Method and device for predicting temperature rise of optical fiber panel workpiece in vacuum hot pressing furnace

Abstract

The invention provides a temperature rise prediction method and device for an optical fiber panel workpiece in a vacuum hot pressing furnace, and aims to realize temperature prediction of the temperature rise process of the optical fiber panel workpiece by utilizing material parameters and combining a three-dimensional radiation conduction coupling model. The invention discloses a method for predicting the temperature rise of an optical fiber panel workpiece in a vacuum hot-pressing furnace. An integrated geometric model of the optical fiber panel workpiece, a mold and the vacuum hot-pressing furnace is established; establishing a coupling physical model based on the integrated geometric model in combination with the material parameters of the mold, the vacuum hot pressing furnace and the optical fiber panel workpiece and the actually measured temperature of a heating element in the vacuum hot pressing furnace; determining the required surface emissivity and the heat conductivity coefficient of the optical fiber panel workpiece by using the simulated temperature curve and the actual temperature curve and adopting inversion calculation; and inputting the surface emissivity and the heat conductivity coefficient of the optical fiber panel workpiece into the coupling physical model to obtain a three-dimensional radiation conduction coupling physical model, and performing temperature prediction by using the three-dimensional radiation conduction coupling physical model.

Description

Technical Field

[0001] This application relates to the field of optical fiber panel manufacturing technology, and in particular to a method and apparatus for predicting the temperature rise of optical fiber panel workpieces in a vacuum hot press furnace. Background Technology

[0002] As a core component of high-end optical equipment such as low-light night vision and medical imaging, the manufacturing process of fiber optic panels plays a decisive role in product quality. During the vacuum hot-pressing process of fiber optic panels, precise temperature control is crucial, as it directly affects the microstructure and performance of the fiber optic panel.

[0003] In actual production, there is no means to predict the temperature of fiber optic panel workpieces inside the vacuum hot press furnace. This situation makes it impossible for production personnel to anticipate the temperature change trend of the fiber optic panel workpieces during the heating phase, forcing them to rely heavily on past experience when adjusting process parameters. However, this reliance on experience is highly susceptible to errors due to inaccurate temperature control, ultimately leading to unstable fiber optic panel quality and affecting the overall performance of the product.

[0004] Therefore, there is an urgent need for a new method that can accurately predict the temperature rise data of fiber optic panels in a vacuum autoclave to fill the gap in existing technology and provide reliable guidance for process optimization. Summary of the Invention

[0005] This application provides a method and apparatus for predicting the temperature rise of fiber optic panel workpieces in a vacuum hot press furnace. The purpose is to obtain key thermophysical parameters through integrated modeling, coupled physical simulation and inversion calculation, and to use the key thermophysical parameters in combination with a three-dimensional radiation and conduction coupled model to solve the heat exchange law transiently, so as to predict the temperature rise process of fiber optic panel workpieces.

[0006] To address the aforementioned technical problems, this application provides the following technical solutions: The first aspect of this application provides a method for predicting the temperature rise of fiber optic panel workpieces in a vacuum hot press furnace, including: Obtain the geometric and material parameters of the fiber optic panel workpiece, mold, and vacuum hot press furnace, as well as the measured temperature of the heating element in the current vacuum hot press furnace; Temperature data of the first target position in the fiber optic panel workpiece or mold is collected at preset time intervals within a preset time period, and the actual temperature curve is generated. After constructing an integrated geometric model based on the geometric parameters, a coupled physical model simulating solid heat transfer and surface radiation heat transfer is established by combining material parameters and the measured temperature of the heating element in the current vacuum hot press furnace. The coupled physical model is used to output the simulated temperature curve of the second target position within a preset time and at the same preset time interval. The second target position is the simulated position corresponding to the first target position. Using simulated temperature curves and actual temperature curves, inversion calculations were employed to determine the surface emissivity of the fiber optic panel workpiece, the mold, and the vacuum hot press furnace, as well as the thermal conductivity of the fiber optic panel workpiece. Substituting the surface emissivity and thermal conductivity into the coupled physical model, a three-dimensional radiation-conduction coupled physical model is constructed. The three-dimensional radiation-conduction coupled physical model uses the finite element method for transient solution to determine the temperature change law of the optical fiber panel workpiece during the heating process and predict the temperature of the optical fiber panel workpiece. The temperature of the heating element corresponding to the working condition to be predicted is input into the three-dimensional radiation conduction coupled physical model to obtain the predicted temperature during the heating process of the optical fiber panel workpiece.

[0007] A second aspect of this application provides a device for predicting the temperature rise of fiber optic panel workpieces inside a vacuum hot press furnace, comprising: The acquisition unit is used to acquire the geometric and material parameters of the fiber optic panel workpiece, mold, and vacuum hot press furnace, as well as the measured temperature of the heating element in the current vacuum hot press furnace. The acquisition unit is used to collect temperature data of the first target position in the optical fiber panel workpiece or mold within a preset time interval, and generate an actual temperature curve. The establishment unit is used to build an integrated geometric model based on the geometric parameters in the acquisition unit, and then, in combination with material parameters and the measured temperature of the heating element in the current vacuum hot press furnace, establish a coupled physical model to simulate solid heat transfer and surface radiation heat transfer. The coupled physical model is used to output the simulated temperature curve of the second target position within a preset time and at the same preset time interval. The second target position is the simulation position corresponding to the first target position. The calculation data unit is used to determine the surface emissivity of the fiber optic panel workpiece, the mold, and the vacuum hot press furnace, as well as the thermal conductivity of the fiber optic panel workpiece, by using the simulated temperature curve in the establishment unit and the actual temperature curve in the acquisition unit and by inversion calculation. A model unit is defined to substitute the surface emissivity and thermal conductivity of the calculation data unit into the coupled physical model to construct a three-dimensional radiation and conduction coupled physical model. The three-dimensional radiation and conduction coupled physical model uses the finite element method for transient solution to determine the temperature change law of the optical fiber panel workpiece during the heating process and predict the temperature of the optical fiber panel workpiece. The prediction unit is used to input the temperature of the heating element corresponding to the working condition to be predicted into the three-dimensional radiation conduction coupled physical model in the determination model unit to obtain the predicted temperature during the heating process of the optical fiber panel workpiece.

[0008] A third aspect of this application provides a storage medium comprising a stored program that, when the program is executed, controls the device containing the storage medium to perform the aforementioned method for predicting the temperature rise of an optical fiber panel workpiece in a vacuum hot press furnace.

[0009] A fourth aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described method for predicting the temperature rise of a fiber optic panel workpiece in a vacuum hot press furnace.

[0010] Compared to existing technologies, this application provides a method for predicting the temperature rise of fiber optic panel workpieces in a vacuum hot press furnace. Firstly, by constructing a three-dimensional radiation-conduction coupled physical model encompassing the fiber optic panel workpiece, mold, and vacuum hot press furnace, this method realistically recreates the strong coupling heat transfer mechanism of solid conduction and surface radiation in a vacuum hot press environment, overcoming simulation distortion caused by traditional isolated modeling that ignores system thermal interaction. Secondly, addressing the problem that in actual production processes, the surface emissivity of the mold, vacuum hot press furnace, and fiber optic panel, as well as the thermal conductivity of the fiber optic panel workpiece, are affected by multiple factors such as material surface condition, oxidation level, microstructure, and high-temperature environment, making direct measurement impossible and leading to significant errors in predicted temperature values ​​when directly applying theoretical values, this application introduces an inversion calculation method based on measured temperature curves and simulated curves to determine the surface emissivity and thermal conductivity of the fiber optic panel workpiece, effectively avoiding prediction errors caused by theoretical value deviations. Finally, the three-dimensional radiation-conduction coupled physical model uses the finite element method for transient solution to determine the temperature change law of the fiber optic panel workpiece during the heating process. Therefore, the above method achieves temperature prediction of the fiber optic panel workpiece during the heating process, filling a gap in existing technologies and providing reliable guidance for process optimization. Attached Figure Description

[0011] The above and other objects, features, and advantages of exemplary embodiments of this application will become readily understood by reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of this application are illustrated by way of example and not limitation, with the same or corresponding reference numerals denoteing the same or corresponding parts, wherein: Figure 1 A flowchart illustrating a method for predicting the temperature rise of fiber optic panel workpieces in a vacuum hot press furnace is shown schematically. Figure 2 A flowchart illustrating another method for predicting the temperature rise of fiber optic panel workpieces in a vacuum hot press furnace is shown schematically. Figure 3A schematic diagram of the geometric model of the fiber optic panel sample and the mold is shown. Figure 4a The diagram schematically illustrates the material parameters defined by interpolation functions and the parameter input diagrams required for each material sub-node and the solid heat transfer physics field. Figure 4b The diagram schematically illustrates the nodes for solid heat transfer and surface-to-surface radiation, as well as the respective sub-nodes of these two nodes. Figure 5 The diagram schematically illustrates the K-type thermocouple arrangement and mold component names of the fiber optic panel; Figure 6 The diagram illustrates the results of the inversion calculation of material parameters. Figure 7 A schematic diagram of a device for predicting the temperature rise of fiber optic panel workpieces inside a vacuum hot press furnace is shown. Figure 8 A schematic diagram of another fiber optic panel workpiece temperature prediction device in a vacuum hot press furnace is shown. Detailed Implementation

[0012] Exemplary embodiments of this application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of this application are shown in the drawings, it should be understood that this application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of this application and to fully convey the scope of this application to those skilled in the art.

[0013] It should be noted that, unless otherwise stated, the technical or scientific terms used in this application shall have the ordinary meaning as understood by one of ordinary skill in the art to which this application pertains.

[0014] As a core component of high-end optical equipment such as low-light night vision and medical imaging, the manufacturing process of fiber optic panels plays a decisive role in product quality. During the vacuum hot pressing process of fiber optic panels, precise temperature control is crucial, directly affecting the microstructure and performance of the panel. In actual production, there is no means to predict the temperature of the fiber optic panel workpiece within the vacuum hot pressing furnace. This situation prevents production personnel from anticipating the temperature change trend of the fiber optic panel workpiece during the heating phase, forcing them to rely heavily on past experience when adjusting process parameters. However, this reliance on experience is highly susceptible to errors due to inaccurate temperature control, ultimately leading to unstable fiber optic panel quality and affecting the overall performance of the product.

[0015] Based on this, the applicant of this application provides a method for predicting the temperature rise of fiber optic panel workpieces in a vacuum hot press furnace, specifically as follows: Figure 1 As shown, it specifically includes: Step 101: Obtain the geometric and material parameters of the fiber optic panel workpiece, mold, and vacuum hot press furnace, as well as the measured temperature of the heating element in the current vacuum hot press furnace.

