopamine / waste clay is obtained.

[0173] 6. Perform plasma treatment on the aramid fiber / polytetrafluoroethylene fiber blended fabric. The power of the plasma treatment is 100 W and the time is 30 min to obtain an activated aramid fiber / polytetrafluoroethylene fiber blended fabric;

[0174] 7. Mix the carbonized polydopamine / waste clay and phenolic resin solution to prepare an impregnating solution;

[0175] 8. Immerse and dry the activated aramid fiber / polytetrafluoroethylene fiber blended fabric in the impregnating solution 10 times to obtain a fabric prepreg;

[0176] 9. Cure the fabric prepreg to obtain an aramid fiber / polytetrafluoroethylene fiber self-lubricating gasket fabric composite material.

[0177] Example 2

[0178] 1. Grind 10 g of waste clay in a ball mill, set the rotation speed of the ball mill to 400 r / min, and the ball milling time to 12 h;

[0179] 2. Dissolve 3.128 g of Tris and 612 μL of hydrochloric acid and make up to 500 mL to obtain a Tris solution. Add the above-mentioned waste clay powder and dopamine into the Tris solution.

[0180] 3. Stir the mixed solution at a rotation speed of 850 r / min for 24 h to cause a polymerization reaction to obtain a polydopamine / waste clay dispersion.

[0181] 4. Centrifuge and wash the polydopamine / waste clay dispersion three times in a cycle, and then place it in an oven for drying to obtain polydopamine / waste clay powder.

[0182] 5. Place the polydopamine / waste clay powder in a crucible and carry out carbonization treatment in a tube furnace. Set the reaction temperature to 600 °C and the reaction time to 5 h. After the reaction is completed, carbonized polydopamine / waste clay is obtained.

[0183] 6. Carry out plasma treatment on the carbon fiber fabric at a power of 50 W for 35 min to obtain an activated aramid fiber / polytetrafluoroethylene fiber blended fabric;

[0184] 7. Mix the carbonized polydopamine / waste clay composite reinforcing filler and the phenolic resin solution to prepare an impregnating solution;

[0185] 8. Immerse the activated glass fiber fabric in the impregnating solution and repeat the impregnation-drying process 7 times to obtain a fabric prepreg;

[0186] 9. Cure the fabric prepreg to obtain a glass fiber self-lubricating gasket fabric composite.

[0187] Example 3

[0188] 1. Ball-mill 10 g of waste clay in a ball mill at a rotation speed of 400 r / min for 12 h;

[0189] 2. Dissolve 3.128 g of Tris and 612 μL of hydrochloric acid and make up to 500 mL to obtain a Tris solution. Add the above-mentioned waste clay powder and dopamine to the Tris solution.

[0190] 3. Stir the mixed solution at a rotation speed of 850 r / min for 24 h to cause a polymerization reaction to obtain a polydopamine / waste clay dispersion.

[0191] 4. Centrifuge and wash the polydopamine / waste clay dispersion three times in a cycle, and then place it in an oven for drying to obtain polydopamine / waste clay powder.

[0192] 5. Place the polydopamine / waste clay powder in a crucible and carry out carbonization treatment in a tube furnace. Set the reaction temperature to 600 °C and the reaction time to 5 h. After the reaction is completed, carbonized polydopamine / waste clay is obtained.

[0193] 6. The polyimide fiber / polytetrafluoroethylene fiber blended fabric is subjected to plasma treatment with a power of 50 W and a time of 35 min to obtain an activated aramid fiber / polytetrafluoroethylene fiber blended fabric;

[0194] 7. The carbonized polydopamine / waste clay composite reinforcing filler and the phenolic resin solution are mixed to prepare an impregnating solution;

[0195] 8. The activated carbon fiber fabric is repeatedly impregnated and dried 10 times in the impregnating solution to obtain a fabric prepreg;

[0196] 9. The fabric prepreg is cured to obtain a polyimide fiber / polytetrafluoroethylene fiber self-lubricating liner fabric composite.

[0197] Example 4

[0198] The optimization goal of an intelligent optimization algorithm for polymerization reaction conditions is that the coverage rate of the polymer on the substrate reaches 95%.

