Heating and curing method and apparatus for carbon fiber composite material

Abstract

The present application relates to the technical field of carbon fiber composite material processing. Provided are a heating and curing method and apparatus for a carbon fiber composite material. The method comprises: randomly generating a first curing parameter, and performing multiple instances of heating and curing bubble simulation generation and resin loss simulation generation, so as to generate a first bubble map and a first resin loss map; performing bubble and resin loss scale analysis, so as to obtain bubble and resin loss scale information, performing bubble and resin loss reliability analysis, so as to obtain bubble and resin loss reliability, and performing calculation to obtain a first curing fitness; and on the basis of the first curing fitness, continuing to optimize curing parameters, so as to obtain an optimal curing parameter. The present application can solve the technical problem of the lack of accuracy and adaptability in the settings of curing parameters in the existing heating and curing processes leading to ineffective control of material bubble generation and resin loss, thereby effectively reducing material bubble generation and resin loss during curing, and significantly improving the material curing quality.

Description

A method and apparatus for heat curing carbon fiber composite materials Technical Field

[0001] This application relates to the field of carbon fiber composite material processing technology, and in particular to a method and apparatus for heating and curing carbon fiber composite materials. Background Technology

[0002] Carbon fiber composites are widely used in high-end manufacturing fields such as aerospace and automobile manufacturing due to their excellent strength, rigidity and lightweight properties.

[0003] In the manufacturing process of carbon fiber composites, heat curing is a crucial step that directly affects the mechanical properties and service life of the material. However, in existing heat curing processes, curing parameters (heating rate, pressure, etc.) mainly rely on experience-based settings or fixed process standards. This often results in a lack of precision and adaptability in setting curing parameters, leading to significant quality fluctuations within the material, particularly issues such as bubble formation and resin loss. This severely impacts the overall performance and stability of the material. Summary of the Invention

[0004] The purpose of this application is to provide a method and apparatus for heating and curing carbon fiber composite materials, in order to solve the technical problems in the existing heating and curing processes for carbon fiber composite materials, which are characterized by the lack of precision and adaptability in the setting of curing parameters, resulting in the inability to effectively control the generation of material bubbles and resin loss.

[0005] In view of the above problems, this application provides a method and apparatus for heat curing carbon fiber composite materials.

[0006] In a first aspect, this application provides a method for heat curing carbon fiber composite materials, implemented using a heat curing device for carbon fiber composite materials, comprising: laying up the carbon fiber composite material to be heat cured and obtaining a curing parameter space for heat curing, wherein the curing parameters include pressure parameters and heating rate parameters; randomly generating first curing parameters within the curing parameter space, performing multiple heat curing bubble simulations and resin loss simulations to obtain multiple first bubble parameters and first resin loss parameters, and generating a first bubble diagram and a first resin loss diagram; performing bubble size analysis and resin loss size analysis based on the first bubble diagram and the first resin loss diagram to obtain bubble size information and resin loss size information, and performing bubble reliability analysis and resin loss reliability analysis to obtain bubble reliability and resin loss reliability, and calculating a first curing fitness of the first curing parameters; further optimizing the curing parameters based on the first curing fitness to obtain the optimal curing parameters with the highest curing fitness, and performing heat curing of the carbon fiber composite material.

[0007] Secondly, this application also provides a heat curing apparatus for carbon fiber composite materials, used to perform a heat curing method for carbon fiber composite materials as described in the first aspect, comprising: a curing parameter space acquisition module, used to lay up the carbon fiber composite material to be heat cured and acquire a curing parameter space for heat curing, wherein the curing parameters include pressure parameters and heating rate parameters; a simulated heat curing module, used to randomly generate first curing parameters within the curing parameter space, perform multiple simulated bubble generation and resin loss generation for heat curing, obtain multiple first bubble parameters and first resin loss parameters, and generate a first bubble diagram and a first resin loss diagram; a curing fitness calculation module, used to perform bubble size analysis and resin loss size analysis based on the first bubble diagram and the first resin loss diagram, obtain bubble size information and resin loss size information, and perform bubble confidence analysis and resin loss confidence analysis, obtain bubble confidence and resin loss confidence, and calculate a first curing fitness of the first curing parameters; and a curing parameter optimization module, used to further optimize the curing parameters based on the first curing fitness, obtain the optimal curing parameters with the largest curing fitness, and perform heat curing of the carbon fiber composite material.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] By randomly generating first curing parameters within the curing parameter space, and simulating bubble generation and resin loss during multiple heating and curing processes based on these first curing parameters, multiple first bubble parameters and first resin loss parameters are obtained. These first bubble parameters and first resin loss parameters are then used as pixel values ​​to construct first bubble maps and first resin loss maps. Next, bubble size analysis and resin loss size analysis are performed based on these first bubble maps and first resin loss maps to obtain bubble size information and resin loss size information, respectively. Furthermore, bubble reliability analysis and resin loss reliability analysis are performed to obtain bubble reliability and resin loss reliability. Based on the bubble size information, resin loss size information, bubble reliability, and resin loss reliability, the first curing fitness of the first curing parameters is calculated. Finally, the curing parameters are further optimized based on the first curing fitness to obtain the optimal curing parameters with the highest curing fitness. Heating and curing of carbon fiber composite materials are then performed based on these optimal curing parameters. This approach improves the scientific, rational, and accurate setting of curing parameters, effectively reduces material bubble generation and resin loss during the curing process, and significantly improves the curing quality of carbon fiber composite materials.

