Digital thermal manipulation method based on image programming

Abstract

The application discloses a digital heat manipulation method based on image programming and relates to the field of computer-aided (CAX) technology, characterized in that for a heat manipulation process involving multiple heat sources, in order to realize accurate control of a target surface temperature field, the process is first discretized according to a given time step to obtain the target temperature field in each time step; the temperature field of the target surface is characterized by a gray-scale image, the image is processed in a fuzzy manner in combination with a heat transfer equation to simulate the evolution process of the temperature field over time, and the required theoretical optimal heating mode is reversely solved; and the input power corresponding to each heat source is calculated by a characteristic decomposition algorithm to approximate the heating mode. Thus, the input power state corresponding to each heat source at each moment is determined, and digital heat manipulation is realized. The application provides a new heat manipulation idea, provides more efficient, accurate and unified operation, and lays a theoretical and technical foundation for automatic and accurate control of a microwave solidification manufacturing process.

Description

Technical Field

[0001] This invention relates to a computer-aided (CAX) technology, particularly a multi-heat source heating control technology for composite materials, specifically a digital thermal control method based on image programming. Background Technology

[0002] Most manufacturing processes require a complementary heat treatment method, such as curing or annealing, which essentially involves controlling the temperature distribution over a relatively long period. Inappropriate temperatures applied to the material during heat treatment can lead to a decline in part quality, such as deformation or excessive residual stress. Therefore, precise and controllable thermal manipulation of the temperature field in target areas of a part is an effective technical approach to improving part quality and performance.

[0003] There are currently various methods for controlling the temperature field in multi-heat-source heating systems. In terms of hardware, some researchers have implanted distributed heating units or microcircuits into substrates, others have arranged multiple microwave sources in resonant cavities to achieve microwave power feeding, and still others have utilized thermal fluidization within anisotropic artificial structural materials (also known as thermal metamaterials). Regarding control algorithms, some researchers adjust process curves by setting temperature control programs, others have built neural networks to predict the correspondence between heat source states and temperature fields, and still others have introduced topological consistency algorithms from algebraic graph theory to represent the power feed state of multiple heat sources, and so on.

[0004] While the aforementioned methods achieve some degree of temperature field control, most remain quasi-static, targeting only the final temperature field and neglecting to consider the time-varying temperature field. In principle, dynamic temperature field control is challenging, requiring precise characterization of the heat transfer process. Furthermore, calculating the input power of each heat source to accurately track the target temperature field is mathematically an inverse problem; existing iterative optimization methods are inefficient and struggle to provide optimal solutions, failing to meet the demands for precise and efficient thermal control.

[0005] This invention proposes a digital thermal control method based on digital image programming, which accurately calculates the evolution of the temperature field over time and then achieves digital thermal control by controlling the time-varying power state. This method provides more efficient, accurate, and consistent operation, offering a new approach to digital thermal control and laying the theoretical and technical foundation for the automated and precise control of manufacturing processes such as microwave curing. Summary of the Invention

[0006] The purpose of this invention is to address the problem that most existing multi-heat source heating controls are quasi-static controls, which result in low control accuracy. The invention proposes a digital thermal control method based on digital image programming to accurately realize numerical control of the heating process.

[0007] The technical solution of this invention is:

[0008] A digital thermal control method based on digital image programming, comprising the following steps:

[0009] First, for a thermal manipulation process involving multiple heat sources, in order to achieve precise control of the target surface temperature field, the process is first discretized according to a given time step to obtain the target temperature field within each time step. ;

[0010] Secondly, the temperature field of the target surface The temperature field evolution over time is simulated by characterizing the image using grayscale images and then processing the image by blurring it in conjunction with the heat transfer equation.

[0011] Third, the evolution process is solved in reverse to obtain the results at each time step. achieve The required theoretical optimal heating mode The input power corresponding to each heat source was calculated using an eigenvalue decomposition algorithm. To approximate this heating mode;

[0012] Finally, the input power state of each heat source at each moment is determined by the above method, thereby realizing digital thermal control.

[0013] in:

[0014] In the aforementioned thermal manipulation process, the temperature field of the target surface is generated by the superposition of multiple heat sources, which can be represented as a grayscale image, where the grayscale value of each pixel represents the temperature value at that location.

