A temperature control method and feedback system for autoclaves
By acquiring temperature and parameter data of hot-pressed material products, calculating variation factors and dispersion coefficients, and using temperature data to predict weights to adjust hot-pressing parameters, the problem of uneven temperature during the hot-press molding process was solved, achieving uniform heat distribution and high-quality curing of composite materials.
Patent Information
- Application Number
- CN202510981442.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-16
- Publication Date
- 2026-01-27
- Estimated Expiration
- 2045-07-16
AI Technical Summary
During the autoclave molding process, uneven surface temperature of composite products leads to inconsistent porosity, affecting the quality and performance of the finished product. In particular, under complex geometric structures and differences in material thermal conductivity, local heat flow is blocked or heat accumulates, resulting in significant temperature gradient differences.
By acquiring temperature and parameter data of hot-pressed material products at different hot-pressing stages, calculating variation factors and discreteness coefficients, using temperature data to predict weights to adjust hot-pressing parameters, and employing the STL time-series decomposition algorithm for temperature control, uniform heat distribution is achieved.
It improves the uniformity of surface temperature of hot-pressed materials, reduces the risk of local temperature anomalies, ensures the curing quality and consistency of composite materials, and enhances product performance.
Smart Images
Figure CN120848631B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of temperature control technology, and specifically to a temperature control method and feedback system for autoclaves. Background Technology
[0002] In the aerospace industry, autoclave molding technology, characterized by stable product quality and low porosity, is one of the most widely used methods in the manufacture of composite material components. Its main process includes: spreading the cut prepreg of the composite material onto a pre-prepared special mold; trimming and compacting the material; sealing the mold with a vacuum bag; and finally placing it into an autoclave. The autoclave process involves heating, pressurizing, maintaining the temperature and pressure, and then cooling and depressurizing to complete the hot pressing, thereby solidifying and shaping the material structure. Autoclave molding technology solidifies composite materials under strictly controlled temperature and pressure conditions, resulting in stable product quality, low porosity, and excellent mechanical properties. It is particularly suitable for manufacturing high-performance components with complex geometries.
[0003] The design of the mold structure within the autoclave has a significant impact on temperature distribution. Complex geometries, differences in material thermal conductivity, and internal structural inhomogeneities often lead to uneven temperature distribution on the mold surface. This is especially true for composite materials, which generally suffer from poor performance repeatability and large data dispersion depending on the batch. Slight fluctuations in raw materials and the working environment can easily cause localized heat flow obstruction or heat accumulation at sharp corners, internal cavities, and uneven thicknesses of the mold, resulting in underheating or overheating in certain areas. Performance differences can further exacerbate temperature gradients. Furthermore, the temperature distribution on the mold surface within the autoclave is also affected by airflow and heat transfer. Complex mold structures can hinder uniform hot air flow, creating eddies or dead zones in certain areas, reducing local convective heat transfer efficiency. In actual autoclave processing of composite materials, different stage parameter settings, such as the duration and rate of heating and pressurization, will cause varying degrees of temperature changes during the material heating process. This results in uneven surface temperature of the hot-pressed material, leading to varying degrees of porosity in the finished product and affecting its quality. Summary of the Invention
[0004] To address the problem of uneven temperature distribution on the surface of hot-pressed materials due to inappropriate parameter settings in existing methods, the present invention aims to provide a temperature control method and feedback system for autoclaves. The specific technical solution adopted is as follows:
[0005] In a first aspect, the present invention provides a temperature control method for an autoclave, the method comprising the following steps:
[0006] Acquire temperature data and various hot-pressing parameter data for different hot-pressed material products at different hot-pressing stages;
[0007] Based on the temperature variation characteristics of different regions of each hot-pressed material product in the same hot-pressing stage, the variation factor of each hot-pressed material product in each hot-pressing stage is obtained; combined with the variation trend of the variation factor of the same hot-pressed material product in adjacent hot-pressing stages and the similarity between the hot-pressing parameter data and the standard parameter data, the discrete state coefficient of each hot-pressed material product in each hot-pressing stage is determined.
[0008] Based on the overall difference between the discrete condition coefficients of each hot-pressing material product and other types of hot-pressing material products in all hot-pressing stages, the temperature data prediction weight of each hot-pressing material product is obtained; the hot-pressing parameters of different hot-pressing stages are adjusted using the temperature data prediction weight.
