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

Abstract

The invention provides a preparation method of a carbonized polydopamine / spent bleaching clay reinforced self-lubricating fabric composite material, and belongs to the technical field of self-lubricating fabric composite materials. Carrying out ball milling on the spent bleaching clay in a ball mill to obtain spent bleaching clay powder; stirring the mixed solution at normal temperature to enable the mixed solution to be subjected to a polymerization reaction to obtain a polydopamine / spent bleaching clay dispersion liquid; centrifuging, washing and drying the polydopamine / spent bleaching clay dispersion liquid to obtain polydopamine / spent bleaching clay powder; carrying out carbonization treatment on the polydopamine / spent bleaching clay powder in a tubular furnace to obtain carbonized polydopamine / spent bleaching clay; performing plasma treatment on the fiber fabric to obtain an activated fiber fabric; and finally, heating and curing to obtain the self-lubricating liner fabric composite material. The shearing force applied to the material surface can be effectively reduced, and the fiber matrix is effectively prevented from being damaged.

Description

Technical Field

[0001] The invention relates to the technical field of self-lubricating liner fabric composite materials, in particular to a method for preparing a carbonized polydopamine / waste clay reinforced self-lubricating liner fabric composite material. Background Art

[0002] As an important component in the machinery industry, the main function of bearings is to support mechanical rotating bodies, reduce the friction of rotating bodies during rotation, and ensure the rotation accuracy of rotating bodies. Self-lubricating spherical bearings, as important functional components with high load-bearing, wear resistance, self-aligning and rotation, have become widely used in the fields of aviation, aerospace, navigation, weapons and high-end civilian equipment. They are indispensable components of aircraft engines, landing gears, rudders and other parts. Self-lubricating pads are very important components of self-lubricating spherical bearings. They can reduce wear and tear, isolate metals, resist impact, and extend the service life of bearings. Self-lubricating pads also have the characteristics of high strength and maintenance-free, and they show excellent self-lubricating and anti-wear properties in harsh service environments such as high temperature, heavy load and high frequency. With the development of high-tech fields, various machines have higher and higher performance requirements for bearings, and at the same time, higher requirements are also put forward for self-lubricating pads.

[0003] For self-lubricating cushion fabric composites, the reinforcement is a two-dimensional composite fabric woven from reinforcing fibers and lubricating fibers, in which the interfacial bonding force between the fiber and the resin has a decisive influence on the performance of the composite. The interface between the fiber and the resin has good bonding strength, and the internal stress of the material can be released through the continuous generation of microcracks between the interfaces; if there is no 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 interface modification layer between the fiber and the resin, which can greatly improve the mechanical and tribological properties of the self-lubricating cushion fabric composite.

[0004] At present, the main methods for strengthening self-lubricating gasket fabric composite materials include chemical etching, surface coating, grafting polymer, etc. The above methods can achieve the purpose of improving the fiber strength in the self-lubricating gasket fabric, but the tribological properties of the self-lubricating gasket fabric material and the interface 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 interface bonding effect, low friction coefficient and high wear resistance. Summary of the invention

[0005] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a method for preparing a carbonized polydopamine / waste clay reinforced self-lubricating pad fabric composite material, which can significantly improve the interface bonding effect and wear resistance of the self-lubricating pad fabric material and reduce the friction coefficient.

[0006] In the first aspect, in order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions: The present invention provides a method for preparing a carbonized polydopamine / waste clay reinforced self-lubricating liner fabric composite material; ball-milling the waste clay in a ball mill to obtain waste clay powder; Furthermore, the rotation speed during the ball milling process is 400 r / min, and the ball milling time is 12 h; Prepare a Tris solution, and add the waste clay powder and dopamine into the Tris solution; Furthermore, the Tris solution is obtained by dissolving 3.128 g Tris and 612 μL hydrochloric acid to 500 mL; The mixed solution is stirred at room temperature to cause a polymerization reaction to obtain a polydopamine / waste clay dispersion; Furthermore, the polymerization reaction time is 24 hours, and the speed of the stirrer is set to 850 r / min; Centrifuging, washing and drying the polydopamine / waste clay dispersion to obtain polydopamine / waste clay powder; Furthermore, the centrifuge speed in the centrifuge treatment is 8000 r / min, the centrifugation time is 5 min, and the washing operation is cyclically performed for 3-5 times; Carbonizing the polydopamine / waste clay powder in a tube furnace to obtain carbonized polydopamine / waste clay; Furthermore, the carbonization treatment of polydopamine / waste clay was performed by setting the temperature of the tube furnace to 600° C. for 5 hours, and introducing argon gas into the tube furnace.

