Process control method and system for forming composite material

Abstract

The invention discloses a composite material forming process control method and system, and relates to the technical field of composite material forming control. Comprising the following steps: obtaining composite material finished products produced by different types and different addition amounts of fillers, obtaining the thermal conductivity of the finished products through experimental detection, and mapping the types and addition amounts of the fillers with the thermal conductivity to generate a training sample data set; a neural network prediction model is established based on the data set, and training is performed by using the filler type and the additive amount as input and taking the thermal conductivity as a label. And then, inputting filler information of a to-be-produced finished product to obtain a thermal conductivity predicted value. And correcting the curing degree and calculating a thermal demand coefficient, a resin reaction degree coefficient and a pressure loss coefficient in combination with the real-time technological process parameters and the curing degree of the finished product. Finally, process control parameters are dynamically corrected according to the coefficients, and accurate control over the forming process of the composite material is achieved. And the forming efficiency and the finished product quality of the composite material are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of composite material molding control, and in particular to a composite material molding process control method and system. Background Art

[0002] In the production of modern composite materials, with the increasing requirements for material performance, especially in terms of thermal conductivity, strength, corrosion resistance, etc., the molding process control of composite materials has become increasingly complex. Traditional composite molding processes usually rely on experience and test data for parameter optimization. This method is not only time-consuming and labor-intensive, but also difficult to cope with the diverse requirements of material performance. The choice of filler has a significant impact on the final performance of the composite material, and different types and different addition amounts of fillers may lead to significant differences in the thermal conductivity of the finished product during the production process, which makes the development of an effective process control method a technical problem that needs to be solved urgently.

[0003] Existing technologies often lack systematic research on the relationship between filler type and addition amount and composite material performance, resulting in the inability to accurately predict the thermal conductivity of materials during the production process. Due to the lack of effective control measures, the thermal conductivity may not be consistent with expectations during the processing process, thus affecting the quality and performance of the finished product. In addition, process parameters during the molding process such as resin viscosity, heat flux density, ambient humidity, etc. also have an important impact on the final performance of the finished product, and these parameters are often difficult to monitor and adjust in real time during the production process.

[0004] Therefore, a new composite material molding process control method is urgently needed to achieve precise control of composite material performance by introducing advanced prediction models and dynamic correction mechanisms. Advanced machine learning technologies such as neural networks are used to establish a mapping relationship between filler type, addition amount and finished product thermal conductivity, thereby achieving accurate prediction of finished product thermal conductivity. At the same time, real-time monitoring and adjustment of process parameters can ensure that the actual situation can be flexibly responded to during the molding process, thereby improving the overall performance and production efficiency of composite materials.

[0005] In the prior art, the publication number CN118664933A discloses a process control method and device for composite material molding, wherein the method includes: obtaining initial parameters of the composite material; establishing a mathematical model of the composite material molding process according to the initial parameters; performing numerical simulation of the composite material molding process based on the mathematical model to obtain numerical simulation results; comparing the numerical simulation results with the preset molding results, screening out the optimal process parameter combination that meets the preset molding results as the composite material molding control parameters; setting parameters for the temperature control system and the pressure control system of the composite material molding equipment, and starting the composite material molding process to complete the production of composite material products. However, in this method, the molding process of the composite material involves complex physical and chemical reactions. Establishing an accurate mathematical model requires not only deep knowledge of materials science, but also full consideration of various factors in the molding process, including temperature, pressure, flow characteristics, etc. If the model cannot accurately capture the relationship between these factors, it may lead to deviations in the prediction results, thereby affecting the selection of process parameters. At the same time, during the production process, product performance and process parameters may change with time and conditions. The method fails to mention how to monitor the performance of the finished product in real time and make adjustments based on actual feedback. This control method without real-time feedback may lead to low production efficiency and unstable finished product quality. Therefore, this method reduces the accuracy and effectiveness of process parameter control.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The object of the present invention is to provide a process control method and system for composite material molding to solve the problems raised in the above background technology.

[0008] To achieve the above object, the present invention provides the following technical solutions: A process control method for composite material molding, the specific steps comprising: Obtain a number of finished composite materials produced with different types and different addition amounts of fillers, test the finished composite materials using experiments, obtain the corresponding finished thermal conductivity, map the type and addition amount of fillers to the corresponding finished thermal conductivity, and generate a training sample data set; Based on the data in the training sample data set, a neural network prediction model is established, the type and addition amount of the filler in the training sample data set are used as the input of the model, and the corresponding finished product thermal conductivity is used as a label to train the neural network prediction model to obtain a thermal conductivity prediction model; Obtaining the type and amount of filler added to the finished composite material product to be produced, inputting the type and amount of filler added to the finished composite material product to be produced into the trained thermal conductivity prediction model to obtain a predicted value of thermal conductivity of the finished product to be produced, and simultaneously obtaining real-time process flow parameters during the processing, wherein the process flow parameters include resin viscosity, heat flux density, ambient humidity, resin injection speed, and air flow rate; Obtain the real-time curing degree of the finished product during the processing, correct the curing degree of the finished product based on the process parameters, and obtain the precise value of the curing degree of the finished product. According to the precise value of the curing degree of the finished product and the predicted value of thermal conductivity, combined with the process parameters, calculate the heat demand coefficient, resin reaction degree coefficient and pressure loss coefficient; According to the obtained heat demand coefficient, resin reaction degree coefficient and pressure loss coefficient, the process control parameters in the processing process are dynamically corrected to obtain real-time correction values ​​of the process control parameters. Based on the obtained real-time correction values ​​of the process control parameters, the processing process is controlled to complete the process control of composite material molding. The process control parameters include mold temperature, curing pressure and curing time.

