System and method for estimating product simulation parameters

Abstract

The invention provides a system and a method for estimating product simulation parameters. The method comprises the following steps that a product thermal image corresponding to a product is obtained, the product temperature of the product in a detected oven is obtained, and equipment information, the product thermal image and the product temperature correspond to the same time point; and utilizing the product thermal image, the product temperature, the product information and the equipment information to obtain a product virtual coefficient corresponding to the product, and utilizing the product virtual coefficient to estimate a first product simulation parameter when the product is in the oven to be tested.

Description

Technical Field

[0001] The present invention relates to a system and method for estimating simulation parameters of a product. Background Art

[0002] In the technical field of hot processing processes, usually only a small number of sensors are arranged in some areas of hot processing equipment, resulting in the inability to obtain product parameters of a product in different ovens of the hot processing equipment, such as product temperature and product moisture content. However, if a large number of sensors are arranged, although more information can be obtained, due to the high price of the sensors, the cost will increase significantly. Therefore, in the technical field of hot processing processes, usually only the experience of personnel can be relied on to adjust the hot processing equipment to ensure the quality of the product. Summary of the Invention

[0003] The system for estimating simulation parameters of a product according to the present invention is used for hot processing equipment and a product, wherein the hot processing equipment includes a measured oven and at least one to-be-measured oven, wherein the product corresponds to product information, wherein the hot processing equipment corresponds to equipment information, and wherein the system includes a first thermal imaging sensor, a second thermal imaging sensor, a temperature sensor, a storage medium, and a processor. The first thermal imaging sensor is arranged at the inlet section of the hot processing equipment. The second thermal imaging sensor is arranged at the outlet section of the hot processing equipment. The temperature sensor is arranged in the measured oven. The storage medium stores a sensing data obtaining unit and a first product simulation parameter estimating unit, wherein the sensing data obtaining unit is used to obtain a product thermal image corresponding to the product through the first thermal imaging sensor and the second thermal imaging sensor, and to obtain the product temperature of the product in the measured oven through the temperature sensor, wherein the equipment information, the product thermal image, and the product temperature correspond to the same time point; the first product simulation parameter estimating unit is used to obtain a product virtual coefficient corresponding to the product by using the product thermal image, the product temperature, the product information, and the equipment information, and to estimate a first product simulation parameter of the product in the to-be-measured oven by using the product virtual coefficient. The processor is coupled to the first thermal imaging sensor, the second thermal imaging sensor, the temperature sensor, and the storage medium, and is used to access and execute the sensing data obtaining unit and the first product simulation parameter estimating unit.

[0004] The method for estimating product simulation parameters of the present invention is used for a hot processing device and a product, wherein the hot processing device includes a measured oven and at least one oven to be measured, wherein the product corresponds to product information, wherein the hot processing device corresponds to device information, and the method includes the following steps: obtaining a product thermal image corresponding to the product, and obtaining the product temperature when the product is in the measured oven, wherein the device information, the product thermal image, and the product temperature correspond to the same time point; and using the product thermal image, the product temperature, the product information, and the device information to obtain a product virtual coefficient corresponding to the product, and using the product virtual coefficient to estimate a first product simulation parameter when the product is in the oven to be measured. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] Figure 1 FIG. is a schematic diagram of a system for estimating product simulation parameters according to an embodiment of the present invention.

[0006] Figure 2 FIG. is a flowchart of a method for estimating product simulation parameters according to an embodiment of the present invention.

[0007] Figure 3A is Figure 2 An exemplary embodiment of "obtaining a product virtual coefficient corresponding to the product" in step S230 shown.

[0008] Figure 3B is Figure 2 Another exemplary embodiment of "obtaining a product virtual coefficient corresponding to the product" in step S230 shown.

[0009] Figure 4A FIG. is a schematic diagram of estimating a two-dimensional second product simulation parameter according to an embodiment of the present invention.

[0010] Figure 4B is Figure 4A Further illustration of.

