A PVDC coating method, electronic device and system based on finite element analysis

Abstract

This invention discloses a PVDC coating method, electronic device, and system based on finite element analysis, comprising the following steps: First, simulating the coating process using finite element analysis, obtaining different coating qualities based on different coating standards and coating parameters; then, establishing and training a BP network model; next, optimizing the coating quality based on the trained BP network model according to different coating standards, and establishing a database based on coating standards, coating parameters, and coating quality; finally, matching the coating parameters in the database with the coating standards of the material to be coated, and performing coating based on the coating parameters. This method uses finite element analysis to simulate the coating process and establishes a relevant database. During actual coating processes, coating parameters are determined based on actual coating standards, enabling intelligent matching of coating parameters, thereby improving coating efficiency and ensuring coating quality.

Description

Technical Field

[0001] This invention relates to the field of composite packaging material production, and more specifically, to a PVDC coating method, electronic device, and system based on finite element analysis. Background Technology

[0002] PVDC coated film, also known as PVDC coated film, coated film, or K film for short, includes KOP (including matte KOP), KPET, KPA, KCPP, KCPE, and other coated films. K film is produced by coating one or more layers of polyvinylidene chloride (PVDC) latex onto various film materials using specialized equipment, resulting in a film with high barrier properties. Its superior barrier properties are mainly manifested in its ability to reduce oxygen permeability by hundreds or thousands of times, thereby significantly improving shelf life, aroma retention, freshness preservation, and oil resistance. Furthermore, it possesses the same printability and lamination properties as ordinary films, and can also be heat-sealed on both sides (heat-sealing strength ≥0.8N / 15mm) as needed.

[0003] The barrier properties of PVDC coated films are directly related to both coating thickness and coating quality. In current coating processes, coating parameters need to be determined based on the coating thickness, and these parameters directly affect the coating quality. Currently, the setting of these parameters is usually done by directly querying relevant process parameters based on the coating thickness and manually setting them. While ensuring the coating thickness may be achieved, optimal coating quality may not be attained.

[0004] In view of the above, this application is hereby submitted. Summary of the Invention

[0005] To address the aforementioned problems, this invention provides a PVDC coating method, electronic device, and system based on finite element analysis. The method simulates the coating process using finite element analysis, obtaining different coating qualities based on different coating standards and parameters. A BP network model is then established based on this model, and a database is built by training the BP network model. During actual coating processes, coating parameters are determined based on the actual coating standards, enabling intelligent matching of coating parameters, thereby improving coating efficiency and ensuring coating quality.

[0006] To achieve the above-mentioned objectives, the present invention provides the following technical solution:

[0007] First aspect

[0008] This invention provides a PVDC coating method based on finite element analysis, comprising the following steps:

[0009] S1. Based on the finite element analysis method, the coating process is simulated, and different coating qualities are obtained based on different coating parameters for different coating standards.

[0010] S2. Establish a BP network model and train the BP network model;

[0011] S3. Based on the trained BP network model, the coating quality is optimized according to different coating standards, and a database is established based on coating standards, coating parameters and coating quality.

[0012] S4. Match the coating parameters in the database according to the coating standards of the material to be coated, and perform coating based on the coating parameters.

[0013] In this scheme, the coating process is simulated using finite element analysis, and the corresponding data between coating standards, coating parameters, and coating quality are obtained. A BP network model is established and trained based on different data. For the trained network model, the optimal coating parameters for coating quality are found and matched for different coating standards, and coating is performed based on these parameters. This method, using finite element analysis to simulate the coating process, effectively reduces experimental costs, generates large amounts of sample data, trains the data on the sample data to ensure accuracy, and establishes a database based on the trained data. This enables rapid matching of corresponding coating parameters, thereby improving coating efficiency and ensuring coating quality.

[0014] Furthermore, the coating standards include coating thickness, coating width, and base film material.

[0015] Furthermore, the coating parameters include latex concentration, coating roller speed, and curing temperature.

[0016] Furthermore, in step S1, the coating process is simulated based on the finite element analysis method to establish a coating model, with the coating standard and coating parameters as input parameters; the quality of the formed coating is used as the output parameter.

[0017] Furthermore, in step S2, a BP network model is established, where the input layer inputs coating standards and coating parameters, and the output layer outputs coating quality.