[0016] Obtaining material parameters includes the density, constant-pressure heat capacity, and thermal conductivity of the mold and vacuum hot press furnace, as well as the density and specific heat capacity of the fiber optic panel workpiece. In this step, geometric parameter data refers to the dimensions (such as length, width, height, and opposite side lengths), structural morphology, and component assembly relationships of the fiber optic panel workpiece, mold, and vacuum hot press furnace. The integrated geometric model is a three-dimensional model constructed based on the geometric parameter data of the three components, integrating the structural features of the fiber optic panel, mold, and vacuum hot press furnace to achieve the basic model for coupled simulation of the thermal processes of the three. The measured temperature of the heating element in the current vacuum hot press furnace is the temperature boundary condition, which is divided into three types: The specific division method is to divide the vacuum hot press furnace according to the layout of the heating element, such as (upper / middle / lower region or other geometrically shaped region), to obtain the temperature boundary condition data of different regions. The upper region temperature boundary region selects the surface of the heating element in the upper region and the inner and outer walls of the furnace chamber in the upper region; the middle region temperature boundary region selects the surface of the heating element in the middle region and the inner and outer walls of the furnace chamber in the middle region; the lower region temperature boundary region selects the surface of the heating element in the lower region and the inner and outer walls of the furnace chamber in the lower region. In this step, the geometric parameter data of the fiber optic panel workpiece, mold, and vacuum hot press furnace can be obtained directly or through measurement. For example, if there is a CAD or Solidworks 3D model, the model size data (such as the length, width, and height of the fiber optic panel) can be directly extracted; if there is no model, the key dimensions and structural assembly relationships of the fiber optic panel, mold, and vacuum hot press furnace are measured using high-precision measuring tools.

[0017] The material parameters (thermophysical parameters) are obtained as follows: the density, constant-pressure heat capacity, and thermal conductivity of the mold and vacuum hot press are extracted from the manufacturer's technical manual; the density and specific heat capacity of the fiber optic panel are obtained through experimental measurement (e.g., using the water displacement method to determine the density of the fiber optic panel workpiece: prepare an electronic balance, graduated cylinder, distilled water, and degreased cotton. First, cut the fiber optic panel sample into regular small pieces (remove surface impurities), weigh the sample mass m using an electronic balance and record it. Add an appropriate amount of distilled water to the graduated cylinder and read the initial volume V1. Use degreased cotton to absorb the surface moisture of the sample, slowly place it into the graduated cylinder, ensuring that the sample is completely submerged and free of air bubbles, and read the final volume V2. According to density = m / (V2-V1), repeat 3 times). The average value of each experiment was taken to ensure measurement accuracy. The specific heat capacity was determined using a mixing method: a calorimeter, electronic balance, thermometer (accuracy 0.1℃), and constant temperature water bath were used. A dry fiber optic panel sample of mass m1 was weighed and heated in a constant temperature water bath to a stable temperature t1. Distilled water of known mass m2 and initial temperature t2 was added to the calorimeter. After the system stabilized, the initial mixing temperature t2 was recorded. The heated sample was quickly placed into the calorimeter and stirred until the temperature stabilized. The final equilibrium temperature t was recorded. According to the heat conservation formula m1c1(t1-t)=m2c 水 (t-t2)(c 水 Given that the specific heat capacity of water is 4.2 J / (g・℃), the specific heat capacity c1 of the sample was calculated. The experiment was repeated three times and the average value was taken. The temperature of the heating element inside the vacuum hot press furnace was collected in real time by the K-type thermocouples (PID-controlled temperature feedback sensors) installed at the factory in the upper / middle / lower zones of the furnace inner wall.

[0018] Step 102: Collect temperature data of the first target position in the fiber optic panel workpiece or mold within a preset time interval, and generate the actual temperature curve.

[0019] The first target location refers to the preset temperature monitoring points on the fiber optic panel workpiece (center of the upper surface, center of the inner surface, center of the lower surface) or the mold (the gap between the collar and the slider, and the gap between the collar and the outer ring are unknown) to obtain the actual temperature curve. The actual temperature curve (Tex) is the temperature-time change curve obtained by thermocouples placed at the first target location within a preset time period based on a preset acquisition frequency (preset time interval). The specific implementation is as follows: First, define the first target location. These locations are key temperature monitoring points pre-set on the fiber optic panel workpiece and the mold. Specifically, K-type thermocouples are placed at the center of the upper surface, center of the inner surface, and center of the lower surface of the fiber optic panel workpiece. Additional K-type thermocouples are placed at the gap between the collar and the slider, and at the gap between the collar and the outer ring of the mold. Second, perform data acquisition. During the specific heating process in the vacuum autoclave, temperature data is continuously acquired at fixed preset time intervals (e.g., once per minute) using all the aforementioned K-type thermocouples. This data can be directly exported as a data file (e.g., a date file) from the terminal in the central control room. Finally, generate the actual temperature curve. By organizing the collected temperature data at each target location and its corresponding timestamp, a temperature-time variation curve (Tex) can be plotted for that location within a preset process time period. These curves will serve as the benchmark for comparison with simulation results in subsequent inversion calculations. This systematic data acquisition process ensures the accuracy and reliability of the obtained experimental data, laying a solid foundation for building a high-precision numerical model.

[0020] It is worth noting that the actual temperature profile (Tex) is obtained in this step as follows: K-type thermocouples are placed at the center of the upper surface, the inner center, and the lower surface of the fiber optic panel, or at the gap between the mold collar and the slider, and at the gap between the collar and the outer ring. Temperature data is continuously collected at a preset frequency (once per minute) within a preset process time period, and the actual temperature profile is generated based on the collected data. The data can be exported as a date file from the central control room terminal. The number of actual temperature profiles can be one or more, which is not limited here. This paper provides three actual temperature profiles, with the measurement locations corresponding to the three temperature profiles being the inner center of the fiber optic panel, the gap between the mold collar and the slider, and the gap between the collar and the outer ring.

[0021] Step 103: After constructing an integrated geometric model based on the geometric parameters, and combining the material parameters and the measured temperature of the heating element in the current vacuum hot press furnace, establish a coupled physical model simulating solid heat transfer and surface radiation heat transfer.

[0022] Integrated geometric model construction: Import the acquired geometric parameter data into Comsol software (such as vacuum hot press furnace and fiber optic panel workpieces can be drawn directly in the Comsol geometry module, and molds are imported through third-party software). Set the three as an assembly to form a contact pair and complete the construction of the integrated geometric model.

[0023] In this step, the coupled physical model is a three-dimensional physical model that integrates solid-state heat transfer and surface-to-surface radiation, capable of simulating the radiation-conduction coupled thermal process between the fiber optic panel, the mold, and the vacuum autoclave. The coupled physical model outputs simulated temperature curves of the second target location within a preset time period at the same preset time intervals. The second target location corresponds to the simulated location of the first target location, specifically the center of the upper surface, the inner center, and the lower surface of the fiber optic panel, as well as the corresponding positions in the model for the gaps between the mold collar and the slider, and between the collar and the outer ring. The simulated temperature curve (Tcal) is the temperature-time variation curve of the second target location calculated by the coupled physical model. It is used to compare with the actual temperature curve to obtain the surface emissivity of the fiber optic panel workpiece, the mold, and the vacuum autoclave, as well as the thermal conductivity of the fiber optic panel workpiece.

[0024] Based on the integrated geometric model constructed in step 101, the physics field settings and parameter configurations are performed in Comsol software: solid heat transfer and surface-to-surface radiation physics fields are selected and coupled; corresponding sub-nodes are created in the material node; and parameters such as density, constant-pressure heat capacity, and thermal conductivity of the mold, vacuum hot press furnace, fiber optic panel, and insulation layer inside the vacuum hot press furnace are entered into the corresponding regions. Tungsten material is selected for the heating element and called from the software material library; the surface of the heating element and the inner and outer walls of the furnace are selected as temperature boundary regions according to the upper, middle, and lower regions; the collected heating element temperature is used as the boundary condition input; and the reference temperature is set to 30℃, and the convective heat flux and heat transfer coefficient are set. Including parameters such as water-cooled boundary; after completing the physical field and parameter configuration, run the model to simulate the transient thermal process and output the simulated temperature curve at the second target location.

[0025] Step 104: Using simulated temperature curves and actual temperature curves, inversion calculations are used to determine the surface emissivity of the fiber optic panel workpiece, the mold, and the vacuum hot press furnace, as well as the thermal conductivity of the fiber optic panel workpiece.

[0026] In this step, surface emissivity refers to the ratio of the thermal radiation energy emitted by an object's surface per unit time and per unit area to the thermal radiation energy emitted by a blackbody under the same conditions at the same temperature. Its value ranges from 0 to 1, and it is a core thermophysical parameter characterizing an object's radiative heat dissipation capacity. Thermal conductivity is a core thermophysical parameter characterizing a material's ability to conduct heat. It is defined as the amount of heat passing through a unit area of ​​material per unit time under a unit temperature gradient; the higher the value, the stronger the material's thermal conductivity.

[0027] Using simulated and actual temperature curves, inversion calculations are employed to determine the surface emissivity of the fiber optic panel workpiece, mold, and vacuum hot press furnace, as well as the thermal conductivity of the fiber optic panel workpiece. This includes: setting initial values ​​for the surface emissivity of the mold, vacuum hot press furnace, and fiber optic panel based on the surface oxidation of the materials; setting initial values ​​for the experimental thermal conductivity of the secondary multifilament of the fiber optic panel in the fiber direction and perpendicular to the fiber direction; inputting the measured temperature of the heating element in the current vacuum hot press furnace, the initial values ​​for surface emissivity, and the initial values ​​for the fiber direction and perpendicular to the fiber direction into the coupled physical model; then simulating the transient thermal process using the finite element method through the coupled physical model to output the simulated temperature curve at the second position; when the degree of agreement between the simulated temperature curve and the actual temperature curve is greater than a preset degree of agreement, obtaining the surface emissivity of the fiber optic panel workpiece, mold, and vacuum hot press furnace, as well as the thermal conductivity of the fiber optic panel workpiece in the coupled physical model.

