Optimized preparation method of high-strength flame-retardant polyamide-based composite material

Abstract

The invention discloses an optimized preparation method of a high-strength flame-retardant polyamide-based composite material in the technical field of information, which comprises the following steps: in the preparation process of the composite material, acquiring online dispersed state monitoring data of a modified filler by using a laser particle size analyzer and an optical microscope, including particle size distribution, agglomeration degree and dispersion uniformity of the filler; according to online dispersion state monitoring data, parameter ranges of melt flow property characterization are determined, the parameter ranges comprise the shearing rate and the temperature, particle size distribution data are used for determining the shearing rate range, small particle size distribution corresponds to the high shearing rate range, and agglomeration degree data are used for determining the temperature range; the higher degree of agglomeration corresponds to a higher temperature range to promote the dispersion of the filler, and these parameters are directly used to guide the setting of the subsequent rheological test.

Description

Technical Field

[0001] The invention relates to the field of information technology, and in particular to an optimized preparation method of a high-strength flame-retardant polyamide-based composite material. Background Art

[0002] In the process of preparing high-strength flame-retardant polyamide, how to evenly disperse the reinforcing filler into the polyamide matrix and form a good interface bonding is a key technical issue. Common reinforcing fillers include glass fiber, carbon fiber, nanomaterials, etc., which have large differences in compatibility with the polyamide matrix and are easy to agglomerate during the mixing process, resulting in uneven dispersion of the filler. In addition, the interfacial bonding force between the filler and the matrix also directly affects the mechanical properties of the material. Poor interfacial bonding force will cause the stress to be unable to be effectively transmitted, causing the strength and toughness of the material to decrease. Therefore, it is necessary to select a suitable addition method for different fillers and modify the surface of the filler to improve its compatibility with the polyamide matrix and interfacial bonding strength.

[0003] When choosing the addition method, it is necessary to comprehensively consider factors such as the type, morphology, size of the filler, and the required material properties. For example, for fillers with larger sizes, melt blending may cause uneven dispersion of the filler in the matrix, while solution blending can achieve better dispersion effects. For nanomaterials, due to their large specific surface area, they are more likely to agglomerate, so it is necessary to introduce suitable dispersants or coupling agents during the mixing process to improve their dispersibility and compatibility with the matrix. At the same time, during the mixing process, it is also necessary to strictly control process parameters such as temperature, shear rate, mixing time, etc., to prevent the filler from breaking or degrading, affecting the final performance of the material. Therefore, how to prepare reinforced polyamide composites with good dispersibility and high interface bonding strength by rationally designing the addition method and optimizing the process parameters is a technical problem that needs to be solved urgently. Summary of the invention

[0004] The present invention provides an optimized preparation method of a high-strength flame-retardant polyamide-based composite material, which comprises the following steps:

[0005] Step S101, obtaining a filler after surface modification, the filler has different surface modification effects, including hydrophilicity, hydrophobicity, polarity and non-polarity. According to the surface modification effect and size of the filler, different adding methods are used to add the filler to the polyamide matrix. When the filler size is in the range of 1-100 microns, a solution blending method is used to dissolve the filler and the polyamide in the same solvent, and the solvent is evaporated after stirring evenly. When the filler size is nanometer-level, a surfactant is added as a dispersant during the melt blending process, and the filler, the surfactant and the polyamide are added to a twin-screw extruder together for melt mixing. The surfactant is selected based on the surface characteristics of the filler and the compatibility of the polyamide matrix to ensure a good dispersion effect. In the preparation process of the composite material, the dispersion state of the filler in the polyamide matrix is ​​controlled by adjusting the screw speed, temperature distribution and residence time of the twin-screw extruder;

[0006] Step S102, during the preparation of the composite material, using a laser particle size analyzer and an optical microscope to obtain online dispersion state monitoring data of the modified filler, including the particle size distribution, agglomeration degree and dispersion uniformity of the filler, and determining the parameter range for characterizing the melt flow performance according to the online dispersion state monitoring data, including shear rate and temperature, the particle size distribution data is used to determine the shear rate range, a smaller particle size distribution corresponds to a higher shear rate range, and the agglomeration degree data is used to determine the temperature range, a higher agglomeration degree corresponds to a higher temperature range, so as to promote the dispersion of the filler, and these parameters are directly used to guide the setting of subsequent rheological tests;

[0007] Step S103, using a rheometer to characterize the melt flow performance of the prepared composite material to obtain a viscosity shear rate curve of the composite material, judging the dispersion uniformity of the modified filler according to the platform area of ​​the viscosity shear rate curve, and the width of the platform area is positively correlated with the dispersion uniformity, using a scanning electron microscope to observe the cross-sectional morphology of the prepared composite material, verifying the analysis results of the viscosity shear rate curve, and combining the dispersion uniformity judgment results to calculate the dispersion index of the modified filler in the polyamide matrix. The dispersion index is calculated by the ratio of the number of filler particles per unit area to the number of particles in theoretical uniform distribution. The specific calculation method is: first, the number of filler particles actually observed per unit area is counted, and then the number of particles in theoretical uniform distribution is calculated according to the volume fraction and average particle size of the filler. The calculation formula for the number of particles in theoretical uniform distribution is: V filler / (4 / 3πr 3 ), where Vfilling is the volume of the filler per unit volume, r is the average radius of the filler, and finally the actual observed number is divided by the theoretical number to obtain the dispersion index;