[0015] The aforementioned evolution process refers to characterizing the temperature field of the target region using grayscale images. and the heating modes of each heat source acting on this area. The temperature field is obtained by combining the heat transfer equation with blurring and other processing of the image. The evolution process over time under the coupled effects of heat source heating, heat diffusion, and heat dissipation.

[0016] The heating mode It refers to the power density distribution received by the target area to be heated under the action of the heat source in the heating system.

[0017] The inverse solution mentioned above refers to the inverse problem of the aforementioned evolution process. Based on the given temperature field evolution objective... Assuming a heating mode The temperature field under this heating mode was calculated using an evolutionary process model at a specific time step. The actual temperature field after , target and The difference is used as a compensation term to optimize the heating mode, and the solution is iteratively solved to improve the actual temperature field after this time step. Approaching the evolutionary goal as closely as possible To obtain the optimal heating mode .

[0018] The aforementioned feature decomposition algorithm refers to a linear optimization algorithm that identifies the optimal heating mode. Heating modes corresponding to each heat source under unit power linear superposition Characterization is performed to achieve the optimal heating mode. The maximum approximation.

[0019] The aforementioned digital thermal control refers to the fact that the power state of each heat source calculated by the above method at each time step can be described by digital control codes to control the heating system.

[0020] The beneficial effects of this invention are:

[0021] (1) This invention provides a digital thermal control method based on digital image programming, which accurately and quickly determines the input power state of each heat source at each moment during the heating process, and uses it as the control command for thermal control. Moreover, this control command strategy can ensure excellent temperature uniformity and consistency with the target heating curve.

[0022] (2) This invention provides more efficient, accurate and uniform operation, and provides a new thermal control idea of ​​digital thermal control, laying the foundation for the automation of heating processes such as microwave curing.

[0023] (3) The present invention is simple, effective and versatile. It can be used in many multi-heat source heating systems, such as microwave curing of carbon fiber composite materials, zone heating, and annealing of metal materials. Attached Figure Description

[0024] Figure 1 This is the overall flowchart of the present invention (Digital thermal control framework for advanced composite material curing process).

[0025] Figure 2 For heat transfer simulation in a unified digital image domain.

[0026] Figure 3 For the accuracy evaluation of image-based heat transfer models.

[0027] Figure 4 The calculation process for the theoretically optimal heating mode that varies with time.

[0028] Figure 5 The simulation results are for two different heating processes. Figure 5 (a) is a simulation result diagram with a uniform temperature field as the heating target. Figure 5 (b) is a simulation result of heating a non-uniform temperature field.

[0029] Figure 6 The results of the actual heating experiment are as follows: (a) Experimental setup; (b) Simulation results; (c) Actual heating results. Specific implementation methods

[0030] The specific implementation method of the present invention will be described below with reference to the accompanying drawings and examples. However, the present invention is not limited to these examples.

[0031] like Figure 1-6 As shown.

[0032] A digital thermal control method based on digital image programming, taking the microwave curing process of carbon fiber composite materials as an example, is illustrated in the overall flowchart below. Figure 1 As shown, the specific steps are as follows:

[0033] Step 1: For a heating process involving multiple heat sources, the time-varying temperature field of the target area and the heating mode of each heat source acting on the area are characterized by digital images. The evolution of the temperature field over time under the coupling effect of heat source heating, heat diffusion and heat dissipation is obtained by digital image processing simulation.

[0034] Given a thin-walled part placed inside a microwave curing jar We define the surface domain of the component. Temperature distribution over time To simulate a simplified heat transfer process, considering the temperature gradient along the thickness direction. This can be ignored. Therefore, the differential heat conduction equation can be expressed as:

[0035]

[0036] in, and These are density, specific heat, and in-plane thermal conductivity, respectively. It is the distribution of internal heat sources generated by the heating provider. It is the heat dissipation distribution from the surface to the air, which can be expressed as a thermal convection function using Newton's law of cooling:

[0037]

[0038] in It is the heat transfer coefficient. Indicates the temperature of the surrounding air fluid. V is the thickness of the part, A is the volume, A is the surface area, and T is the temperature on the surface.