[0028] Specifically, the initial parameters are first set: for the mold, vacuum hot press (including stainless steel parts), and fiber optic panel, the initial surface emissivity is set through visual observation and oxidation degree assessment. For example, the initial emissivity of parts with thick oxide layers (such as the surface of molds that have been used for a long time) is set to 0.75-0.85, and the initial emissivity of new parts with light oxidation (such as the surface of fiber optic panels that have not been used for a long time) is set to 0.65-0.75. The surface of the heat insulation layer is uniformly set to 0.70-0.80. The initial values ​​of the thermal conductivity of the fiber optic panel in the fiber direction and perpendicular to the fiber direction are directly adopted from the experimental test values ​​of its double-fiber structure. The initial value of the thermal conductivity in the fiber direction is set to 1.0-1.2 W / (mK), and the initial value of the thermal conductivity perpendicular to the fiber direction is set to 0.8-1.0 W / (mK).

[0029] Next, input parameters and perform transient simulation: input the measured temperature data of the heating elements in the upper / middle / lower zones of the vacuum hot press furnace, the initial values ​​of the surface emissivity and the initial values ​​of the thermal conductivity of the fiber optic panel into the constructed coupled physical model simultaneously; use the finite element method to calculate the transient thermal process, and keep the simulation sampling time interval consistent with the actual temperature data acquisition interval (both are time intervals corresponding to preset frequencies), and output the simulated temperature curve of the second target position through model calculation.

[0030] Finally, the matching degree is determined and parameters are acquired: the matching degree between the simulated temperature curve and the actual temperature curve is judged. When the matching degree between the simulated temperature curve and the actual temperature curve is greater than the preset matching degree, the surface emissivity of the fiber optic panel workpiece, mold, and vacuum hot press furnace in the coupled physical model, as well as the thermal conductivity of the fiber optic panel workpiece, are acquired. If the result of a single simulation does not reach the threshold, the surface emissivity is finely adjusted according to the preset gradient (0.01-0.05 gradient), and the thermal conductivity of the fiber optic panel is finely adjusted according to the preset adjustment gradient. The above transient simulation process is repeated. When the matching degree between the simulated curve and the actual curve meets the preset requirements, and the absolute value of the temperature error at the end of the heating is <5℃, the iteration is stopped, and the parameters in the current coupled physical model are extracted, which are the finally determined surface emissivity of the mold, vacuum hot press furnace, and fiber optic panel, as well as the thermal conductivity of the fiber optic panel in the fiber direction and perpendicular to the fiber direction.

[0031] Step 105: Substitute the surface emissivity and thermal conductivity into the coupled physical model to construct a three-dimensional radiation-conduction coupled physical model.

[0032] In this step, the three-dimensional radiation-conduction coupled physical model uses the finite element method (FEM) transient solution to determine the temperature change law of the fiber optic panel workpiece during the heating process and predict the temperature of the fiber optic panel workpiece. Specifically, the FEM transient solution is used to reconstruct the heat exchange law between the fiber optic panel workpiece, the mold, and the vacuum hot press furnace under vacuum conditions, and based on the temperature change law of the fiber optic panel workpiece over time during the heating process, the temperature of the fiber optic panel workpiece is predicted.

[0033] Specifically, the surface emissivity of the fiber optic panel workpiece, mold, and vacuum hot press furnace (including stainless steel components and insulation layer) obtained from step 104, as well as the thermal conductivity of the fiber optic panel along the fiber direction and perpendicular to the fiber direction, are accurately entered into the coupled physical model, replacing the original initial parameters. Simultaneously, the configured solid-state heat transfer and surface-to-surface radiation coupled physical field settings, temperature boundary conditions in the upper / middle / lower zones of the furnace, and geometric assembly relationships are retained in the model, forming a complete three-dimensional radiation-conduction coupled physical model. This ensures that the model simultaneously covers both heat conduction and radiation heat transfer mechanisms, closely matching the actual heat transfer scenario inside the vacuum hot press furnace.

[0034] Next, the finite element transient solution configuration and calculation were performed: In the COMSOL platform, an adaptive mesh generation strategy was adopted to refine the mesh in temperature-sensitive areas such as the fiber optic panel and mold gaps, while using a coarse mesh in non-critical areas to balance solution accuracy and efficiency. A transient study was created, setting the solution time range to be consistent with the actual temperature data acquisition cycle in step 101, maintaining a sampling interval of 1 minute, selecting a split solver, and setting a maximum number of iterations of 30 to ensure solution convergence. After starting the solution, the model, based on the law of conservation of energy and the radiation transfer equation, transiently calculated the radiation-conduction coupled heat exchange process between the fiber optic panel, mold, and vacuum autoclave at various moments under vacuum conditions, outputting dynamic data of the global temperature field to reconstruct the heat exchange law. From the global temperature field data output by the solution, data of any point or key monitoring point (center of the upper surface, center of the interior, center of the lower surface) of the fiber optic panel workpiece was accurately extracted. Based on the above data, the temperature data of the fiber optic panel workpiece during the heating process was plotted using data visualization tools, intuitively presenting the predicted temperature change trend of the fiber optic panel during the heating process.

[0035] Specifically, based on the temperature change over time of the optical fiber panel workpiece during the heating process in the heat exchange law, the temperature of the optical fiber panel workpiece is predicted. The specific steps are as follows: After extracting temperature-time series data from any point or key monitoring point of the optical fiber panel workpiece, the temperature field is obtained through a four-step scheme of "data calibration - pattern fitting - trend inference - accuracy verification", as follows: The first step is data calibration and preprocessing. Abnormal fluctuations (such as jump values ​​caused by instantaneous sensor interference) are removed, and the data is smoothed using a moving average method. The temperature data from the three monitoring points are aligned by timestamps to ensure that there is complete three-dimensional temperature data at each time point, laying the foundation for subsequent analysis.

[0036] The second step is fitting the heat exchange law. Based on the calibrated data, a piecewise fitting strategy is adopted: in the initial stage of heating (temperature < 300℃), a linear function is used to reflect the rapid heating characteristics dominated by radiation; in the middle stage (300℃-600℃), an exponential function is used to fit the gradual heating stage of radiation and conduction coupling; and in the later stage (temperature ≥ 600℃), a polynomial function is used to fit the steady-state characteristics approaching the target temperature. At the same time, combined with the heat exchange mechanism of the three-dimensional radiation-conduction coupling model, a temperature gradient correction term is introduced to make the fitted function more closely match the actual heat transfer law.

[0037] The third step is to extrapolate the heating trend. The piecewise fitting functions are integrated into a complete temperature prediction model. A preset heating time is input, and the predicted temperature values ​​of the three monitoring points at each time point are extrapolated. For the temperature differences between different monitoring points, the average temperature of the fiber optic panel is calculated through weight allocation (0.5 for the internal center and 0.25 for the centers of the upper and lower surfaces), generating a comprehensive heating trend curve to intuitively present the overall heating process.

[0038] The fourth step is accuracy verification and correction. The predicted curve obtained from the derivation is compared with the actual temperature curve under the same basic conditions. The temperature error at the end of the heating process and the overall curve consistency are calculated. If the error is >5℃ or the consistency is <94.33%, the fitting function parameters are adjusted (such as correcting the attenuation coefficient of the exponential function and the order of the polynomial function). Steps two and three are repeated until the accuracy requirements are met, ultimately outputting an accurate fiber optic panel heating prediction curve. This scheme eliminates interference through data preprocessing, optimizes the fitting model based on the heat transfer mechanism, and ensures accuracy through iterative verification. It achieves a reliable conversion from discrete data to a continuous prediction curve, and the entire process closely matches the actual heat exchange law of radiation-conduction coupling in a vacuum autoclave.

[0039] Step 106: Input the temperature of the heating element corresponding to the working condition to be predicted into the three-dimensional radiation conduction coupled physical model to obtain the predicted temperature of the optical fiber panel workpiece during the heating process.

[0040] The temperature data of the heating elements inside the vacuum hot press furnace under the predicted operating conditions (temperature and time series data corresponding to the upper / middle / lower regions are used as boundary conditions) are accurately input into the three-dimensional radiation-conduction coupled physical model to ensure a one-to-one correspondence with the model's preset temperature boundary regions. Keeping the model's original adaptive mesh generation, separate solver configuration, and solution time step unchanged, the transient solution process is initiated. Based on the radiation-conduction coupled heat exchange law, the model calculates the temperature field data at all times across the entire domain, extracts the temperature information of key monitoring points of the fiber optic panel (center of the upper surface, center of the interior, and center of the lower surface), and generates a complete heating process prediction curve, which is the predicted temperature of the fiber optic panel workpiece during the heating process.

[0041] In summary, a method for predicting the temperature rise of fiber optic panel workpieces in a vacuum hot press furnace involves several key steps. First, obtaining the measured temperatures of the geometry, materials, and heating elements, and then acquiring the actual temperature curve at the target location. This provides a realistic input benchmark for the coupled physical model, crucial for preventing the model from deviating from reality. Second, constructing an integrated geometric model rather than isolated component models ensures that the heat transfer of the workpiece, mold, and vacuum hot press furnace during vacuum hot pressing is interconnected, avoiding the problem of neglecting the coupling effect of radiation and conduction caused by only constructing a fiber optic panel model. Third, in actual production, the surface emissivity of the mold, vacuum hot press furnace, and fiber optic panel, as well as the thermal conductivity of the fiber optic panel workpiece, are affected by multiple factors such as material surface condition, oxidation degree, microstructure, and high-temperature environment, making direct measurement impossible. Directly using theoretical values ​​would lead to significant errors in the predicted temperature. To address the issue of the inability to directly measure the surface emissivity and thermal conductivity of the fiber optic panel workpiece, this application employs inverse calculations using simulated and actual curves to derive parameter data. This method overcomes the technical bottleneck of difficult material parameter calibration and improves the physical realism of the model. Finally, the three-dimensional radiation-conduction coupling model, combined with finite element transient solution, enabled the capture of temperature change patterns and the prediction of temperature during the heating process of fiber optic panel workpieces. This fills a gap in existing technology and provides reliable guidance for process optimization.

[0042] Furthermore, this application also provides another method for predicting the temperature rise of fiber optic panel workpieces in a vacuum hot press furnace, specifically as follows: Figure 2 As shown, it specifically includes: Step 201: Build the assembly database.

[0043] The assembly database stores standard geometric models of fiber optic panel workpieces, molds, and vacuum autoclaves. These standard geometric models cover different sizes and structural types. The assembly database refers to a pre-established, structured library of geometric models within the simulation system, used to store standard three-dimensional geometric models of key components such as fiber optic panel workpieces, molds, and vacuum autoclaves. These models are categorized and managed according to size specifications (e.g., height, opposite side length) and structural types (e.g., D / R / G6000 molds, X / Z / P type panels), supporting rapid retrieval and matching. Standard geometric models are CAD / COMSOL compatible three-dimensional models that have undergone parametric or standardized processing, possessing complete topological structures and assemblable interfaces, and accurately reflecting the shape, internal cavities, contact surfaces, and other characteristics of actual physical components.