[0008] Step S104, establishing a quantitative relationship between the dispersion index and the mechanical properties of the composite material. The mechanical properties of the composite material include tensile strength, flexural strength and impact strength. The dispersion index reflects the distribution state of the filler in the matrix and directly affects the mechanical properties of the composite material. By adjusting the preparation parameters, composite material samples with different dispersion indexes are prepared, and their mechanical properties are tested. A mathematical model is established through regression analysis. The mathematical model is expressed as: Y=aX+b, where Y represents the mechanical property index, X represents the dispersion index, and a and b are regression coefficients. This model is used to predict the mechanical properties of the composite material under different dispersion indexes, providing a basis for subsequent optimization;

[0009] Step S105, according to the established quantitative relationship mathematical model, iterative optimization is performed through genetic algorithm to determine the formulation design and process optimization parameters of the polyamide-based composite material, including filler type, content, surface modification method, processing temperature and shear rate. The specific application process of the genetic algorithm includes: initializing the parameter population, evaluating the fitness of each group of parameters (i.e., predicted mechanical properties), selecting excellent individuals, performing crossover and mutation operations to generate new parameter combinations, repeating the evaluation-selection-crossover-mutation process until the preset number of iterations or performance indicators are reached. In this way, the parameter combination is continuously iterated and optimized to achieve the optimization of the mechanical properties of the composite material.

[0010] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0011] The present invention discloses an optimized preparation method of a high-strength flame-retardant polyamide-based composite material. During the preparation process, a suitable adding method is first selected according to the surface modification effect and size of the filler. For micron-level fillers, a solution blending method is adopted, while for nano-level fillers, a surfactant is added by melt blending to improve dispersibility. The dispersion state of the filler is regulated by online monitoring such as a laser particle size analyzer and an optical microscope, and then the preparation parameters are adjusted by the data of the rheometer to optimize the flow properties of the composite material. In addition, the present invention achieves the optimization of the mechanical properties of the composite material by establishing a mathematical model between the dispersion index and the mechanical properties of the composite material and iteratively optimizing the preparation parameters using a genetic algorithm. This method not only improves the mechanical properties of the material, but also ensures the predictability and repeatability of the material properties by accurately controlling the parameters in the preparation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 The present invention is a flow chart of an optimized preparation method of a high-strength flame-retardant polyamide-based composite material.

[0013] Figure 2 It is a schematic diagram of the optimized preparation method of the high-strength flame-retardant polyamide-based composite material of the present invention.

[0014] Figure 3 It is another schematic diagram of the optimized preparation method of the high-strength flame-retardant polyamide-based composite material of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0016] like Figure 1-3 The optimized preparation method of the high-strength flame-retardant polyamide-based composite material of this embodiment may specifically include the following steps:

[0017] Step S101, obtain the filler after surface modification, the filler has different surface modification effects, including hydrophilicity, hydrophobicity, polarity and non-polarity. According to the surface modification effect and size of the filler, different adding methods are adopted to add the filler to the polyamide matrix. When the filler size is within the range of 1-100 microns, a solution blending method is adopted to dissolve the filler and polyamide in the same solvent, and the solvent is evaporated after stirring. When the filler size is nanometer-level, a surfactant is added as a dispersant during the melt blending process, and the filler, surfactant and polyamide are added to a twin-screw extruder together for melt mixing. The selection of surfactant is based on the surface characteristics of the filler and the compatibility of the polyamide matrix to ensure a good dispersion effect. In the preparation process of the composite material, the dispersed state of the filler in the polyamide matrix is ​​controlled by adjusting the screw speed, temperature distribution and residence time of the twin-screw extruder.

[0018] A filler after surface modification is obtained, and the surface characteristics of the filler are judged according to the surface modification effect of the filler, and the compatibility of the filler with the polyamide matrix is ​​determined; different adding methods are adopted according to the size of the filler; if the size of the filler is within a preset first size range, a solution blending method is adopted to dissolve the filler and polyamide in the same solvent, and a uniformly dispersed composite material solution is obtained by stirring and mixing; if the size of the filler is within a preset second size range, a melt blending method is adopted, and a surfactant is selected as a dispersant according to the surface characteristics of the filler and the compatibility of the polyamide matrix, and the filler, the surfactant and the polyamide are added to a twin-screw extruder together; the screw speed, temperature distribution and residence time of the twin-screw extruder are adjusted to obtain a uniformly dispersed composite material solution; The process parameters are used to control the dispersion state of the filler in the polyamide matrix; a machine learning algorithm is used to establish a relationship model between the surface characteristics and dimensions of the filler and the process parameters of the twin-screw extruder; according to the properties of the filler, the optimal combination of process parameters is predicted to guide the preparation process of the composite material; during the preparation process of the composite material, the operation data of the twin-screw extruder is collected in real time, and the operation data includes screw speed, temperature distribution and pressure parameters; the collected data is input into the machine learning model, the process parameters are dynamically optimized, and the dispersion state and interface compatibility of the filler in the polyamide matrix are evaluated; the characterization results are compared with the prediction results of the machine learning model, and the machine learning model is continuously optimized and improved to improve the intelligence level of the composite material preparation process.