[0009] Furthermore, the method for generating the training sample data set is: mapping the type and addition amount of the filler to the finished thermal conductivity parameters of the corresponding composite material finished product one by one to form a corresponding grid, and recording the formed grid as the training sample data set; Based on the convolutional neural network, a thermal conductivity prediction model is established, wherein the convolutional neural network consists of an input layer, a convolutional layer, a pooling layer, a fully connected layer and an output layer, wherein the activation function in the convolutional layer is function, The specific expression of the function is: ; in, Indicates The corresponding convolutional layers, It means the The corresponding convolutional layer The first feature data eigenvalues, where is the index of the convolutional layer, is the index of the feature data, is the index of the eigenvalue in the feature data, where , , , where N, M and O are the total number of convolutional layers, feature data and feature values ​​in the feature data respectively; For the fully connected layer, the number of neurons in the fully connected layer is set to 32, the initial neural network learning rate is set to 0.001, and the number of training rounds is 100; The trained thermal conductivity prediction model takes as input the type and amount of filler added, and outputs the predicted value of thermal conductivity of the finished composite material.

[0010] Furthermore, the real-time curing degree of the finished product during the processing is obtained, and the curing degree of the finished product is corrected based on the process parameters to obtain the precise value of the curing degree of the finished product, wherein the formula for calculating the precise value of the curing degree of the finished product is: ; In the formula, is the precise value of the curing degree of the finished product at the current moment, is the curing degree of the finished product collected at the current moment, is the initial resin viscosity, is the resin viscosity at the current moment, is the resin viscosity influence constant; The curing degree of the finished product at the current moment Specifically, the degree of curing is monitored in real time through techniques such as infrared spectroscopy or thermogravimetric analysis.

[0011] Furthermore, according to the precise value of the curing degree of the finished product and the predicted value of the thermal conductivity, combined with the process parameters, the heat demand coefficient, the resin reaction degree coefficient and the pressure loss coefficient are calculated, wherein the heat demand coefficient is calculated based on the formula: ; In the formula, is the heat demand coefficient at the current moment, is the air flow rate of the processing environment at the current moment, is the cumulative reaction heat at the current moment, is the predicted value of thermal conductivity of the finished product; The cumulative reaction heat at the current moment The calculation is performed through heat flux density, where the specific calculation is based on the formula: ; In the formula, is the initial reaction heat, is the processing start time, For the current moment, is the heat flux density, where the heat flux density is collected in real time by a heat flux meter.

[0012] Furthermore, the formula for calculating the resin reaction degree coefficient is: ; In the formula, is the resin reaction degree coefficient at the current moment, is the ambient humidity; The formula for calculating the pressure loss coefficient is: ; In the formula, is the pressure loss coefficient of the injected resin at the current moment, is the length of the resin injection channel, is the diameter of the resin injection channel, is the density of the resin, is the resin injection speed of the resin, is the friction factor.

[0013] Furthermore, the process control parameters in the processing process are dynamically corrected according to the obtained heat demand coefficient, resin reaction degree coefficient and pressure loss coefficient to obtain real-time correction values ​​of the process control parameters, wherein the specific formula for calculating the mold temperature correction value is: ; In the formula, is the mold temperature correction value at the current moment, is the initial value of the mold temperature. is the heat demand coefficient at the current moment; The specific formula for calculating the curing time correction value is: ; In the formula, is the curing time correction value at the current moment, It is the initial value of the set curing time; The specific formula for calculating the curing pressure correction value is: ; In the formula, is the curing pressure correction value at the current moment, is the initial value of the curing pressure, where , and are the weight constants of heat demand coefficient, resin reaction degree coefficient and pressure loss coefficient, respectively, where and , and Both are greater than 0.