[0044] In this embodiment, the assembly database is first constructed based on historical production data and equipment manuals, collecting geometric parameters (such as height h, opposite side length, diameter, cavity depth, etc.) of common fiber optic panels (e.g., X, Z, P types), matching molds (e.g., D, R, G6000 types), and vacuum autoclaves. Subsequently, parametric 3D geometric models are created for each type of component in platforms such as COMSOL Multiphysics or SolidWorks: for example, a P-type panel is modeled as a rectangular solid with length × width × height = 123mm × 112mm × 100mm; a D-type mold includes substructures such as collars, sliders, and outer rings. All models are stored in a local or cloud database as "standard geometric models" with attached metadata tags (e.g., "mold type = D", "applicable panel = X"). The database is organized in a tree structure, supporting multi-dimensional queries and additions by component type, size range, material category, etc. When a new workpiece needs to be simulated, the system can automatically call the matching model, avoiding redundant modeling and significantly improving simulation preparation efficiency. The database also supports expansion. New models can be added simply by entering geometric parameters and generating the corresponding model, ensuring the method's universality across different product specifications.

[0045] Step 202: Select a matching standard geometric model from the assembly database based on the geometric parameter data of the fiber optic panel workpiece, mold, and vacuum hot press furnace.

[0046] In this step, geometric parameter data refers to key dimensional values ​​describing the physical shape of the actual workpiece, such as the height, side-to-side distance, and radius of curvature of the fiber optic panel; the internal cavity dimensions and fitting clearance of the mold; and the effective heating zone diameter and height of the furnace. Matching refers to comparing the input geometric parameters with the preset parameter ranges of standard models in the database to select models with consistent geometric features or whose errors are within tolerance.

[0047] Specifically, the first step is to collect the geometric parameter data of the target component. Accurate data is obtained through two methods: for components with 3D models, key dimensions (such as the height and opposite side length of the fiber optic panel, and the gap between the mold's collar and slider) are directly extracted from CAD or Solidworks files; for solid components without models, high-precision measuring tools (such as laser rangefinders and vernier calipers) are used for actual measurement, recording dimensional values ​​and structural features (such as whether the heating element layout of the vacuum autoclave is divided into upper / middle / lower sections). Simultaneously, the compatibility relationships of each component are clarified (e.g., the fiber optic panel of sample X needs to be matched with a type D mold). Next, matching principles are established, such as prioritizing dimensional errors ≤ ±0.5mm and complete structural consistency as primary matching conditions; for non-core parameters (such as the fine structure of non-critical areas on the component surface), small differences are allowed, but must ensure that they do not affect simulation logic such as heat conduction and radiation. For example, when matching the fiber optic panel model, priority is given to comparing core dimensions such as opposite side length and height, and the fiber arrangement direction must be completely consistent; when matching the mold model, the focus is on confirming the gap dimensions between the collar and slider, the outer ring, and the assembly method. Finally, the retrieval and filtering process is performed: The target geometric parameters are input into the assembly database management system, and the system automatically retrieves standard geometric models that meet the criteria, generating a candidate model list. The parameter specifications of the candidate models are manually reviewed to confirm that the dimensions and structure perfectly match the target component. Then, the model files (e.g., .step, .iges format) are extracted to prepare for subsequent integrated geometric model assembly. If no perfectly matching model is found, the parameters can be fine-tuned based on the closest standard model, or a new standard model can be constructed and stored in the database.

[0048] Step 203: Based on the component relationships between the fiber optic panel workpiece, the mold, and the vacuum hot press furnace, assemble the standard geometric model of the fiber optic panel workpiece, the mold, and the vacuum hot press furnace to obtain an integrated geometric model.

[0049] In this step, component relationships refer to the spatial constraints and contact logic of each component in physical assembly, such as the mold being placed at the center of the furnace base, the fiber optic panel being embedded in the mold cavity, and the heating element surrounding the furnace. An integrated geometric model refers to a single, continuous three-dimensional geometric body formed by combining multiple independent component models according to their actual assembly relationships, used for multiphysics coupling simulation in a unified coordinate system.

[0050] Specifically, the first step is to clarify the relationships between the components: define the assembly sequence and spatial rules of the three parts. The vacuum hot press furnace serves as the basic carrier, and the mold must be assembled in a designated area within the furnace chamber (such as the center of the furnace chamber). The fiber optic panel workpiece must be embedded inside the mold, ensuring close contact (without additional gaps) with the mold's rings, sliders, and other components. Simultaneously, key contact surfaces (such as the contact surface between the fiber optic panel and the mold, and the contact surface between the mold and the inner wall of the furnace chamber) must be marked. These surfaces are critical areas for heat conduction and radiative heat transfer, and their precise positioning must be ensured during assembly.

[0051] Next, the model is imported and positioned: the three standard geometric models selected in step 202 are imported into the Comsol simulation software and placed sequentially according to the assembly order. First, the vacuum autoclave model is fixed as the reference. Then, based on the furnace zoning and mold adaptation position, the mold model is moved to the specified coordinate area, ensuring that the contact surfaces of the mold and the inner wall of the furnace are completely aligned. Finally, the fiber optic panel model is embedded inside the mold, and its position is adjusted so that key monitoring points such as the center of the upper surface and the center of the inner wall of the fiber optic panel are precisely matched with the corresponding positions of the mold. Using the software's "assembly constraint" function, fixed constraints (such as fixing the relative position of the mold and the furnace) and contact constraints (such as the contact surfaces of the fiber optic panel and the mold) are set to prevent model position shifts during the simulation.

[0052] Finally, model verification and optimization are performed: After assembly, the relative positions of each component are checked to ensure they conform to the actual production scenario, and that key contact surfaces are fully fitted without overlap or excessive gaps. The model is pre-meshed to confirm the structural integrity of the geometric model (e.g., no damage or gaps) to ensure it does not affect subsequent physics settings and simulation calculations. If assembly deviations are found (e.g., misalignment between the fiber optic panel and the mold), they are corrected by adjusting model coordinates and modifying constraints. If geometric conflicts exist (e.g., overlapping components), step 202 is revisited to re-select a matching standard model, or the model structure is fine-tuned. After successful verification, the model is saved as an integrated geometric model file for subsequent construction of the coupled physical model.

[0053] The integrated modeling method based on an assembly database, constructed in steps 201 to 203, significantly improves the accuracy, efficiency, and versatility of simulations of the heating process of fiber optic panel workpieces in a vacuum hot press furnace. First, by pre-establishing a standardized geometric model library covering various specifications and structural types, the tedious process of modeling from scratch for each simulation is avoided, greatly shortening preprocessing time. Second, an intelligent matching mechanism based on actual geometric parameters ensures that the selected model closely matches the real working conditions, effectively reducing simulation deviations caused by model distortion. Finally, by accurately assembling each component according to physical assembly relationships, a complete and continuous integrated geometric model is formed, providing a high-fidelity geometric foundation for subsequent radiation-conduction coupled heat transfer analysis. This method not only supports rapid response to simulation needs of different product models but also has good scalability, suitable for process optimization and temperature field prediction of multi-batch, multi-specification fiber optic panels, thereby improving the yield and process stability of hot pressing molding.

[0054] Step 204: Establish a coupled physical model and calculate material parameters.

[0055] Using simulated and actual temperature curves, inversion calculations are employed to determine the surface emissivity of the fiber optic panel workpiece, mold, and vacuum hot press furnace, as well as the thermal conductivity of the fiber optic panel workpiece. This includes: setting initial values ​​for the surface emissivity of the mold, vacuum hot press furnace, and fiber optic panel based on the surface oxidation condition of the materials; setting initial values ​​for the experimental thermal conductivity of the secondary multifilament of the fiber optic panel in the fiber direction and perpendicular to the fiber direction; inputting the measured temperature of the heating element in the current vacuum hot press furnace, the initial values ​​for surface emissivity, and the initial values ​​for the fiber direction and perpendicular to the fiber direction into a coupled physical model; and then using the coupled physical model to simulate the transient thermal process using the finite element method to output the simulated temperature curve at a second location; when the degree of agreement between the simulated temperature curve and the actual temperature curve is greater than a preset degree of agreement, the surface emissivity of the fiber optic panel workpiece, mold, and vacuum hot press furnace, as well as the thermal conductivity of the fiber optic panel workpiece, are obtained from the coupled physical model.

[0056] Specifically, in the COMSOL simulation platform, based on the integrated geometric model constructed in step 203, the coupled physical fields are set and their parameters are configured. Two physical fields, "Solid Heat Transfer" and "Surface-to-Surface Radiation," are selected and their coupling function is enabled to accurately reflect the heat transfer mechanism dominated by heat conduction and radiation in a vacuum environment. Under the material node, four empty material sub-nodes (corresponding domains) are created: mold, vacuum hot press furnace (including pressure rod, furnace chamber, and support platform), fiber optic panel, and vacuum hot press furnace inner insulation layer (including insulation layer and furnace cover). The built-in "tungsten" material library is used separately for the heating element. Known material parameters—such as the density, constant-pressure heat capacity, and thermal conductivity of the mold and vacuum hot press, and the density and specific heat capacity of the fiber optic panel workpiece (usually provided by the manufacturer)—are filled into the corresponding regions. For the thermal conductivity of the fiber optic panel, due to its significant anisotropy (different thermal conductivity in the fiber direction and perpendicular direction), two initial values ​​need to be set separately—usually taken from experimental measurements of double-fiber structures, for example, 1.1 W / (m·K) in the fiber direction and 0.9 W / (m·K) in the perpendicular direction. The surface emissivity of the fiber optic panel workpiece, mold, and vacuum hot press, as well as the thermal conductivity of the fiber optic panel workpiece, cannot be directly obtained and must be determined through further inversion calculations.