[0007] The fiber fabric is subjected to plasma treatment to obtain an activated fiber fabric; Furthermore, the power of the plasma treatment is 50-150 W, and the time is 30-35 min.

[0008] The activated fiber fabric is immersed in an impregnation liquid containing a mixture of carbonized polydopamine / waste clay powder and a resin solution, and then repeatedly immersed and dried, and finally heated and cured to obtain a self-lubricating liner fabric composite material; Further, the resin includes one or more of epoxy resin, polyimide resin, polyamide imide 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; Further, the ratio of the carbonized polydopamine / waste clay powder to the resin solution is 1 g: 40 mL; Furthermore, the mass fraction of the resin in the self-lubricating liner fabric composite material is 30% to 40%; the curing temperature is 100 to 200° C., the pressure is 0.5 to 1 MPa, and the curing time is 1 to 2 hours; Furthermore, an intelligent optimization algorithm for polymerization reaction conditions is introduced, and the specific steps are as follows: Step 1: Sensor installation and parameter variable description; The installation and data collection of sensors are the basis of intelligent optimization algorithms. The following is a detailed description of the installation location, function and related parameters of various sensors: Temperature Sensor: Installation location: The temperature sensor is installed inside the reactor near the agitator to ensure that the measured temperature represents the actual temperature of the entire reaction system; Function: Temperature is a key parameter that affects the speed and effect of dopamine polymerization reaction. The temperature sensor is used to monitor the temperature changes during the reaction in real time and provide important input data for the optimization model. Accuracy: The accuracy of the temperature sensor is within ±0.1℃ to ensure the reliability of the data; formula: ; in, is the average temperature over a period of time, is the number of measurements, is the temperature value of each measurement, Indicates the index of the current time point. represents the i-th time point; pH sensor: Installation location: The pH sensor should be installed in the solution part of the reactor, close to the reactant area, so as to accurately measure the pH of the solution; Function: pH value will affect the polymerization rate and reaction degree of dopamine. Real-time monitoring of pH changes can help determine the progress of the reaction and adjust the reaction conditions in a timely manner. Measuring range: In this reaction system, it is concentrated between 6 and 8; formula: ; in, is the average pH value over a certain period of time, is the pH value of each measurement; Viscosity sensor: Installation location: The viscosity sensor should be installed in the area of ​​the reactor where the liquid flow is most uniform in order to obtain accurate viscosity readings; Function: Viscosity is an important characterization of the polymer formed during the reaction. Changes in viscosity reflect the growth and cross-linking of polymer chains, which directly affects the final properties of the material; Measuring range: 0.1Pa·s to 10Pa·s; formula: ; in, is the average viscosity within a certain period of time, is the viscosity value of each measurement; Agitator speed sensor: Installation location: The agitator speed sensor is directly installed on the agitator shaft to monitor the agitator speed in real time; Function: Stirring speed is an important parameter for controlling the uniformity of reaction mixing. By real-time monitoring of the stirring speed, the system can adjust the speed of the stirrer as needed to optimize the reaction conditions. Speed ​​range: set between 600 r / min and 1000 r / min; formula: ; in, is the average stirring speed within a certain period of time, is the speed value measured each time; Step 2: Data collection and preliminary processing During the reaction process, the reaction data of various installed sensors (temperature sensor, pH sensor, viscosity sensor, stirrer 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 efficient transmission of real-time data; Data collection frequency: Temperature data: collect once every minute and record the temperature value ; pH data: collected once every minute, recording pH value ; Viscosity data: collect and record viscosity values ​​every 5 minutes ; Agitator speed data: collected once per second, recording the speed value ; Initial data processing: Moving average filtering: used to eliminate random noise and smooth the data by calculating the average of multiple consecutive data points; ; in, is the data value after moving average filtering. Indicates at a point in time The original data collected at is the window size of the moving average filter, is the total width of the window, Indicates the offset, the value range is from -k to k; Median filtering: eliminates outliers by replacing them with the median of nearby data; ; in, is the denoised data value, represents the median operation, Indicates at a point in time The original data value collected at Exponentially Weighted Moving Average (EWMA): assigns higher weights to new data and lower weights to old data, so that the latest data has a greater impact on the smoothed results; ; in, is the smoothed data, is the smoothing coefficient, is the smoothed data value after exponentially weighted moving average processing, For time point The original data value of Step 3: Application of ISOA-DELM model ISOA-DELM (Deep Extreme Learning Machine Based on Improved Seagull Optimization Algorithm) 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; in this patented technology, the ISOA-DELM model will be used to optimize key parameters in the polymerization process; Input variables: ; ; ; ; ; ; DELM structure: Input layer: receives normalized input data; Hidden layer: multiple layers of hidden neurons to capture complex nonlinear relationships in the input data; Output layer: Output predicted target variable ; ISOA Optimization: The improved Seagull Optimization Algorithm (ISOA) is used to