[0014] The present invention also provides a process control system for composite material molding, wherein the process control system for composite material molding is used to execute the above-mentioned process control method for composite material molding, comprising: The sample data acquisition module is used to obtain a number of composite material products produced by different types and different addition amounts of fillers, test the composite material products by experiments, obtain the corresponding finished product thermal conductivity, map the filler type and addition amount with the corresponding finished product thermal conductivity, and generate a training sample data set; The prediction model training module is used to establish a neural network prediction model based on the data in the training sample data set, take the type and addition amount of the filler in the training sample data set as the input of the model, and take the corresponding finished product thermal conductivity as the label to train the neural network prediction model to obtain a thermal conductivity prediction model; A process parameter analysis module is used to obtain the type and amount of fillers added to the finished composite material product to be produced, input the type and amount of fillers added to the finished composite material product to be produced into the trained thermal conductivity prediction model, obtain the predicted value of the thermal conductivity of the finished product to be produced, and simultaneously obtain the real-time process parameters during the processing, wherein the process parameters include resin viscosity, heat flux density, ambient humidity, resin injection speed and air flow rate; The curing degree detection module is used to obtain the real-time curing degree of the finished product during the processing, and to correct the curing degree of the finished product based on the process parameters to obtain the precise value of the curing degree of the finished product. According to the precise value of the curing degree of the finished product and the predicted value of the thermal conductivity, combined with the process parameters, the heat demand coefficient, the resin reaction degree coefficient and the pressure loss coefficient are calculated; The control parameter correction module is used to dynamically correct the process control parameters in the processing process according to the obtained heat demand coefficient, resin reaction degree coefficient and pressure loss coefficient, obtain real-time correction values ​​of the process control parameters, control the processing process based on the obtained real-time correction values ​​of the process control parameters, and complete the process control of composite material molding. The process control parameters include mold temperature, curing pressure and curing time.

[0015] Compared with the prior art, the present invention has the following beneficial effects: First, by creating a training sample data set based on the corresponding relationship between filler type and addition amount and the thermal conductivity of the finished product, and using neural network for predictive modeling, the thermal conductivity of the composite material to be produced can be predicted quickly and accurately. This improves the predictability of production, provides a scientific basis for material selection and ratio, and reduces material waste and production risks caused by mismatching. Secondly, this method combines the real-time monitored process parameters with the curing degree of the finished product for dynamic correction, ensuring a high degree of consistency between the performance of the finished product and the production conditions. By monitoring and adjusting parameters such as resin viscosity, heat flux density, and ambient humidity during the processing process, the process control parameters such as mold temperature, curing pressure, and curing time can be optimized in real time. This flexible dynamic adjustment mechanism effectively enhances the adaptability of the processing process and ensures that the composite material can meet the expected performance standards under different production conditions. Finally, the implementation of this method not only improves the quality and reliability of the finished product, but also significantly reduces production costs and time. The production process is optimized by reducing unnecessary testing and correction time. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the overall method flow of the present invention; Figure 2 It is a schematic diagram of the overall system structure of the present invention. DETAILED DESCRIPTION

[0017] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0018] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0019] Example: See also Figure 1 , the present invention provides a technical solution: A process control method for composite material molding, the specific steps comprising: Step 1: Obtain several finished composite materials produced with different types and different addition amounts of fillers, test the finished composite materials through experiments, obtain the corresponding finished thermal conductivity, map the type and addition amount of fillers with the corresponding finished thermal conductivity, and generate a training sample data set.

[0020] In the production of composite materials, the type and amount of filler added have an important influence on the thermal conductivity of the finished product. Fillers are materials used to improve the performance of composite materials. Common filler types include: Inorganic fillers: such as alumina, silicate, graphite, calcium carbonate, etc. Such fillers usually have good thermal conductivity and chemical stability; Organic fillers: such as polymer particles, glass fibers, etc. These fillers usually have little effect on thermal conductivity, but can improve the toughness of the material; Thermally conductive fillers: such as metal powders (copper powder, aluminum powder), thermally conductive plastics, etc. These specially designed fillers have high thermal conductivity and can significantly improve the thermal conductivity of composite materials; Nano fillers: such as carbon nanotubes, graphene, etc. These fillers are widely studied and used in high-performance composite materials due to their high specific surface area and excellent thermal conductivity.

[0021] The amount of filler added is usually expressed as a mass percentage (%). Different addition amounts have a significant impact on the final performance of the composite material; for example: low addition amount (1% - 5%): may be mainly used to improve the physical properties of the material, such as toughness, wear resistance, etc., while having little effect on thermal conductivity; medium addition amount (5% - 15%): within this range, the thermal conductivity of the filler will gradually emerge, which can effectively enhance the thermal conductivity of the composite material; high addition amount (above 15%): the filler may form a continuous heat conduction channel, greatly improving thermal conductivity, but it may also affect other properties, such as mechanical strength and toughness.

[0022] The thermal conductivity of the filler itself is the main factor affecting the thermal conductivity of the composite material. For example, the thermal conductivity of copper powder is higher than that of graphite powder, so adding copper powder will significantly increase the thermal conductivity. There may be interactions between different types of fillers, affecting the thermal conductivity. For example, adding graphite filler can improve the thermal conductivity of the polymer matrix, etc.

[0023] The method for generating the training sample data set is as follows: mapping the type and addition amount of filler to the finished thermal conductivity parameters of the corresponding composite material finished product one by one to form a corresponding grid, and recording the formed grid as the training sample data set.