[0019] Specifically, the surface properties of the filler after surface modification will vary depending on the modification method. For example, after the filler is treated with a silane coupling agent, hydrophobic groups will be introduced on the surface to enhance the compatibility with the polyamide matrix; treatment with a titanate coupling agent will enhance the interfacial bonding between the filler and the matrix. Assuming that the calcium carbonate is surface modified by the silane coupling agent KH550, the surface of the calcium carbonate changes from hydrophilic to hydrophobic, and the compatibility with the equally hydrophobic polyamide 6 is improved. The filler size is crucial to the selection of the addition method. If the filler is calcium carbonate with an average particle size of 50 microns, which is in the range of 1-100 microns, solution blending is used. Dissolve polyamide 6 in formic acid, disperse the surface-modified calcium carbonate in formic acid, and then mix the two and stir them evenly to obtain a composite material solution. If the filler is nano-silica with an average particle size of 50 nanometers, melt blending is used. The choice of surfactant depends on the surface properties of the filler and the polyamide matrix. For hydrophobic fillers and polyamide 6 systems, oleic acid or stearic acid can be selected as dispersants to improve the dispersion of fillers in the matrix and prevent filler agglomeration. If the filler is alumina treated with a titanate coupling agent, the coupling agent itself or other surfactants with similar structures can be considered. The process parameters of the twin-screw extruder are crucial to the dispersion state of the filler. The screw speed affects the shear force, the temperature distribution affects the melt viscosity, and the residence time affects the dispersion effect. For example, for nano-silica and polyamide 6 systems, the screw speed can be set to 300 rpm, the temperature can be set to 240-260°C, and the residence time can be 3 minutes. If the filler is calcium carbonate, due to its large particle size, a higher screw speed and longer residence time may be required to ensure its full dispersion. Machine learning algorithms can establish a relationship model between the surface characteristics and size of the filler and the process parameters of the twin-screw extruder. For example, the dispersion state data (characterized by the dispersion index) of calcium carbonate fillers with different surface characteristics (contact angle) and sizes at different screw speeds, temperatures, and residence times can be collected, and the model can be trained using algorithms such as support vector machines or artificial neural networks. Then, for a new filler, such as calcium carbonate with a surface contact angle of 110° and a particle size of 20 microns, the model can predict the optimal combination of screw speed, temperature, and residence time. During the preparation of the composite material, the operating data of the twin-screw extruder is collected in real time and input into the machine learning model for dynamic optimization. For example, if the melt pressure is too high, the model may suggest reducing the screw speed or increasing the temperature to avoid equipment overload and ensure the dispersion of the filler. If the dispersion index is not ideal, the model may suggest extending the residence time or increasing the screw speed. The prepared composite material is characterized and analyzed. For example, a scanning electron microscope is used to observe the dispersion state and interface bonding of the filler in the polyamide matrix, a transmission electron microscope is used to observe the dispersion of the nanofiller, and a mechanical property test is used to evaluate the performance improvement of the composite material.Compare the characterization results with the prediction results of the machine learning model. If it is found that the performance of the composite material does not meet expectations under the optimal parameters predicted by the model, it is necessary to adjust the model parameters or increase the training data to continuously optimize and improve the model. By continuously accumulating data and optimizing the model, the intelligent and precise control of the composite material preparation process can be finally achieved.

[0020] According to the surface characteristics and size of the filler, a relationship model between the surface characteristics and size of the filler and the process parameters of the twin-screw extruder was established using the support vector machine algorithm. The extruder operation data was collected in real time and input into the model to dynamically optimize the parameters, thereby obtaining the filler dispersion state and interface compatibility. The characterization results were compared with the model prediction results, and the model was continuously improved to obtain an intelligent process flow for preparing composite materials.

[0021] Acquire filler surface characteristics and size data, and use support vector machine algorithm training to establish a relationship model between the filler characteristics and the process parameters of a twin-screw extruder; input the collected real-time process parameter data into the support vector machine model to obtain optimized process parameters; control the operation of the twin-screw extruder according to the optimized process parameters to obtain filler dispersibility and interface compatibility data; compare the filler dispersibility and interface compatibility data with the prediction results of the support vector machine model to determine the accuracy of the model prediction; if the model prediction accuracy does not reach a preset threshold, use the filler dispersibility and interface compatibility data as new training data to retrain the support vector machine model.

[0022] Specifically, in the preparation of modern composite materials, the use of support vector machine (SVM) algorithms to establish a relationship model between filler characteristics and twin-screw extruder process parameters is an efficient method. For example, assume that when producing polymer composites, the particle size and surface treatment of the filler will affect its dispersion and interfacial compatibility in the matrix. By collecting filler data with different particle sizes and surface treatments, a model can be trained using the SVM algorithm to predict the dispersion and interfacial compatibility of the filler under specific process parameters. Installing sensors on the twin-screw extruder to collect process parameters such as temperature, pressure, and screw speed in real time is key to ensuring that the production process is controllable and optimized. For example, during the production process, if the sensor shows that the temperature of the extruder is lower than the temperature recommended by the model, the system can automatically adjust the power of the heater to achieve the ideal processing conditions. The real-time collected process parameter data is input into the established SVM model, and the process parameters of the model can be dynamically optimized. A specific application of this method is in the production of high-performance composites containing nanofillers. Through the dynamic adjustment of the model, the dispersion state of the nanofiller can be optimized in real time, thereby improving the mechanical properties and durability of the product. Through characterization methods such as scanning electron microscopy (SEM), the microscopic morphology and interface bonding state of the composite material can be observed in detail. For example, by using SEM to observe the composite material of polymer and carbon nanotubes, the distribution of carbon nanotubes in the polymer matrix and the interface bonding between carbon nanotubes and polymer can be clearly seen. Comparing the characterization results of filler dispersion and interface compatibility with the prediction results of the support vector machine model is an important step to verify the accuracy of the model. If it is found that there is a large deviation between the dispersion predicted by the model and the actual observed dispersion, these new characterization results can be used as new training data to retrain the SVM model. This iterative optimization process can continuously improve the prediction accuracy of the model until it meets the production requirements. Through this intelligent preparation process, not only can the production efficiency and product quality of composite materials be improved, but also the automation and intelligence of the process can be realized, which greatly reduces labor costs and production costs, while improving the market competitiveness of products.