[0011] Wherein, the reference numerals are

[0012] 100: System for estimating product simulation parameters

[0013] 110: First thermal image sensor

[0014] 120: Second thermal image sensor

[0015] 130: Temperature sensor

[0016] 140: Storage medium

[0017] 141: Sensing data acquisition unit

[0018] 142: First product simulation parameter estimation unit

[0019] 143: Second product simulation parameter estimation unit

[0020] 150: Processor

[0021] 160: Moisture content sensor

[0022] S210, S230: Steps

[0023] T i1 and T i2 and T i3 : First product simulation parameters

[0024] M o1 and M o2 and M o3 : Second product simulation parameters

[0025] 1, 2, 3: Pixel points Detailed implementation mode

[0026] Figure 1 is a schematic diagram of a system 100 for estimating product simulation parameters illustrated according to an embodiment of the present invention. Please refer to Figure 1 . It should be noted first that the system 100 for estimating product simulation parameters can be used for thermal processing equipment and products, and can further estimate the simulation parameters of the products (such as temperature and moisture content). Specifically, the thermal processing equipment may include a measured oven and at least one oven to be measured. The oven to be measured may be the same oven as the measured oven, and there is no limitation here. In addition, the product may correspond to product information. On the other hand, the thermal processing equipment may correspond to equipment information. In this embodiment, the system 100 for estimating product simulation parameters may include a first thermal imaging sensor 110, a second thermal imaging sensor 120, a temperature sensor 130, a storage medium 140, a processor 150, and a moisture content sensor 160. The first thermal imaging sensor 110 may be disposed at the inlet section of the thermal processing equipment. The second thermal imaging sensor 120 may be disposed at the outlet section of the thermal processing equipment. The temperature sensor 130 may be disposed in the measured oven. The storage medium 140 may store a sensing data acquisition unit 141, a first product simulation parameter estimation unit 142, and a second product simulation parameter estimation unit 143. The processor 150 may be coupled to the first thermal imaging sensor 110, the second thermal imaging sensor 120, the temperature sensor 130, the storage medium 140, and the moisture content sensor 160, and access and execute the sensing data acquisition unit 141, the first product simulation parameter estimation unit 142, and the second product simulation parameter estimation unit 143. The moisture content sensor 160 may be disposed at the (outlet section of the) thermal processing equipment. In other embodiments, the system 100 for estimating product simulation parameters may omit the moisture content sensor 160 and the second product simulation parameter estimation unit 143.

[0027] Figure 2 is a flow chart of a method for estimating product simulation parameters according to an embodiment of the present invention, wherein the method may be Figure 1 The system 100 for estimating product simulation parameters is shown. Figure 1 and Figure 2 .

[0028] First, step S210 is performed to obtain a product thermal image corresponding to the product, and obtain the product temperature of the product when it is in the measured oven, wherein the device information, the product thermal image, and the product temperature correspond to the same time point. Specifically, in step S210, the sensing data obtaining unit 141 can obtain the product thermal image corresponding to the product through the first thermal image sensor 110 and the second thermal image sensor 120, and can obtain the product temperature of the product in the measured oven through the temperature sensor 130, wherein the device information, the product thermal image, and the product temperature correspond to the same time point. In other words, the device information, the product thermal image, and the product temperature obtained at the same time point can be used in the subsequent steps of the present invention after being integrated / imaged.

[0029] Then, step S230 is executed to obtain a product virtual coefficient corresponding to the product using the product thermal image, product temperature, product information, and equipment information, and to estimate a first product simulation parameter of the product when it is in the oven to be tested using the product virtual coefficient. Specifically, in step S230, the first product simulation parameter estimation unit 142 can obtain a product virtual coefficient corresponding to the product using the product thermal image, product temperature, product information, and equipment information, and to estimate a first product simulation parameter of the product when it is in the oven to be tested using the product virtual coefficient. In this embodiment, the first product simulation parameter is the simulated temperature of the product. The following will describe an implementation example of "obtaining a product virtual coefficient corresponding to the product" in step S230.