[0010] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0012] Figure 1 is a schematic flowchart of a heat curing method for carbon fiber composite material according to this application;

[0013] Figure 2 is a schematic diagram of the process for generating the first bubble map and the first resin loss map in a heat curing method for carbon fiber composite material according to this application;

[0014] Figure 3 is a schematic diagram of the structure of a heating and curing device for carbon fiber composite materials according to this application.

[0015] Explanation of reference numerals in the attached figures:

[0016] Curing parameter space acquisition module 11, simulated heating and curing module 12, curing adaptability calculation module 13, curing parameter optimization module 14. Detailed Implementation

[0017] This application provides a method and apparatus for heat curing carbon fiber composite materials, solving the technical problem in existing heat curing processes for carbon fiber composite materials where the lack of precision and adaptability in curing parameter settings leads to the inability to effectively control material bubble formation and resin loss. It improves the scientific, rational, and precise nature of curing parameter settings, effectively reducing material bubble formation and resin loss during the curing process, thus significantly improving the curing quality of carbon fiber composite materials.

[0018] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0019] Example 1, please refer to Figure 1. This application provides a method for heat curing carbon fiber composite materials, applied to a heat curing device for carbon fiber composite materials, specifically including the following steps:

[0020] S100: Lay up the carbon fiber composite material to be heat-cured and obtain the curing parameter space for heat curing, wherein the curing parameters include pressure parameters and heating rate parameters.

[0021] Specifically, carbon fiber composite layup refers to the process of laying pre-impregnated carbon fiber material layer by layer onto a mold or other substrate according to specific design requirements during the manufacturing of carbon fiber composites. The purpose of this process is to ultimately form a composite material structure with the required shape and strength through the superposition of multiple layers of carbon fiber material. First, the carbon fiber composite material to be heat-cured is laid up, that is, according to the design structural parameters of the carbon fiber composite material (such as the number of layers, thickness, fiber orientation, etc.), the carbon fiber composite material to be heat-cured is laid up. During the layup process, it is ensured that each layer of carbon fiber prepreg (pre-impregnated fiber material) is evenly arranged, avoiding wrinkles, gaps or fiber misalignment, so as to ensure the mechanical properties of the final material.

[0022] Next, based on the design structural parameters of the carbon fiber composite material, curing parameters for similar carbon fiber composite materials are indexed for heat curing. These curing parameters include pressure parameters and heating rate parameters. Pressure parameters refer to the pressure applied to the composite material during heat curing, used to expel air bubbles and pores, enhancing density. Excessive curing pressure may lead to resin loss, while insufficient pressure may result in incomplete gas expulsion and the formation of air bubbles. Heating rate parameters refer to the rate at which the temperature rises from the initial temperature to the curing temperature. The heating rate directly affects the curing quality of the material; too fast or too slow a rate will affect the final material properties. For example, an excessively fast heating rate can cause the resin to soften and flow rapidly, potentially exacerbating resin loss, especially in components with complex geometries. Rapid heating may also prevent the resin from expelling air bubbles sufficiently, causing internal defects in the material. Multiple pressure parameters and multiple heating rate parameters are obtained, and a pressure parameter space is constructed based on the multiple pressure parameters, and a heating rate parameter space is constructed based on the multiple heating rate parameters. Finally, the curing parameter space is obtained by combining the pressure parameter space and the heating rate parameter space. By constructing a curing parameter space by indexing the curing parameters of similar materials, the scientificity and rationality of the curing parameter space setting can be improved, providing support for subsequent curing parameter optimization analysis. At the same time, it reduces blind parameter optimization and improves the efficiency and accuracy of curing parameter optimization analysis.

[0023] S200: Randomly generate first curing parameters within the curing parameter space, perform multiple heating and curing bubble simulations and resin loss simulations to obtain multiple first bubble parameters and first resin loss parameters, and generate a first bubble diagram and a first resin loss diagram.

[0024] Specifically, within the curing parameter space, any pressure parameter and any heating rate parameter are randomly selected and combined to generate the first curing parameter. On the other hand, a bubble simulator and a resin loss simulator are constructed based on a BP neural network to simulate heating and curing. The bubble simulator is used to predict bubbles based on the curing parameters; the input data is the curing parameters, and the output data is the bubble parameter, i.e., the predicted number of bubbles. The resin loss simulator is used to predict resin loss based on the curing parameters; the input data is the curing parameters, and the output data is the resin loss parameter, i.e., the proportion of resin loss by weight. A sample dataset is obtained to supervise the training of the bubble simulator and the resin loss simulator, resulting in bubble simulators and resin loss simulators that meet the expected convergence conditions. Then, the first curing parameter is input into the trained bubble simulator and resin loss simulator to perform multiple heating and curing bubble simulations and resin loss simulations, outputting multiple first bubble parameters (number of first bubbles) and multiple first resin loss parameters (proportion of first resin loss by weight).

[0025] On the other hand, a first bubble map is constructed using multiple first bubble parameters as pixel values. Each pixel in the bubble map corresponds to a bubble generation result, and the pixel value represents the number of generated bubbles. Similarly, a first resin loss map is constructed using multiple first resin loss parameters as pixel values. Each pixel in the resin loss map represents a resin loss result, and the pixel value represents the proportion of resin loss by weight. By constructing the first bubble map and the first resin loss map, the influence of different curing parameters on bubble generation and resin loss can be observed intuitively, thereby improving the efficiency of subsequent bubble size analysis and resin loss size analysis.