optimize the weights and biases of the DELM model to minimize the prediction error. ; in, is the weight matrix, is the deviation vector; Weight update: ; in, The generalized inverse matrix of the hidden layer output matrix, is the target output matrix, is the weight matrix; Seagull optimization update formula: ; in, 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 group, each position represents a possible solution (i.e., a combination of a set of weights and biases); 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; Update position: Update the position of each seagull based on its fitness value and its predation behavior (including exploration and exploitation). The new position should be closer to the global optimal solution. Exploration phase: Seagulls explore new areas through random flights to avoid falling into local optimality; Development stage: Seagull adjusts its flight direction and speed based on the known optimal solution, and gradually converges to the global optimal solution; The ultimate goal: Through multiple iterations, the seagull group gradually approaches the global optimal solution, that is, to find the set of weights and biases that minimize the prediction error; Real-time prediction and optimization: Real-time input: : Real-time collected temperature data; : pH value data collected in real time; : Real-time collection of viscosity data; : Stirring speed data collected in real time; Real-time output: : Real-time prediction of polymer coverage effect; :Optimal stirring speed; : Optimal reaction temperature; : Optimally collected viscosity data; : Optimal reaction time adjustment value; Implement the prediction formula: ; in, Represents the trained ISOA-DELM model function; Dynamic Adjustment: Stirring speed adjustment: ; Temperature adjustment: ; Reaction time adjustment: ; in, is the adjusted stirring speed, is the optimal value suggested by the model, is the adjusted reaction temperature, is the optimal value suggested by the model, is the adjusted reaction time, is the original reaction time, The time adjustment values ​​suggested for the model; Step 4: Actuator Operation Intelligent speed controller: The agitator is equipped with an intelligent speed 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 the polymer; Control formula: ; in, is the speed regulation coefficient, It's time point The optimal stirring speed recommended by the ISOA-DELM model is It's time point When the actual speed of the current agitator is It's time point When the stirrer speed is adjusted, Operation process: The intelligent speed controller receives the optimal stirring speed output by the ISOA-DELM model in real time ; According to the current stirring speed , the intelligent speed controller calculates and adjusts to the new stirring speed , ensure that the reaction solution is evenly mixed to prevent local overheating or incomplete polymerization; Temperature regulation system: The heating system in the reactor is closely connected to the temperature sensor. According to the temperature adjustment suggestions of the ISOA-DELM model, the temperature of the reactor is dynamically adjusted to ensure that the reaction temperature is always kept in the optimal range; Control formula: ; in, is the temperature regulation coefficient, It's time point The optimal temperature suggested by the ISOA-DELM model is It's time point When the current reactor temperature is Time When the temperature of the new reactor is adjusted; Operation process: The temperature control system monitors the reaction temperature in real time through the temperature sensor and receives the optimal temperature recommendation from the ISOA-DELM model; the new temperature calculated according to the formula , adjust the heating power or cooling system, dynamically adjust the reactor temperature to prevent overheating or insufficient temperature from causing incomplete polymerization reaction; Time control system: The reaction time control system relies on the ISOA-DELM model to predict the optimal reaction time; 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: ; in, is the current elapsed reaction time, is the time adjustment suggested by the model, It is the reaction time of the adjusted heart; Operation process: Based on 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 close to completion, the system will sound an alarm and automatically shut down the heating and stirring devices to prevent over- or under-reaction; Step 5: Feedback and adaptive learning; Feedback Process: Data collection: After each polymerization reaction is completed, the system will collect actual data about the polymer coverage effect including coverage, uniformity, and thickness; Feedback to the model: These actual measured data will be input into the ISOA-DELM model as feedback signals and compared with the results predicted by the model; Error calculation: ; in, It's time point The prediction error of Based on the error data, the model automatically adjusts its internal parameters, weights W and biases b, to reduce future prediction errors; Step 6: Human-machine interface settings Real-time monitoring: Operators can monitor the entire reaction process in real time through the human-machine interface, including sensor data, model prediction results, and actuator operating status; the interface can intuitively display the current reaction conditions, including temperature curves, pH changes, and viscosity changes; Data visualization: The human-machine interface provides data visualization function, and operators can view and analyze the changing trends of key parameters in the form of charts, curves, etc. Data visualization can help operators better understand and judge the changes in the reaction process; Manual intervention and parameter adjustment: In special circumstances, operators can manually intervene through the human-machine interface to adjust key parameters (such as temperature and stirring speed). The system will be updated in real time according to the operator's adjustments and recalculated through the ISOA-DELM model to ensure that the adjusted conditions still meet the optimal reaction requirements.