[0009] Preferably, obtaining the variation factor of each hot-pressed material product in each hot-pressing stage based on the variation characteristics of temperature data in different regions of each hot-pressed material product during the same hot-pressing stage includes:
[0010] For any hot-pressed material product:
[0011] Calculate the sum of the differences in temperature data between two adjacent moments in each region of any hot-pressed material product during the candidate stage, and record it as the temperature change characteristic value of each region during the candidate stage;
[0012] Based on the degree of dispersion of the temperature change characteristic values of all regions of any hot-pressed material product in the candidate stage, the variation factor of any hot-pressed material product in the candidate stage is obtained, and the degree of dispersion is negatively correlated with the variation factor.
[0013] The candidate stage is any hot pressing stage.
[0014] Preferably, obtaining the variation factor of any hot-pressed material product in the candidate stage based on the dispersion of temperature change characteristic values in all regions of the hot-pressed material product includes:
[0015] Calculate the variance of the temperature change characteristic values of all regions of any hot-pressed material product, whereby the variance is used to characterize the degree of dispersion of the temperature change characteristic values of all regions of any hot-pressed material product.
[0016] The negative correlation normalization result between the variance and the number of regions of any hot-pressed material product is determined as the variation factor of any hot-pressed material product in the candidate stage.
[0017] Preferably, the step of determining the dispersion coefficient of each hot-pressing material product in each hot-pressing stage by combining the changing trends of the variation factors of the same hot-pressing material product in adjacent hot-pressing stages and the similarity between the hot-pressing parameter data and the standard parameter data includes:
[0018] For any type of hot-pressed material product:
[0019] Based on the average value of the variation factor of any hot-pressed material product in the candidate stage and the similarity between the hot-pressing parameter data and the standard parameter data of any hot-pressed material product in the candidate stage, the dispersion coefficient of any hot-pressed material product in the candidate stage is calculated. The similarity is positively correlated with the dispersion coefficient, and the average value of the variation is negatively correlated with the dispersion coefficient.
[0020] Wherein, the change value of the variation factor of any hot-pressed material product in the candidate stage is the difference between the variation factor of the previous stage and the variation factor of the hot-pressed material product in the candidate stage.
[0021] Preferably, the acquisition of the similarity between the hot-pressing parameter data and standard parameter data of any hot-pressing material product in the candidate stage includes:
[0022] The cosine similarity between the hot-pressing parameter vector of any hot-pressing material product in the candidate stage and the corresponding standard parameter vector is determined as the similarity between the hot-pressing parameter data and the standard parameter data of any hot-pressing material product in the candidate stage.
[0023] The hot-pressing parameter vector of any hot-pressing material product in the candidate stage is composed of all the hot-pressing parameter data of the hot-pressing material product in the candidate stage, and the standard parameter vector is composed of standard hot-pressing parameter data.
[0024] Preferably, the step of obtaining the temperature data prediction weight for each hot-pressed material product based on the overall difference between the dispersion coefficients of each hot-pressed material product and other types of hot-pressed material products at all hot-pressing stages includes:
[0025] Calculate the average difference between the discrete condition coefficients of each hot-pressed material product across all adjacent hot-pressing stages;
[0026] The average difference between the average difference of the hot-pressed material product of the type to be analyzed and the average difference of the hot-pressed material product of all other types is calculated and denoted as the first average value.
[0027] The temperature data prediction weight of the hot-pressed material product to be analyzed is determined based on the first average value, and the first average value is negatively correlated with the temperature data prediction weight.
[0028] The hot-pressed material product to be analyzed can be any type of hot-pressed material product.
[0029] Preferably, determining the temperature data prediction weights for the type of hot-pressed material product to be analyzed based on the first average value includes:
[0030] The value of the exponential function, with the natural constant as the base and the negative first average value as the exponent, is determined as the prediction weight for the temperature data of the hot-pressed material products to be analyzed.
[0031] Preferably, the step of adjusting the hot-pressing parameters for different hot-pressing stages using temperature data prediction weights includes:
[0032] The STL time-series decomposition algorithm was used to process the temperature data of each hot-pressed material product at all hot-pressing stages to extract time-series decomposition parameters;
[0033] The corresponding time-series decomposition parameters are corrected using the predicted weights based on the temperature data, and the hot-pressing parameters for different hot-pressing stages are adjusted based on the corrected results.
[0034] Preferably, the time-series decomposition parameters are corrected using the temperature data prediction weights, including:
[0035] The product of the temperature data prediction weights and the corresponding time series decomposition parameters is determined as the corrected time series decomposition parameters.