[0039] Temperature distribution on the surface domain at any given time Can be derived from grayscale images A precise representation, where each pixel Store the temperature value at the corresponding location. Simultaneously, the heat source... It is possible to be with Represented in the same domain. In this way, the differential heat conduction process can be uniquely transformed into an iterative image processing step, such as... Figure 2 As shown:

[0040]

[0041] in It is a small time step. Represents the Laplace nucleus.

[0042] As long as the initial temperature distribution is provided and heat source distribution The temperature change can then be directly determined using the above formula, with acceptable accuracy and extremely high efficiency. Compared to actual measurements, Figure 3 The preliminary simulation test shown demonstrates a temperature prediction accuracy exceeding 99%. However, the temperature distribution... The expected behavior is to follow a prescribed temperature curve, therefore the problem is conversely defined as optimizing a time-dependent heat source distribution. This is also known as heat source estimation.

[0043] Step 2: Based on the given temperature field evolution target, at a given time step... The heating process is discretized, and the theoretically optimal heating mode is solved in reverse for the temperature field evolution target within each time step.

[0044] By continuously adjusting the heat source, the actual curing temperature is controlled to approximate the target temperature curve. arrive The time interval, The initial temperature is known, denoted as The target temperature image is set according to the curing temperature curve. The key is to estimate a sufficient distribution of heat sources. This causes the temperature to be distributed over time. Time to reach .

[0045] This paper employs an iterative approach to solve the aforementioned inverse problem, utilizing an image processing-based model that is both accurate and computationally efficient. (Heat source) The estimation begins with an initial guess by eliminating in-plane heat conduction processes. Although this initial value barely satisfies the accuracy of the estimation, we can use it to apply to heat transfer simulations to obtain the resulting temperature. . With the goal The discrepancies will trigger updates to the estimated heat sources, and after several iterations, will gradually converge to a level close to the actual ones. This results in a heat source distribution... This is only the theoretically optimal heating mode. The actual heat source is a weighted sum of various heat sources, so we need to find the optimal power state to most closely approximate the required heat source distribution.

[0046] Step 3: Calculate the input power corresponding to each heat source using the feature decomposition algorithm. To approximate the optimal heating mode, the input power state of each heat source at each moment is determined, and this is used as the control command for the heating system to achieve digital thermal control.

[0047] Assuming the curing system has Each heat source, under a uniform power condition, generates a unique heat source distribution. Furthermore, each heat source is linearly independent and does not affect the others. Therefore, it is necessary to find a weighted power ratio. To determine the power state, such that the weighted sum It can best approach the theoretical optimal value. This allows for the manipulation of time-varying temperature distribution by setting a power state that changes over time, thereby numerically controlling the heating element. With the aid of thermal imaging, algorithms determine the heat source distribution for each heating element under a uniform power state. . There is. Find the weighted power ratio The problem can be defined as a linear programming problem:

[0048]

[0049] in and These are the minimum and maximum allowable power ratios specified for the heating element. To solve this problem, the QR decomposition method is first used to distribute the linear independent heat sources. Transform into an orthogonal basis The QR decomposition method decomposes matrix M into the product of an orthogonal matrix Q and an upper triangular matrix R, where each column of the orthogonal matrix Q represents an orthogonal basis of the column space of M. Once the orthogonal basis of the embedding matrix Q is calculated, the weighted power ratio... That's how you get the result.

[0050] The weighted power ratio First, regularization is needed to round the values ​​to a specified range. This process of identifying the power state is... Figure 4 The process is defined and executed periodically to determine the optimal heating strategy for the upcoming temperature profile. Ultimately, the time-related power state can be encoded into a format similar to "G-code" to digitally control the curing system, such as... Figure 4 As shown.

[0051] To verify the effectiveness of the present invention, the following implementation case is provided, using the thermal control of microwave curing of carbon fiber composite materials as an application scenario.