[0009] In a second aspect, the present invention provides a self-lubricating liner fabric composite material of carbonized polydopamine / waste clay reinforced filler.

[0010] The present invention provides a self-lubricating cushion fabric composite material with carbonized polydopamine / waste clay reinforced filler; wherein the carbonized modified polydopamine / waste clay can promote the uniform distribution of waste clay in the filler in the resin matrix, and improve its dispersion performance in the self-lubricating cushion fabric, and on the other hand, the carbonization treatment can weaken the interlayer force of the waste clay, and further improve the lubrication performance of the waste clay; and the carbonized polydopamine / waste clay has excellent mechanical properties and a large specific surface area, which is helpful to form a synergistic enhancement effect on the friction and wear performance of the self-lubricating cushion fabric; The present invention introduces the reinforcing filler carbonized polydopamine / waste clay reinforcing particles to enhance the interfacial bonding strength between the fabric and the resin, thereby ensuring that the stress during the stretching process in all directions can be evenly transferred to the fiber fabric, thereby improving the mechanical properties of the material. The present invention introduces carbonized polydopamine / waste clay into the self-lubricating liner fabric material to effectively reduce the shear force applied to the surface of the material and thereby effectively prevent the fiber matrix from being damaged, thereby achieving a significant improvement in the lubrication performance of the liner fabric composite material and the friction and wear performance.

[0011] 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), real-time prediction and dynamic optimization of the polymerization reaction process are realized. 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 product quality. This intelligent optimization algorithm significantly improves the automation level and reliability of the polymerization reaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 Scanning electron microscope images of polydopamine / waste clay before and after carbonization modification; (a) before modification; (b) after modification; Figure 2 Scanning images of carbonized polydopamine / waste clay reinforced carbon fiber self-lubricating liner fabric; (a) is a scanning image of the fabric surface; (b) is a high-magnification cross-sectional scanning image; (c) is a low-magnification cross-sectional scanning image; Figure 3 The friction coefficient of three carbonized polydopamine / waste clay reinforced self-lubricating liner fabric examples and three unreinforced self-lubricating liner fabric comparison examples are compared; Figure 4 The tensile strength and elastic modulus comparison of three carbonized polydopamine / waste clay reinforced self-lubricating liner fabric examples and a comparative example of unreinforced self-lubricating liner fabric; Figure 5The actual picture and scanning electron microscope image of the friction and wear surface of carbonized polydopamine / waste clay reinforced carbon fiber self-lubricating pad fabric; the upper part is the actual picture of the self-lubricating pad fabric after friction test under different load conditions, (a) is a load of 200g and a speed of 300r / min; (b) is a load of 500g and a speed of 300r / min; (c) is a load of 800g and a speed of 300r / min; (d) is a load of 1000g and a speed of 300r / min; the lower part is the electron scanning microscope image of the wear mark of the self-lubricating pad fabric under different experimental conditions, where (e) corresponds to (a); (f) corresponds to (b); (g) corresponds to (c); (h) corresponds to (d); Figure 6 The figure is a flow chart of an intelligent optimization algorithm for polymerization reaction conditions. DETAILED DESCRIPTION

[0013] The specific contents of the present invention are further explained below in conjunction with embodiments.

[0014] Example 1 1. Mill 10g of waste clay in a ball mill at a speed of 400r / min for 12h; 2. 3. Dissolve 128 g Tris and 612 μL hydrochloric acid to 500 mL to obtain a Tris solution, and add the above-mentioned waste clay powder and dopamine to the Tris solution.

[0015] 3. Stir the mixed solution at a speed of 850 r / min for 24 hours to allow a polymerization reaction to occur to obtain a polydopamine / waste clay dispersion.