[0024] Step 2: Based on the data in the training sample data set, a neural network prediction model is established. The type and addition amount of the filler in the training sample data set are used as the input of the model, and the corresponding finished product thermal conductivity is used as the label to train the neural network prediction model to obtain a thermal conductivity prediction model.

[0025] Based on the convolutional neural network, a thermal conductivity prediction model is established, wherein the convolutional neural network consists of an input layer, a convolutional layer, a pooling layer, a fully connected layer and an output layer, wherein the activation function in the convolutional layer is function, The specific expression of the function is: ; in, Indicates The corresponding convolutional layers, It means the The corresponding convolutional layer The first feature data eigenvalues, where is the index of the convolutional layer, is the index of the feature data, is the index of the eigenvalue in the feature data, where , , , where N, M and O are the total number of convolutional layers, feature data and feature values ​​in the feature data respectively; For the fully connected layer, the number of neurons in the fully connected layer is set to 32, the initial neural network learning rate is set to 0.001, and the number of training rounds is 100; The trained thermal conductivity prediction model takes as input the type and amount of filler added, and outputs the predicted value of thermal conductivity of the finished composite material.

[0026] One of the biggest advantages of convolutional neural networks (CNNs) is the ability to automatically extract features from input data. When building a thermal conductivity prediction model, filler type and addition amount are used as input data, and CNN can automatically identify and extract potential features related to thermal conductivity without the need for manual feature extraction. This is especially important because the type and amount of fillers may contain complex nonlinear relationships, and manual extraction of these features is usually very difficult and error-prone.

[0027] The relationship between thermal conductivity and filler type and addition amount may be nonlinear. Traditional linear regression models may not be able to accurately capture this complex nonlinear relationship. CNN, through its multi-layer structure, is able to learn this complex nonlinear relationship and thus improve the model's predictive ability.

[0028] CNN can learn high-level features of input data through multiple convolutional layers, while pooling layers help reduce dimensions and extract the most representative features. In this way, CNN can capture deep features that affect thermal conductivity from raw inputs such as filler type and addition amount.

[0029] Step 3: Obtain the type and amount of filler added to the finished composite material product to be produced, input the type and amount of filler added to the finished composite material product to be produced into the trained thermal conductivity prediction model to obtain the predicted value of thermal conductivity of the finished product to be produced, and simultaneously obtain the real-time process flow parameters during the processing, wherein the process flow parameters include resin viscosity, heat flux density, ambient humidity, resin injection speed and air flow rate.

[0030] Resin viscosity is an important parameter that affects composite molding. It can be obtained by using online viscosity monitoring systems (such as ultrasonic viscometers or optical viscometers). These devices can be installed in the resin delivery pipeline to monitor viscosity changes in real time; or using special instruments such as rotational viscometers or capillary viscometers to directly measure the viscosity of the resin. Rotational viscometers measure the resistance of the fluid by rotating a propeller or rod to calculate the viscosity.

[0031] Heat flux density describes the amount of heat passing through a unit area and is often obtained through the following methods: Heat flux meter: Use heat flux sensors (such as thermocouples or heat flow sheets) to measure the heat flux density at various locations during the composite molding process. Heat flux meters are usually embedded in the mold and can monitor temperature changes in real time; Infrared thermal imagers: Infrared thermal imagers can be used to obtain the temperature distribution on the mold surface in a non-contact manner and calculate the heat flux density through a heat conduction model.

[0032] Ambient humidity has an important influence on material properties and can be obtained through the following methods: Humidity sensor: Use digital or analog humidity sensors (such as capacitive humidity sensors or impedance humidity sensors), which can monitor the relative humidity in the air in real time.

[0033] The resin injection speed directly affects the molding process and can be obtained by the following methods: Use a flow meter (such as an electromagnetic flow meter or ultrasonic flow meter) to measure the flow rate of the resin. The flow meter can monitor the fluid flow in real time and calculate the injection speed through the volume change of the fluid, or install a position sensor or speed sensor in the injection system to monitor the working speed of the syringe to obtain the actual injection speed.

[0034] Air flow rate usually affects the molding process and is usually obtained by the following methods: using an anemometer (such as a hot wire anemometer or a rotary anemometer) to measure the air flow speed. The anemometer can provide air flow rate data in real time; or using a differential pressure sensor in the air flow duct to monitor the pressure change of the air flow and calculate the air flow rate based on the principles of fluid dynamics.

[0035] Step 4: Obtain the real-time curing degree of the finished product during the processing, correct the curing degree of the finished product based on the process parameters, and obtain the precise value of the curing degree of the finished product. According to the precise value of the curing degree of the finished product and the predicted value of thermal conductivity, combined with the process parameters, calculate the heat demand coefficient, resin reaction degree coefficient and pressure loss coefficient.