[0026] S300: Based on the first bubble diagram and the first resin loss diagram, perform bubble size analysis and resin loss size analysis to obtain bubble size information and resin loss size information, and perform bubble reliability analysis and resin loss reliability analysis to obtain bubble reliability and resin loss reliability, and calculate the first curing adaptability of the first curing parameter.

[0027] Specifically, bubble size analysis is performed based on the first bubble chart, that is, the average value of multiple first bubble parameters (number of first bubbles) within the first bubble chart is calculated to obtain a first average bubble parameter, and the first average bubble parameter is used as bubble size information. On the other hand, resin loss scale analysis is performed based on the first resin loss chart, that is, the average value of multiple first resin loss parameters (first resin loss weight ratio) within the first resin loss chart is calculated, and the first average resin loss parameter is used as resin loss scale information.

[0028] On the other hand, bubble reliability analysis and resin loss reliability analysis are performed based on the first bubble diagram and the first resin loss diagram. Bubble generation and resin loss may be accidental due to improper material handling, such as improper pre-impregnation of carbon fiber composites, uneven fiber layer arrangement, or uneven resin distribution. Therefore, reliability analysis can effectively distinguish between accidental issues and curing parameter setting problems. First, bubble reliability analysis is performed based on the first bubble diagram, i.e., calculating the parameter deviation of each bubble parameter from other bubble parameters, determining multiple bubble parameter deviations, and averaging the multiple bubble parameter deviations. The reciprocal of the average calculation result is set as the bubble reliability. A larger bubble parameter deviation indicates a greater randomness in bubble generation under the first curing parameter, resulting in a lower bubble reliability; a smaller bubble parameter deviation indicates a higher probability of bubble generation under the first curing parameter, resulting in a higher bubble reliability. Then, based on the same method used to obtain the bubble reliability, resin loss reliability analysis is performed based on the first resin loss diagram, i.e., calculating the parameter deviation of each resin loss parameter from other resin loss parameters, averaging the multiple resin loss parameter deviations, and setting the reciprocal of the average calculation result as the resin loss reliability.

[0029] Construct a function to calculate the fitness of the solidification process: In the curing fitness calculation function, HCA represents the curing fitness, and K... p Let P represent the bubble confidence level, and P represent the bubble size information. y To preset bubble size information, K s S represents the confidence level of resin loss, and S represents the scale of resin loss. y This is to preset the amount of resin loss.

[0030] Using the curing fitness calculation function, the curing fitness of the first curing parameter is calculated based on the bubble size information, resin loss size information, bubble confidence, and resin loss confidence. The first curing fitness is output, where a higher curing fitness indicates a better curing effect of the composite material. By constructing the curing fitness calculation function to calculate the curing fitness, the curing effect of the curing parameters can be accurately evaluated, providing a basis for subsequent optimization analysis of the curing parameters.

[0031] S400: Based on the first curing adaptability, continue to optimize the curing parameters to obtain the optimal curing parameters with the greatest curing adaptability, and perform heat curing of carbon fiber composite materials.

[0032] Specifically, a second curing parameter is randomly selected within the curing parameter space, wherein the second curing parameter is different from the first curing parameter. Using the same method as described above for obtaining the first curing fitness, the second curing fitness of the second curing parameter is calculated. The same method is used to continue iterative selection and curing fitness calculation within the curing parameter space until a predetermined number of selections is met, at which point selection stops, resulting in multiple curing fitnesss for multiple curing parameters. Finally, the curing parameter with the highest curing fitness is output as the optimal curing parameter, and the carbon fiber composite material is heated and cured according to the optimal curing parameter.

[0033] The described heating and curing method for carbon fiber composite materials is applied to a heating and curing device for carbon fiber composite materials. It can solve the technical problems in existing heating and curing processes for carbon fiber composite materials, where the lack of precision and adaptability in curing parameter settings leads to the inability to effectively control the generation of material bubbles and resin loss. By randomly generating first curing parameters within the curing parameter space, and simulating bubble generation and resin loss during multiple heating and curing processes based on these first curing parameters, multiple first bubble parameters and first resin loss parameters are obtained. These first bubble parameters and first resin loss parameters are then used as pixel values ​​to construct first bubble maps and first resin loss maps. Next, bubble size analysis and resin loss size analysis are performed based on these first bubble maps and first resin loss maps to obtain bubble size information and resin loss size information, respectively. Furthermore, bubble reliability analysis and resin loss reliability analysis are performed to obtain bubble reliability and resin loss reliability. Based on the bubble size information, resin loss size information, bubble reliability, and resin loss reliability, the first curing fitness of the first curing parameters is calculated. Finally, the curing parameters are further optimized based on the first curing fitness to obtain the optimal curing parameters with the highest curing fitness. Heating and curing of carbon fiber composite materials are then performed based on these optimal curing parameters. This approach improves the scientific, rational, and accurate setting of curing parameters, effectively reduces material bubble generation and resin loss during the curing process, and significantly improves the curing quality of carbon fiber composite materials.