[0016] 4. The polydopamine / waste clay dispersion is centrifuged, washed, and recycled three times, and then placed in an oven for drying to obtain polydopamine / waste clay powder.

[0017] 5. The polydopamine / waste clay powder is placed in a tube furnace using a crucible for carbonization treatment, the reaction temperature is set to 600° C., the reaction time is set to 5 h, and the carbonized polydopamine / waste clay is obtained after the reaction is completed.

[0018] 6. The aramid fiber / polytetrafluoroethylene fiber blended fabric was subjected to plasma treatment at a power of 100 W for 30 min to obtain an activated aramid fiber / polytetrafluoroethylene fiber blended fabric; 7. Mixing the carbonized polydopamine / waste clay and the phenolic resin solution to prepare an impregnation solution; 8. Repeating the dipping-drying process for 10 times on the activated aramid fiber / polytetrafluoroethylene fiber blended fabric in the dipping solution to obtain a fabric prepreg; 9. The fabric prepreg is cured to obtain an aramid fiber / polytetrafluoroethylene fiber self-lubricating liner fabric composite material.

[0019] Example 2 1. Mill 10 g of waste clay in a ball mill at a speed of 400 r / min for 12 h; 2. 3. Dissolve 128 g Tris and 612 μL hydrochloric acid to 500 mL to obtain a Tris solution, and add the above-mentioned waste clay powder and dopamine to the Tris solution.

[0020] 3. Stir the mixed solution at a speed of 850 r / min for 24 hours to allow a polymerization reaction to occur to obtain a polydopamine / waste clay dispersion.

[0021] 4. The polydopamine / waste clay dispersion is centrifuged, washed, and recycled three times, and then placed in an oven for drying to obtain polydopamine / waste clay powder.

[0022] 5. The polydopamine / waste clay powder is placed in a tube furnace using a crucible for carbonization treatment, the reaction temperature is set to 600° C., the reaction time is set to 5 h, and the carbonized polydopamine / waste clay is obtained after the reaction is completed.

[0023] 6. The carbon fiber fabric was subjected to plasma treatment at a power of 50 W for 35 min to obtain an activated aramid fiber / polytetrafluoroethylene fiber blended fabric; 7. The carbonized polydopamine / waste clay composite reinforcing filler and the phenolic resin solution are mixed to prepare an impregnation solution; 8. Repeating the dipping-drying process for 7 times on the activated glass fiber fabric in the dipping solution to obtain a fabric prepreg; 9. The fabric prepreg is cured to obtain a glass fiber self-lubricating liner fabric composite material.

[0024] Example 3 1. Mill 10g of waste clay in a ball mill at a speed of 400r / min for 12h; 2. 3. Dissolve 128 g Tris and 612 μL hydrochloric acid to 500 mL to obtain a Tris solution, and add the above-mentioned waste clay powder and dopamine to the Tris solution.

[0025] 3. Stir the mixed solution at a speed of 850 r / min for 24 hours to allow a polymerization reaction to occur to obtain a polydopamine / waste clay dispersion.

[0026] 4. The polydopamine / waste clay dispersion is centrifuged, washed, and recycled three times, and then placed in an oven for drying to obtain polydopamine / waste clay powder.

[0027] 5. The polydopamine / waste clay powder is placed in a tube furnace using a crucible for carbonization treatment, the reaction temperature is set to 600° C., the reaction time is set to 5 h, and the carbonized polydopamine / waste clay is obtained after the reaction is completed.

[0028] 6. The polyimide fiber / polytetrafluoroethylene fiber blended fabric was subjected to plasma treatment at a power of 50 W for 35 min to obtain an activated aramid fiber / polytetrafluoroethylene fiber blended fabric; 7. The carbonized polydopamine / waste clay composite reinforcing filler and the phenolic resin solution are mixed to prepare an impregnation solution; 8. Repeating the impregnation-drying of the activated carbon fiber fabric in the impregnation solution for 10 times to obtain a fabric prepreg; 9. The fabric prepreg is cured to obtain a polyimide fiber / polytetrafluoroethylene fiber self-lubricating liner fabric composite material.

[0029] Example 4 An intelligent optimization algorithm for polymerization reaction conditions has an optimization target of 95% coverage of the polymer on the substrate.