[0030] Figure 3A yes Figure 2 An example of an implementation of "obtaining a product virtual coefficient corresponding to a product" in step S230 shown in FIG. Figure 1 , Figure 2 and Figure 3A In this embodiment, the first product simulation parameter estimation unit 142 can use the product thermal image, product temperature, product information and equipment information to convert the energy conservation equation φ(τ)=h(τ)[T w (τ)-T(τ)] is converted into an ordinary differential equation In one embodiment, the equipment information may include the measured oven temperature corresponding to the measured oven, and the equipment information may include the oven temperature to be measured corresponding to the oven to be measured. In other embodiments, the equipment information may further include, but is not limited to, process parameters, host speed, and exhaust air speed. In one embodiment, the product information may include, but is not limited to, fabric, yard weight, additives, width value, density, specific heat capacity, and thickness.

[0031] Then, the first product simulation parameter estimation unit 142 may analyze the ordinary differential equation to obtain an iterative equation Then, the first product simulation parameter estimation unit 142 may perform Bayesian optimization fitting on the product virtual coefficient by using the product information and the equipment information. Next, the first product simulation parameter estimation unit 142 may substitute the product information, the equipment information, and the product virtual coefficient into the iterative equation to obtain the first product simulation parameter. In one embodiment, the above Bayesian optimization fitting may include the following steps (a), (b), and (c).

[0032] (a) The first product simulation parameter estimation unit 142 uses the iterative equation to solve for the first product simulation parameter, where the first product simulation parameter includes the minimum value of the first product simulation parameter.

[0033] (b) The first product simulation parameter estimation unit 142 updates the product virtual coefficient by using the difference between the product temperature and the minimum value of the first product simulation parameter.

[0034] (c) The first product simulation parameter estimation unit 142 determines whether the difference between the product temperature and the minimum value of the first product simulation parameter corresponds to the minimum mean absolute error percentage value. Wherein, if the judgment result is "no", step (b) is repeated; conversely, if the judgment result is "yes", the product virtual coefficient is obtained

[0035] Figure 3B Yes Figure 2 Another implementation example of "obtaining the product virtual coefficient corresponding to the product" in step S230 shown. Please refer to Figure 1 、 Figure 2 And Figure 3B . In this embodiment, the product and the thermal processing equipment may correspond to the heating stage. Specifically, the heating stage may include a first stage (product temperature is less than T transit ), a second stage (product temperature reaches T transit ), and a third stage (product temperature exceeds T transit ). T transitFor example, it is 100 °C. In other words, the above heating stage is the different stages of the product being heated in the thermal processing equipment. The first product simulation parameter estimation unit 142 can obtain the product virtual coefficient by using the energy supply, mass change, and temperature difference change of the product during the heating stage.

[0036] In this embodiment, assume that T transit is the evaporation temperature, T product is the product temperature, X0 is the initial moisture content of the product, m total is the total inlet mass of the product, and u is the speed of the product in the thermal processing equipment. As shown in Equation 1, the moisture content (MC) can be obtained based on the moisture regain (MR). For example, the moisture content can be 40%. As shown in Equation 2, the mass of the product (m product ) can be obtained through the outlet weight of the product (m final ), the initial moisture content of the product (X0), and the outlet moisture content of the product (X1). As shown in Equation 3, the total inlet mass of the product ( mtotal ) can be obtained from the initial moisture content of the product (X0) and the mass of the product (m product ). Before the moisture of the product reaches the boiling point (T transit ), the product and moisture coexist. Therefore, the first product simulation parameter estimation unit 142 can use the method of mixed specific heat to estimate the theoretical value of heat transfer of the actual product in the oven. The calculation method of the mixed density (ρ mix ) can be as shown in Equation 4. On the other hand, the calculation method of the mixed specific heat (Cp mix ) can be as shown in Equation 5. The calculation method of the Reynolds number (Re) can be as shown in Equation 6, where ρa is the density of hot air, V j et is the flow velocity of the orifice jet, D is the orifice diameter, and μ is the dynamic viscosity. The calculation method of the Nusselt number (Nu) can be as shown in Equation 7, where Pr is the Prandtl number, H is the distance from the orifice to the product, D is the orifice diameter, f is the relative orifice area, and Re is the Reynolds number. The calculation method of the heat transfer coefficient (h) can be as shown in Equation 8, where Nu is the Nusselt number, k is the thermal conductivity, and D is the orifice diameter.