[0036] Secondly, the present invention provides a feedback system for temperature control of an autoclave, the system comprising:
[0037] The data acquisition module is used to acquire temperature data and various hot pressing parameter data of different hot pressing materials at different hot pressing stages;
[0038] The data processing module is used to obtain the variation factor of each hot-pressed material product in each hot-pressing stage based on the variation characteristics of temperature data in different regions of each hot-pressed material product in the same hot-pressing stage; and to determine the discrete state coefficient of each hot-pressed material product in each hot-pressing stage by combining the variation trend of the variation factor of the same hot-pressed material product in adjacent hot-pressing stages and the similarity between the hot-pressing parameter data and the standard parameter data.
[0039] The adjustment module is used to obtain the temperature data prediction weight for each hot-pressed material product based on the overall difference between the discrete condition coefficients of each hot-pressed material product and other types of hot-pressed material products in all hot-pressing stages; and to adjust the hot-pressing parameters for different hot-pressing stages using the temperature data prediction weight.
[0040] The present invention has at least the following beneficial effects:
[0041] This invention first acquires temperature data for various hot-pressed material products at each hot-pressing stage. Considering that the temperature changes at different locations of the same hot-pressed material product may differ, possibly due to the influence of the mold shape, the variation factor of the hot-pressed material product at each hot-pressing stage is determined based on the temperature data variation characteristics of different regions of each hot-pressed material product at the same hot-pressing stage. Since the hot-pressing parameters for the same hot-pressed material product are the same at the same hot-pressing stage, but may differ from the standard parameters, the variation trend of the variation factor of the same hot-pressed material product in adjacent hot-pressing stages and the similarity between the hot-pressing parameters and the standard parameters are considered to evaluate the data dispersion of each hot-pressed material product at each hot-pressing stage, obtaining a dispersion coefficient. Furthermore, based on the overall difference between the dispersion coefficients of each hot-pressed material product and other types of hot-pressed material products at all hot-pressing stages, a temperature data prediction weight for each hot-pressed material product is obtained. The temperature data prediction weight is used to adjust the hot-pressing parameters at different hot-pressing stages, promoting uniform heat distribution, improving the surface temperature uniformity of the hot-pressed material product during hot-pressing, and ensuring the quality and consistency of the composite material curing. The method provided by this invention can not only significantly reduce the risk of defects caused by local temperature anomalies, but also improve product performance, providing a strong guarantee for the molding of high-quality composite materials. Attached Figure Description
[0042] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention 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 only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0043] Figure 1 A flowchart of a temperature control method for an autoclave provided in an embodiment of the present invention;
[0044] Figure 2 This is a structural block diagram of a feedback system for temperature control of an autoclave, provided in an embodiment of the present invention. Detailed Implementation
[0045] To further illustrate the technical means and effects adopted by the present invention to achieve the intended purpose, the following detailed description, in conjunction with the accompanying drawings and preferred embodiments, describes a temperature control method and feedback system for an autoclave according to the present invention.
[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.
[0047] The following detailed description, in conjunction with the accompanying drawings, illustrates a specific scheme for a temperature control method and feedback system for an autoclave provided by the present invention.
[0048] An embodiment of a temperature control method for autoclaves:
[0049] The specific scenario addressed in this embodiment is as follows: During the hot pressing process of hot pressing materials, there may be situations where the hot pressing parameters are not set appropriately. In this embodiment, different initial hot pressing parameters are first set for each hot pressing stage, and different types of hot pressing material products are hot pressed. Based on their hot pressing conditions, the hot pressing parameters are adjusted to ensure the subsequent hot pressing effect and improve the product qualification rate.
[0050] This embodiment proposes a temperature control method for autoclaves, such as... Figure 1 As shown, a temperature control method and feedback system for an autoclave according to this embodiment includes the following steps:
[0051] Step S1: Obtain temperature data and various hot pressing parameter data for different hot pressing material products at different hot pressing stages.
[0052] First, set the initial hot-pressing parameters for each hot-pressing stage. It should be noted that the initial hot-pressing parameters are the same for the same hot-pressing material product at the same hot-pressing stage. The implementer sets these initial hot-pressing parameters based on experience. Initial hot-pressing parameters include temperature and pressure, which can be set according to specific circumstances in practical applications.
[0053] Temperature sensors (such as thermocouples, infrared thermal imagers, or embedded sensors) are placed at different locations on the mold, covering key areas such as sharp corners, edges, and thickness transition zones. These sensors collect real-time temperature data at their respective locations. In this embodiment, multiple temperature sensors are installed on the mold to collect temperature data for each hot-pressed material product at different stages of the hot-pressing process. Simultaneously, the hot-pressing parameter data for each type of hot-pressed material product at each stage is recorded, i.e., the initial hot-pressing parameters.
[0054] Thus, this embodiment has collected temperature data and hot pressing parameter data for each hot-pressed material product at each hot-pressing stage during the hot-pressing process.