[0023] Step S102, during the preparation of the composite material, a laser particle size analyzer and an optical microscope are used to obtain online dispersion state monitoring data of the modified filler, including the particle size distribution, degree of agglomeration and dispersion uniformity of the filler. According to the online dispersion state monitoring data, the parameter range for characterizing the melt flow performance is determined, including shear rate and temperature. The particle size distribution data is used to determine the shear rate range, and a smaller particle size distribution corresponds to a higher shear rate range. The degree of agglomeration data is used to determine the temperature range, and a higher degree of agglomeration corresponds to a higher temperature range to promote the dispersion of the filler. These parameters are directly used to guide the setting of subsequent rheological tests.

[0024] Obtain filler particle size distribution data, perform statistical analysis on the particle size distribution data to obtain the mean and variance of the filler particle size, and determine a suitable shear rate range based on the mean and variance; obtain a microscopic image of the filler, segment and extract features on the microscopic image through an image processing algorithm to obtain a filler agglomeration index, and determine a suitable temperature range based on the agglomeration index; in the process of preparing the composite material, collect laser particle size data and optical microscopy data in real time, input the laser particle size data and optical microscopy data into a pre-established mapping model, and predict the melt flow properties under current process parameters.

[0025] Specifically, during the preparation of the composite material, a laser particle size analyzer is first used to obtain the particle size distribution data of the filler. For example, when preparing a polymer composite material, if the filler is silica particles, the particle size distribution can be measured by a laser particle size analyzer, and the mean and variance of the particle size can be calculated. These data help researchers determine the appropriate shear rate range to ensure that the filler can be well dispersed and not aggregated during the extrusion process. For example, if the mean particle size is 10 microns and the variance is small, it indicates that the particle distribution is relatively uniform and a higher shear rate can be selected for processing. Next, an optical microscope is used to image and analyze the dispersion state of the filler. Through specific image processing algorithms, such as edge detection and image segmentation techniques, filler agglomeration indicators can be extracted from the microscopic image. This step is critical because it directly affects the final performance of the composite material. For example, if a large amount of filler agglomeration is observed, it may be necessary to adjust the temperature or add more dispersant to optimize the dispersion effect. The data obtained from laser particle size analysis and optical microscopy are used as input, and a support vector machine algorithm is used to establish a mapping model between melt flow properties and filler dispersion state parameters. This model can predict the flow properties of composite melts, such as viscosity and shear thinning index, under specific process parameters. For example, the model may indicate that the melt flow properties are best at lower shear rates and higher processing temperatures. In the actual composite preparation process, laser particle size and optical microscopy data are collected in real time and input into the established mapping model. According to the melt flow properties predicted by the model, the shear rate and temperature parameters can be dynamically adjusted. For example, if the model predicts that the flow properties under the current parameters are not good, the shear rate can be increased or the temperature can be adjusted through the fuzzy control algorithm until the ideal filler dispersion state is achieved. Finally, the optimized shear rate and temperature parameters are used for rheological testing to test the melt flow performance curve of the composite. The data obtained by the rheometer, such as viscosity and shear thinning index, are correlated with the online dispersion state monitoring data. These analysis results can be further used to guide the formulation design and process optimization of composite materials to ensure the preparation of composite materials with excellent performance.

[0026] Step S103, using a rheometer to characterize the melt flow properties of the prepared composite material to obtain a viscosity shear rate curve of the composite material. The dispersion uniformity of the modified filler is judged according to the platform area of ​​the viscosity shear rate curve, and the width of the platform area is positively correlated with the dispersion uniformity. A scanning electron microscope is used to observe the cross-sectional morphology of the prepared composite material to verify the analysis results of the viscosity shear rate curve. Combined with the results of the dispersion uniformity judgment, the dispersion index of the modified filler in the polyamide matrix is ​​calculated. The dispersion index is calculated by the ratio of the number of filler particles per unit area to the number of particles in theoretical uniform distribution. The specific calculation method is: first, the number of filler particles actually observed per unit area is counted, and then the number of particles in theoretical uniform distribution is calculated based on the volume fraction and average particle size of the filler. The calculation formula for the number of particles in theoretical uniform distribution is: V filler / (4 / 3πr 3 ), where Vfilling is the volume of the filler per unit volume, and r is the average radius of the filler. Finally, the actual observed number is divided by the theoretical number to obtain the dispersion index.

[0027] The rheological test data of the composite material are obtained to obtain viscosity data under different shear rates; the viscosity-shear rate curve of the viscosity data and the corresponding shear rate is plotted; it is determined whether there is a platform area in the viscosity-shear rate curve, and if so, the width of the platform area is determined; a scanning electron microscope is used to observe the morphology of multiple areas of the composite cross section to obtain the average number of filler particles per unit area; the number of particles per unit volume when theoretically uniformly distributed is calculated according to the filler volume fraction of the composite material and the average particle size of the filler particles; the average number of filler particles per unit area is divided by the theoretical number of particles per unit volume to obtain the dispersion index of the filler; the platform area width and the dispersion index are comprehensively judged to determine the dispersion uniformity of the filler in the composite material; a correlation model between the dispersion index and the characteristic parameters of the viscosity-shear rate curve is established; and the predicted dispersion index of the filler is calculated according to the viscosity-shear rate curve of the composite material using the correlation model.