[0057] Secondly, the measured temperature data (temperature boundary conditions) and initial parameters of the heating elements in the vacuum hot press furnace are set. Measured temperature data of the heating elements in the upper, middle, and lower zones of the vacuum hot press furnace are obtained (the sampling time interval is a preset time interval, such as 1 minute). The surface of the heating elements in the upper zone and the inner and outer walls of the furnace are set as the "heat source temperature boundary - upper zone", and the middle and lower zones are set similarly. At the same time, the furnace shell is set as the convective heat flux boundary, and the water-cooled area is set as the fixed low temperature boundary. The initial temperature is uniformly set to 30℃. For the key parameters to be inverted, the initial surface emissivity is set according to the oxidation state of the material: for example, 0.80 for the surface of a long-term used mold (thick oxidation), 0.70 for a newly made optical fiber panel (light oxidation), and 0.75 for the insulation layer; the thermal conductivity of the optical fiber panel is based on the experimental value of the secondary multifilament, with the fiber direction set to 1.1 W / (m·K) and the vertical direction set to 0.9 W / (m·K).

[0058] Subsequently, an inversion calculation based on temperature response is performed. The initial parameters are substituted into the model, and transient thermal simulation is executed. The output is a simulated temperature-time curve for the "second target position" (such as the panel center, mold slider gap, etc.) that strictly corresponds to the spatial location of the measured point. The time sampling points of this curve are completely synchronized with the actual temperature data of the "first target position" collected by thermocouples placed on-site. By calculating the overall consistency between the two curves, it is determined whether the current parameter combination accurately reflects the actual heat transfer behavior. If the consistency does not reach the preset threshold, the surface emissivity and thermal conductivity are slightly adjusted—prioritizing the correction of the parameters most sensitive to temperature response (usually the panel thermal conductivity and mold emissivity), and the simulation is run again. This iterative process continues until the consistency between the simulated curve and the measured curve throughout the entire heating process (especially the steady-state segment and inflection point) is greater than the preset consistency (e.g., absolute temperature error at the end of heating < 5℃, curve consistency ≥ 94.33%). The final converged emissivity and thermal conductivity are then determined as the effective thermophysical parameters of this batch of workpieces under the current process conditions. This method can be used to construct high-precision three-dimensional radiation-conduction coupled physical models, providing reliable input for subsequent temperature field prediction. It effectively solves the problem of directly measuring material parameters, improving simulation accuracy and engineering applicability. It is worth noting that when obtaining the surface emissivity of multiple sets of fiber optic panel workpieces, molds, and vacuum hot press furnaces, as well as the thermal conductivity of the fiber optic panel workpieces, to obtain the final set of data, one can compare the degree of agreement among the various sets of data and select the set with the highest degree of agreement, or choose according to the actual situation.

[0059] Based on this, it can be seen that by constructing a physical model that deeply couples solid heat transfer and surface-to-surface radiation, and combining it with an inversion algorithm driven by measured temperature, the anisotropic thermal conductivity and surface emissivity of key components of the fiber optic panel workpiece, which are difficult to measure directly, can be accurately identified. This method is based on a high-fidelity integrated geometric model, employing boundary conditions and sampling timing completely synchronized with the actual process to ensure strict alignment between simulation and measured data in the spatiotemporal dimensions. Through iterative optimization, the simulated temperature curve closely matches the measured curve, thereby obtaining effective material parameters applicable to the current batch and operating conditions. The obtained parameters significantly improve the accuracy of temperature field simulation during vacuum hot pressing, providing reliable technical support for process stability control, heating regime optimization, and product yield improvement, combining high precision, strong adaptability, and engineering feasibility.

[0060] Step 205: Based on material parameters and combined with the coupled physical model, determine the three-dimensional radiation and conduction coupled physical model, and predict the temperature of the optical fiber panel workpiece based on the temperature change law of the optical fiber panel workpiece during the heating process in the heat exchange law.

[0061] In this embodiment, the material parameters determined by the inversion in step 204 are accurately entered into the coupled physical model, replacing the original corresponding values ​​to ensure that the thermal properties of each component in the model truly reflect the current process conditions. Subsequently, the configured "solid heat transfer" and "surface-to-surface radiation" coupled physical field structures, the temperature boundary conditions of the upper / middle / lower zones of the furnace, and the component assembly relationships are retained in the COMSOL Multiphysics platform to form a complete three-dimensional radiation-conduction coupled physical model. To balance computational efficiency and accuracy, an adaptive meshing strategy is adopted: local mesh refinement is performed in areas with large temperature gradients or dense monitoring points, such as inside the fiber optic panel and in the gaps between molds, while a coarser mesh is used in other areas. Next, a transient study is created, setting the solution time range to be consistent with the actual process cycle, maintaining a time step of 1 minute to match the sampling frequency of the measured data, and using a split solver to ensure convergence stability. After starting the solution, the model, based on the law of conservation of energy and the radiation transfer equation, transiently calculates the radiation-conduction coupled heat exchange process between the workpiece, mold, and furnace body at each moment under vacuum conditions, and outputs dynamic evolution data of the global temperature field. Finally, the temperature-time series of key locations (such as the center of the upper surface, the center of the interior, and the center of the lower surface) of the fiber optic panel workpiece are extracted from the results. Heating curves are then plotted using data visualization tools to visually present the temperature change trend throughout the hot pressing process, providing a high-precision prediction basis for process optimization and defect control. Furthermore, the three-dimensional radiation-conduction coupled physical model can also output the temperature fields of the vacuum hot press furnace, mold, insulation layer, and fiber optic panel workpiece. Transient finite element analysis is performed in COMSOL to calculate the temperature value at each time step of any point (preset point / second target location point) across the entire fiber optic panel workpiece. The temperature-time series of key locations are then extracted from these values ​​to obtain the predicted heating curves and temperature fields.

[0062] It is worth noting that this embodiment also provides the heat transfer control equations for determining the coupled physical model and the three-dimensional radiation-conduction coupled physical model based on the law of conservation of energy; The heat transfer control equation is: Under vacuum conditions, neglecting convection and considering only the heating element as the heat source, the heat transfer control equation simplifies to: in, , Let Cp be the density of the object under study, and Cp be the specific heat capacity of the object under study. The thermal conductivity of the research object is given. The research object can be any one of the following: fiber optic panel workpiece, mold, or vacuum hot press furnace. For transient terms, For convection terms, For heat conduction, This is a radiation source term, determined by the characteristics of the heating element. For other conductive heat sources, P represents the power of the heating element, and V represents the volume per unit area. Here, T is the radiative heat flux vector, and T is the target temperature data.

[0063] In this embodiment, the heat exchange equation on the outer surface of the vacuum hot press furnace is: The radiation transfer within the furnace is described by the radiation transfer equation (RTE), as follows: Under vacuum non-scattering conditions ( The furnace radiation transfer equation can be simplified to: The equation for radiative heat flux divergence is: Furthermore, this embodiment is also based on the simplified heat transfer control equation, The radiative heat flux divergence equation is used to obtain the coupled heat conduction equation of the optical fiber panel workpiece in the vacuum hot press furnace; The coupled heat conduction equation for the fiber optic panel workpiece in the vacuum hot press furnace is: in, Indicates position direction The radiation intensity, where 'a' represents the absorption coefficient. Represents the scattering coefficient, under vacuum conditions. Where n represents the refractive index and T represents the ambient temperature. Represents the scattering phase function. Represents solid angle, It is the Stefan-Boltzmann constant. The physical meaning of radiation energy is the direction. Changes in net flux on It is the radiation attenuation term caused by absorption and scattering by the medium in space. It is a source of heat radiation. It is the scattering contribution of incident radiation from all directions. Launch item, It is an absorption term. The convective heat transfer coefficient is... For surface emissivity, and These are the surface temperature and the ambient temperature, respectively.

[0064] It is worth noting that the coupled heat conduction equation for the fiber optic panel workpiece in the vacuum hot press furnace is used to characterize the temperature change over time. The heat exchange equation on the outer surface of the vacuum hot press furnace is used to ensure that the predicted temperature calculation conforms to the actual heat exchange law of the scenario (the coupled heat conduction equation for the fiber optic panel workpiece in the vacuum hot press furnace), thus avoiding result distortion.

[0065] Step 206: Integrate the three-dimensional radiation and conduction coupled physical model into boundary probe feedback PID control.

[0066] The PID control equation is in the form of: Where u(t) is the control output, u bias This is the reference output, Cset is the set value, c(t) is the measured value, and Cset-c(t) is the error signal. It is an integral variable. It is the rate of change of the measured value, K p It is proportional gain, K i It is the integral gain, K d It is differential gain, It is a proportional term. It is an integral term. This is the differential term. The proportional gain k used in this model is... p =10W / ℃, integral gain k i =0.5W / (℃·s), differential gain k d =0.001Ws / ℃, deviation u bias =0. Maximum power u max =5000W, c set These are measured temperature data. The PID control equations are used to simulate the temperature control logic of an actual furnace, correct the heating power, and improve prediction accuracy.

[0067] Specifically, the implementation of PID control involves: First, clarifying the boundary probe placement rules: selecting only the inner surface of the furnace zone as the detection area to ensure the probes can accurately capture the actual temperature feedback inside the furnace. Dividing the furnace into upper, middle, and lower zones, the heating elements in the upper zone are bound to the inner wall boundary of the upper zone, the heating elements in the middle zone to the inner wall boundary of the middle zone, and the heating elements in the lower zone to the inner wall boundary of the lower zone. This forms a one-to-one correspondence between the probe, heating element, and control area, achieving independent temperature control for each zone, conforming to the zoned heating logic of the actual operation of the vacuum autoclave. PID control parameter configuration: Based on the temperature control characteristics and simulation accuracy requirements of the vacuum autoclave, the core control parameter is configured as follows: proportional gain K. p =10W / ℃, used for rapid response to temperature deviations (e.g., instantly increasing heating power when the central zone temperature is lower than the set value); Integral gain K i=0.5W / (℃・s), used to eliminate steady-state errors (such as small temperature drifts during long-term heat preservation); differential gain K d =0.001W・s / ℃, used to suppress temperature overshoot (e.g., to prevent the temperature from rapidly rising above the target value during the heating phase); reference output u bias =0, maximum power u max =5000W (the upper limit of the rated power of the matching heating element). Setting value C set The measured temperature data of the furnace partition obtained in step 203 is directly used to ensure that the control target is consistent with the actual production process curve. Model integration and solution configuration: In the COMSOL Multiphysics platform, the PID control module is integrated with the three-dimensional radiation and conduction coupled physical model. Through the software's built-in multiphysics coupling interface, the correlation between the control output and the heating element power is established. The input of the PID control equation is the real-time temperature (measured value c(t)) collected by the boundary probe, and the output is the dynamic power u(t) of the heating element, which is fed back to the coupled physical model in real time to replace the original fixed temperature boundary conditions. During the solution, the transient study settings are kept unchanged, the solution time range is consistent with the actual temperature acquisition cycle, and the sampling interval is 1 minute to ensure that the control response and temperature change are synchronized. Control logic operation flow: During the simulation, the PID controller dynamically adjusts according to the following logic: real-time calculation of the error signal e(t)=Cset-c(t), when e(t)>0 (actual temperature is lower than the set value), through the proportional term Rapidly increase heating power, integral term Accumulate deviations and continuously correct them; differential term Anticipate temperature change trends (e.g., increase power output in advance when deviation increases rapidly). When e(t) = 0 (actual temperature equals set value), maintain the current heating power and offset deviations caused by small disturbances through the integral term; when e(t) < 0 (actual temperature is higher than set value), reduce heating power to avoid temperature overshoot and ensure temperature control stability.