[0199] Initial parameter setting:

[0200] Temperature : Initially set to 600 °C;

[0201] pH value : Initially set to 7.0;

[0202] Viscosity : The initial measured value is 1.5 Pa·s;

[0203] Stirring speed : Initially set to 850 r / min;

[0204] Temperature data processing: The moving average filtering method is used to smooth the temperature data and eliminate short-term fluctuations:

[0205] ;

[0206] pH data processing: The median filtering method is used to remove outliers in the pH value:

[0207] ;

[0208] Viscosity data processing: The exponentially weighted moving average method is used to smooth the viscosity data and highlight the overall trend:

[0209] ;

[0210] The preprocessed temperature, pH value, viscosity, and stirring speed are used as the input variables of the ISOA-DELM model, and the finally output value is:

[0211] Optimal temperature : The optimal temperature recommended after model calculation is 630 °C;

[0212] Optimal pH value: The optimal pH value recommended by model calculation is 7.1;

[0213] Optimal stirring speed ): The optimal stirring speed recommended by model calculation is 872 r / min;

[0214] Optimal viscosity data : The optimal viscosity data recommended by model calculation is 2.5 Pa·s;

[0215] Optimal reaction time : The total reaction time recommended by the model is 24 hours;

[0216] Temperature adjustment: The initial temperature is 600 °C and it is adjusted to 630 °C. The heating power of the control system is increased from 80% to 85%;

[0217] Stirring speed adjustment: The initial stirring speed is 850 r / min and it is adjusted to 872 r / min. The intelligent speed controller automatically adjusts according to the optimal value recommended by the model;

[0218] pH value adjustment: The initial pH value is 7.0 and a small amount of acidic regulator is added to adjust the pH value to 7.1;

[0219] Actual coverage effect:

[0220] After the reaction, the actually measured polymer coverage rate is 93%, which is different from the target of 95%. The error is:

[0221] ;

[0222] Based on the error, the ISOA-DELM model automatically adjusts the weight W and bias b to reduce the error of future predictions. The adjusted new weight and bias are applied to the next reaction;

[0223] Human-machine interface operation:

[0224] The operator views the real-time data through the interface and confirms whether the reaction is carried out according to the optimal conditions recommended by the model; if necessary, the operator can manually adjust the temperature or stirring speed and immediately see the adjusted effect in the system.

[0225] Comparative example 1

[0226] 1. Plasma-treat the aramid fiber / polytetrafluoroethylene fiber blended fabric at a power of 100 W for 30 min to obtain an activated aramid fiber / polytetrafluoroethylene fiber blended fabric;

[0227] 2. Prepare a phenolic resin solution into an impregnating solution;

[0228] 3. Immerse and dry the activated aramid fiber / polytetrafluoroethylene fiber blended fabric in the impregnating solution 10 times repeatedly to obtain a fabric prepreg;

[0229] 4. Cure the fabric prepreg to obtain an aramid fiber / polytetrafluoroethylene fiber self-lubricating gasket fabric.

[0230] Comparative Example 2

[0231] 1. Plasma-treat the carbon fiber fabric at a power of 100 W for 30 min to obtain an activated aramid fiber / polytetrafluoroethylene fiber blended fabric;

[0232] 2. Prepare a phenolic resin solution into an impregnating solution;

[0233] 3. Immerse and dry the activated aramid fiber / polytetrafluoroethylene fiber blended fabric in the impregnating solution 10 times repeatedly to obtain a fabric prepreg;

[0234] 4. Cure the fabric prepreg to obtain a carbon fiber self-lubricating gasket fabric.

[0235] Comparative Example 3

[0236] 1. Plasma-treat the polyimide fiber / polytetrafluoroethylene fiber blended fabric at a power of 100 W for 30 min to obtain an activated aramid fiber / polytetrafluoroethylene fiber blended fabric;

[0237] 2. Prepare a phenolic resin solution into an impregnating solution;

[0238] 3. Immerse and dry the activated aramid fiber / polytetrafluoroethylene fiber blended fabric in the impregnating solution 10 times repeatedly to obtain a fabric prepreg;

[0239] 4. Cure the fabric prepreg to obtain a polyimide fiber / polytetrafluoroethylene fiber self-lubricating gasket fabric.