[0037]

[0038]

[0039] m totat =(X0 × m product ) + m product ...(Equation 3)

[0040]

[0041]

[0042] where ρ mix is the mixed density, m wi is the weight of the inlet water, ρ w is the density of water, ρ product is the density of the fabric of the product, C p_mix is the mixed specific heat, C p_w is the specific heat of water, C p_product is the specific heat of the product.

[0043]

[0044]

[0045]

[0046] Furthermore, the convective heat quantity (Q) can be calculated as in Formula 9, where h is the heat transfer coefficient, A is the fabric area, T oven is the measured oven temperature, T product is the product temperature. The radiative heat quantity (Q rad ) can be calculated as in Formula 10, where ε is the emissivity, σ is the Stefan-Boltzmann constant, T oven is the measured oven temperature, T product is the product temperature. The temperature change (ΔT) can be calculated as in Formula 11. The water removal amount (m del ) can be calculated as in Formula 12, where h fg is the latent heat of vaporization of water.

[0047] Q = h × 2A × (T oven - T product )...(Formula 9)

[0048] Q rad = ε × σ × 2A × (T owen 4 - T product 4 )...(Formula 10)

[0049]

[0050]

[0051] In the first product simulation parameter estimation unit 142 (using the above Figure 3A or Figure 3BAfter obtaining the product virtual coefficient corresponding to the product (in the implementation manner), the first product simulation parameter estimation unit 142 can use the product virtual coefficient to estimate the first product simulation parameter of the product when it is in the oven to be measured.

[0052] In one embodiment, the product may include a printed circuit board (PCB, Printed Circuit Board).

[0053] In one embodiment, the product may include fabric. After the first product simulation parameter estimation unit 142 finishes Figure 2 performing step S230 (also known as the step of estimating the first product simulation parameter using the "physical model"), the second product simulation parameter estimation unit 143 can then estimate the second product simulation parameter corresponding to the product. This will be further described below.

[0054] Figure 4A is a schematic diagram for estimating the two-dimensional second product simulation parameter according to an embodiment of the present invention. Figure 4B is Figure 4A a further description. Please refer to Figure 1 , Figure 2 , Figure 4A and Figure 4B simultaneously. In this embodiment, the second product simulation parameter estimation unit 143 can input the first product simulation parameter, product information, and equipment information into the neural model to establish a two-dimensional second product simulation parameter distribution model, such as a two-dimensional moisture content distribution model, that is, in this embodiment, the second product simulation parameter corresponding to the product to be estimated is the moisture content. In one embodiment, the neural model may include 5 network layers, and the normal distribution can be used as the initialization (Kernel initializer) of the weight. Further, the neural model can use the Rectified Linear Unit (ReLU) as the activation function (Activation function), and the batch size for each update is 150, and the number of epochs is 128, and the root mean square error can be used as the loss function. In this embodiment, the sensing data acquisition unit 141 can obtain the product moisture content corresponding to the product through the moisture content sensor 160. Then, the second product simulation parameter estimation unit 143 can input the first product simulation parameter and the product moisture content into the two-dimensional second product simulation parameter distribution model to estimate the two-dimensional second product simulation parameter corresponding to the product.