[0068] In this step, the accuracy of the model calculation increases as the sampling time interval decreases, based on the data from the temperature sensor controlled by the PID controller. Boundary probes are selected only on the inner surfaces of the furnace zones. The boundary controlled by the upper zone heating element is the inner wall boundary of the upper zone of the furnace; the boundary controlled by the middle zone heating element is the inner wall boundary of the middle zone of the furnace; and the boundary controlled by the lower zone heating element is the inner wall boundary of the lower zone of the furnace.

[0069] The advantages of the above embodiments are as follows: The core advantages of this implementation method are: (1) It restores the actual operating state of the furnace and improves the accuracy of the prediction. The traditional temperature boundary model only takes the highest temperature of the process curve as the boundary condition, which cannot reflect the dynamic power adjustment of the heating element under PID control (such as the actual temperature in the middle zone may be about 10°C higher than the process target), and also ignores the real working condition that the surface of the heating element can reach 1000°C. However, the PID model can simulate the closed-loop temperature control process of the furnace "detection-comparison-adjustment" by integrating boundary probe feedback PID control. It can not only accurately restore the dynamic distribution of the temperature field in the furnace, but also truly present the power fluctuation of the heating element, making the temperature prediction more in line with the actual industrial production and providing a more reliable simulation basis for the design of new hot press furnaces. (2) It omits the development of complex independent PID algorithms and reduces the model development cost. That is, the native PID control algorithm of the vacuum hot press furnace is usually the core asset of the equipment manufacturer, which is difficult to obtain and has a high development difficulty. This implementation directly calls the built-in PID control function of COMSOL, combined with the furnace zoning strategy and measured temperature data. There is no need to write complex control code independently. High-precision temperature control simulation can be achieved simply by configuring parameters and binding boundaries. Compared with the traditional mode of independently developing PID algorithms, the development cycle is shortened by more than 60%, and the large trial and error costs in the algorithm debugging process are avoided, which significantly improves the efficiency of model development. (3) Optimize temperature control accuracy and reduce prediction error. That is, through the synergistic effect of the proportional, integral and derivative terms, PID control can effectively solve the problem of temperature response lag under fixed boundary conditions. For example, in the heating stage, the proportional term responds quickly to the deviation, and the derivative term suppresses overshoot, avoiding stress defects in the fiber optic panel due to excessive local temperature; in the heat preservation stage, the integral term eliminates steady-state error, ensuring the temperature uniformity of each area of ​​the fiber optic panel, so that the absolute value of the temperature error at the end of the heating is <5℃ and the curve matching degree is ≥94.33% (in some embodiments, the absolute value of the temperature error at the end of the heating is <3℃ and the curve matching degree is ≥94.33%), which meets the requirements of high-precision prediction. In addition, independent zone control can specifically adjust the heating power of each zone, alleviate the problem of uneven temperature in the upper and lower zones of the furnace, and further improve the accuracy of temperature field prediction. (4) Supporting the dual needs of process optimization and equipment design. This implementation method can not only be used for process optimization of existing furnaces (such as adjusting PID parameters through simulation to optimize the heating curve to reduce color difference of fiber optic panels), but also provide data support for the design of new vacuum hot press furnaces. For example, by simulating the influence of different probe placement positions and control parameters on the temperature field, the furnace structure and heating element layout can be optimized to avoid product defects caused by unreasonable temperature control design in actual production. At the same time, the model can be flexibly adapted to fiber optic panels and molds of different sizes and shapes, keeping the PID control logic and core parameters unchanged, only adjusting the control area boundary, demonstrating good universality.

[0070] Specifically, this application also provides specific examples, as follows: For example, existing molds (mold types include D, R, and G6000) Figure 3 The images in group a show the shape of the fiber optic panel sample and the fiber optic panel workpieces (workpiece types include X, Z, and P). Figure 3 The images in section b show the mold's external shape and internal structure, as well as the vacuum hot press furnace. Hardware environment: A dual-socket AMD 96-core (192 threads) server ensures efficient large-scale model solving; Software environment: A three-dimensional radiation-conduction coupled model is built based on simulation platforms such as COMSOL Multiphysics to achieve multiphysics solution and control logic integration. Specific fiber optic panel dimensions are as follows: Sample X (with mold D): Height h = 133 mm, length of opposite sides l = 34.5 mm Sample Z (with mold R): Height h = 90mm, length of opposite sides l = 45mm Sample P (with G6000 mold): Length x = 123mm, Width y = 112mm, Height h = 100mm Given the existing geometric parameter data for the mold, fiber optic panel workpiece, and vacuum hot press furnace, corresponding twin geometric models of the mold, fiber optic panel workpiece, and vacuum hot press furnace are established respectively. These geometric models are then imported into the COMSOL Multiphysics simulation platform to obtain an integrated geometric model. The integrated geometric model is constructed within COMSOL. If the geometric models of the mold, fiber optic panel workpiece, or vacuum hot press furnace are not yet established, they can be created based on CAD or COMSOL. For example, if there is no 3D model of the vacuum hot press furnace, a planar axisymmetric geometry needs to be drawn in COMSOL based on the geometric parameters of the vacuum hot press furnace (furnace) and then rotated to obtain the geometric model of the vacuum hot press furnace.

[0071] After constructing the integrated geometric model, this embodiment also obtains the measured temperature of the heating elements in the current vacuum autoclave. To ensure the accuracy of the heating element temperature data and the accuracy of the predicted temperature, the furnace is divided according to the layout of the heating elements within the furnace chamber, and the boundary conditions of each furnace chamber section are determined, which are the temperature data of the heating elements within each section. If the heating element layout is upper / middle / lower zones, the furnace chamber is divided into upper / middle / lower geometric regions. It is worth noting that the temperature boundary region selection rules are as follows: the upper region temperature boundary region selects the surface of the heating elements in the upper zone and the inner and outer walls of the upper furnace chamber; the middle region temperature boundary region selects the surface of the heating elements in the middle zone and the inner and outer walls of the middle furnace chamber; the lower region temperature boundary region selects the surface of the heating elements in the lower zone and the inner and outer walls of the lower furnace chamber. The measured temperature data serves as the input for the temperature boundaries. Specifically, through a furnace temperature sensor (a sensor used for PID control), measured temperature rise data (temperature curves at different sampling intervals) of the inner surface of the furnace chamber section are collected and imported into the integrated geometric model as boundary conditions.

[0072] After obtaining the integrated geometric model, material parameters and their corresponding regions are set in COMSOL according to the physical fields. The physical fields are solid heat transfer and surface-to-surface radiation, set as follows: Figure 4a As shown (a illustrates the material parameters defined by interpolation functions; b illustrates the parameter inputs required for each material sub-node and the solid heat transfer physics field), as... Figure 4b As shown, the details are as follows: Material parameter area selection: (1) Material area for solid heat transfer: Create 4 empty material sub-nodes in the material node (set the window's geometry entity layer selection - domain in each empty material sub-node) and name them respectively: mold, vacuum hot press furnace, fiber optic panel and insulation layer in the vacuum hot press furnace. Select all geometric areas of the mold material, select the pressure rod, furnace chamber and furnace chamber support platform geometric areas for the vacuum hot press furnace (stainless steel material), select the fiber optic panel geometric area for the fiber optic panel material, and select the insulation layer and furnace cover insulation layer geometric areas for the insulation layer material; if the heating element is tungsten material, you can directly create a tungsten material sub-node in the software's material library and select all the geometric areas of the heating element. (2) Material area for surface-to-surface radiation: Create 4 empty material sub-nodes in the material node (set the window's geometry entity layer selection - boundary in each empty material sub-node) and name them respectively: mold surface, furnace stainless steel surface, fiber optic panel surface and insulation layer surface. Similarly, you can select the corresponding boundary according to the material name. (3) Create an interpolation function in the global definition node to transform the values ​​of the material parameters into functions; at the same time, use the interpolation function to create the boundary condition experimental heating curves of the three temperature boundary regions; finally, name and define each function in the parameter sub-node of the global definition node and fill it into the attribute row of each material sub-node.

[0073] Solid heat transfer: (1) In the settings window of the solid heat transfer node, make the following settings: select all domains in the domain selection bar (domain refers to geometric entity); enter the reference temperature of 30℃ in the reference temperature bar; select linear discretization in the discretization bar. (2) The solid 1 sub-node is set to default and is not changed. (3) Change the temperature in the initial value bar of the initial value 1 sub-node to 30℃. (4) The thermal insulation 1 and continuity 1 sub-nodes are set to default and are not changed. (5) Add a heat flux 1 sub-node to the solid heat transfer node and name it furnace shell; locate the boundary selection bar in the heat flux setting window and select all shell boundaries (surfaces) of the furnace in the graphics window; locate the heat flux bar and select convective heat flux, set the heat transfer coefficient to 5W / m2·℃, and set the external temperature to 30℃ (actual temperature in the workshop). (6) Add four temperature sub-nodes to the solid heat transfer node; name the first temperature sub-node as water-cooled boundary and select the water-cooled boundary region in the graphics window; name the remaining three temperature sub-nodes as heat source temperature boundary---upper zone / middle zone / lower zone respectively and select the boundary region according to the above selection rules. (7) Material parameters of the solid heat transfer physical field: The material parameters of solid heat transfer are density, constant pressure heat capacity, and thermal conductivity. The density, constant pressure heat capacity, and thermal conductivity of the mold, furnace stainless steel and insulation layer used in the experiment can be obtained from the manufacturer. The density and specific heat capacity of the fiber optic panel can be obtained from the experiment.