[0036] The real-time curing degree of the finished product during the processing is obtained, and the curing degree of the finished product is corrected based on the process parameters to obtain the precise value of the curing degree of the finished product. The formula for calculating the precise value of the curing degree of the finished product is: ; In the formula, is the precise value of the curing degree of the finished product at the current moment, is the curing degree of the finished product collected at the current moment, is the initial resin viscosity, is the resin viscosity at the current moment, is the resin viscosity influence constant; The curing degree of the finished product at the current moment Specifically, it is the degree of cure monitored in real time by infrared spectroscopy or thermogravimetric analysis. Specifically, Fourier transform infrared spectroscopy (FTIR) is a technology that uses infrared radiation to analyze materials. During the curing process, by monitoring the absorption of infrared light of a specific wavelength by the material, information about the chemical reaction can be obtained. The curing reaction usually involves changes in certain functional groups, such as the formation or breaking of hydrogen bonds, the conversion of double bonds, etc. By tracking the absorption peaks of these functional groups before and after curing, the degree of cure can be inferred. However, due to the influence of environmental factors such as temperature and light conditions during the infrared spectrum data collection process, the disappearance or transformation of certain functional groups during the curing process may not be fully reflected in the infrared spectrum. For example, some reactions may lead to the generation of new chemical structures or changes in functional groups, and the infrared spectrum may not fully capture these changes, especially in the case of weak absorption at low concentrations or specific wavelengths, so that the infrared spectrum data detection leads to a small degree of cure of the finished product. Therefore, there is an error in determining the degree of cure of the finished product only through infrared spectroscopy. By combining the change of the resin viscosity with the gradual increase of the curing reaction and the determination of the degree of cure of the finished product by infrared spectroscopy, a comprehensive judgment can be made to compensate for the error and improve the accuracy of the degree of cure detection.

[0037] It should be noted that Indicates the exact value of the curing degree of the finished product at the current moment. The curing degree is usually used to describe the degree to which the material changes from liquid or semi-solid to solid during the curing process. The larger the value, the higher the degree of curing of the material, and the more complete the chemical reactions within the material (such as cross-linking, polymerization, etc.) have been.

[0038] The viscosity of the resin changes during the curing process as the reaction proceeds. Generally, the viscosity increases as the curing proceeds. This change in viscosity directly affects the fluidity of the resin and the efficiency of the curing reaction. Used to compare the relationship between the current viscosity and the initial viscosity. The square root of this ratio can be regarded as a proportional correction to the degree of cure, reflecting the influence of the current resin state on the degree of cure, in the form of square root Using the square root to adjust the cure degree can more smoothly reflect the effects of viscosity changes. Viscosity changes are usually not linear, and using the square root can reduce the effect on the cure degree to a certain extent, especially when the viscosity change is not large.

[0039] The resin viscosity affects the constant Used to adjust the degree of cure calculation to reflect the effects of specific process flows or conditions, generally ranging from 0.02 to 0.3.

[0040] According to the precise value of the curing degree of the finished product and the predicted value of the thermal conductivity, combined with the process parameters, the heat demand coefficient, the resin reaction degree coefficient and the pressure loss coefficient are calculated. The formula for calculating the heat demand coefficient is: ; In the formula, is the heat demand coefficient at the current moment, is the air flow rate of the processing environment at the current moment, is the cumulative reaction heat at the current moment, is the predicted value of thermal conductivity of the finished product; It should be noted that the heat demand coefficient at the current moment The heat demand coefficient at the current moment is characterized by air flow rate, cumulative reaction heat and predicted thermal conductivity of the finished product. The larger the value, the higher the heat required for the reaction, and the mold temperature can be increased to meet the needs of the curing reaction.

[0041] The air flow rate of the processing environment at the current moment , air flow rate has a significant effect on heat conduction and convection heat transfer. Flowing air can take away or transfer heat. Air flow will take away excess heat or introduce cold air, thus affecting the temperature distribution and reaction rate of the material. Therefore, the heat demand coefficient at the current moment The air flow rate of the processing environment at the current moment Proportional, through the exponential function It shows that the effect of increasing air flow rate on heat consumption is nonlinear, and higher air flow rate will significantly increase heat loss.

[0042] The cumulative reaction heat at the current moment The larger the value, the more heat is generated, so the mold temperature can be lowered to keep the temperature constant while reducing energy consumption. The heat demand coefficient at the current moment Inversely proportional. By squaring Indicates the inhibitory effect of reaction heat on temperature increase. As the reaction heat increases, the material temperature rises, the mold temperature can be lowered, and consumption can be reduced.

[0043] Predicted thermal conductivity of finished product , a parameter that describes the thermal conductivity of a material. Materials with high thermal conductivity can transfer heat more efficiently, so only a relatively low temperature supply is required to achieve the target effect. Therefore, the cumulative reaction heat at the current moment is The heat demand coefficient at the current moment The higher the thermal conductivity, the more efficiently the material can transfer heat, resulting in increased heat transfer efficiency.

[0044] pass Represents the synergistic effect of the cumulative reaction heat and thermal conductivity on temperature control. The square root is used to smooth the effect of the synergistic effect.

[0045] The cumulative reaction heat at the current moment The calculation is performed through heat flux density, where the specific calculation is based on the formula: ; In the formula, is the initial reaction heat, is the processing start time, For the current moment, is the heat flux density, where the heat flux density is collected in real time by a heat flow meter. Usually 0.