[0030] Initial parameter settings: temperature : The initial setting is 600℃; pH : The initial setting is 7.0; Viscosity : The initial measurement value is 1.5 Pa·s; Stirring speed : The initial setting is 850 r / min; Temperature data processing: Use moving average filtering to smooth temperature data and eliminate short-term fluctuations: ; pH data processing: Use median filtering to remove abnormal points in pH values: ; Viscosity data processing: Use exponentially weighted moving average to smooth the viscosity data and highlight the overall trend: ; The pre-treated temperature, pH value, viscosity, and stirring speed are used as input variables of the ISOA-DELM model, and the final output value is: Optimum temperature : The optimal temperature recommended by the model calculation is 630℃; Optimal pH value: The optimal pH value recommended by model calculation is 7.1; Optimum stirring speed ): The optimal stirring speed recommended by the model calculation is 872 r / min; Optimal viscosity data : The optimal viscosity data suggested by the model calculation is 2.5 Pa·s; Optimal response time : The total reaction time suggested by the model is 24 hours; Temperature adjustment: The initial temperature is 600°C, which is adjusted to 630°C. The heating power of the control system is increased from 80% to 85%; Stirring speed adjustment: The initial stirring speed is 850 r / min, which is adjusted to 872 r / min. The intelligent speed controller automatically adjusts according to the optimal value recommended by the model; pH adjustment: The initial pH is 7.0, add a small amount of acid regulator to adjust the pH to 7.1; Actual coverage effect: After the reaction, the actual measured polymer coverage was 93%, which was different from the target 95%, with an error of: ; Based on the error, the ISOA-DELM model automatically adjusts the weights W and biases b to reduce the error in future predictions, and the adjusted new weights and biases are applied to the next reaction; Human-machine interface operation: The operator views real-time data through the interface and confirms whether the reaction is proceeding 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 effect of the adjustment in the system.

[0031] Comparative Example 1 1. The aramid fiber / polytetrafluoroethylene fiber blended fabric was subjected to plasma treatment at a power of 100 W for 30 min to obtain an activated aramid fiber / polytetrafluoroethylene fiber blended fabric; 2. preparing a phenolic resin solution as an impregnation solution; 3. Repeating the dipping-drying process for 10 times of the activated aramid fiber / polytetrafluoroethylene fiber blended fabric in the dipping solution to obtain a fabric prepreg; 4. The fabric prepreg is cured to obtain an aramid fiber / polytetrafluoroethylene fiber self-lubricating cushion fabric.

[0032] Comparative Example 2 1. The carbon fiber fabric was subjected to plasma treatment at a power of 100 W for 30 min to obtain an activated aramid fiber / polytetrafluoroethylene fiber blended fabric; 2. preparing a phenolic resin solution as an impregnation solution; 3. Repeating the dipping-drying process for 10 times of the activated aramid fiber / polytetrafluoroethylene fiber blended fabric in the dipping solution to obtain a fabric prepreg; 4. Curing the fabric prepreg to obtain carbon fiber self-lubricating liner fabric.

[0033] Comparative Example 3 1. The polyimide fiber / polytetrafluoroethylene fiber blended fabric was subjected to plasma treatment at a power of 100 W for 30 min to obtain an activated aramid fiber / polytetrafluoroethylene fiber blended fabric; 2. preparing a phenolic resin solution as an impregnation solution; 3. Repeating the dipping-drying process for 10 times of the activated aramid fiber / polytetrafluoroethylene fiber blended fabric in the dipping solution to obtain a fabric prepreg; 4. The fabric prepreg is cured to obtain a polyimide fiber / polytetrafluoroethylene fiber self-lubricating cushion fabric.

Claims

1. A method for preparing a carbonized polydopamine / waste clay reinforced self-lubricating fabric composite material, characterized in that: The following steps are involved: ball-milling the waste clay in a ball mill to obtain waste clay powder; Prepare a Tris solution, and add the waste clay powder and dopamine into the Tris solution; The mixed solution is stirred at room temperature to cause a polymerization reaction to obtain a polydopamine / waste clay dispersion; Centrifuging, washing and drying the polydopamine / waste clay dispersion to obtain polydopamine / waste clay powder; Carbonizing the polydopamine / waste clay powder in a tube furnace to obtain carbonized polydopamine / waste clay; The fiber fabric is subjected to plasma treatment to obtain an activated fiber fabric; The activated fiber fabric is immersed in an impregnation solution mixed with carbonized polydopamine / waste clay and resin solution, and is repeatedly impregnated and dried, and finally heated and cured to obtain a self-lubricating liner fabric composite material.