[0055] Step S2: Based on the temperature data variation characteristics of different regions of each hot-pressed material product in the same hot-pressing stage, obtain the variation factor of each hot-pressed material product in each hot-pressing stage; combine the variation trend of the variation factor of the same hot-pressed material product in adjacent hot-pressing stages and the similarity between the hot-pressing parameter data and the standard parameter data, determine the discrete state coefficient of each hot-pressed material product in each hot-pressing stage.
[0056] Due to the inherent variability in the hot pressing process of composite materials, in the current scenario, the variability of the hot pressing stage is mainly reflected in the changes in the hot pressing condition parameters of each stage. This will further lead to a certain degree of change in the temperature data of multiple locations of the composite material in subsequent hot pressing stages. This type of change is more pronounced in composite material products with complex shapes.
[0057] In this embodiment, each hot-pressed material product is divided into a preset number of vertical regions. The temperature data variation in different regions may be affected by the shape; that is, the greater the difference in temperature data variation at different locations, the more the temperature change is location-dependent, meaning that the location is more affected by the shape. Next, this embodiment will evaluate the variation of each hot-pressed material product at each stage based on the temperature data variation characteristics of different regions of each hot-pressed material product during the same hot-pressing stage, and obtain the corresponding variation factor. In this embodiment, the preset number is 6, but in specific applications, the implementer can set it according to specific circumstances.
[0058] Next, this embodiment will be described using a hot pressing stage as an example. The method provided in this embodiment can be used to process other hot pressing stages.
[0059] Specifically, any hot pressing stage is designated as a candidate stage.
[0060] For any hot-pressed material product: First, calculate the absolute value of the difference between the temperature data of each position in each region of the hot-pressed material product at every two adjacent moments in the candidate stage, as the difference between the temperature data of each position in each region of the hot-pressed material product at every two adjacent moments in the candidate stage; then, calculate the sum of the differences between the temperature data of all positions in each region of the hot-pressed material product at every two adjacent moments in the candidate stage, and record it as the temperature change characteristic value of each region in the candidate stage; based on the dispersion of the temperature change characteristic values of all regions of the hot-pressed material product in the candidate stage, obtain the variation factor of the hot-pressed material product in the candidate stage, wherein the dispersion is negatively correlated with the variation factor.
[0061] In this embodiment, variance is used to characterize the degree of dispersion, that is, the variance of the temperature change characteristic value of all regions of the hot-pressed material product is calculated, and the negative correlation normalization result between the variance and the number of regions of the hot-pressed material product is determined as the variation factor of any hot-pressed material product in the candidate stage.
[0062] Among them, a negative correlation means that the dependent variable decreases as the independent variable increases, and the dependent variable increases as the independent variable decreases. It can be a subtraction relationship, a division relationship, etc., which is determined by the actual application.
[0063] In this embodiment, a specific calculation formula for the variation factor is given. The variation factor of the i-th hot-pressed material product in the j-th hot-pressing stage can be expressed as:
[0064]
[0065] Among them, R i,j σ represents the variation factor of the i-th hot-pressed material product in the j-th hot-pressing stage. i,j Let n represent the variance of the temperature change characteristic values of all regions of the i-th hot-pressed material product during the j-th hot-pressing stage. i The number of regions for the i-th hot-pressed material product is represented by exp(), which represents an exponential function with the natural constant as the base, and || represents the absolute value sign.
[0066] The variance of the temperature change characteristic values of all regions of the i-th hot-pressed material product is used to reflect the degree of dispersion of the temperature change characteristic values of all regions of the hot-pressed material product. The larger the variance, the more dispersed the distribution of the temperature change characteristic values of all regions of the i-th hot-pressed material product is, that is, the smaller the variation factor of the i-th hot-pressed material product in the j-th stage.
[0067] The influence of shape on temperature changes in composite material products during the hot pressing stage should be analyzed based on the overall temperature change on the surface of the product itself. The greater the influence of shape, the less likely it is to be affected by the poor repeatability of the autoclave. Correspondingly, the degree of variation of different models of products in multiple hot pressing stages will be further reduced. This is due to the relatively stable control of parameters in the multi-stage hot pressing process. In other words, parameter control leads to a reduction in the dispersion of the overall performance data of the hot pressing process, which is more reflected in the stable control conditions.