[0034] Furthermore, this application includes laying up the carbon fiber composite material to be heat-cured and obtaining the curing parameter space for heat curing, comprising:

[0035] According to the structural parameters of the carbon fiber composite material, the carbon fiber composite material to be heat-cured is laid up; based on the structural parameters, the pressure parameter space and heating rate parameter space of similar carbon fiber composite materials for heat curing are indexed and combined to obtain the curing parameter space.

[0036] Specifically, firstly, the structural parameters of the carbon fiber composite material are obtained, including the number of layers, thickness, and fiber orientation, which can be determined through design drawings or material design requirements. Then, based on the structural parameters, the carbon fiber composite material to be heat-cured is laid up, and during the layup process, it is ensured that each layer of carbon fiber prepreg (pre-impregnated resin fiber material) is evenly arranged to avoid wrinkles, gaps, or fiber misalignment, so as to ensure the mechanical properties of the final material.

[0037] On the other hand, based on the structural parameters, a parameter matching database is invoked to index the curing parameters of similar carbon fiber composite materials for heat curing. This database contains various carbon fiber composite materials, storing structural parameters and corresponding curing process parameters for different materials. The curing parameters include pressure parameters and heating rate parameters. Pressure parameters refer to the pressure applied to the composite material during heat curing, used to expel air bubbles and pores, enhancing density. Heating rate parameters refer to the rate at which the temperature rises from the initial temperature to the curing temperature; the heating rate directly affects the curing quality of the material, as too fast or too slow heating will affect the final material performance. Multiple pressure parameters and multiple heating rate parameters of similar carbon fiber composite materials are retrieved, and a pressure parameter space is constructed based on the multiple pressure parameters, and a heating rate parameter space is constructed based on the multiple heating rate parameters. Finally, the curing parameter space is obtained by combining the pressure parameter space and the heating rate parameter space.

[0038] By calling the parameter matching database and indexing the curing parameters of similar materials to construct a curing parameter space, the scientificity and rationality of the curing parameter space setting can be improved, providing support for subsequent curing parameter optimization analysis. At the same time, it can avoid blindly seeking parameter optimization and improve the efficiency and accuracy of curing parameter optimization analysis.

[0039] Furthermore, as shown in Figure 2, a first curing parameter is randomly generated within the curing parameter space, and multiple heating and curing processes are simulated to generate bubbles and resin loss. This application includes:

[0040] A bubble simulator and a resin loss simulator for pre-training heat curing simulation are used, wherein the bubble simulator and the resin loss simulator each include multiple bubble simulation branches and multiple resin loss simulation branches. First curing parameters are randomly generated within the curing parameter space. These first curing parameters are then input into the multiple bubble simulation branches and the multiple resin loss simulation branches, and multiple heat curing bubble simulations and resin loss simulations are performed to obtain multiple first bubble parameters and first resin loss parameters. These multiple first bubble parameters and first resin loss parameters are used as pixels to construct and generate a first bubble map and a first resin loss map.

[0041] Specifically, a backpropagation (BP) neural network is a type of feedforward neural network capable of learning complex nonlinear relationships through training data, and is widely used in classification, regression, and other fields. A bubble simulator and a resin loss simulator are constructed based on the BP neural network for simulating heat curing. These simulators are iteratively optimized neural network models used in machine learning, obtained through supervised training with sample data. The bubble simulator includes multiple bubble simulation branches, which predict bubbles based on curing parameters. The input data is the curing parameters, and the output data is the bubble parameter, i.e., the predicted number of bubbles. The resin loss simulator includes multiple resin loss simulation branches, which predict resin loss based on curing parameters. The input data is the curing parameters, and the output data is the resin loss parameter, i.e., the proportion of resin lost by weight.

[0042] Next, any pressure parameter and any heating rate parameter are randomly selected and combined within the curing parameter space to obtain the first curing parameter. Then, the first curing parameter is input into the multiple bubble simulation branches and multiple resin loss simulation branches, respectively, to simulate bubble generation and resin loss through multiple heating and curing processes, resulting in multiple first bubble parameters and multiple first resin loss parameters. The first bubble parameter represents the number of first bubbles, and the first resin loss parameter represents the weight percentage of first resin loss. The multiple first bubble parameters may be the same or different. Then, using the multiple first bubble parameters as pixel values, a first bubble map is constructed, where each pixel in the bubble map corresponds to a bubble generation result, and the pixel value represents the number of generated bubbles. Similarly, using the multiple first resin loss parameters as pixel values, a first resin loss map is constructed, where each pixel in the resin loss map represents a resin loss result, and the pixel value represents the weight percentage of resin loss. By constructing the first bubble map and the first resin loss map, the influence of different curing parameters on bubble generation and resin loss can be observed intuitively, thereby improving the efficiency of subsequent bubble size analysis and resin loss size analysis.

[0043] Furthermore, this application includes a pre-trained bubble simulator and a resin loss simulator for simulating heat curing, which are further included in the following:

[0044] Based on historical heat curing data of similar carbon fiber composites, a set of sample curing parameters was collected, and the number of bubbles and the proportion of resin loss weight in the cured carbon fiber composites under different sample curing parameters were collected to obtain a set of sample bubble parameters and a set of sample resin loss parameters. The set of sample curing parameters and the set of sample bubble parameters were combined and divided to obtain multiple sets of bubble simulation training data. Using the multiple sets of bubble simulation training data, supervised training was conducted to obtain multiple bubble simulation branches to obtain a bubble simulator. The set of sample curing parameters and the set of sample resin loss parameters were combined and divided, and supervised training was conducted to obtain multiple resin loss simulation branches to obtain a resin loss simulator.