2. The preparation method according to claim 1, characterized in that: The rotation speed during the ball milling process is 400 r / min, and the ball milling time is 12 h.

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

4. The preparation method according to claim 1, characterized in that: The centrifuge speed in the centrifuge treatment is 8000 r / min, the centrifugation time is 5 min, and the centrifugation is cyclically performed with the washing operation, and the cycle needs to be repeated 3-5 times.

5. The preparation method according to claim 1, characterized in that: The temperature of the tubular furnace in the carbonization treatment is 600° C., and the carbonization time is 5 hours; the power of the plasma treatment is 50-150 W, and the time is 30-35 minutes.

6. 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 polyetheretherketone fiber / polytetrafluoroethylene fiber blended fabric.

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

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

9. The preparation method according to claim 1, characterized in that: It also includes introducing an intelligent optimization algorithm for polymerization reaction conditions, the specific steps are as follows: Step 1: Sensor installation and parameter variable description The installation and data collection of sensors are the basis of intelligent optimization algorithms. The following is a detailed description of the installation location, function and related parameters of various sensors: Temperature Sensor: Installation location: The temperature sensor is installed inside the reactor near the agitator 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℃ to ensure the reliability of the data; formula: ； in, is the average temperature over a period of time, is the number of measurements, is the temperature value of each measurement, Indicates the index of the current time point. represents the i-th time point; pH sensor: Installation location: The pH sensor should be installed in the solution part of the reactor, close to the reactant area, so as to accurately measure the pH of the solution; Function: pH value will affect the polymerization rate and reaction degree of dopamine. Real-time monitoring of pH changes can help determine the progress of the reaction and adjust the reaction conditions in a timely manner. Measuring range: In this reaction system, it is concentrated between 6 and 8; formula: ； in, is the average pH value over a certain period of time, is the pH value of each measurement; Viscosity sensor: Installation location: The viscosity sensor should be installed in the area of ​​the reactor where the liquid flow is most uniform in order 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 the polymer chain, which directly affects the final performance of the material. Measuring range: 0.1Pa·s to 10Pa·s; formula: ； in, is the average viscosity over a certain period of time, is the viscosity value of each measurement; Agitator speed sensor: Installation location: The agitator speed sensor is directly installed on the agitator shaft to monitor the agitator speed in real time; Function: Stirring speed is an important parameter for controlling the uniformity of reaction mixing. By real-time monitoring of the stirring speed, the system can adjust the speed of the stirrer as needed to optimize the reaction conditions. Speed ​​range: set between 600 r / min and 1000 r / min; formula: ； in, is the average stirring speed within a certain period of time, is the speed value measured each time; Step 2: Data collection and preliminary processing During the reaction process, data from temperature sensors, pH sensors, viscosity sensors, and agitator speed sensors are collected in real time. These sensors are connected to the data acquisition system through the IoT interface to ensure efficient transmission of real-time data. Data collection frequency: Temperature data: collect once every minute and record the temperature value ; pH data: collected once every minute, recording pH value ; Viscosity data: collect and record viscosity values ​​every 5 minutes ; Agitator speed data: collected once per second, recording the speed value ; Initial data processing: Moving average filtering: used to eliminate random noise and smooth the data by calculating the average of multiple consecutive data points; ； in, is the data value after moving average filtering. Indicates at a point in time The original data collected at is the window size of the moving average filter, is the total width of the window, Indicates the offset, the value range is from -k to k; Median filtering: eliminates outliers by replacing them with the median of nearby data; ； in, is the denoised data value, represents the median operation, Indicates at a point in time The original data value collected at Exponentially Weighted Moving Average (EWMA): gives higher weights to new data and lower weights to old data, so that the latest data has a greater impact on the smoothed results; ； in, is the smoothed data, is the smoothing coefficient, is the smoothed data value after exponentially weighted moving average processing, For time point The original data value of Step 3: Application of ISOA-DELM model ISOA-DELM, a deep extreme learning machine based on improved Seagull algorithm optimization, 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 key parameters in the polymerization process; Input variables: ； ； ； ； ； ； DELM structure: Input layer: receives normalized input data; Hidden layer: multiple layers of hidden neurons to capture complex nonlinear relationships in the input data; Output layer: Output predicted target variable ; ISOA Optimization: The improved Seagull optimization algorithm ISOA is used to optimize the weights and biases of the DELM model to minimize the prediction error. ； in, is the weight matrix, is the deviation