[0068] For any type of hot-pressed material product:
[0069] All hot-pressing parameter data for this type of hot-pressed material product during the candidate stage constitutes the hot-pressing parameter vector for that product during the candidate stage, while standard hot-pressing parameter data constitutes the standard parameter vector. It should be noted that the standard parameter data is manually set according to specific circumstances, and each type of hot-pressed material product has corresponding standard parameter data for each hot-pressing stage. The cosine similarity between the hot-pressing parameter vector for this type of hot-pressed material product during the candidate stage and the corresponding standard parameter vector is calculated. This cosine similarity is determined as the similarity between the hot-pressing parameter data and the standard parameter data for this type of hot-pressed material product during the candidate stage.
[0070] Furthermore, based on the average value of the variation factor of the hot-pressed material product in the candidate stage and the similarity between the hot-pressing parameter data and the standard parameter data of the hot-pressed material product in the candidate stage, the dispersion coefficient of the hot-pressed material product in the candidate stage is calculated. The similarity is positively correlated with the dispersion coefficient, and the average value of the variation is negatively correlated with the dispersion coefficient.
[0071] Specifically, the change value of the variation factor of this type of hot-pressed material product in the candidate stage is the difference between the variation factor of the previous stage and the variation factor of this type of hot-pressed material product in the candidate stage. The average value of the change value of the variation factor of each type of hot-pressed material product in the candidate stage is the average value of the change values of the variation factor of all hot-pressed material products under each type of hot-pressed material product in the candidate stage.
[0072] In this embodiment, a specific formula for calculating the discrete state coefficient is given. The discrete state coefficient of the r-th hot-pressed material product in the j-th hot-pressing stage can be expressed as:
[0073]
[0074] Among them, Z r,j a represents the discreteness coefficient of the r-th hot-pressed material product in the j-th hot-pressing stage. r,j Let a represent the hot-pressing parameter vector of the r-th hot-pressed material product in the j-th hot-pressing stage. 0,j Let cos(a) represent the standard parameter vector of the r-th hot-pressed material product in the j-th hot-pressing stage. r,j ,a 0,j ) indicates taking a r,j With a 0,j The cosine similarity between them, where I represents the number of products of the r-th hot-pressed material, and R i,j-1 R represents the variation factor of the r-th hot-pressed material product in the (j-1)-th hot-pressing stage. i,jλ represents the variation factor of the r-th hot-pressed material product in the j-th hot-pressing stage, and λ represents the preset adjustment parameter.
[0075] The preset adjustment parameter is introduced into the formula for calculating the discrete condition coefficient to prevent the denominator from being 0. In this embodiment, the preset adjustment parameter is 0.01. In specific applications, the implementer can set it according to the specific situation. This represents the average value of the variation factor of the r-th hot-pressed material product in the j-th hot-pressing stage, reflecting the decreasing trend of the variation of the r-th hot-pressed material product in the j-th hot-pressing stage. This represents the comparison between the temperature data variation and parameter variation under the influence of shape in a single stage. The larger the value, the greater the influence of shape, which means the more obvious the data dispersion characteristics. The greater the dispersion coefficient of the r-th hot-pressed material product in the j-th hot-pressing stage.
[0076] Using the above method, the discrete state coefficient of each hot-pressed material product at each hot-pressing stage can be obtained.
[0077] Step S3: Based on the overall difference between the discrete condition coefficients of each hot-pressing material product and other types of hot-pressing material products in all hot-pressing stages, obtain the temperature data prediction weight for each hot-pressing material product; use the temperature data prediction weight to adjust the hot-pressing parameters for different hot-pressing stages.
[0078] In this embodiment, the discrete state coefficient calculated in step S2 represents the discrete characteristics affecting a single stage. However, for actual composite material hot pressing process control, achieving multi-stage operation process control requires ensuring the overall stability of temperature changes. Therefore, considering temperature data changes across multiple stages, the less obvious the discrete characteristics of the multiple stages of the composite material hot pressing process, the higher the confidence level of using its data for temperature data prediction control. Based on this, this embodiment determines the confidence level based on the overall difference between the discrete state coefficients of each hot pressing material product and other types of hot pressing material products in all hot pressing stages, i.e., obtains the temperature data prediction weight, and uses the temperature data prediction weight to adjust the hot pressing parameters for different hot pressing stages.
[0079] First, calculate the absolute value of the difference between the discrete condition coefficients of each type of hot-pressed material product in every two adjacent hot-pressing stages. This value is taken as the difference between the discrete condition coefficients of each type of hot-pressed material product in every two adjacent hot-pressing stages. Then, based on the difference between the discrete condition coefficients of each type of hot-pressed material product in every two adjacent hot-pressing stages, calculate the average value of the difference between the discrete condition coefficients of each type of hot-pressed material product in all adjacent hot-pressing stages. This average value is taken as the average difference between the discrete condition coefficients of each type of hot-pressed material product in all adjacent hot-pressing stages. Using this method, the average difference corresponding to each type of hot-pressed material product can be obtained. It should be noted that each type of hot-pressed material product has a corresponding average difference.