[0045] Specifically, firstly, curing parameters are extracted based on historical heating and curing data of similar carbon fiber composites to obtain a set of sample curing parameters; then, the number of bubbles and the proportion of resin loss in the cured carbon fiber composites under different sample curing parameters are collected. Under the same sample curing parameters, due to randomness and other reasons, the number of bubbles and the proportion of resin loss may be different after multiple heating and curing processes. Therefore, one sample curing parameter may correspond to multiple different sample bubble parameters. The number of bubbles is used as the bubble parameter, and the proportion of resin loss is used as the resin loss parameter to obtain a set of sample bubble parameters and a set of sample resin loss parameters.

[0046] Then, the sample solidification parameter set and the sample bubble parameter set are combined, with one sample solidification parameter corresponding to multiple sample bubble parameters. The combined result is then divided equally to obtain multiple sets of bubble simulation training data. Each set of bubble simulation training data contains the same number of data points, but the specific data points are different. Next, using the sample solidification parameter as input and the sample bubble parameters as supervision, the multiple sets of bubble simulation training data are used to supervise the training of multiple bubble simulation branches. The deviation between the model's prediction results and the sample data is calculated using a loss function, and the backpropagation algorithm is used to adjust the model's weights to reduce errors during training. Multiple convergent bubble simulation branches that meet the preset convergence conditions are obtained, and a bubble simulator is constructed based on the multiple bubble simulation branches.

[0047] On the other hand, the sample solidification parameter set and the sample resin loss parameter set are combined, with one sample solidification parameter corresponding to multiple sample resin loss parameters. Then, the combination result is divided equally to obtain multiple sets of resin loss simulation training data. Furthermore, the multiple sets of resin loss simulation training data are used to supervise the training of multiple resin loss simulation branches to obtain multiple convergent resin loss simulation branches and construct a resin loss simulator.

[0048] By constructing a bubble simulator based on multiple convergent bubble simulation branches and a resin loss simulator based on multiple convergent resin loss simulation branches, each branch uses different training data. This allows for the analysis of potentially different output bubble parameters based on the input curing parameters. Integrating the outputs of multiple convergent bubble simulation branches yields the final bubble parameters, improving the accuracy of the simulation analysis. Furthermore, the smaller training data required for each bubble simulation branch enhances training convergence efficiency. This approach enables the prediction of the number of bubbles and the proportion of resin loss weight under various scenarios, thereby improving the comprehensiveness and rationality of the obtained first bubble parameters and first resin loss parameters, ultimately enhancing the accuracy of the constructed first bubble diagram and first resin loss diagram.

[0049] Furthermore, based on the first bubble diagram and the first resin loss diagram, bubble size analysis and resin loss size analysis are performed to obtain bubble size information and resin loss size information. This application includes:

[0050] Based on the first bubble diagram, the average value of multiple first bubble parameters within the first bubble diagram is calculated to obtain a first average bubble parameter, which serves as bubble size information; based on the first resin loss diagram, the average value of multiple first resin loss parameters within the first resin loss diagram is calculated to obtain a first average resin loss parameter, which serves as resin loss size information.

[0051] Specifically, bubble size analysis is performed based on the first bubble chart, that is, the average value of multiple first bubble parameters (number of first bubbles) within the first bubble chart is calculated to obtain a first average bubble parameter, which is the average number of bubbles obtained from the analysis, and the first average bubble parameter is used as bubble size information. On the other hand, resin loss scale analysis is performed based on the first resin loss chart, that is, the average value of multiple first resin loss parameters (first resin loss weight ratio) within the first resin loss chart is calculated, and the first average resin loss parameter is used as resin loss scale information.

[0052] Furthermore, bubble reliability analysis and resin loss reliability analysis are performed to obtain bubble reliability and resin loss reliability, and the first curing adaptability of the first curing parameter is calculated. This application includes:

[0053] Within the first bubble map, multiple sets of first bubble parameters are randomly selected multiple times, and the average values ​​of multiple random bubble parameters are calculated. The deviation amplitude between each first bubble parameter and the average value of each random bubble parameter is calculated, and the average deviation value of the bubble parameters is calculated. The number of the average values ​​of the multiple random bubble parameters is the same as the number of the multiple first bubble parameters. Based on the average deviation value of the bubble parameters, bubble reliability is calculated, where the magnitude of the average deviation value of the bubble parameters is negatively correlated with the magnitude of the bubble reliability. Based on the first resin loss map, resin loss reliability is calculated. Based on the bubble size information, bubble reliability, resin loss size information, and resin loss reliability, the first curing adaptability of the first curing parameter is calculated, as follows:

[0054] Where HCA represents curing adaptability, K p Let P represent the bubble confidence level, and P represent the bubble size information. y To preset bubble size information, K s S represents the confidence level of resin loss, and S represents the scale of resin loss. y This is to preset the amount of resin loss.

