Digital twin-based dispensing system and method for creating a physical model of a fluid

Abstract

A coating system is provided, comprising: a computer device including: a coating parameter setting module for setting at least one coating parameter; and a coating simulation module for performing a colloidal physics simulation based on the coating parameters to generate a digital twin of the simulated coating result; the colloidal physics simulation is based on at least one of the Navier-Stokes equations and a surface tension physics model.

Description

Technical Field

[0001] This invention relates to an adhesive application system, and more particularly to an adhesive application system based on digital twins. Prior Technology

[0002] Glue application plays a crucial role in electronics manufacturing, with applications including solder paste application, sealant application, grease application, thermal paste application, and printed circuit board encapsulation. Traditional glue application machines utilize robotic arms or XY(-Z) positioning slides paired with glue valves to achieve automated glue application. However, existing glue application systems and methods have some drawbacks, mainly in the following aspects:

[0003] (1) Path planning: Existing path planning methods mainly rely on manually setting the glue application points and adjusting the spacing between the points based on experience, which is inefficient and prone to errors.

[0004] (2) Parameter setting: The parameter setting of the glue applicator, such as the moving speed, glue dispensing speed, glue valve height, etc., usually needs to be adjusted by human experience and lacks scientific basis.

[0005] (3) Detection of coating results: The detection of coating results mostly relies on manual visual inspection or existing automated optical inspection (AOI) equipment. However, these methods are inefficient, costly, and require a large amount of data collection and training for different coating forms.

[0006] (4) Parameter optimization: Existing parameter optimization methods mainly rely on manual experience and lack effective automated optimization methods, which makes it difficult to improve the coating quality.

[0007] The aforementioned shortcomings mainly stem from the following reasons: various parameters of the coating machine affect the width and height of the adhesive coating, thus impacting the coating quality. However, the complex relationships between these parameters are difficult to quantify, and currently, judgment and adjustment rely primarily on manual experience. Furthermore, the various stages of the coating process are relatively independent, lacking effective integration and utilization of information. For instance, most existing methods for detecting coating results do not consider information obtained during path planning, leading to reduced detection efficiency and accuracy.

[0008] To solve the above problems, a glue application system that can simplify path planning, defect detection, and parameter optimization is needed. Summary of the Invention

[0009] This disclosure provides a coating system, including: a computer device, comprising: a coating parameter setting module for setting at least one coating parameter; and a coating simulation module for performing a colloidal physics simulation based on the coating parameters to generate a digital twin of the simulated coating result; the colloidal physics simulation is based on at least one of the Navier-Stokes equations and a surface tension physics model.

[0010] In one embodiment, the adhesive application parameters include at least one of the following: viscosity coefficient, surface tension coefficient, moving speed, dispensing speed, adhesive valve height, adhesive height, and adhesive width.

[0011] In one embodiment, the adhesive application system further includes an adhesive application device, which performs experimental adhesive application according to the adhesive application parameters to produce an experimental colloid.

[0012] In one embodiment, it further includes: an imaging device for capturing an image of the experimental colloid and transmitting the image to the computer device; the computer device further includes a comparison module for comparing the obtained measurement results of the experimental colloid with the simulated coating results and detecting whether there are abnormal results. If it is determined that there are abnormal results, the coating parameter setting module adjusts the coating parameters.

[0013] In one embodiment, the comparison module compares the obtained image of the experimental colloid with the simulated coating result, detects the difference between the image of the experimental colloid and the simulated coating result, and corrects the model of the colloid physical simulation based on the difference, so that the simulated coating result generated by the colloid physical simulation is close to the measurement result of the experimental colloid.

[0014] In one embodiment, the computer device further includes a path planning module that plans an adhesive application path based on the adhesive application parameters; the planning of the adhesive application path is based on at least one of the adhesive width and the adhesive height in the simulated adhesive application results.

[0015] In one embodiment, the adhesive applicator includes a robotic arm or positioning slide for carrying an adhesive valve system.

[0016] In one embodiment, the imaging device is a 2D camera or a 3D camera, used to capture images of the experimental colloid.