[0080] The following embodiment uses a hot-pressed material product as an example for explanation. The method provided in this embodiment can be used to process other types of hot-pressed material products. Specifically, any type of hot-pressed material product is designated as the type to be analyzed. The average difference between the average difference corresponding to the type to be analyzed and the average difference corresponding to all other types of hot-pressed material products is calculated and designated as the first average value. The temperature data prediction weight of the type to be analyzed is determined based on the first average value, and the first average value is negatively correlated with the temperature data prediction weight.
[0081] In this embodiment, the value of an exponential function with the natural constant as the base and the negative first average value as the exponent is determined as the prediction weight for the temperature data of the hot-pressed material products of the type to be analyzed.
[0082] In this embodiment, a specific calculation formula for the temperature data prediction weight is given. The temperature data prediction weight for the r-th hot-pressed material product can be expressed as:
[0083]
[0084] in, This represents the predicted weight of temperature data for the r-th type of hot-pressed material product. This represents the average difference in the dispersion coefficients of the r-th hot-pressed material product across all adjacent hot-pressing stages. Let R represent the average difference between the discrete state coefficients of the k-th hot-pressed material product (excluding the r-th hot-pressed material product) in all adjacent hot-pressing stages, where R represents the number of hot-pressed material products, || represents the absolute value sign, and exp() represents an exponential function with the natural constant as the base.
[0085] This characterizes the difference between the average difference corresponding to the r-th type of hot-pressed material product and the average difference corresponding to the k-th type of hot-pressed material product (excluding the r-th type). The first average value is used to reflect the difference between the average difference corresponding to the r-th hot-pressed material product and the average difference corresponding to other types of hot-pressed material products. The smaller the value, the less obvious the discrete characteristics of the r-th hot-pressed material product are in multiple stages of the hot-pressing process, and the higher the confidence level of its data for temperature data prediction and control. That is, the greater the prediction weight of the temperature data of the r-th hot-pressed material product.
[0086] Using the above method, the temperature data prediction weights for each hot-pressed material product can be obtained. Then, the hot-pressing parameters for different hot-pressing stages can be adjusted using the temperature data prediction weights.
[0087] Specifically, the STL (Seasonal-Trend decomposition procedure based on Loess) time-series decomposition algorithm is used to process the temperature data of each hot-pressing stage for each type of hot-pressed material product, extracting time-series decomposition parameters. The STL time-series decomposition algorithm is existing technology and will not be elaborated upon here. The corresponding time-series decomposition parameters are corrected using the predicted weights of the temperature data; that is, the product of the predicted weights and the corresponding time-series decomposition parameters is determined as the corrected time-series decomposition parameters. This constructs a precise temperature control model, enabling temperature prediction for multiple process stages of hot-pressed composite materials and allowing adjustment of parameters in relevant stages. For example, taking the heating rate of the heating stage as an example, the precise temperature control model predicts the temperature data for the heating stage, calculates the expected heating rate using the current autoclave control temperature data and the predicted data, and implements temperature control during the heating stage within the autoclave based on this expected heating rate.
[0088] This embodiment first acquires temperature data for various hot-pressing material products at each hot-pressing stage. Considering that the temperature changes at different locations of the same hot-pressing material product may differ, possibly due to the influence of the mold shape, the variation factor of the hot-pressing material product at each hot-pressing stage is determined based on the temperature data variation characteristics of different regions of each hot-pressing material product at the same hot-pressing stage. Since the hot-pressing parameters for the same hot-pressing material product are the same at the same hot-pressing stage, but may differ from the standard parameters, the variation trend of the variation factor of the same hot-pressing material product in adjacent hot-pressing stages and the similarity between the hot-pressing parameters and the standard parameters are considered to evaluate the data dispersion of each hot-pressing material product at each hot-pressing stage, obtaining a dispersion coefficient. Furthermore, based on the overall difference between the dispersion coefficients of each hot-pressing material product and other types of hot-pressing material products at all hot-pressing stages, a temperature data prediction weight for each hot-pressing material product is obtained. The temperature data prediction weight is used to adjust the hot-pressing parameters at different hot-pressing stages, promoting uniform heat distribution, improving the surface temperature uniformity of the hot-pressing material product during hot-pressing, and ensuring the quality and consistency of the composite material curing. The method provided in this embodiment can not only significantly reduce the risk of defects caused by local temperature anomalies, but also improve product performance, providing a strong guarantee for the molding of high-quality composite materials.