[0028] Specifically, rheometer testing is an important means to characterize the melt flow properties of composite materials. Put the prepared composite material sample into the rheometer, set different shear rates, such as 0.1s-1, 1s-1, 10s-1, 100s-1, etc., and measure the viscosity value of the sample at different shear rates. The measured viscosity value and the corresponding shear rate are plotted into a curve, namely the viscosity-shear rate curve. Taking ABS resin as the matrix and adding 20% ​​calcium carbonate filler as an example, if the viscosity-shear rate curve has a flat area when the shear rate is low, it indicates that the calcium carbonate filler is dispersed more evenly in the ABS matrix. The wider the platform area, the better the dispersion. The possible reason is that the interaction force between the fillers is weak and it is not easy to agglomerate. On the contrary, if the curve has no platform area or the platform area is very narrow, it indicates that the filler may have agglomerated and the dispersion is uneven. The scanning electron microscope can more intuitively observe the dispersion state of the filler in the composite material. After the composite material sample is cross-sectioned, it is placed in a scanning electron microscope for observation. Zoom in to a suitable multiple, such as 5000 times, and take photos of multiple areas, for example, select 5 different areas. Count the number of filler particles in each area, and then calculate the average. Assuming that 100, 110, 95, 105, and 100 filler particles are observed in the 5 areas respectively, the average number of filler particles in each area is 102. It is known that the volume fraction of the filler in the composite material formula is 20%, and the average particle size of the filler particles is 1μm. The number of particles per unit volume when theoretically uniformly distributed can be calculated according to the formula. Assume that the calculation result is 100 / μm 3 The average number of filler particles per unit area observed by the scanning electron microscope (102) is divided by the theoretically calculated number of particles per unit volume (100 / μm). 3, and the dispersion index is 1.02. The closer the dispersion index is to 1, the more uniform the filler is dispersed. Combining the results of rheological testing and scanning electron microscopy, the uniformity of the dispersion of the filler can be judged more comprehensively. For example, if there is an obvious platform area in the viscosity-shear rate curve and the dispersion index is close to 1, it can be determined that the filler is evenly dispersed in the composite material. If the viscosity-shear rate curve has no platform area and the dispersion index deviates from 1 more, such as 0.5 or 1.5, it means that the filler is unevenly dispersed. Using the support vector machine algorithm, an association model between the dispersion index and the characteristic parameters of the viscosity-shear rate curve can be established. For example, characteristic parameters such as the platform area width, initial viscosity, and curve slope of the viscosity-shear rate curve can be extracted as input, and the dispersion index is used as output to train the support vector machine model. After the training is completed, the dispersion index of the filler can be quickly predicted by inputting the characteristic parameters of the viscosity-shear rate curve, thereby improving the efficiency of the evaluation of dispersion uniformity. For example, for a new set of composite materials, the viscosity-shear rate curve is obtained through rheological testing, and the characteristic parameters are extracted and input into the trained support vector machine model, so that its dispersion index can be quickly predicted without tedious SEM observation and statistics. This is of great significance for the formulation design and process optimization of composite materials.

[0029] Step S104, establish a quantitative relationship between the dispersion index and the mechanical properties of the composite material, the mechanical properties of the composite material include tensile strength, flexural strength and impact strength. The dispersion index reflects the distribution state of the filler in the matrix and directly affects the mechanical properties of the composite material. By adjusting the preparation parameters, composite samples with different dispersion indexes are prepared, their mechanical properties are tested, and a mathematical model is established through regression analysis. The mathematical model is expressed as: Y=aX+b, where Y represents the mechanical performance index, X represents the dispersion index, and a and b are regression coefficients. This model is used to predict the mechanical properties of composite materials under different dispersion indexes, providing a basis for subsequent optimization.

[0030] A series of composite material samples with different dispersion indexes are obtained, wherein the samples are prepared by designing multiple groups of preparation experiments and adopting different preparation parameter values; for the samples, tensile strength, bending strength and impact strength data are obtained by using mechanical property testing equipment; a mathematical model is established, wherein the dispersion index is used as an independent variable and the mechanical property index is used as a dependent variable, and a linear regression is performed using the least squares method to obtain the regression coefficient, thereby determining the quantitative relationship between the dispersion index and the mechanical property.

[0031] Specifically, the selection of preparation parameters has a crucial influence on the performance of composite materials. Taking polypropylene / calcium carbonate composite materials as an example, the preparation parameters mainly include calcium carbonate content, type and amount of coupling agent, melt blending temperature, screw speed, etc. A series of experiments can be designed, for example, keeping other parameters unchanged, changing the calcium carbonate content, and preparing composite samples with calcium carbonate content of 10%, 20%, 30% and 40% respectively. Similarly, the type of coupling agent can be changed, such as using titanate coupling agent and silane coupling agent, and changing the amount of coupling agent, such as 1%, 2% and 3%. Change the melt blending temperature, for example, set it to 170℃, 180℃ and 190℃. Change the screw speed, for example, set it to 50rpm, 100rpm and 150rpm. In this way, a series of composite samples with different dispersion indexes can be obtained. The tensile strength, bending strength and impact strength of the samples are tested respectively by a tensile testing machine, a bending testing machine and an impact testing machine. For example, a universal testing machine is used to test the tensile properties of the sample at a tensile rate of 2mm / min to obtain a tensile strength value. The bending properties of the samples were tested using a three-point bending test to obtain the bending strength and bending modulus values. The impact properties of the samples were tested using a pendulum impact tester to obtain the impact strength values. The measured dispersion index and mechanical properties data were subjected to linear regression analysis. For example, assuming that a sample with a calcium carbonate content of 10% has a dispersion index of 0.8, a tensile strength of 50 MPa, a bending strength of 80 MPa, and an impact strength of 10 kJ / m 2 The sample with a calcium carbonate content of 20% has a dispersion index of 0.9, a tensile strength of 60MPa, a flexural strength of 90MPa, and an impact strength of 12kJ / m 2. Through the least squares method, the linear relationship between the dispersion index and the tensile strength, bending strength and impact strength can be obtained, for example: tensile strength = 20 × dispersion index + 34. The established linear regression model is statistically tested. For example, the overall significance of the model is judged by the F test. If the p value of the F test is less than 0.05, the model is considered significant. The significance of the regression coefficient is judged by the t test. If the p value of the t test is less than 0.05, the regression coefficient is considered significant. Assuming that the F test p value of the tensile strength regression equation is 0.01 and the t test p value of the regression coefficient is 0.03, it indicates that the regression model is effective and reliable. Using the established mathematical model, the mechanical properties corresponding to different dispersion indices can be predicted. For example, when the dispersion index is 0.7, the tensile strength can be predicted to be 48MPa. The prediction results are then verified by experiments. For example, the measured tensile strength is 47MPa, which is not much different from the predicted value, indicating that the model prediction accuracy is high. If the predicted value is significantly different from the measured value, for example, the predicted value is 48MPa and the measured value is 40MPa, it is necessary to supplement the experimental data, re-perform regression analysis, and optimize the mathematical model. The optimized mathematical model is applied to the composite material formulation design and process optimization. For example, according to the model, it can be predicted that when the dispersion index is 0.95, the best mechanical properties can be obtained. Therefore, in actual production, the dispersion state of the filler can be controlled by adjusting the preparation parameters, such as increasing the amount of coupling agent, increasing the screw speed, etc., so that the dispersion index reaches 0.95, thereby obtaining a composite material with ideal mechanical properties.