[0046] The formula for calculating the resin reaction coefficient is: ; In the formula, is the resin reaction degree coefficient at the current moment, is the ambient humidity; It should be noted that the resin reaction coefficient at the current moment The larger the value, the higher the degree of resin reaction and the more complete the curing reaction.

[0047] in The larger the value, the higher the curing degree of the material, and the more complete the chemical reactions (such as cross-linking, polymerization, etc.) in the material have been. Therefore, the resin reaction degree coefficient at the current moment and Directly proportional to the progress of the reaction.

[0048] The viscosity of the resin changes during the curing process as the reaction proceeds. Generally, the viscosity increases as the curing proceeds, so the resin viscosity The larger the reaction progress, the higher the resin viscosity Resin reaction coefficient at the current moment Directly proportional.

[0049] pass The combination of the degree of cure and viscosity characterizes the degree of reaction of the resin. provides information about the progress of the reaction, while the resin viscosity It reflects the change of the state of the resin during the curing process. Using the square root form can balance the influence of these two parameters and avoid one parameter being too dominant in value. Through such a combination, the reaction degree of the resin can be more comprehensively reflected.

[0050] Humidity can affect the curing process of resins, especially in certain types of resins (such as water-based resins), whose reaction characteristics may be significantly affected by ambient humidity, resulting in a slowdown in curing progress. Resin reaction coefficient at the current moment Inversely proportional. Through the logarithmic function This reflects the inhibitory effect of humidity on the resin curing process. Excessive humidity may slow down the resin curing reaction or cause incomplete curing. The introduction of the logarithmic function can smooth the effect of humidity, especially under low humidity conditions, where the effect of humidity changes on the curing process will be more significant.

[0051] The formula for calculating the pressure loss coefficient is: ; In the formula, is the pressure loss coefficient of the injected resin at the current moment, is the length of the resin injection channel, is the diameter of the resin injection channel, is the density of the resin, is the resin injection speed of the resin, is the friction factor.

[0052] During the flow process, the resin will be subject to friction and local flow resistance, resulting in pressure loss. The Darcy-Weisbach formula is used to describe the pressure loss. The pressure loss coefficient of the injected resin at the current moment is The larger the value, the more pressure loss there is during processing, so the pressure should be increased to prevent bubbles and gas accumulation. At the same time, pressure helps to reduce the voids in the material and improve the density and mechanical properties of the final product.

[0053] The friction factor It can be set according to the material of the injection channel and expert experience, generally between 0.01 and 0.1.

[0054] Step 5: Dynamically correct the process control parameters during the processing according to the obtained heat demand coefficient, resin reaction degree coefficient and pressure loss coefficient to obtain real-time correction values ​​of the process control parameters. Control the processing process based on the obtained real-time correction values ​​of the process control parameters to complete the process control of composite material molding. The process control parameters include mold temperature, curing pressure and curing time.

[0055] According to the obtained heat demand coefficient, resin reaction degree coefficient and pressure loss coefficient, the process control parameters in the processing process are dynamically corrected to obtain the real-time correction value of the process control parameters. The specific formula for calculating the mold temperature correction value is: ; In the formula, is the mold temperature correction value at the current moment, is the initial value of the mold temperature. is the heat demand coefficient at the current moment; Among them, the heat demand coefficient at the current moment The larger the value, the higher the heat demand. The mold temperature can be increased to meet the needs of the curing reaction. Therefore, the mold temperature correction value at the current moment is and Proportional, through the logarithmic function Indicates that with The increase of mold temperature correction value The impact gradually decreases.

[0056] The resin reaction coefficient at the current moment The larger the value, the higher the degree of resin reaction and the more complete the curing reaction. Conversely, the weaker the curing reaction. Therefore, the temperature should be increased to promote the curing reaction. and Inversely proportional, through Indicates an inverse relationship, through the square root Avoid overcorrection.

[0057] The specific formula for calculating the curing time correction value is: ; In the formula, is the curing time correction value at the current moment, It is the initial value of the set curing time; The current resin reaction coefficient The larger the value, the higher the degree of resin reaction and the more complete the curing reaction. The curing time can be appropriately reduced. On the contrary, the curing time should be increased to make the curing reaction complete. Therefore, the curing time correction value at the current moment and Inversely proportional, through the square Indicates a significant impact on the cure time correction value.

[0058] The specific formula for calculating the curing pressure correction value is: ; In the formula, is the curing pressure correction value at the current moment, is the initial value of the curing pressure, where , and are the weight constants of heat demand coefficient, resin reaction degree coefficient and pressure loss coefficient, respectively, where and , and Both are greater than 0.

[0059] Among them, the pressure loss coefficient of the injected resin at the current moment is The larger the value, the more pressure loss there is during the processing, so the pressure should be increased. Therefore, the curing pressure correction value at the current moment is and Proportional, through the exponential function express The significant impact on pressure control, while the volume is constant, the temperature rises, the pressure rises accordingly, so the pressure should be appropriately reduced when the temperature rises to maintain a stable pressure. Therefore, the current curing pressure correction value is and Inversely proportional.