[0089] An embodiment of a feedback system for temperature control of an autoclave:
[0090] See Figure 2 The diagram illustrates a structural block diagram of a feedback system for temperature control of an autoclave according to an embodiment of the present invention. The system may include a data acquisition module, a data processing module, and an adjustment module.
[0091] The data acquisition module is used to acquire temperature data and various hot-pressing parameter data of different hot-pressing material products at different hot-pressing stages.
[0092] The data processing module is used to obtain the variation factor of each hot-pressed material product in each hot-pressing stage based on the variation characteristics of temperature data in different regions of each hot-pressed material product in the same hot-pressing stage; and to determine the discrete state coefficient of each hot-pressed material product in each hot-pressing stage by combining the variation trend of the variation factor of the same hot-pressed material product in adjacent hot-pressing stages and the similarity between the hot-pressing parameter data and the standard parameter data.
[0093] The adjustment module is used to obtain the temperature data prediction weight for each hot-pressed material product based on the overall difference between the discrete condition coefficients of each hot-pressed material product and other types of hot-pressed material products in all hot-pressing stages; and to adjust the hot-pressing parameters for different hot-pressing stages using the temperature data prediction weight.
[0094] It should be understood that Figure 2 The block diagram and modules of the feedback system for temperature control of an autoclave shown can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated hardware. Those skilled in the art will understand that the above-described methods and systems can be implemented using computer-executable instructions and / or included in processor control code, for example, on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The systems and modules of this specification can be implemented not only by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, or by a combination of the above-described hardware circuits and software (e.g., firmware).
[0095] For more details about the above modules, please refer to other parts of this manual; they will not be repeated here.
[0096] In other embodiments, a medium is also provided that stores at least one computer-executable program, which, when executed by a computer, causes the computer to perform the steps in the temperature control feedback method for an autoclave described above, and the medium may be a computer-readable storage medium.
[0097] The provided apparatus, system, and medium are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects of the corresponding methods provided above, and will not be repeated here.
[0098] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A temperature control method for an autoclave, characterized in that, The method includes the following steps: Acquire temperature data and various hot-pressing parameter data for different hot-pressed material products at different hot-pressing stages; Based on the temperature variation characteristics of different regions of each hot-pressed material product in the same hot-pressing stage, the variation factor of each hot-pressed material product in each hot-pressing stage is obtained; combined with the variation trend of the variation factor of the same hot-pressed material product in adjacent hot-pressing stages and the similarity between the hot-pressing parameter data and the standard parameter data, the discrete state coefficient of each hot-pressed material product in each hot-pressing stage is determined. Based on the overall difference between the discrete condition coefficients of each hot-pressing material product and other types of hot-pressing material products in all hot-pressing stages, the temperature data prediction weight of each hot-pressing material product is obtained; the hot-pressing parameters of different hot-pressing stages are adjusted using the temperature data prediction weight; The step of obtaining the variation factor for each hot-pressed material product in each hot-pressing stage based on the temperature variation characteristics of different regions of each hot-pressed material product during the same hot-pressing stage includes: For any hot-pressed material product: Calculate the sum of the differences in temperature data between two adjacent moments in each region of any hot-pressed material product during the candidate stage, and record it as the temperature change characteristic value of each region during the candidate stage; Based on the degree of dispersion of the temperature change characteristic values of all regions of any hot-pressed material product in the candidate stage, the variation factor of any hot-pressed material product in the candidate stage is obtained, and the degree of dispersion is negatively correlated with the variation factor. The candidate stage is any hot pressing stage.
2. The temperature control method for an autoclave according to claim 1, characterized in that, The step of obtaining the variation factor of any hot-pressed material product in the candidate stage based on the dispersion of temperature change characteristic values of all regions of the product includes: Calculate the variance of the temperature change characteristic values of all regions of any hot-pressed material product, whereby the variance is used to characterize the degree of dispersion of the temperature change characteristic values of all regions of any hot-pressed material product. The negative correlation normalization result of the ratio between the variance and the number of regions of any hot-pressed material product is determined as the variation factor of any hot-pressed material product in the candidate stage.