[0052] This implementation case includes two parts: simulation and actual heating test, both of which are conducted in an octagonal microwave cavity, with 12 microwave magnetrons distributed on the side walls as the heat source (see...). Figure 6 (a) A rectangular composite laminate is placed inside the cavity and heated according to a given temperature profile. Uniform heat source distribution for each magnetron. The system was individually calibrated by heating for 5 minutes with a 100-watt power input. The time-varying power state planned for numerical control is presented in matrix form, where each row represents a weighted power ratio for a specific time step (30 seconds in the simulation test). The allowable range of the power ratio is set as follows: This means that each magnetron can be switched off or powered at 100 to 1000 watts. The output temperature is expected to match the target curve.

[0053] Simulation results show that when the target temperature field is set to a uniform target, the maximum in-plane temperature difference can be controlled within a certain range, such as... Figure 5 As shown in (a); however, when the target temperature field is set as a non-uniform target (such as...), Figure 5 (b) shows that the temperature field tends to converge towards the target distribution, such as Figure 5 As shown in (c).

[0054] The experimental heating test involved heating the composite material plate to 100°C. The entire heating process was numerically controlled using an optimized power state, with a time step set to 300 seconds per row. The actual temperature distribution was recorded using a thermal imager and analyzed by [the relevant data - likely a data processing unit]. Figure 6 The temperature curves in (c) are shown. The actual temperature changes are... Figure 6 The simulation results shown in (b) are in good agreement, qualifying the proposed method for precise thermal control in practical applications. Therefore, the effectiveness of the method proposed in this invention is demonstrated.

[0055] The parts not covered in this invention are the same as or can be implemented using existing technologies.

Claims

1. A digital thermal control method based on digital image programming, characterized in that: For a thermal manipulation process involving multiple heat sources, in order to achieve precise control of the target surface temperature field, the process is first discretized according to a given time step to obtain the target temperature field within each time step. ; the temperature field of the target surface The image is represented by a grayscale image, and then blurred using the heat transfer equation to simulate the evolution of the temperature field over time, thereby inversely solving for the temperature at each time step. achieve The required theoretical optimal heating mode The input power corresponding to each heat source was calculated using an eigenvalue decomposition algorithm. To approximate the heating mode; thereby determining the input power state of each heat source at each moment, realizing digital thermal control; the inverse solution refers to the inverse problem of the above evolution process; based on the given temperature field evolution target Assuming a heating mode The temperature field under this heating mode was calculated using an evolutionary process model at a specific time step. The actual temperature field after , target and The difference is used as a compensation term to optimize the heating mode, and the solution is iteratively solved to improve the actual temperature field after this time step. Approaching the evolutionary goal as closely as possible To obtain the optimal heating mode The aforementioned eigenvalue decomposition algorithm refers to a linear optimization algorithm that identifies the optimal heating mode. Heating modes corresponding to each heat source under unit power linear superposition Characterization is performed to achieve the optimal heating mode. The maximum approximation; for any given time, the temperature distribution on the surface domain. From grayscale image A precise representation, where each pixel Store the temperature value at the corresponding location; simultaneously, the heat source In Represented in the same domain; thus, the differential heat conduction process is uniquely transformed into an iterative image processing step: ； In the formula It is a time step. Represents the Laplace nucleus; For the initial temperature distribution, , It is the in-plane thermal conductivity. It is the heat transfer coefficient. It is the temperature of the surrounding air fluid. It refers to the thickness of the part.

2. The method according to claim 1, characterized in that: In the aforementioned thermal manipulation process, the temperature field of the target surface is generated by the superposition of multiple heat sources, which can be represented as a grayscale image, where the grayscale value of each pixel represents the temperature value at that location.

3. The method according to claim 1, characterized in that: The aforementioned evolution process refers to characterizing the temperature field of the target region using grayscale images. and the heating modes of each heat source acting on this area. The temperature field is obtained by blurring the image using the heat transfer equation. The evolution process over time under the coupled effects of heat source heating, heat diffusion, and heat dissipation.

4. The method according to claim 3, characterized in that: The heating mode It refers to the power density distribution received by the target area to be heated under the action of the heat source in the heating system.

5. The method according to claim 1, characterized in that: The aforementioned digital thermal control refers to the description of the power state of each heat source at each time step, calculated by the above method, through digital control codes, in order to control the heating system.

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