[0055] Specifically, firstly, multiple sets of first bubble parameters are randomly selected multiple times within the first bubble diagram. Each set of first bubble parameters includes, for example, three first bubble parameters. Then, the average of the multiple sets of first bubble parameters is calculated to obtain multiple random bubble parameter averages, where the number of multiple random bubble parameter averages is the same as the number of first bubble parameters. Next, the deviation between each first bubble parameter and each random bubble parameter average is calculated, for example, the ratio of the difference between each first bubble parameter and each random bubble parameter average to each first bubble parameter, resulting in multiple bubble parameter deviation values. Then, the average of the multiple bubble parameter deviation values ​​is calculated to obtain the bubble parameter deviation average. Further, bubble confidence is calculated based on the bubble parameter deviation average. For example, the reciprocal of the bubble parameter deviation average is used as the bubble confidence. The magnitude of the bubble parameter deviation average is negatively correlated with the bubble confidence; that is, the larger the bubble parameter deviation average, the more random the number of bubbles generated under the first curing parameter may be due to chance, such as occasionally having more bubbles and occasionally having fewer bubbles, and the lower the corresponding bubble confidence.

[0056] Using the same method for calculating the bubble confidence level, the mean deviation of the resin loss parameters is calculated based on the first resin loss map, and the resin loss confidence level is calculated based on the mean deviation of the resin loss parameters. The magnitude of the mean deviation of the resin loss parameters is negatively correlated with the magnitude of the resin loss confidence level. For example, the reciprocal of the mean deviation of the resin loss parameters can be used to obtain the resin loss confidence level.

[0057] Then, construct the solidification fitness calculation function: In the curing fitness calculation function, HCA represents the curing fitness; a higher curing fitness indicates a better curing effect; K p Let P represent the bubble confidence level, and P represent the bubble size information. y The preset bubble size information is a predetermined bubble quantity threshold, which can be set according to material quality requirements, for example, 2; K s S represents the confidence level of resin loss, and S represents the scale of resin loss. y The preset resin loss scale information is a predetermined resin loss weight percentage, for example, 5%.

[0058] By constructing a curing fitness calculation function to calculate the curing fitness, the curing effect of curing parameters can be accurately evaluated, providing a basis for subsequent comparison and evaluation of curing parameters. Higher bubble confidence and resin loss confidence indicate more accurate and reliable bubble size and resin loss scale information in the current analysis and calculation, resulting in higher curing fitness. Conversely, lower bubble size and resin loss scale information indicate better quality of the cured carbon fiber composite material and higher curing fitness.

[0059] Finally, based on the curing fitness calculation function, the first curing fitness of the first curing parameter is calculated according to the bubble size information, bubble confidence, resin loss size information, and resin loss confidence. Further, based on the first curing fitness, the curing parameters are further optimized to obtain the optimal curing parameters with the highest curing fitness. This application includes:

[0060] Continue optimizing the curing parameters within the curing parameter space until convergence; output the curing parameters with the highest curing adaptability during the optimization process to obtain the optimal curing parameters.

[0061] Specifically, the same method is then used to iteratively select curing parameters and calculate curing fitness within the curing parameter space until a predetermined number of selections is met, at which point the selection stops, resulting in multiple curing fitness values ​​for multiple curing parameters. These multiple curing fitness values ​​are compared, and the curing parameter with the highest curing fitness value is selected as the optimal curing parameter. The carbon fiber composite material is then heat-cured according to the optimal curing parameter.

[0062] In summary, the heat curing method for carbon fiber composite materials provided in this application has the following technical effects:

[0063] By randomly generating first curing parameters within the curing parameter space, and simulating bubble generation and resin loss during multiple heating and curing processes based on these first curing parameters, multiple first bubble parameters and first resin loss parameters are obtained. These first bubble parameters and first resin loss parameters are then used as pixel values ​​to construct first bubble maps and first resin loss maps. Next, bubble size analysis and resin loss size analysis are performed based on these first bubble maps and first resin loss maps to obtain bubble size information and resin loss size information, respectively. Furthermore, bubble reliability analysis and resin loss reliability analysis are performed to obtain bubble reliability and resin loss reliability. Based on the bubble size information, resin loss size information, bubble reliability, and resin loss reliability, the first curing fitness of the first curing parameters is calculated. Finally, the curing parameters are further optimized based on the first curing fitness to obtain the optimal curing parameters with the highest curing fitness. Heating and curing of carbon fiber composite materials are then performed based on these optimal curing parameters. This approach improves the scientific, rational, and accurate setting of curing parameters, effectively reduces material bubble generation and resin loss during the curing process, and significantly improves the curing quality of carbon fiber composite materials.

[0064] Example 2: Based on the same inventive concept as the heat curing method for carbon fiber composite materials in the foregoing examples, this application also provides a heat curing apparatus for carbon fiber composite materials, as shown in Figure 3, including:

[0065] The curing parameter space acquisition module 11 is used to lay up the carbon fiber composite material to be heat-cured and acquire the curing parameter space for heat curing, wherein the curing parameters include pressure parameters and heating rate parameters; the simulated heat curing module 12 is used to randomly generate first curing parameters within the curing parameter space, perform multiple heat curing bubble simulation generation and resin loss simulation generation, obtain multiple first bubble parameters and first resin loss parameters, and generate a first bubble diagram and a first resin loss diagram; the curing fitness calculation module 13 is used to perform bubble size analysis and resin loss size analysis based on the first bubble diagram and the first resin loss diagram, obtain bubble size information and resin loss size information, and perform bubble confidence analysis and resin loss confidence analysis, obtain bubble confidence and resin loss confidence, and calculate the first curing fitness of the first curing parameters; the curing parameter optimization module 14 is used to further optimize the curing parameters based on the first curing fitness, obtain the optimal curing parameters with the largest curing fitness, and perform heat curing of the carbon fiber composite material.