3. The temperature control method for an autoclave according to claim 1, characterized in that, The method combines the changing trends of variation factors of the same hot-pressed material product in adjacent hot-pressing stages with the similarity between hot-pressing parameter data and standard parameter data to determine the dispersion coefficient of each hot-pressed material product in each hot-pressing stage, including: For any type of hot-pressed material product: Based on the average value of the variation factor of any hot-pressed material product in the candidate stage and the similarity between the hot-pressing parameter data and the standard parameter data of any hot-pressed material product in the candidate stage, the dispersion coefficient of any hot-pressed material product in the candidate stage is calculated. The similarity is positively correlated with the dispersion coefficient, and the average value of the variation is negatively correlated with the dispersion coefficient. Wherein, the change value of the variation factor of any hot-pressed material product in the candidate stage is the difference between the variation factor of the previous stage and the variation factor of the hot-pressed material product in the candidate stage.
4. The temperature control method for an autoclave according to claim 3, characterized in that, The acquisition of the similarity between the hot-pressing parameter data and standard parameter data of any hot-pressing material product in the candidate stage includes: The cosine similarity between the hot-pressing parameter vector of any hot-pressing material product in the candidate stage and the corresponding standard parameter vector is determined as the similarity between the hot-pressing parameter data and the standard parameter data of any hot-pressing material product in the candidate stage. The hot-pressing parameter vector of any hot-pressing material product in the candidate stage is composed of all the hot-pressing parameter data of the hot-pressing material product in the candidate stage, and the standard parameter vector is composed of standard hot-pressing parameter data.
5. The temperature control method for an autoclave according to claim 1, characterized in that, The method for determining the temperature data prediction weight for each hot-pressed material product based on the overall difference between the dispersion coefficients of each hot-pressed material product and other types of hot-pressed material products across all hot-pressing stages includes: Calculate the average difference between the discrete condition coefficients of each hot-pressed material product in all adjacent hot-pressing stages; The average difference between the average difference of the hot-pressed material product of the type to be analyzed and the average difference of the hot-pressed material product of all other types is calculated and denoted as the first average value. The temperature data prediction weight of the hot-pressed material product to be analyzed is determined based on the first average value, and the first average value is negatively correlated with the temperature data prediction weight. The hot-pressed material product to be analyzed can be any type of hot-pressed material product.
6. A temperature control method for an autoclave according to claim 5, characterized in that, The step of determining the temperature data prediction weight of the hot-pressed material product of the type to be analyzed based on the first average value includes: The value of the exponential function, with the natural constant as the base and the negative first average value as the exponent, is determined as the prediction weight for the temperature data of the hot-pressed material products to be analyzed.
7. The temperature control method for an autoclave according to claim 1, characterized in that, The method of adjusting the hot-pressing parameters for different hot-pressing stages using temperature data prediction weights includes: The STL time-series decomposition algorithm was used to process the temperature data of each hot-pressed material product at all hot-pressing stages to extract time-series decomposition parameters; The corresponding time-series decomposition parameters are corrected using the predicted weights based on the temperature data, and the hot-pressing parameters for different hot-pressing stages are adjusted based on the corrected results.
8. A temperature control method for an autoclave according to claim 7, characterized in that, The time-series decomposition parameters are corrected using the predicted weights based on the temperature data, including: The product of the temperature data prediction weights and the corresponding time series decomposition parameters is determined as the corrected time series decomposition parameters.
9. A feedback system for temperature control of an autoclave, characterized in that, The system includes: The data acquisition module is used to acquire temperature data and various hot-pressing parameter data of different hot-pressed material products at different hot-pressing stages; The data processing module is used to obtain the variation factor of each hot-pressed material product in each hot-pressing stage based on the variation characteristics of temperature data in different regions of each hot-pressed material product in the same hot-pressing stage; and to determine the discrete state coefficient of each hot-pressed material product in each hot-pressing stage by combining the variation trend of the variation factor of the same hot-pressed material product in adjacent hot-pressing stages and the similarity between the hot-pressing parameter data and the standard parameter data. The adjustment module is used to obtain the temperature data prediction weight for each hot-pressed material product based on the overall difference between the discrete condition coefficients of each hot-pressed material product and other types of hot-pressed material products in all hot-pressing stages; and to adjust the hot-pressing parameters for different hot-pressing stages using the temperature data prediction weight. The step of obtaining the variation factor for each hot-pressed material product in each hot-pressing stage based on the temperature variation characteristics of different regions of each hot-pressed material product during the same hot-pressing stage includes: For any hot-pressed material product: Calculate the sum of the differences in temperature data between two adjacent moments in each region of any hot-pressed material product during the candidate stage, and record it as the temperature change characteristic value of each region during the candidate stage; Based on the degree of dispersion of the temperature change characteristic values of all regions of any hot-pressed material product in the candidate stage, the variation factor of any hot-pressed material product in the candidate stage is obtained, and the degree of dispersion is negatively correlated with the variation factor. The candidate stage is any hot pressing stage.
Citation Information
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