Claims

1. A preparation method of a carbonized polydopamine / waste clay reinforced self-lubricating fabric composite material, characterized in that It includes the following steps: Ball-mill the waste bleaching earth in a ball mill to obtain waste bleaching earth powder; Prepare a Tris solution, and add the waste bleaching earth powder and dopamine into the Tris solution; Stir the mixed solution at room temperature to cause a polymerization reaction to obtain a polydopamine / waste bleaching earth dispersion; Centrifuge, wash, and dry the polydopamine / waste bleaching earth dispersion to obtain polydopamine / waste bleaching earth powder; Carry out carbonization treatment on the polydopamine / waste bleaching earth powder in a tube furnace to obtain carbonized polydopamine / waste bleaching earth; Carry out plasma treatment on the fiber fabric to obtain an activated fiber fabric; Immerse the activated fiber fabric in an impregnating solution mixed with carbonized polydopamine / waste bleaching earth and a resin solution, repeat impregnation-drying, and finally heat and cure to obtain a self-lubricating gasket fabric composite; During the ball-milling process, the rotation speed is 400 r / min and the ball-milling time is 12 h.

2. The preparation method according to claim 1, characterized in that, The Tris solution is obtained by dissolving 3.128 g of Tris and 612 μL of hydrochloric acid and making up to 500 mL; the polymerization reaction time is 24 h, and the rotation speed of the stirrer is set to 850 r / min.

3. The preparation method according to claim 1, characterized in that, In the centrifugation treatment, the rotation speed of the centrifuge is 8000 r / min, the centrifugation time is 5 min, and it is carried out in a cycle with the washing operation, and the cycle needs to be carried out 3 - 5 times.

4. The preparation method according to claim 1, characterized in that, In the carbonization treatment, the temperature of the tube furnace is 600 °C and the carbonization time is 5 h; the power of the plasma treatment is 50 - 150 W and the time is 30 - 35 min.

5. The preparation method according to claim 1, characterized in that, The resin includes one or more of epoxy resin, polyimide resin, polyamide-imide, or phenolic resin; the fiber fabric includes one of glass fiber fabric, carbon fiber fabric, aramid fiber / polytetrafluoroethylene fiber blended fabric, polyimide fiber / polytetrafluoroethylene fiber blended fabric, or polyether ether ketone fiber / polytetrafluoroethylene fiber blended fabric.

6. The preparation method according to claim 1, characterized in that, The ratio of the carbonized polydopamine / waste bleaching earth powder to the resin solution is 1 g:40 mL.

7. The preparation method according to claim 1, characterized in that, In the self-lubricating gasket fabric composite, the mass fraction of the resin is 30% - 40%; the curing temperature is 100 - 200 °C, the pressure is 0.5 - 1 MPa, and the curing time is 1 - 2 h.