[0018] Furthermore, the method includes step S5, which involves coating based on the coating parameters determined in step S4, obtaining the first coating quality based on the coating parameters, determining the gap between the first coating quality and the optimal coating quality, and adjusting the coating parameters to reduce the gap between the first coating quality and the optimal coating quality.

[0019] Furthermore, step S5 specifically includes the following steps:

[0020] S51. Based on the trained BP network model, under the premise of determining the coating standard, obtain the variation law of coating parameters and coating quality.

[0021] S52. Based on the changing pattern, adjust the coating parameters and obtain the adjusted coating quality. Based on the adjusted coating quality, the first coating quality, and the optimal coating quality, determine whether further adjustment is needed.

[0022] Second aspect

[0023] This invention also provides an electronic device, including a processor, a network interface, and a memory, wherein the processor, the network interface, and the memory are interconnected, wherein the memory is used to store a computer program, the computer program including program instructions, and the processor is configured to call the program instructions to execute a PVDC coating method based on finite element analysis as described above.

[0024] Third aspect

[0025] This invention also provides a computer-readable storage medium storing a computer program that, when executed, implements the steps of the PVDC coating method based on finite element analysis described above.

[0026] Fourth aspect

[0027] This invention also provides a PVDC coating system based on finite element analysis, comprising:

[0028] Simulation module: The simulation module is used to simulate the coating process and obtain different coating qualities based on different coating parameters for different coating standards;

[0029] Neural network establishment and training module: The neural network establishment and training module is used to establish a BP network model and train the BP network model;

[0030] Database creation and storage module: The database creation and storage module is used to optimize the coating quality based on the trained BP network model and different coating standards, and to create a database based on the coating standards, coating parameters and coating quality.

[0031] Control module: The control module is used to match the coating parameters in the database according to the coating standards of the material to be coated, and to perform coating based on the coating parameters.

[0032] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0033] This invention relates to a PVDC coating method, electronic device, and system based on finite element analysis. Using finite element analysis, the coating process is simulated, and corresponding data between coating standards, coating parameters, and coating quality are obtained. A BP network model is established and trained based on different data. For the trained network model, the optimal coating parameters for coating quality are found and matched for different coating standards, and coating is performed based on these parameters. By using this method, the coating process is simulated using finite element analysis, which effectively reduces experimental costs, generates large amounts of sample data, trains the data on the sample data to ensure accuracy, and establishes a database based on the trained data. This enables rapid matching of corresponding coating parameters, thereby improving coating efficiency and ensuring coating quality. Attached Figure Description

[0034] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0035] Figure 1 A flowchart of a PVDC coating method based on finite element analysis is provided for an embodiment of the present invention;

[0036] Figure 2 A logic diagram of a coating method provided in another embodiment of the present invention;

[0037] Figure 3 A schematic diagram of the structure of an electronic device is provided for an embodiment of the present invention. Detailed Implementation

[0038] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0039] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to practice the invention. In other instances, well-known structures, circuits, materials, or methods have not been specifically described in order to avoid obscuring the invention.

[0040] Throughout this specification, references to "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with that embodiment or example is included in at least one embodiment of the invention. Therefore, the phrases "an embodiment," "an example," "an example," or "an example" appearing in various places throughout the specification do not necessarily refer to the same embodiment or example. Furthermore, specific features, structures, or characteristics can be combined in one or more embodiments or examples in any suitable combination and / or sub-combination. Moreover, those skilled in the art will understand that the illustrations provided herein are for illustrative purposes and are not necessarily drawn to scale. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0041] In the description of this invention, it should be understood that the terms "front", "rear", "left", "right", "up", "down", "vertical", "horizontal", "high", "low", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting the scope of protection of this invention. Example

[0042] like Figure 1 As shown, this embodiment of the invention provides a PVDC coating method based on finite element analysis, including the following steps:

[0043] S1. Based on the finite element analysis method, the coating process is simulated, and different coating qualities are obtained based on different coating parameters for different coating standards.

[0044] S2. Establish a BP network model and train the BP network model;

[0045] S3. Based on the trained BP network model, the coating quality is optimized according to different coating standards, and a database is established based on coating standards, coating parameters and coating quality.

[0046] S4. Match the coating parameters in the database according to the coating standards of the material to be coated, and perform coating based on the coating parameters.

[0047] In step S1, the coating process is simulated based on the finite element analysis method. Specifically, as those skilled in the art should know, a finite element model of the coating process can be established based on Ansys software, with the coating standard and coating parameters as input parameters and the quality of the resulting coating as output parameters.