vector; Weight update: ； in, The generalized inverse matrix of the hidden layer output matrix, is the target output matrix, is the weight matrix; Seagull optimization update formula: ； in, 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 group, 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 based on the fitness value and the seagull's predation behavior. The new position should be closer to the global optimal solution. Exploration phase: Seagulls explore new areas through random flights to avoid falling into local optimality; Development stage: Seagull adjusts its flight direction and speed based on the known optimal solution, and gradually converges to the global optimal solution; The ultimate goal: Through multiple iterations, the seagull group gradually approaches the global optimal solution, that is, to find the set of weights and biases that minimize the prediction error; Real-time prediction and optimization: Real-time input: : Real-time temperature data collection; : pH value data collected in real time; : Real-time collection of viscosity data; : Stirring speed data collected in real time; Real-time output: : Real-time prediction of polymer coverage effect; :Optimal stirring speed; : Optimal reaction temperature; : Optimally collected viscosity data; : Optimal reaction time adjustment value; Implement the prediction formula: ； in, Represents the trained ISOA-DELM model function; Dynamic Adjustment: Stirring speed adjustment: ； Temperature adjustment: ； Reaction time adjustment: ； in, is the adjusted stirring speed, is the optimal value suggested by the model, is the adjusted reaction temperature, is the optimal value suggested by the model, is the adjusted reaction time, is the original reaction time, The time adjustment values ​​suggested for the model; Step 4: Actuator Operation Intelligent speed controller: The agitator is equipped with an intelligent speed 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 the polymer; Control formula: ； in, is the speed regulation coefficient, It's time point The optimal stirring speed recommended by the ISOA-DELM model is It's time point When the actual speed of the current agitator is It's time point When the stirrer speed is adjusted, Operation process: The intelligent speed controller receives the optimal stirring speed output by the ISOA-DELM model in real time ; According to the current stirring speed , the intelligent speed controller calculates and adjusts to the new stirring speed , ensure that the reaction solution is evenly mixed to prevent local overheating or incomplete polymerization; Temperature regulation system: The heating system in the reactor is closely connected to the temperature sensor. According to the temperature adjustment suggestions of the ISOA-DELM model, the temperature of the reactor is dynamically adjusted to ensure that the reaction temperature is always kept in the optimal range; Control formula: ； in, is the temperature regulation coefficient, It's time point The optimal temperature suggested by the ISOA-DELM model is It's time point When the current reactor temperature is Time When the temperature of the new reactor is adjusted; Operation process: The temperature control system monitors the reaction temperature in real time through the temperature sensor and receives the optimal temperature recommendation from the ISOA-DELM model; the new temperature calculated according to the formula , adjust the heating power or cooling system, dynamically adjust the reactor temperature to prevent overheating or insufficient temperature from causing incomplete polymerization reaction; Time control system: The reaction time control system relies on the ISOA-DELM model to predict the optimal reaction time; 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: ； in, is the current elapsed reaction time, is the time adjustment suggested by the model, It is the reaction time of the adjusted heart; Operation process: Based on 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 close to completion, the system will sound an alarm and automatically shut down the heating and stirring devices to prevent over- or under-reaction; Step 5: Feedback and adaptive learning; Feedback Process: Data collection: After each polymerization reaction is completed, the system will collect actual data about the polymer coverage effect including coverage, uniformity, and thickness; Feedback to the model: These actual measured data will be input into the ISOA-DELM model as feedback signals and compared with the results predicted by the model; Error calculation: ； in, It's time point The prediction error of Based on the error data, the model automatically adjusts its internal parameters, weights W and biases b, to reduce future prediction errors; Step 6: Human-machine interface settings Real-time monitoring: Operators can monitor the entire reaction process in real time through the human-machine interface, including sensor data, model prediction results, and actuator operating status; the interface can intuitively display the current reaction conditions, including temperature curves, pH changes, and viscosity changes; Data visualization: The human-machine interface provides data visualization function, and operators can view and analyze the changing trends of key parameters in the form of charts, curves, etc. Data visualization can help operators better understand and judge the changes in the reaction process; Manual intervention and parameter adjustment: In special circumstances, the operator can manually intervene through the human-machine interface to adjust the temperature and stirring speed. The system will be updated in real time according to the operator's adjustments and recalculated through the ISOA-DELM model to ensure that the adjusted conditions still meet the optimal reaction requirements.

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