[0055] For example, as Figure 4B shown, assume that the first product simulation parameter includes T i1 , T i2 and T i3, and the first product simulation parameter T i1 , the first product simulation parameter T i2 , and the first product simulation parameter T i3 respectively correspond to pixel points 1, 2, and 3. The second product simulation parameter estimation unit 143 can input the first product simulation parameter T i1 and the product moisture content (obtained by the moisture content sensor 160 disposed at the outlet section of the thermal processing equipment) into the two-dimensional second product simulation parameter distribution model to estimate the second product simulation parameter M of pixel point 1 o1 . Similarly, the second product simulation parameter estimation unit 143 can input the first product simulation parameter T i2 and the product moisture content (obtained by the moisture content sensor 160 disposed at the outlet section of the thermal processing equipment) into the two-dimensional second product simulation parameter distribution model to estimate the second product simulation parameter M of pixel point 2 o2 . Similarly, the second product simulation parameter estimation unit 143 can input the first product simulation parameter T i3 and the product moisture content (obtained by the moisture content sensor 160 disposed at the outlet section of the thermal processing equipment) into the two-dimensional second product simulation parameter distribution model to estimate the second product simulation parameter M of pixel point 3 o3 . Then, the second product simulation parameter estimation unit 143 can use the second product simulation parameter M o1 , the second product simulation parameter M o2 , and the second product simulation parameter M o3 to obtain the two-dimensional second product simulation parameter corresponding to the product.

[0056] In summary, the system and method for estimating product simulation parameters of the present invention can, after obtaining the product virtual coefficient corresponding to the product, use the product virtual coefficient to estimate the first product simulation parameter of the product when it is in the oven to be tested. In particular, the present invention can use the product thermal image, product temperature, product information, and equipment information to obtain the product virtual coefficient, and thus can more accurately (based on the "physical model") estimate the first product simulation parameter. Furthermore, the system and method for estimating product simulation parameters of the present invention can also use the first product simulation parameter to estimate the second product simulation parameter. Based on this, the convenience of adjusting the thermal processing equipment can be significantly improved.

Claims

1. A system for estimating product simulation parameters, characterized in that, For a hot processing device and a product, wherein the hot processing device includes a measured oven and at least one to-be-measured oven, wherein the product corresponds to product information, wherein the hot processing device corresponds to device information, and wherein the system includes: A first thermal imaging sensor disposed at an inlet section of the hot processing device; A second thermal imaging sensor disposed at an outlet section of the hot processing device; A temperature sensor disposed in the measured oven; A storage medium storing a sensing data acquisition unit and a first product simulation parameter estimation unit, wherein the sensing data acquisition unit is configured to obtain a product thermal image corresponding to the product through the first thermal imaging sensor and the second thermal imaging sensor, and obtain a product temperature of the product in the measured oven through the temperature sensor, wherein the device information, the product thermal image, and the product temperature correspond to the same time point; the first product simulation parameter estimation unit is configured to use the product thermal image, the product temperature, the product information, and the device information to obtain a product virtual coefficient corresponding to the product, and use the product virtual coefficient to estimate a first product simulation parameter of the product in the to-be-measured oven; and A processor coupled to the first thermal imaging sensor, the second thermal imaging sensor, the temperature sensor, and the storage medium, and configured to access and execute the sensing data acquisition unit and the first product simulation parameter estimation unit.

2. The system according to claim 1, characterized in that, The first product simulation parameter estimation unit is configured to convert an energy conservation equation into an ordinary differential equation by using the product thermal image, the product temperature, the product information, and the device information; The first product simulation parameter estimation unit is configured to analyze the ordinary differential equation to obtain an iterative equation; The first product simulation parameter estimation unit is configured to perform Bayesian optimization fitting on the product virtual coefficient by using the product information and the device information; The first product simulation parameter estimation unit is configured to substitute the product information, the device information, and the product virtual coefficient into the iterative equation to obtain the first product simulation parameter.

3. The system according to claim 2, characterized in that, The Bayesian optimization fitting includes the following steps: The first product simulation parameter estimation unit uses the iterative equation to solve for the first product simulation parameter, wherein the first product simulation parameter includes a minimum value of the first product simulation parameter; and The first product simulation parameter estimation unit updates the product virtual coefficient by using a difference between the product temperature and the minimum value of the first product simulation parameter; and The first product simulation parameter estimation unit determines whether the difference between the product temperature and the minimum value of the first product simulation parameter corresponds to a minimum mean absolute error percentage value to obtain the product virtual coefficient.

4. The system according to claim 1, characterized in that, The device information includes a measured oven temperature corresponding to the measured oven, and the device information includes a to-be-measured oven temperature corresponding to the to-be-measured oven.