[0048] The coating standards include, but are not limited to, coating thickness, coating width, and base film material; the coating parameters include, but are not limited to, latex concentration, coating roller speed, and curing temperature.

[0049] As should be known to those skilled in the art, the quality of a coating can be evaluated by defect detection. Specifically, this can be achieved using a macro camera and detection system, or by using the deep learning-based image detection scheme described in application number 202010932832.2. It should be noted that the specific methods for evaluating coating quality are actually existing technologies, and any method that can achieve coating quality evaluation is acceptable, which will not be elaborated here.

[0050] Specifically, in step S2, a BP network model is established and trained. In this scheme, for the establishment and training of the BP network model, the input data of the input layer is the coating standard and coating parameters, and the output parameters of the output layer are the coating quality. The specific establishment method is not described in detail here.

[0051] Specifically, in step S3, the coating quality is optimized, and a genetic algorithm can be used to obtain the optimal solution.

[0052] Specifically, the coating parameters in the database are matched according to the coating standards of the material to be coated, and the matching method adopts a for loop.

[0053] In this scheme, the coating process is simulated using finite element analysis, and the corresponding data between coating standards, coating parameters, and coating quality are obtained. A BP network model is established and trained based on different data. For the trained network model, the optimal coating parameters for coating quality are found and matched for different coating standards, and coating is performed based on these parameters. This method, using finite element analysis to simulate the coating process, effectively reduces experimental costs, generates large amounts of sample data, trains the data on the sample data to ensure accuracy, and establishes a database based on the trained data. This enables rapid matching of corresponding coating parameters, thereby improving coating efficiency and ensuring coating quality.

[0054] like Figure 2 As shown, in some embodiments, the method further includes step S5: applying coating based on the coating parameters determined in step S4, obtaining the first coating quality based on the coating parameters, determining the gap between the first coating quality and the optimal coating quality, and adjusting the coating parameters to reduce the gap between the first coating quality and the optimal coating quality.

[0055] This solution provides a technical approach that enables online control of coating quality. By monitoring coating quality and adjusting coating parameters, the coating effect can be further guaranteed.

[0056] Furthermore, step S5 specifically includes the following steps:

[0057] S51. Based on the trained BP network model, under the premise of determining the coating standard, obtain the variation law of coating parameters and coating quality.

[0058] S52. Based on the changing pattern, adjust the coating parameters and obtain the adjusted coating quality. Based on the adjusted coating quality, the first coating quality, and the optimal coating quality, determine whether further adjustment is needed.

[0059] As those skilled in the art should know, given a defined coating standard, different coating parameters are needed to determine the optimal coating quality. Based on this, multiple sets of data relating coating parameters and coatings under the same coating standard can be obtained. Furthermore, during the training of the neural network, a single variable can be controlled. For example, when the coating parameters include latex concentration, coating roller speed, and curing temperature, the latex concentration and coating roller speed can be kept constant while the curing temperature is controlled to obtain the relative relationship between coating quality and curing temperature. Based on this relationship, coating quality can be controlled by adjusting the curing temperature.

[0060] In some optional implementations, for different types of coating parameters, the sensitivity between different types of coating parameters and changes in coating quality can be obtained. That is, under the premise of ensuring a single variable, the sensitivity of coating quality changes after adjusting different types of coating parameters can be determined. In specific embodiments, the size of the difference between the first coating quality and the optimal coating quality can be used to determine which type of coating parameter should be adjusted to achieve the adjustment of coating parameters.

[0061] like Figure 3 As shown, this embodiment of the invention also provides an electronic device, including a processor, a network interface, and a memory. The processor, the network interface, and the memory are interconnected. The memory is used to store a computer program, which includes program instructions. The processor is configured to call the program instructions to execute a PVDC coating method based on finite element analysis as described above.

[0062] This invention also provides a computer-readable storage medium storing a computer program that, when executed, implements the steps of the PVDC coating method based on finite element analysis described above.

[0063] In embodiments of the present invention, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0064] The various methods, steps, and logic diagrams disclosed in the embodiments of this invention can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The processor reads information from the storage medium and, in conjunction with its hardware, completes the steps of the above methods.