[0032] Step S105, according to the established quantitative relationship mathematical model, iterative optimization is performed through genetic algorithm to determine the formulation design and process optimization parameters of the polyamide-based composite material, including filler type, content, surface modification method, processing temperature and shear rate. The specific application process of the genetic algorithm includes: initializing the parameter population, evaluating the fitness of each group of parameters (i.e., predicted mechanical properties), selecting excellent individuals, performing crossover and mutation operations to generate new parameter combinations, and repeating the evaluation-selection-crossover-mutation process until the preset number of iterations or performance indicators are reached. In this way, the parameter combination is continuously iterated and optimized to achieve the optimization of the mechanical properties of the composite material.

[0033] The mechanical property data of the polyamide-based composite material are obtained, wherein the mechanical property data include filler type, content, surface modification method, processing temperature and shear rate parameter information; based on the mechanical property data, a mathematical model of the quantitative relationship between the mechanical property of the polyamide-based composite material and the filler type, content, surface modification method, processing temperature and shear rate is established; the filler type, content, surface modification method, processing temperature and shear rate are used as optimization variables, and the material mechanical properties are used as optimization targets to construct a fitness function of the genetic algorithm; a certain number of parameter combinations of filler type, content, surface modification method, processing temperature and shear rate are randomly generated as The invention discloses an initial population of a genetic algorithm; substituting each parameter combination in the initial population into the mathematical model, calculating the corresponding mechanical property prediction value, and using the prediction value as the fitness of the individual; selecting a certain number of excellent individuals by roulette selection or tournament selection according to the fitness of the individual; performing a crossover operation on the excellent individuals to generate a new parameter combination; performing a mutation operation on the newly generated individuals to introduce a new value; repeatedly performing the selection, crossover and mutation operations, and continuously iterating and updating the population until a preset number of iterations or mechanical property indicators are reached; and outputting the optimal parameter combination as the formulation design and process optimization parameters of the polyamide-based composite material.

[0034] Specifically, the mechanical properties of polyamide-based composites are closely related to their composition and processing technology. A mathematical model can be established to quantitatively describe this relationship. For example, the following model can be established: Mechanical properties = a* filler content + b* processing temperature + c* shear rate + d, where a, b, c, and d are unknown coefficients that need to be fitted by experimental data. This model takes into account the effects of filler content, processing temperature, and shear rate on mechanical properties. Of course, the actual model may be more complex and require consideration of more factors, such as filler type, surface modification method, etc. The type of filler has a significant effect on the mechanical properties of the composite. For example, glass fiber can significantly improve the strength and stiffness of the composite, while carbon nanotubes can simultaneously improve strength, stiffness, and toughness. Assume that glass fiber is selected as a filler and the effect of its content on mechanical properties is studied. A series of composite samples with different glass fiber contents, such as 10%, 20%, 30%, etc., can be prepared and their mechanical properties can be tested. The surface modification method of the filler will also affect its bonding strength with the matrix, thereby affecting the mechanical properties of the composite. For example, treating glass fiber with silane coupling agent can improve its compatibility with polyamide matrix, thereby improving the mechanical properties of the composite material. Assuming that the glass fiber is surface treated with silane coupling agent KH550, the interface bonding strength between the treated glass fiber and the polyamide matrix is ​​higher, and the mechanical properties of the composite material are also improved accordingly. Processing temperature and shear rate affect the molding quality of the composite material and the dispersion state of the filler, thereby affecting the mechanical properties. For example, a higher processing temperature can reduce the viscosity of polyamide and facilitate the dispersion of fillers, but too high a temperature may cause polyamide degradation. A higher shear rate can promote the dispersion of fillers, but it may also cause the polyamide molecular chain to break. Assume that 280℃ is selected as the processing temperature and 100rpm is selected as the shear rate. Genetic algorithm is a commonly used optimization algorithm that can be used to find the optimal composite material formula and processing parameters. First, the fitness function needs to be defined, such as taking the tensile strength of the composite material as the fitness function. Then, a set of initial parameter combinations is randomly generated, such as different combinations of filler content, processing temperature and shear rate. These parameter combinations are substituted into the mathematical model, and the corresponding mechanical property prediction values ​​are calculated and used as the individual fitness values. Next, select excellent individuals according to their fitness, and perform crossover and mutation operations to generate new parameter combinations. For example, you can select two individuals with the highest fitness values ​​for crossover, swap their filler content parameters, and generate two new individuals. Then, perform mutation operations on the newly generated individuals, such as randomly increasing or decreasing the processing temperature parameter by a certain value. Repeat the selection, crossover, and mutation operations until the preset number of iterations or mechanical performance indicators are reached. Finally, output the optimal parameter combination, such as a filler content of 25%, a processing temperature of 275°C, and a shear rate of 90rpm.These parameters can be used as a reference for the formulation design and process optimization of polyamide-based composites. For example, by controlling the filler content to 25%, the best mechanical properties can be obtained.