[0066] Furthermore, the heating and curing device for the carbon fiber composite material is also used for:

[0067] According to the structural parameters of the carbon fiber composite material, the carbon fiber composite material to be heat-cured is laid up; based on the structural parameters, the pressure parameter space and heating rate parameter space of similar carbon fiber composite materials for heat curing are indexed and combined to obtain the curing parameter space.

[0068] Furthermore, the heating and curing device for the carbon fiber composite material is also used for:

[0069] A bubble simulator and a resin loss simulator for pre-training heat curing simulation are used, wherein the bubble simulator and the resin loss simulator each include multiple bubble simulation branches and multiple resin loss simulation branches. First curing parameters are randomly generated within the curing parameter space. These first curing parameters are then input into the multiple bubble simulation branches and the multiple resin loss simulation branches, and multiple heat curing bubble simulations and resin loss simulations are performed to obtain multiple first bubble parameters and first resin loss parameters. These multiple first bubble parameters and first resin loss parameters are used as pixels to construct and generate a first bubble map and a first resin loss map.

[0070] Furthermore, the heating and curing device for the carbon fiber composite material is also used for:

[0071] Based on historical heat curing data of similar carbon fiber composites, a set of sample curing parameters was collected, and the number of bubbles and the proportion of resin loss weight in the cured carbon fiber composites under different sample curing parameters were collected to obtain a set of sample bubble parameters and a set of sample resin loss parameters. The set of sample curing parameters and the set of sample bubble parameters were combined and divided to obtain multiple sets of bubble simulation training data. Using the multiple sets of bubble simulation training data, supervised training was conducted to obtain multiple bubble simulation branches to obtain a bubble simulator. The set of sample curing parameters and the set of sample resin loss parameters were combined and divided, and supervised training was conducted to obtain multiple resin loss simulation branches to obtain a resin loss simulator.

[0072] Furthermore, the heating and curing device for the carbon fiber composite material is also used for:

[0073] Based on the first bubble diagram, the average value of multiple first bubble parameters within the first bubble diagram is calculated to obtain a first average bubble parameter, which serves as bubble size information; based on the first resin loss diagram, the average value of multiple first resin loss parameters within the first resin loss diagram is calculated to obtain a first average resin loss parameter, which serves as resin loss size information.

[0074] Furthermore, the heating and curing device for carbon fiber composite materials is also used for: randomly selecting multiple sets of first bubble parameters multiple times within the first bubble diagram, calculating the average value of multiple random bubble parameters, calculating the deviation amplitude between each first bubble parameter and the average value of each random bubble parameter, and calculating the average deviation value of bubble parameters, wherein the number of the average values ​​of multiple random bubble parameters is the same as the number of multiple first bubble parameters; calculating bubble reliability based on the average deviation value of bubble parameters, wherein the magnitude of the average deviation value of bubble parameters is negatively correlated with the magnitude of bubble reliability; calculating resin loss reliability based on the first resin loss diagram; and calculating the first curing adaptability of the first curing parameter based on the bubble size information, bubble reliability, resin loss size information, and resin loss reliability, as follows:

[0075] Where HCA represents curing adaptability, K p Let P represent the bubble confidence level, and P represent the bubble size information. y To preset bubble size information, K s S represents the confidence level of resin loss, and S represents the scale of resin loss. y This is to preset the amount of resin loss.

[0076] Furthermore, the heating and curing device for the carbon fiber composite material is also used for:

[0077] Continue optimizing the curing parameters within the curing parameter space until convergence; output the curing parameters with the highest curing adaptability during the optimization process to obtain the optimal curing parameters.

[0078] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The heat curing method and specific examples of carbon fiber composite materials in the foregoing Embodiment 1 are also applicable to the heat curing apparatus for carbon fiber composite materials in this embodiment. Through the foregoing detailed description of the heat curing method for carbon fiber composite materials, those skilled in the art can clearly understand the heat curing apparatus for carbon fiber composite materials in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here. As for the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and relevant parts can be referred to in the method section.

[0079] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0080] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method of heat curing a carbon fiber composite material, characterized by, The method includes: The carbon fiber composite material to be heat-cured is laid up, and the curing parameter space of heat curing is obtained, including pressure parameters and heating rate parameters. Within the curing parameter space, first curing parameters are randomly generated, and multiple heating and curing bubble simulations and resin loss simulations are performed to obtain multiple first bubble parameters and first resin loss parameters, and to generate a first bubble map and a first resin loss map. Based on the first bubble diagram and the first resin loss diagram, bubble size analysis and resin loss size analysis are performed to obtain bubble size information and resin loss size information. Bubble reliability analysis and resin loss reliability analysis are also performed to obtain bubble reliability and resin loss reliability. The first curing adaptability of the first curing parameter is then calculated. Based on the first curing adaptability, the curing parameters are further optimized to obtain the optimal curing parameters with the greatest curing adaptability, and the carbon fiber composite material is then heat-cured.