[0074] Surface-to-surface radiation: (1) In the settings window of the surface-to-surface radiation node, make the following settings: select all surfaces in the boundary region; locate the radiation settings bar, set the wavelength correlation of the radiation attribute to constant, the refractive index of the transparent medium n=1, select half cube for the surface-to-surface radiation method, and set the radiation resolution to 256; select linear discretization in the discretization bar. (2) Locate the diffuse surface 1 sub-node, select opacity control for the emitted radiation direction; set the ambient temperature in the environment bar. Environmental radiation (3) Locate the initial value 1 sub-node, and select blackbody / graybody for the initial value in the initial value column. (4) Material parameters of the surface-to-surface radiation physical field: surface emissivity. The surface emissivity of the mold, vacuum hot press, heat insulation layer and fiber optic panel needs to be determined by inversion calculation.

[0075] It is worth noting that, since the optical fiber panel sample is composed of a large number of secondary multifilaments and there are gaps between the multifilaments, the thermal conductivity of the secondary multifilaments cannot be directly used as the thermal conductivity of the optical fiber panel sample in the simulation, but can only be used to reflect the changing trend of the thermal conductivity of the optical fiber panel. At the same time, the surface oxidation degree of the vacuum hot press furnace, mold, heat insulation layer and optical fiber panel is unevenly distributed, and the surface emissivity is difficult to obtain accurately through conventional experiments. Therefore, a set of material parameters (material surface emissivity, thermal conductivity) of optical fiber panels with specific vacuum hot press furnace, specific mold and specific shape and size can be determined by inversion calculation, and the material parameters are a function of temperature. The material parameter inversion calculation can be obtained by the following methods: (1) Given the initial value of the material surface emissivity according to the oxidation condition, the initial value of the thermal conductivity of the optical fiber panel (fiber direction and perpendicular to fiber direction) is set as the experimental value of the secondary multifilament. (2) Perform model calculation (finite element method) based on the temperature boundary condition of a sampling time interval of 1 min. (3) Compare the actual temperature curves of the three locations (the gap between the ring and the slider, the gap between the ring and the outer ring, and the midpoint of the fiber optic panel workpiece) with the simulated temperature curves of the corresponding locations in the model output by the coupled physical model. If the matching degree of the three locations is above the preset matching degree, a set of material parameters is obtained from the parameter iteration optimization results of the coupled physical model.

[0076] Specifically, the surface emissivity of the fiber optic panel workpiece, mold, and vacuum hot press (including the vacuum hot press itself and the insulation layer inside the vacuum hot press), as well as the thermal conductivity of the fiber optic panel workpiece, need to be determined through inversion calculations. The specific determination steps are as follows: Let's take mold D as an example first, such as... Figure 5As shown, (a) illustrates the placement of K-type thermocouples at three locations on the upper surface center (represented by position 1 in the image), the inner center (position 2), and the lower surface center (position 3) of the fiber optic panel workpiece, along with a PID-controlled temperature feedback sensor. (b) shows the component names of the mold and the locations of the thermocouples used to measure the mold temperature. Additional thermocouples are placed in the gaps between the collar and slider, and between the collar and outer ring of mold D, in conjunction with the thermocouple at the inner midpoint of the fiber optic panel workpiece, to monitor temperature changes. Simulations were conducted based on a temperature boundary condition with a sampling time interval of 1 minute. By adjusting material parameters (the surface emissivity of the fiber optic panel workpiece, mold, and vacuum hot press furnace (including the furnace itself and its insulation layer), and the thermal conductivity of the fiber optic panel workpiece), the actual temperature curves at the three locations (the gap between the collar and slider, the gap between the collar and outer ring with additional thermocouples, and the midpoint of the fiber optic panel workpiece) were compared with the simulated temperature curves at the corresponding locations in the model output by the coupled physical model. If the matching at the three locations was above a preset degree of agreement, a set of material parameters was obtained from the parameter iterative optimization results of the coupled physical model, such as... Figure 6 The results of the inversion calculation of the material parameters are shown (where the material parameters are a function of temperature).

[0077] After obtaining the surface emissivity of the fiber optic panel workpiece, mold, and vacuum hot press furnace, as well as the thermal conductivity of the fiber optic panel workpiece, these material parameters are input into a coupled physical model (which already contains the density, constant-pressure heat capacity, and thermal conductivity of the mold and vacuum hot press furnace, the density and specific heat capacity of the fiber optic panel workpiece, and the temperature parameters of the heating elements inside the vacuum hot press furnace). This yields a three-dimensional radiation-conduction coupled physical model. This model is solved transiently using the finite element method to reconstruct the heat exchange law between the fiber optic panel workpiece, mold, and vacuum hot press furnace under vacuum conditions. Based on the temperature change of the fiber optic panel workpiece over time during the heating process, the temperature of the fiber optic panel workpiece is predicted. The method for predicting the temperature of the fiber optic panel workpiece involves performing transient finite element simulation in COMSOL using the measured heating curve as boundary conditions, solving the energy conservation and radiation heat transfer equations, and thus obtaining the temperature evolution data of each point inside the fiber optic panel workpiece over time. The temperature-time series of the target location is then extracted to obtain the predicted heating temperature based on the heat exchange law.

[0078] It is worth noting that the prediction accuracy and universality of the three-dimensional radiation-conduction coupling physical model formed in step 205 and the three-dimensional radiation-conduction coupling physical model with PID control formed in step 206 are explained as follows: By comparing the actual temperature curves of the midpoints inside three different sized fiber optic panel samples (X sample with 34.5mm opposite sides, Z sample with 45mm opposite sides, and P sample with 123mm×112mm) with the simulated temperature curves obtained by the two models respectively, the prediction performance of the models can be intuitively presented. The significance of this comparison lies in two aspects. First, through verification with three samples of different sizes, it proves that the model can not only accurately capture the heating law of a single-specification fiber optic panel, but also adapt to actual production scenarios of different sizes and molds, fully demonstrating the model's universality. Second, the PID control model outputs a higher degree of agreement between the simulated and actual temperature curves, indicating that the PID control model has higher prediction accuracy. This directly confirms the scientific nature of the "integrated coupled modeling + parameter inversion + PID control integration" technical solution, verifies that the model can truly reproduce the heating process of the fiber optic panel in the vacuum hot press furnace, provides reliable data support for process optimization and new furnace design, and also proves that the technical path of omitting the independent development of complex PID algorithms is engineering feasible.

[0079] Furthermore, as a response to the above Figure 1-2 In addition to the implementation of the method embodiment shown, this embodiment of the invention also provides a device for predicting the temperature rise of fiber optic panel workpieces in a vacuum hot press furnace. This device predicts the temperature curve of the fiber optic panel workpiece during the heating process. The embodiment of this device corresponds to the foregoing method embodiment. For ease of reading, this embodiment will not repeat the details of the foregoing method embodiment, but it should be clear that the device in this embodiment can implement all the contents of the foregoing method embodiment. Specifically, as shown... Figure 7 As shown, the device includes: The acquisition unit 71 is used to acquire the geometric and material parameters of the optical fiber panel workpiece, mold and vacuum hot press furnace, as well as the measured temperature of the heating element in the current vacuum hot press furnace. The acquisition unit 71 is used to acquire temperature data of the first target position in the optical fiber panel workpiece or mold within a preset time interval, and generate an actual temperature curve. Establishment unit 72 is used to establish a coupled physical model simulating solid heat transfer and surface radiation heat transfer after constructing an integrated geometric model based on the geometric parameters in acquisition unit 71, combined with material parameters and measured temperature of heating element. The coupled physical model is used to output the simulated temperature curve of the second target position within a preset time and at the same preset time interval. The second target position is the simulated position of the first target position. The calculation data unit 73 is used to determine the surface emissivity of the fiber optic panel workpiece, the mold and the vacuum hot press furnace, as well as the thermal conductivity of the fiber optic panel workpiece, by using the simulated temperature curve in the establishment unit 72 and the actual temperature curve in the acquisition unit 71 and by inversion calculation. Model unit 74 is defined to substitute the surface emissivity and thermal conductivity in the calculation data unit 73 into the coupled physical model to construct a three-dimensional radiation conduction coupled physical model. The three-dimensional radiation conduction coupled physical model uses the finite element method to solve transiently to determine the temperature change law of the optical fiber panel workpiece during the heating process and predict the temperature of the optical fiber panel workpiece. The prediction unit 75 is used to input the temperature of the heating element corresponding to the working condition to be predicted into the three-dimensional radiation conduction coupled physical model in the determination model unit 74 to obtain the predicted temperature of the optical fiber panel workpiece during the heating process.

[0080] Furthermore, such as Figure 8 As shown, the model determination unit 74 further includes: Module 741 is used to determine the heat transfer control equations of the three-dimensional radiation-conduction coupled physical model based on the law of conservation of energy. The heat transfer control equation is: Under vacuum conditions, the heat transfer control equations simplify to: in, , Let Cp be the density of the object under study, and Cp be the specific heat capacity of the object under study. The thermal conductivity of the research object is given. The research object can be any one of the following: fiber optic panel workpiece, mold, or vacuum hot press furnace. For transient terms, For convection terms, For heat conduction, For radiation source terms, For other conductive heat sources, P represents the power of the heating element, and V represents the volume per unit area. This is the radiative heat flux vector.

[0081] Furthermore, such as Figure 8 As shown, the determining module 741 further includes: The heat exchange equation for the outer surface of a vacuum hot press furnace is: The furnace radiation transfer equation is The furnace radiation transfer equation can be simplified to: The equation for radiative heat flux divergence is: Furthermore, such as Figure 8 As shown, the determining module 741 further includes: Based on the simplified heat transfer control equation, The radiative heat flux divergence equation is used to obtain the coupled heat conduction equation of the optical fiber panel workpiece in the vacuum hot press furnace; The coupled heat conduction equation for the fiber optic panel workpiece in the vacuum hot press furnace is: in, Indicates position direction The radiation intensity, where 'a' represents the absorption coefficient. Represents the scattering coefficient, under vacuum conditions. Where n represents the refractive index and T represents the ambient temperature. Represents the scattering phase function. Represents solid angle, It is the Stefan-Boltzmann constant. The physical meaning of radiation energy is the direction. Changes in net flux on It is the radiation attenuation term caused by absorption and scattering by the medium in space. It is a source of heat radiation. It is the scattering contribution of incident radiation from all directions. Launch item, It is an absorption term. The convective heat transfer coefficient is... For surface emissivity, and These are the surface temperature and the ambient temperature, respectively.