[0035] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An optimized preparation method of a high-strength flame-retardant polyamide-based composite material, characterized in that: The method comprises the following steps: Step S101, obtaining a filler after surface modification, the filler has different surface modification effects, including hydrophilicity, hydrophobicity, polarity and non-polarity. According to the surface modification effect and size of the filler, different adding methods are used to add the filler to the polyamide matrix. When the filler size is in the range of 1-100 microns, a solution blending method is used to dissolve the filler and the polyamide in the same solvent, and the solvent is evaporated after stirring evenly. When the filler size is nanometer-level, a surfactant is added as a dispersant during the melt blending process, and the filler, the surfactant and the polyamide are added to a twin-screw extruder together for melt mixing. The surfactant is selected based on the surface characteristics of the filler and the compatibility of the polyamide matrix to ensure a good dispersion effect. In the preparation process of the composite material, the dispersion state of the filler in the polyamide matrix is ​​controlled by adjusting the screw speed, temperature distribution and residence time of the twin-screw extruder; Step S102, during the preparation of the composite material, using a laser particle size analyzer and an optical microscope to obtain online dispersion state monitoring data of the modified filler, including the particle size distribution, agglomeration degree and dispersion uniformity of the filler, and determining the parameter range for characterizing the melt flow performance according to the online dispersion state monitoring data, including shear rate and temperature, the particle size distribution data is used to determine the shear rate range, a smaller particle size distribution corresponds to a higher shear rate range, and the agglomeration degree data is used to determine the temperature range, a higher agglomeration degree corresponds to a higher temperature range, so as to promote the dispersion of the filler, and these parameters are directly used to guide the setting of subsequent rheological tests; Step S103, using a rheometer to characterize the melt flow properties of the prepared composite material to obtain a viscosity shear rate curve of the composite material, judging the dispersion uniformity of the modified filler according to the platform area of ​​the viscosity shear rate curve, the width of the platform area is positively correlated with the dispersion uniformity, using a scanning electron microscope to observe the cross-sectional morphology of the prepared composite material, verifying the analysis results of the viscosity shear rate curve, and combining the dispersion uniformity judgment results to calculate the dispersion index of the modified filler in the polyamide matrix. The dispersion index is calculated by the ratio of the number of filler particles per unit area to the number of particles in theoretical uniform distribution. The specific calculation method is: first, the number of filler particles actually observed per unit area is counted, and then the number of particles in theoretical uniform distribution is calculated according to the volume fraction and average particle size of the filler. The calculation formula for the number of particles in theoretical uniform distribution is: Ntheoretical=Vfiller / (4 / 3πr 3 ), where Vfilling is the volume of the filler per unit volume, r is the average radius of the filler, and finally the actual observed number is divided by the theoretical number to obtain the dispersion index; Step S104, establishing a quantitative relationship between the dispersion index and the mechanical properties of the composite material. The mechanical properties of the composite material include tensile strength, flexural strength and impact strength. The dispersion index reflects the distribution state of the filler in the matrix and directly affects the mechanical properties of the composite material. By adjusting the preparation parameters, composite material samples with different dispersion indexes are prepared, and their mechanical properties are tested. A mathematical model is established through regression analysis. The mathematical model is expressed as: Y=aX+b, where Y represents the mechanical property index, X represents the dispersion index, and a and b are regression coefficients. This model is used to predict the mechanical properties of the composite material under different dispersion indexes, providing a basis for subsequent optimization; Step S105, according to the established quantitative relationship mathematical model, iterative optimization is performed through genetic algorithm to determine the formulation design and process optimization parameters of the polyamide-based composite material, including filler type, content, surface modification method, processing temperature and shear rate. The specific application process of the genetic algorithm includes: initializing the parameter population, evaluating the fitness of each group of parameters, selecting excellent individuals, performing crossover and mutation operations to generate new parameter combinations, repeating the evaluation-selection-crossover-mutation process until the preset number of iterations or performance indicators are reached. In this way, the parameter combination is continuously iterated and optimized to achieve the optimization of the mechanical properties of the composite material.

2. The optimized preparation method of the high-strength flame-retardant polyamide-based composite material according to claim 1, characterized in that: The step S101 also includes the following steps: Obtaining a filler after surface modification, judging the surface characteristics of the filler according to the surface modification effect of the filler, and determining the compatibility of the filler with the polyamide matrix; Depending on the size of the filler, different addition methods are used; If the size of the filler is within the preset first size range, a solution blending method is adopted to dissolve the filler and the polyamide in the same solvent, and the mixture is stirred to obtain a uniformly dispersed composite material solution; If the size of the filler is within the preset second size range, a surfactant is selected as a dispersant according to the surface properties of the filler and the compatibility of the polyamide matrix by melt blending, and the filler and the surfactant are added to a twin-screw extruder together with the polyamide; The dispersion state of the filler in the polyamide matrix is ​​controlled by adjusting the screw speed, temperature distribution and residence time process parameters of the twin-screw extruder; Using a machine learning algorithm, a relationship model between the surface characteristics and size of the filler and the process parameters of the twin-screw extruder is established; According to the properties of the filler, the optimal combination of process parameters is predicted to guide the preparation process of the composite material; During the preparation of the composite material, real-time collection of operating data of the twin-screw extruder, the operating data including screw speed, temperature distribution and pressure parameters; Inputting the collected data into the machine learning model, dynamically optimizing process parameters, and evaluating the dispersion state and interfacial compatibility of the filler in the polyamide matrix; Comparing the characterization results with the prediction results of the machine learning model, continuously optimizing and improving the machine learning model, and improving the intelligence level of the composite material preparation process; It also includes: based on the surface characteristics and size of the filler, using the support vector machine algorithm to establish a relationship model with the process parameters of the twin-screw extruder, by collecting the extruder operation data in real time, inputting it into the model to dynamically optimize the parameters, obtaining the filler dispersion state and interface compatibility, comparing the characterization results with the model prediction results, continuously improving the model, and obtaining an intelligent process flow for preparing composite materials.