8. The preparation method according to claim 1, wherein It also includes introducing an intelligent optimization algorithm for polymerization reaction conditions, and the specific steps are as follows: Step 1, Sensor installation and parameter variable description The installation and data acquisition of sensors are the basis of the intelligent optimization algorithm. The following is a detailed description of the installation positions, functions, and related parameters of various sensors: Temperature sensor: Installation position: The temperature sensor is installed inside the reaction kettle near the stirrer to ensure that the measured temperature represents the actual temperature of the entire reaction system; Accuracy: The accuracy of the temperature sensor is within ±0.1 °C to ensure the reliability of the data; Formula: wherein, is the average temperature over a period of time, is the number of measurements, is the temperature value of each measurement, represents the index of the current time point, represents the i-th time point; PH sensor: Installation position: The pH sensor should be installed in the solution part of the reaction kettle, near the reactant area, so as to accurately measure the acidity and alkalinity of the solution; Function: The pH value affects the polymerization rate and reaction degree of dopamine. Real-time monitoring of the change of the pH value helps to judge the progress of the reaction and adjust the reaction conditions in time; Measurement range: It is concentrated between 6 and 8 in this reaction system; Formula: Among them, is the average pH value within a certain period of time, is the pH value measured each time; Viscosity sensor: Installation Location: The viscosity sensor should be installed in the area where the liquid flow in the reactor is the most uniform to obtain accurate viscosity readings; Function: Viscosity is an important characterization of the polymer formed during the reaction process. The change in viscosity reflects the growth and cross-linking of polymer chains and directly affects the final properties of the material; Measurement Range: 0.1 Pa·s to 10 Pa·s; Formula: wherein, is the average viscosity within a certain period of time, is the viscosity value measured each time; Agitator Rotation Speed Sensor: Installation Location: The agitator rotation speed sensor is directly installed on the agitator shaft to monitor the rotation speed of the agitator in real time; Function: The stirring speed is an important parameter for controlling the uniformity of the reaction mixture. By monitoring the stirring speed in real time, the system adjusts the rotation speed of the agitator as needed to optimize the reaction conditions; Rotation Speed Range: Set between 600 r / min and 1000 r / min; Formula: Among them, is the average stirring speed within a certain period of time, is the rotational speed value measured each time; Step 2, Data Acquisition and Preliminary Processing During the reaction process, the data of the temperature sensor, pH sensor, viscosity sensor, and agitator rotation speed sensor are collected in real time. These sensors are connected to the data acquisition system through the Internet of Things (IoT) interface to ensure the efficient transmission of real-time data; Data Acquisition Frequency: Temperature data: collected once per minute, record the temperature value ; pH data: collected once per minute, record the pH value ; Viscosity data: collected every 5 minutes and record the viscosity value ; Agitator rotation speed data: collected once per second, recording the rotation speed value ; Preliminary Data Processing: Moving Average Filtering: Used to eliminate random noise by calculating the average value of multiple consecutive data points to smooth the data; Among them, is the data value after moving average filtering processing, represents the original data collected at the time point and is the window size of the moving average filtering, is the total width of the window, represents the offset, and its value range is from -k to k; Median Filtering: Eliminate outliers by replacing the abnormal values with the median of the nearby data; Among them, is the data value after denoising, represents the median operation, represents at the time point the original data value collected; Exponentially Weighted Moving Average (EWMA): Assign higher weights to new data and lower weights to old data, making the latest data have a greater impact on the smoothed result; Among them, is the smoothed data, is the smoothing coefficient, is the smoothed data value after exponential weighted moving average processing, is the time point of the original data value; Step 3, Application of the ISOA-DELM Model The Improved Seagull Algorithm Optimized Deep Extreme Learning Machine (ISOA-DELM) is an advanced machine learning model that combines the improved seagull optimization algorithm (ISOA) and the deep extreme learning machine (DELM) for prediction and optimization of complex systems; The ISOA-DELM model will be used to optimize the key parameters in the polymerization reaction process; Input Variables: ； ； ； ； Target output actual value, coverage rate or uniformity index of the polymer on the surface of waste clay; The target output predicted by the model, which is the coverage rate or uniformity index of the polymer on the surface of waste clay DELM Structure: Input Layer: Receives the normalized input data; Hidden Layer: Multiple hidden neurons to capture the complex nonlinear relationships in the input data; Output layer: Outputs the predicted target variable ; ISOA Optimization: Use the improved seagull optimization algorithm (ISOA) to optimize the weights and biases of the DELM model to minimize the prediction error; Among them, is the weight matrix, is the bias vector; Weight Update: Among them, the generalized inverse matrix of the hidden layer output matrix, is the target output matrix, is the weight matrix; Seagull Optimization Update Formula: Among them, is the updated parameter position, is the global optimal position, is the convergence factor, which is used to control the convergence speed of the algorithm, is the current parameter position, is the current position of the seagull; Optimization Process: Initialization: First, randomly generate the positions of the seagull population, and each position represents a possible solution; Fitness Calculation: For each seagull, calculate its fitness in the current environment, that is, calculate the corresponding objective function value; The seagull with a higher fitness represents a better solution; Update Position: Update the position of each seagull according to the fitness value and the foraging behavior of the seagull. The new position should be closer to the global optimal solution; Exploration Phase: Seagulls explore new areas by flying randomly to avoid being trapped in local optima; Exploitation Phase: Seagulls adjust their flight directions and speeds according to the known optimal solution and gradually converge to the global optimal solution; Final Goal: Through multiple iterations, make the seagull population gradually approach the global optimal solution, that is, find the set of weights and biases that minimize the prediction error; Real-Time Prediction and Optimization: Real-Time Input: : Temperature data collected in real time; : pH value data collected in real time; : The viscosity data collected in real time; : Stirring speed data collected in real time; Real-Time Output: : Polymer coverage effect for real-time prediction; : Optimal stirring speed; : Optimal reaction temperature; : Optimal viscosity data for collection; Implementation prediction formula: Among them, represents the trained ISOA-DELM model function; Dynamic adjustment: Agitation speed adjustment: Temperature adjustment: Reaction time adjustment: Among them, is the adjusted stirring speed, is the optimal value recommended by the model, is the adjusted reaction temperature, is the optimal value recommended by the model, is the adjusted reaction time, is the original reaction time, is the time adjustment value recommended by the model; Step 4, actuator operation Intelligent speed control controller: The agitator is equipped with an intelligent speed control controller that automatically adjusts the agitation speed through the output signal of the ISOA-DELM model; the agitation speed is crucial for the uniformity of the reaction solution and the formation of polymers; Control formula: Among them, is the speed regulation coefficient, is the time point at which the optimal stirring speed recommended by the ISOA-DELM model, is the time point at which the actual rotational speed of the current stirrer, is the time point at which the new rotational speed of the adjusted stirrer; Operation process: The intelligent speed control controller receives in real time the optimal stirring speed output by the ISOA-DELM model ; According to the current stirring speed , the intelligent speed control controller calculates and adjusts to the new stirring speed , ensuring uniform mixing of the reaction solution and preventing local overheating or incomplete polymerization; Temperature regulation system: The heating system in the reaction kettle is closely connected to the temperature sensor and dynamically regulates the temperature of the reaction kettle according to the temperature adjustment suggestions of the ISOA-DELM model to ensure that the reaction temperature always remains within the optimal range; Control formula: Among them, is the temperature adjustment coefficient, is the time point when the optimal temperature recommended by the ISOA-DELM model, is the time point when the current reactor temperature, time point when the new adjusted reactor temperature; Operation process: The temperature regulation system monitors the reaction temperature in real time through a temperature sensor and receives the optimal temperature recommendation of the ISOA-DELM model; calculates the new temperature according to the formula , adjusts the heating power or the cooling system, and dynamically adjusts the temperature of the reaction kettle to prevent incomplete polymerization reactions caused by overheating or insufficient temperature; Time control system: The reaction time control system relies on the prediction of the optimal reaction time by the ISOA-DELM model; the central control system automatically controls the reaction time according to the output of the model to ensure that the reaction ends at the optimal time point; Control formula: Among them, is the currently consumed reaction time, is the time adjustment amount suggested by the model, is the new reaction time after adjustment; Operation process: According to the reaction completion time predicted by the model, the central control system will end the reaction process at the optimal time point; when the reaction is approaching the end, the system will issue an alarm and automatically turn off the heating and agitation devices to prevent overreaction or underreaction; Step 5, feedback and adaptive learning; Feedback process: Data collection: After each polymerization reaction is completed, the system will collect actual data on the polymer coverage effect, including coverage rate, uniformity, and thickness; Feedback to the model: These actually measured data will be input into the ISOA-DELM model as feedback signals and compared with the results predicted by the model; Error calculation: Among them, is the time point of the prediction error; Based on the error data, the model will automatically adjust its internal parameters, the weight W and the bias b, to reduce future prediction errors; Step 6, human-machine interface setting Real-time monitoring: The operator can monitor the entire reaction process in real time through the human-machine interface, including sensor data, model prediction results, and the operating status of the actuator; the interface intuitively displays the current reaction conditions, including the temperature curve, pH change, and viscosity change; Data visualization: The human-machine interface provides a data visualization function, and the operator can view and analyze the change trends of key parameters in the form of charts and curves; data visualization can help the operator better understand and judge the changes in the reaction process; Manual intervention and parameter adjustment: In special cases, the operator manually intervenes through the human-machine interface to adjust the temperature and agitation speed. The system will be updated in real time according to the operator's adjustment and recalculated through the ISOA-DELM model to ensure that the adjusted conditions still meet the optimal reaction requirements.

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