[0060] Among them, heat transfer efficiency usually has the greatest impact on the entire curing process, so it is given the highest weight. Heat transfer efficiency directly affects the temperature and reaction rate of the material, and has a significant decisive effect on mold temperature and curing pressure. The degree of reaction of the resin also has an important influence on the curing process, affecting the curing time and temperature, but its influence usually comes after heat transfer. The adjustment of curing pressure is often determined by flow characteristics and fluid mechanics factors, which have less direct impact on mold temperature and reaction dynamics. Therefore, setting and , and Both are greater than 0.

[0061] See also Figure 2 The present invention also provides a process control system for composite material molding, wherein the process control system for composite material molding is used to execute the above-mentioned process control method for composite material molding, including: The sample data acquisition module is used to obtain a number of composite material products produced by different types and different addition amounts of fillers, test the composite material products by experiments, obtain the corresponding finished product thermal conductivity, map the filler type and addition amount with the corresponding finished product thermal conductivity, and generate a training sample data set; The prediction model training module is used to establish a neural network prediction model based on the data in the training sample data set, take the type and addition amount of the filler in the training sample data set as the input of the model, and take the corresponding finished product thermal conductivity as the label to train the neural network prediction model to obtain a thermal conductivity prediction model; A process parameter analysis module is used to obtain the type and amount of fillers added to the finished composite material product to be produced, input the type and amount of fillers added to the finished composite material product to be produced into the trained thermal conductivity prediction model, obtain the predicted value of the thermal conductivity of the finished product to be produced, and simultaneously obtain the real-time process parameters during the processing, wherein the process parameters include resin viscosity, heat flux density, ambient humidity, resin injection speed and air flow rate; The curing degree detection module is used to obtain the real-time curing degree of the finished product during the processing, and to correct the curing degree of the finished product based on the process parameters to obtain the precise value of the curing degree of the finished product. According to the precise value of the curing degree of the finished product and the predicted value of the thermal conductivity, combined with the process parameters, the heat demand coefficient, the resin reaction degree coefficient and the pressure loss coefficient are calculated; The control parameter correction module is used to dynamically correct the process control parameters in the processing process according to the obtained heat demand coefficient, resin reaction degree coefficient and pressure loss coefficient, obtain real-time correction values ​​of the process control parameters, control the processing process based on the obtained real-time correction values ​​of the process control parameters, and complete the process control of composite material molding. The process control parameters include mold temperature, curing pressure and curing time.

[0062] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0063] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0064] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0065] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A process control method for composite material molding, characterized in that: The specific steps include: Obtain a number of finished composite materials produced with different types and different addition amounts of fillers, test the finished composite materials using experiments, obtain the corresponding finished thermal conductivity, map the type and addition amount of fillers to the corresponding finished thermal conductivity, and generate a training sample data set; Based on the data in the training sample data set, a neural network prediction model is established, the type and addition amount of the filler in the training sample data set are used as the input of the model, and the corresponding finished product thermal conductivity is used as a label to train the neural network prediction model to obtain a thermal conductivity prediction model; Obtaining the type and amount of filler added to the finished composite material product to be produced, inputting the type and amount of filler added to the finished composite material product to be produced into the trained thermal conductivity prediction model to obtain a predicted value of thermal conductivity of the finished product to be produced, and simultaneously obtaining real-time process flow parameters during the processing, wherein the process flow parameters include resin viscosity, heat flux density, ambient humidity, resin injection speed, and air flow rate; Obtain the real-time curing degree of the finished product during the processing, correct the curing degree of the finished product based on the process parameters, and obtain the precise value of the curing degree of the finished product. According to the precise value of the curing degree of the finished product and the predicted value of thermal conductivity, combined with the process parameters, calculate the heat demand coefficient, resin reaction degree coefficient and pressure loss coefficient; According to the obtained heat demand coefficient, resin reaction degree coefficient and pressure loss coefficient, the process control parameters in the processing process are dynamically corrected to obtain real-time correction values ​​of the process control parameters. Based on the obtained real-time correction values ​​of the process control parameters, the processing process is controlled to complete the process control of composite material molding. The process control parameters include mold temperature, curing pressure and curing time.

2. A composite material molding process control method according to claim 1, characterized in that: The method for generating the training sample data set is as follows: mapping the type and addition amount of the filler to the finished thermal conductivity parameters of the corresponding composite material finished product one by one to form a corresponding grid, and recording the formed grid as the training sample data set; Based on the convolutional neural network, a thermal conductivity prediction model is established, wherein the convolutional neural network consists of an input layer, a convolutional layer, a pooling layer, a fully connected layer and an output layer, wherein the activation function in the convolutional layer is function, The specific expression of the function is: ； in, Indicates The corresponding convolutional layers, It means the The corresponding convolutional layer The first feature data eigenvalues, where is the index of the convolutional layer, is the index of the feature data, is the index of the eigenvalue in the feature data, where , , , where N, M and O are the total number of convolutional layers, feature data and feature values ​​in the feature data respectively; For the fully connected layer, the number of neurons in the fully connected layer is set to 32, the initial neural network learning rate is set to 0.001, and the number of training rounds is 100; The trained thermal conductivity prediction model takes as input the type and amount of filler added, and outputs the predicted value of thermal conductivity of the finished composite material.