3. The optimized preparation method of the high-strength flame-retardant polyamide-based composite material according to claim 2, characterized in that: According to the surface characteristics and size of the filler, a support vector machine algorithm is used to establish a relationship model between the process parameters of the twin-screw extruder, and the extruder operation data is collected in real time and input into the model to dynamically optimize the parameters to obtain the filler dispersion state and interface compatibility. The characterization results are compared with the model prediction results, and the model is continuously improved to obtain the process flow of intelligent preparation of composite materials. The following steps are involved: Obtaining filler surface characteristics and size data, and using support vector machine algorithm training to establish a relationship model between the filler characteristics and twin-screw extruder process parameters; Inputting the collected real-time process parameter data into the support vector machine model to obtain optimized process parameters; According to the optimized process parameters, the operation of the twin-screw extruder is controlled to obtain filler dispersibility and interface compatibility data; Comparing the filler dispersibility and interface compatibility data with the prediction results of the support vector machine model to determine the accuracy of the model prediction; If the prediction accuracy of the model does not reach a preset threshold, the filler dispersibility and interface compatibility data are used as new training data to retrain the support vector machine model.

4. The optimized preparation method of the high-strength flame-retardant polyamide-based composite material according to claim 1, characterized in that: The step S102 also includes the following steps: Obtaining filler particle size distribution data, performing statistical analysis on the particle size distribution data to obtain a mean and variance of filler particle size, and determining an appropriate shear rate range according to the mean and variance; Acquire a microscopic image of the filler, segment and extract features of the microscopic image using an image processing algorithm to obtain an agglomeration index of the filler, and determine a suitable temperature range according to the agglomeration index; During the preparation of the composite material, laser particle size data and optical microscopy data are collected in real time, and the laser particle size data and optical microscopy data are input into a pre-established mapping model to predict the melt flow properties under current process parameters.

5. The optimized preparation method of the high-strength flame-retardant polyamide-based composite material according to claim 1, characterized in that: The step S103 also includes the following steps: Obtain rheological test data of composite materials and obtain viscosity data at different shear rates; Plotting a viscosity-shear rate curve of the viscosity data and the corresponding shear rate; Determine whether the viscosity-shear rate curve has a plateau region, and if so, determine the width of the plateau region; Using a scanning electron microscope to observe the morphology of multiple areas of the composite material cross section to obtain the average number of filler particles per unit area; Calculating the number of particles per unit volume when theoretically uniformly distributed according to the filler volume fraction of the composite material and the average particle size of the filler particles; The average number of filler particles per unit area is divided by the theoretical number of particles per unit volume to obtain a dispersion index of the filler; Comprehensively judging the platform width and the dispersion index to determine the dispersion uniformity of the filler in the composite material; Establishing a correlation model between the dispersion index and characteristic parameters of the viscosity-shear rate curve; According to the viscosity-shear rate curve of the composite material, the predicted dispersion index of the filler is calculated using the correlation model.

6. The optimized preparation method of the high-strength flame-retardant polyamide-based composite material according to claim 1, characterized in that: The step S104 also includes the following steps: A series of composite material samples with different dispersion indices are obtained, wherein the samples are prepared by designing multiple groups of preparation experiments and adopting different preparation parameter values; For the sample, a mechanical properties testing device is used to obtain tensile strength, bending strength and impact strength data; A mathematical model was established, with the dispersion index as the independent variable and the mechanical performance index as the dependent variable. The least square method was used for linear regression to obtain the regression coefficient and determine the quantitative relationship between the dispersion index and the mechanical properties.

7. The optimized preparation method of the high-strength flame-retardant polyamide-based composite material according to claim 1, characterized in that: The step S105 also includes the following steps: Obtaining mechanical property data of the polyamide-based composite material, wherein the mechanical property data includes filler type, content, surface modification method, processing temperature and shear rate parameter information; Based on the mechanical property data, a mathematical model of the quantitative relationship between the mechanical properties of polyamide-based composite materials and the filler type, content, surface modification method, processing temperature and shear rate is established; The filler type, content, surface modification method, processing temperature and shear rate are taken as optimization variables, and the mechanical properties of the material are taken as optimization targets to construct the fitness function of the genetic algorithm. A certain number of parameter combinations of filler type, content, surface modification method, processing temperature and shear rate are randomly generated as the initial population of the genetic algorithm; Substituting each parameter combination in the initial population into the mathematical model, calculating the corresponding mechanical property prediction value, and using the prediction value as the individual fitness; According to the fitness of individuals, a certain number of excellent individuals are selected by roulette wheel selection or tournament selection; Performing a crossover operation on the excellent individuals to generate a new parameter combination; Perform mutation operations on the newly generated individuals and introduce new values; Repeat the selection, crossover and mutation operations to iteratively update the population until the preset number of iterations or mechanical performance index is reached; The optimal parameter combination is output as the formulation design and process optimization parameters for polyamide-based composite materials.

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