[0082] Furthermore, such as Figure 8 As shown, the acquisition unit 71 includes: Module 711 is used to build an assembly database, which stores standard geometric models of fiber optic panel workpieces, molds and vacuum hot press furnaces, and the standard geometric models cover different sizes and structural types. Selection module 712 is used to select a matching standard geometric model from the assembly database in building module 711 based on the geometric parameter data of the fiber optic panel workpiece, mold and vacuum autoclave. Module 713 is obtained, which is used to assemble the standard geometric model of the fiber optic panel workpiece, mold and vacuum hot press furnace in module 712 based on the component association relationship between the fiber optic panel workpiece, mold and vacuum hot press furnace to obtain an integrated geometric model.

[0083] Furthermore, such as Figure 8 As shown, the calculation data unit 73 includes: The setting module 731 is used to set the initial values ​​of the surface emissivity of the mold, vacuum hot press furnace and optical fiber panel based on the oxidation of the material surface, and to set the initial values ​​of the experimental thermal conductivity of the secondary multifilament of the optical fiber panel in the fiber direction and perpendicular to the fiber direction. The simulation module 732 is used to input the initial values ​​of the heating element temperature, surface emissivity, fiber direction and perpendicular fiber direction in the vacuum hot press furnace in the setting module 731 into the coupled physical model, and then use the coupled physical model to simulate the transient thermal process using the finite element method to output the simulated temperature curve at the second position. The sampling time interval between the simulated temperature curve and the actual temperature curve is the same. The output data module 733 is used to obtain the surface emissivity of the fiber optic panel workpiece, the mold, and the vacuum hot press furnace in the coupled physical model, as well as the thermal conductivity of the fiber optic panel workpiece, when the degree of agreement between the simulated temperature curve and the actual temperature curve in the simulation module 732 is greater than the preset degree of agreement.

[0084] Furthermore, such as Figure 8 As shown, the prediction unit 75 further includes: Integrating the three-dimensional radiation-conduction coupled physical model into boundary probe feedback PID control includes: The PID control equation is in the form of: Where u(t) is the control output, u bias This is the reference output, Cset is the set value, c(t) is the measured value, and Cset-c(t) is the error signal. It is an integral variable. Kp is the rate of change of the measured value, Ki is the proportional gain, Kd is the integral gain, and Kd is the differential gain. It is a proportional term. It is an integral term. It is a differential term.

[0085] Furthermore, embodiments of this application also provide a computing device, the computing device comprising: at least one processor, and a memory, wherein the memory stores instructions executable by the processor, the instructions being executed by the processor, thereby enabling the processor to perform the above-described operations. Figure 1 , Figure 2 The method for predicting the temperature rise of fiber optic panel workpieces in a vacuum hot press furnace is described in the article.

[0086] Furthermore, embodiments of this application also provide a readable storage medium for storing a computer program, wherein the computer program, when running, controls the device where the storage medium is located to perform the above-described actions. Figure 1 , Figure 2 The method for predicting the temperature rise of fiber optic panel workpieces in a vacuum hot press furnace is described in the article.

[0087] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for predicting the temperature rise of fiber optic panel workpieces in a vacuum hot press furnace, characterized in that, include: Obtain the geometric and material parameters of the fiber optic panel workpiece, mold, and vacuum hot press furnace, as well as the measured temperature of the heating element in the current vacuum hot press furnace; Temperature data of the first target position in the fiber optic panel workpiece or mold is collected at preset time intervals within a preset time period, and the actual temperature curve is generated. After constructing an integrated geometric model based on the geometric parameters, a coupled physical model simulating solid heat transfer and surface radiation heat transfer is established by combining material parameters and the measured temperature of the heating element in the current vacuum hot press furnace. The coupled physical model is used to output the simulated temperature curve of the second target position within a preset time and at the same preset time interval. The second target position is the simulated position corresponding to the first target position. Using simulated temperature curves and actual temperature curves, inversion calculations were employed to determine the surface emissivity of the fiber optic panel workpiece, the mold, and the vacuum hot press furnace, as well as the thermal conductivity of the fiber optic panel workpiece. Substituting the surface emissivity and thermal conductivity into the coupled physical model, a three-dimensional radiation-conduction coupled physical model is constructed. The three-dimensional radiation-conduction coupled physical model uses the finite element method for transient solution to determine the temperature change law of the optical fiber panel workpiece during the heating process and predict the temperature of the optical fiber panel workpiece. The temperature of the heating element corresponding to the working condition to be predicted is input into the three-dimensional radiation conduction coupled physical model to obtain the predicted temperature during the heating process of the optical fiber panel workpiece.

2. The method according to claim 1, characterized in that, The method includes: Based on the law of conservation of energy, the heat transfer control equations of the three-dimensional radiation-conduction coupled physical model are determined; The heat transfer control equation is: Under vacuum conditions, the heat transfer control equations simplify to: in, , Let Cp be the density of the object under study, and Cp be the specific heat capacity of the object under study. The thermal conductivity of the research object is given. The research object can be any one of the following: fiber optic panel workpiece, mold, or vacuum hot press furnace. For transient terms, For convection terms, For heat conduction, For radiation source terms, For other conductive heat sources, P represents the power of the heating element, and V represents the volume per unit area. This is the radiative heat flux vector.

3. The method according to claim 2, characterized in that, The method includes: The heat exchange equation for the outer surface of a vacuum hot press furnace is: The radiation transfer equation of a vacuum hot press furnace is: The furnace radiation transfer equation can be simplified to: The equation for radiative heat flux divergence is: in, This is the radiative heat flux vector.

4. The method according to claim 1, characterized in that, The method includes: Based on the simplified heat transfer control equation, The radiative heat flux divergence equation is used to obtain the coupled heat conduction equation of the optical fiber panel workpiece in the vacuum hot press furnace; The coupled heat conduction equation for the fiber optic panel workpiece in the vacuum hot press furnace is: in, Indicates position direction The radiation intensity, where 'a' represents the absorption coefficient. Represents the scattering coefficient, under vacuum conditions. Where n represents the refractive index and T represents the ambient temperature. Represents the scattering phase function. Represents solid angle, It is the Stefan-Boltzmann constant. The physical meaning of radiation energy is the direction. Changes in net flux on It is the radiation attenuation term caused by absorption and scattering by the medium in space. It is a source of heat radiation. It is the scattering contribution of incident radiation from all directions. Launch item, It is an absorption term. The convective heat transfer coefficient is... For surface emissivity, and These are the surface temperature and the ambient temperature, respectively.

5. The method according to claim 1, characterized in that, The method includes: An assembly database is constructed, which stores standard geometric models of fiber optic panel workpieces, molds, and vacuum hot press furnaces, and the standard geometric models cover different sizes and structural types. Based on the geometric parameter data of the fiber optic panel workpiece, mold, and vacuum hot press furnace, a matching standard geometric model is selected from the assembly database. Based on the component relationships between the fiber optic panel workpiece, mold, and vacuum hot press furnace, a standard geometric model is assembled from the fiber optic panel workpiece, mold, and vacuum hot press furnace to obtain an integrated geometric model.

6. The method according to claim 3, characterized in that, Using simulated and actual temperature curves, inversion calculations were employed to determine the surface emissivity of the fiber optic panel workpiece, the mold, and the vacuum hot press furnace, as well as the thermal conductivity of the fiber optic panel workpiece, including: The initial values ​​of surface emissivity of the mold, vacuum hot press furnace and optical fiber panel are set based on the surface oxidation of the material. The experimental thermal conductivity of the secondary multifilament of the optical fiber panel is set to the initial values ​​in the fiber direction and perpendicular to the fiber direction. After inputting the measured temperature of the heating element in the current vacuum hot press furnace, the initial value of the surface emissivity, and the initial values ​​of the fiber direction and perpendicular fiber direction into the coupled physical model, the transient thermal process is simulated using the finite element method through the coupled physical model to output the simulated temperature curve at the second position. When the degree of agreement between the simulated temperature curve and the actual temperature curve is greater than the preset degree of agreement, the surface emissivity of the fiber optic panel workpiece, the mold and the vacuum hot press furnace in the coupled physical model, as well as the thermal conductivity of the fiber optic panel workpiece, are obtained.

7. The method according to claim 1, characterized in that, The method further includes integrating the three-dimensional radiation-conduction coupled physical model with boundary probe feedback PID control, and the method also includes: The PID control equation is in the form of: Where u(t) is the control output, u bias This is the reference output, Cset is the set value, c(t) is the measured value, and Cset-c(t) is the error signal. It is an integral variable. Kp is the rate of change of the measured value, Ki is the proportional gain, Kd is the integral gain, and Kd is the differential gain. It is a proportional term. It is an integral term. It is a differential term.

8. A device for predicting the temperature rise of fiber optic panel workpieces in a vacuum hot press furnace, characterized in that, include: The acquisition unit is used to acquire the geometric and material parameters of the fiber optic panel workpiece, mold, and vacuum hot press furnace, as well as the measured temperature of the heating element in the current vacuum hot press furnace. The acquisition unit is used to collect temperature data of the first target position in the optical fiber panel workpiece or mold within a preset time interval, and generate an actual temperature curve. The establishment unit is used to build an integrated geometric model based on the geometric parameters in the acquisition unit, and then, in combination with material parameters and the measured temperature of the heating element in the current vacuum hot press furnace, establish a coupled physical model to simulate solid heat transfer and surface radiation heat transfer. The coupled physical model is used to output the simulated temperature curve of the second target position within a preset time and at the same preset time interval. The second target position is the simulation position corresponding to the first target position. The calculation data unit is used to determine the surface emissivity of the fiber optic panel workpiece, the mold, and the vacuum hot press furnace, as well as the thermal conductivity of the fiber optic panel workpiece, by using the simulated temperature curve in the establishment unit and the actual temperature curve in the acquisition unit and by inversion calculation. A model unit is defined to substitute the surface emissivity and thermal conductivity of the calculation data unit into the coupled physical model to construct a three-dimensional radiation and conduction coupled physical model. The three-dimensional radiation and conduction coupled physical model uses the finite element method for transient solution to determine the temperature change law of the optical fiber panel workpiece during the heating process and predict the temperature of the optical fiber panel workpiece. The prediction unit is used to input the temperature of the heating element corresponding to the working condition to be predicted into the three-dimensional radiation conduction coupled physical model in the determination model unit to obtain the predicted temperature during the heating process of the optical fiber panel workpiece.

9. A storage medium comprising a stored program, characterized in that, When the program is running, it controls the device containing the storage medium to execute the method for predicting the temperature rise of optical fiber panel workpieces in a vacuum hot press furnace as described in any one of claims 1 to 7.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements a method for predicting the temperature rise of fiber optic panel workpieces in a vacuum hot press furnace as described in any one of claims 1 to 7.

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