3. A composite material molding process control method according to claim 2, characterized in that: The real-time curing degree of the finished product during the processing is obtained, and the curing degree of the finished product is corrected based on the process parameters to obtain the precise value of the curing degree of the finished product. The formula for calculating the precise value of the curing degree of the finished product is: ； In the formula, is the precise value of the curing degree of the finished product at the current moment, is the curing degree of the finished product collected at the current moment, is the initial resin viscosity, is the resin viscosity at the current moment, is the resin viscosity influence constant; The curing degree of the finished product at the current moment Real-time monitoring by infrared spectroscopy or thermogravimetric analysis techniques.

4. A composite material molding process control method according to claim 3, characterized in that: According to the precise value of the curing degree of the finished product and the predicted value of the thermal conductivity, combined with the process parameters, the heat demand coefficient, the resin reaction degree coefficient and the pressure loss coefficient are calculated. The formula for calculating the heat demand coefficient is: ； In the formula, is the heat demand coefficient at the current moment, is the air flow rate of the processing environment at the current moment, is the cumulative reaction heat at the current moment, is the predicted value of thermal conductivity of the finished product; The cumulative reaction heat at the current moment The calculation is performed through heat flux density, where the specific calculation is based on the formula: ； In the formula, is the initial reaction heat, is the processing start time, For the current moment, is the heat flux density, where the heat flux density is collected in real time by a heat flux meter.

5. The process control method for composite material molding according to claim 3, characterized in that: The formula for calculating the resin reaction coefficient is: ； In the formula, is the resin reaction degree coefficient at the current moment, is the ambient humidity; The formula for calculating the pressure loss coefficient is: ； In the formula, is the pressure loss coefficient of the injected resin at the current moment, is the length of the resin injection channel, is the diameter of the resin injection channel, is the density of the resin, is the resin injection speed of the resin, is the friction factor.

6. A composite material molding process control method according to claim 5, characterized in that: According to the obtained heat demand coefficient, resin reaction degree coefficient and pressure loss coefficient, the process control parameters in the processing process are dynamically corrected to obtain the real-time correction value of the process control parameters. The specific formula for calculating the mold temperature correction value is: ； In the formula, is the mold temperature correction value at the current moment, is the initial value of the mold temperature. is the heat demand coefficient at the current moment; The specific formula for calculating the curing time correction value is: ； In the formula, is the curing time correction value at the current moment, It is the initial value of the set curing time; The specific formula for calculating the curing pressure correction value is: ； In the formula, is the curing pressure correction value at the current moment, is the initial value of the curing pressure, where , and are the weight constants of heat demand coefficient, resin reaction degree coefficient and pressure loss coefficient, respectively, where and , and Both are greater than 0.

7. A process control system for composite material molding, characterized in that: The process control system for composite material molding is used to execute the process control method for composite material molding according to any one of claims 1 to 6, comprising: The sample data acquisition module is used to obtain a number of composite material products produced by different types and different addition amounts of fillers, test the composite material products by experiments, obtain the corresponding finished product thermal conductivity, map the filler type and addition amount with the corresponding finished product thermal conductivity, and generate a training sample data set; The prediction model training module is used to establish a neural network prediction model based on the data in the training sample data set, take the type and addition amount of the filler in the training sample data set as the input of the model, and take the corresponding finished product thermal conductivity as the label to train the neural network prediction model to obtain a thermal conductivity prediction model; A process parameter analysis module is used to obtain the type and amount of fillers added to the finished composite material product to be produced, input the type and amount of fillers added to the finished composite material product to be produced into the trained thermal conductivity prediction model, obtain the predicted value of the thermal conductivity of the finished product to be produced, and simultaneously obtain the real-time process parameters during the processing, wherein the process parameters include resin viscosity, heat flux density, ambient humidity, resin injection speed and air flow rate; The curing degree detection module is used to obtain the real-time curing degree of the finished product during the processing, and to correct the curing degree of the finished product based on the process parameters to obtain the precise value of the curing degree of the finished product. According to the precise value of the curing degree of the finished product and the predicted value of the thermal conductivity, combined with the process parameters, the heat demand coefficient, the resin reaction degree coefficient and the pressure loss coefficient are calculated; The control parameter correction module is used to dynamically correct the process control parameters in the processing process according to the obtained heat demand coefficient, resin reaction degree coefficient and pressure loss coefficient, obtain real-time correction values ​​of the process control parameters, control the processing process based on the obtained real-time correction values ​​of the process control parameters, and complete the process control of composite material molding. The process control parameters include mold temperature, curing pressure and curing time.

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