[0065] This invention also provides a PVDC coating system based on finite element analysis, comprising:

[0066] Simulation module: The simulation module is used to simulate the coating process and obtain different coating qualities based on different coating parameters for different coating standards;

[0067] Neural network establishment and training module: The neural network establishment and training module is used to establish a BP network model and train the BP network model;

[0068] Database creation and storage module: The database creation and storage module is used to optimize the coating quality based on the trained BP network model and different coating standards, and to create a database based on the coating standards, coating parameters and coating quality.

[0069] Control module: The control module is used to match the coating parameters in the database according to the coating standards of the material to be coated, and to perform coating based on the coating parameters.

[0070] In some embodiments, the control module is connected to the latex dispensing structure, coating roller, and curing structure of the coating machine to adjust the latex concentration, coating roller speed, and curing temperature.

[0071] The above are preferred embodiments of the present invention. Those skilled in the art can make changes and modifications to the above embodiments. Therefore, the present invention is not limited to the specific embodiments described above. Any obvious improvements, substitutions or modifications made by those skilled in the art based on the present invention are within the protection scope of the present invention.

Claims

1. A PVDC coating method based on finite element analysis, characterized in that, Includes the following steps: S1. Simulate the coating process based on the finite element analysis method, and apply different coating parameters to different coating standards. Different coating qualities can be obtained; S2. Establish a BP network model and train the BP network model; S3. Based on the trained BP network model, the coating quality is optimized according to different coating standards, and a parameter database is established based on the coating standards, coating parameters and coating quality. S4. Match the coating parameters in the database according to the coating standards of the material to be coated, and perform coating based on the coating parameters; S5. Apply coating based on the coating parameters determined in step S4, obtain the first coating quality based on these parameters, determine the difference between the first coating quality and the optimal coating quality, and adjust the coating parameters to reduce the difference between the first coating quality and the optimal coating quality. Step S5 specifically includes the following steps: S51. Based on the trained BP network model, under the premise of determining the coating standard, obtain the variation law of coating parameters and coating quality. S52. Based on the changing pattern, adjust the coating parameters and obtain the adjusted coating quality. Based on the adjusted coating quality, the first coating quality, and the optimal coating quality, determine whether further adjustment is needed.

2. The PVDC coating method based on finite element analysis according to claim 1, characterized in that, The coating standards include coating thickness, coating width, and base film material.

3. The PVDC coating method based on finite element analysis according to claim 1, characterized in that, The coating parameters include latex concentration, coating roller speed, and curing temperature.

4. The PVDC coating method based on finite element analysis according to claim 1, characterized in that, In step S1, the coating process is simulated based on the finite element analysis method to establish a coating model, with the coating standard and coating parameters as input parameters; the quality of the formed coating is used as the output parameter.

5. The PVDC coating method based on finite element analysis according to claim 1, characterized in that, In step S2, a BP network model is established. The input layer data consists of coating standards and coating parameters, and the output layer outputs coating quality.

6. An electronic device, characterized in that, The device includes a processor, a network interface, and a memory, which are interconnected. The memory stores a computer program, which includes program instructions. The processor is configured to invoke the program instructions to execute a PVDC coating method based on finite element analysis as described in any one of claims 1 to 5.

7. A computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is executed, it implements the steps of a PVDC coating method based on finite element analysis as described in any one of claims 1 to 5.

8. A PVDC coating system based on finite element analysis, characterized in that, include: Simulation module: The simulation module is used to simulate the coating process and obtain different coating qualities based on different coating parameters for different coating standards; Neural network establishment and training module: The neural network establishment and training module is used to establish a BP network model and train the BP network model; Database creation and storage module: The database creation and storage module is used to optimize the coating quality based on the trained BP network model and different coating standards, and to create a database based on the coating standards, coating parameters and coating quality. Control module: The control module is used to match the coating parameters in the database according to the coating standards of the material to be coated, and to perform coating based on the coating parameters; Coating and parameter adjustment module: The coating and parameter adjustment module is used to obtain the first coating quality according to the coating parameters, determine the gap between the first coating quality and the optimal coating quality, and adjust the coating parameters to reduce the gap between the first coating quality and the optimal coating quality; The coating and parameter adjustment module calls the BP network model trained in the neural network establishment and training module to obtain the variation law of coating parameters and coating quality under the premise of determining the coating standard. The coating and parameter adjustment module adjusts the coating parameters based on the change pattern and obtains the adjusted coating quality. Based on the adjusted coating quality, the first coating quality, and the optimal coating quality, it determines whether further adjustment is needed.

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