2. The heating curing method of carbon fiber composite material according to claim 1, characterized in that, Lay up the carbon fiber composite material to be heat-cured and obtain the curing parameter space for heat curing, including: According to the structural parameters of the carbon fiber composite material, the carbon fiber composite material to be heat-cured is laid up; Based on the structural parameters, the pressure parameter space and heating rate parameter space of similar carbon fiber composite materials for heat curing are indexed and combined to obtain the curing parameter space.

3. The heating curing method of carbon fiber composite material according to claim 1, characterized by, Within the curing parameter space, a first curing parameter is randomly generated, and multiple heating and curing processes are simulated to generate bubbles and resin loss, including: The bubble simulator and resin loss simulator are pre-trained to simulate heating and curing, wherein the bubble simulator and resin loss simulator each include multiple bubble simulation branches and multiple resin loss simulation branches. A first curing parameter is randomly generated within the curing parameter space; The first curing parameter is input into the multiple bubble simulation branches and multiple resin loss simulation branches respectively, and multiple heating and curing bubble simulation generation and resin loss simulation generation are performed to obtain multiple first bubble parameters and first resin loss parameters. The first bubble map and the first resin loss map are constructed and generated by using the multiple first bubble parameters and the first resin loss parameters as pixels.

4. The heating curing method of carbon fiber composite material according to claim 3, characterized in that, Pre-trained bubble simulators and resin loss simulators for heat curing simulation include: Based on historical heating and curing data of similar carbon fiber composites, a set of sample curing parameters was collected, and the number of bubbles and the proportion of resin loss weight in the cured carbon fiber composites under different sample curing parameters were collected to obtain a set of sample bubble parameters and a set of sample resin loss parameters. The sample solidification parameter set and the sample bubble parameter set are combined and divided to obtain multiple sets of bubble simulation training data. Using the aforementioned multiple sets of bubble simulation training data, supervised training is performed to obtain multiple bubble simulation branches, thus obtaining a bubble simulator; The sample curing parameter set and the sample resin loss parameter set are combined and divided, and supervised training is used to obtain multiple resin loss simulation branches, thus obtaining a resin loss simulator.

5. The heating curing method of carbon fiber composite material according to claim 1, characterized in that, Based on the first bubble diagram and the first resin loss diagram, bubble size analysis and resin loss size analysis are performed to obtain bubble size information and resin loss size information, including: Based on the first bubble chart, the mean value of multiple first bubble parameters within the first bubble chart is calculated to obtain the first average bubble parameter, which is used as bubble size information; Based on the first resin loss map, the mean value of multiple first resin loss parameters within the first resin loss map is calculated to obtain the first average resin loss parameter, which serves as information on the scale of resin loss.

6. The heating curing method of carbon fiber composite material according to claim 1, characterized in that, Perform bubble confidence analysis and resin loss confidence analysis to obtain bubble confidence and resin loss confidence, and calculate the first curing fitness of the first curing parameter, including: Within the first bubble chart, multiple sets of first bubble parameters are randomly selected multiple times, and the mean values ​​of multiple random bubble parameters are calculated. The deviation magnitude between each first bubble parameter and the mean value of each random bubble parameter is calculated, and the mean deviation value of the bubble parameters is calculated. The number of the multiple random bubble parameter mean values ​​is the same as the number of the multiple first bubble parameters. The bubble reliability is calculated based on the mean deviation of the bubble parameters, wherein the magnitude of the mean deviation of the bubble parameters is negatively correlated with the magnitude of the bubble reliability. Based on the first resin loss map, the resin loss confidence level is calculated. According to the bubble size information, the bubble reliability, the resin loss size information, and the resin loss reliability, a first curing fitness of a first curing parameter is calculated as follows: wherein HCA is a cure fitness, K p is a bubble credibility, P is a bubble size information, P y is a preset bubble size information, K s is a resin loss credibility, S is a resin loss size information, S y is a preset resin loss size information.

7. The heating curing method of carbon fiber composite material according to claim 1, characterized in that, Based on the first curing adaptability, the curing parameters are further optimized to obtain the optimal curing parameters with the highest curing adaptability, including: Continue optimizing the curing parameters within the curing parameter space until convergence; The optimal curing parameters are obtained by outputting the curing parameters that maximize the curing adaptability during the optimization process.

8. A heating and curing apparatus for carbon fiber composites, characterized by, The apparatus for implementing the heat curing method for a carbon fiber composite material according to any one of claims 1 to 7, the apparatus comprising: The curing parameter space acquisition module is used to lay up the carbon fiber composite material to be heat-cured and acquire the curing parameter space for heat curing, wherein the curing parameters include pressure parameters and heating rate parameters. The simulated heating and curing module is used to randomly generate first curing parameters within the curing parameter space, perform multiple heating and curing bubble simulations and resin loss simulations, obtain multiple first bubble parameters and first resin loss parameters, and generate a first bubble map and a first resin loss map. The curing fitness calculation module is used to perform bubble size analysis and resin loss size analysis based on the first bubble diagram and the first resin loss diagram to obtain bubble size information and resin loss size information, as well as to perform bubble confidence analysis and resin loss confidence analysis to obtain bubble confidence and resin loss confidence, and calculate the first curing fitness of the first curing parameter. The curing parameter optimization module is used to further optimize the curing parameters based on the first curing adaptability to obtain the optimal curing parameters with the greatest curing adaptability for heating and curing carbon fiber composite materials.

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