5. The system according to claim 1, characterized in that, The product and the hot processing device correspond to a heating stage, wherein The first product simulation parameter estimation unit is used to obtain the product virtual coefficient by using the energy supply, mass change, and temperature difference change of the product during the heating stage.

6. The system according to claim 1, characterized in that, It further includes a moisture content sensor coupled to the processor, wherein the moisture content sensor is disposed in the outlet section, wherein the product includes fabric, and the storage medium further stores a second product simulation parameter estimation unit, wherein The second product simulation parameter estimation unit is used to input the first product simulation parameter, the product information, and the equipment information into a neural model to establish a two-dimensional second product simulation parameter distribution model; The sensed data acquisition unit is used to obtain the product moisture content corresponding to the product through the moisture content sensor; The second product simulation parameter estimation unit is used to input the first product simulation parameter and the product moisture content into the two-dimensional second product simulation parameter distribution model to estimate the two-dimensional second product simulation parameter corresponding to the product.

7. The system according to claim 1, characterized in that, The product information includes fabric, yard weight, additives, width value, density, specific heat capacity, and thickness.

8. The system according to claim 1, characterized in that, The product includes a printed circuit board.

9. A method for estimating product simulation parameters, characterized in that, For a thermal processing device and a product, wherein the thermal processing device includes a measured oven and at least one to-be-measured oven, wherein the product corresponds to product information, wherein the thermal processing device corresponds to equipment information, and the method includes the following steps: Obtain a product thermal image corresponding to the product, and obtain the product temperature of the product in the measured oven, wherein the equipment information, the product thermal image, and the product temperature correspond to the same time point; and Use the product thermal image, the product temperature, the product information, and the equipment information to obtain the product virtual coefficient corresponding to the product, and use the product virtual coefficient to estimate the first product simulation parameter of the product in the to-be-measured oven.

10. The method according to claim 9, characterized in that, The step of using the product thermal image, the product temperature, the product information, and the equipment information to obtain the product virtual coefficient corresponding to the product includes: Use the product thermal image, the product temperature, the product information, and the equipment information to convert the energy conservation equation into an ordinary differential equation; Analyze the ordinary differential equation to obtain an iterative equation; Use the product information and the equipment information to perform Bayesian optimization fitting on the product virtual coefficient; Substitute the product information, the equipment information, and the product virtual coefficient into the iterative equation to obtain the first product simulation parameter.

11. The method according to claim 10, wherein, The Bayesian optimization fitting includes the following steps: Use the iterative equation to solve for the first product simulation parameter, wherein the first product simulation parameter includes the minimum value of the first product simulation parameter; Use the difference between the product temperature and the minimum value of the first product simulation parameter to update the product virtual coefficient; and Determine whether the difference between the product temperature and the minimum value of the first product simulation parameter corresponds to the minimum mean absolute error percentage value to obtain the product virtual coefficient.

12. The method according to claim 9, wherein, The device information includes the measured oven temperature corresponding to the measured oven, and the device information includes the to-be-measured oven temperature corresponding to the to-be-measured oven.

13. The method according to claim 9, wherein, The product and the thermal processing device correspond to a heating stage, wherein the step of obtaining the product virtual coefficient corresponding to the product by using the product thermal image, the product temperature, the product information, and the device information includes: Obtaining the product virtual coefficient by using the energy supply, mass change, and temperature difference change of the product during the heating stage.

14. The method according to claim 9, wherein, Further includes the following steps: Inputting the first product simulation parameter, the product information, and the device information into a neural network model to establish a two-dimensional second product simulation parameter distribution model; Obtaining the product moisture content corresponding to the product; And Inputting the first product simulation parameter and the product moisture content into the two-dimensional second product simulation parameter distribution model to estimate the two-dimensional second product simulation parameter corresponding to the product.

15. The method according to claim 14, wherein, The product includes fabric.

16. The method according to claim 9, wherein, The product information includes fabric, yard weight, additives, width value, density, specific heat capacity, and thickness.

17. The method according to claim 9, wherein, The product includes a printed circuit board.

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