[0017] In another embodiment, this disclosure provides a coating method, comprising: a coating parameter setting step, which sets at least one coating parameter; and a coating simulation step, which performs a colloidal physics simulation based on the coating parameters to generate a digital twin of the simulated coating result; the colloidal physics simulation is based on at least one of the Navier-Stokes equations and a surface tension physics model. Simple Explanation of the Diagram

[0018] Figure 1 is a flowchart of an adhesive spraying method according to an embodiment of the present invention. Figure 2 is a schematic diagram of the adhesive coating result according to one embodiment of the present invention, wherein (a) is a schematic diagram of the adhesive width W, (b) is a schematic diagram of the adhesive height H, and (c) is a schematic diagram of the adhesive coating result. Figure 3 is a flowchart of finding the adhesive application parameters according to an embodiment of the present invention. Figure 4 is a schematic diagram illustrating the actual optimization results according to the embodiments disclosed herein. Figure 5 is a block diagram of an adhesive spraying system according to an embodiment of the present invention. Implementation

[0019] This invention is described with reference to the accompanying drawings, wherein the same reference numerals throughout the drawings denote similar or identical elements. The drawings are not depicted to actual scale and are merely illustrative of the invention. Some anatomical forms of the invention are described below as illustrative examples. This means that many specific details, relationships, and methods are set forth to provide a complete understanding of the invention. However, those skilled in the art will recognize that the invention could still be made without one or more specific details or by other means.

[0020] In other examples, well-known structures or operations are not detailed to avoid confusion with the invention. The invention is not limited to the order of the described actions or events, as some actions may occur in a different order or simultaneously with other actions or events. Furthermore, not all described actions or events need to be performed in the same manner as in prior art.

[0021] The digital twin-based adhesive application system and method disclosed herein overcomes the shortcomings of existing technologies that rely on human experience and trial and error through colloidal physics simulation, and realizes automation and intelligence in adhesive application parameter setting, adhesive application result detection and parameter optimization.

[0022] Figure 1 is a flowchart of an adhesive spraying method according to an embodiment of the present invention.

[0023] This disclosure utilizes digital twin technology for colloids to map the real colloid coating process into a virtual simulation environment, thereby automating and intelligently setting coating parameters, detecting coating results, and optimizing parameters.

[0024] Specifically, the colloidal physics simulation program in the computer device can be based on fluid dynamics to simulate the coating behavior of colloids under different parameters, such as based on the Navier-Stokes equations and surface tension physics models, but is not limited to these.

[0025] In one embodiment, by adjusting simulation parameters, such as viscosity coefficient, surface tension coefficient, dispensing speed, glue valve height, moving speed, colloid height, and colloid width, different simulated coating results can be obtained, including the colloid height, width, shape, and whether defects are generated. These simulation results can quantify coating quality and serve as a basis for optimizing coating parameters.

[0026] In one embodiment, in order to make the results of colloidal physical simulation accurate and reliable, it is necessary to obtain the key simulation parameters of the colloid, such as the viscosity coefficient and surface tension coefficient.

[0027] In one embodiment, the process of establishing a digital twin of the colloidal coating process and comparing the simulation results using experimental data and colloidal physical simulation technology is as follows.

[0028] First, the adjustment of the adhesive applicator begins in step S0. The adjustment is carried out based on the physical simulation model of the adhesive.

[0029] In one embodiment, the physical simulation model of the colloid is established based on the material properties of the colloid, such as using the Navier-Stokes equations and surface tension model in fluid mechanics to describe the flow and deformation behavior of the colloid. The equations of this fluid model can be solved using the particle method or the mesh method, but are not limited to these. For example, any method that can solve the equations is acceptable as long as the viscosity coefficient and surface tension coefficient are given in advance.

[0030] In step S1, during the colloidal physical simulation, the first step is to plan the path. Based on the coating parameters, such as the width of the colloid, the height of the colloid, or the radius of the colloid, the coating path is planned to ensure that the colloid can uniformly cover the target area.

[0031] Then, (2) parameter setting is performed. Based on the simulation results and the predetermined coating quality requirements, the various coating parameters of the coating device are set through the simulation and optimization algorithm, such as moving speed, dispensing speed, glue valve height, viscosity coefficient and surface tension coefficient, but not limited to these.

[0032] In one embodiment, each coating parameter may be an initial value input in advance, or an actual measured value obtained based on an experiment, or a simulated value obtained based on simulation calculation, etc., but is not limited to these.

[0033] Next, proceed to step S2 to perform the experimental adhesive application, and then proceed to step S3.

[0034] In step S3, the experimental glue application results are detected by comparing the image of the experimental glue application results with the simulation results, and detecting any abnormal results such as glue application defects, such as insufficient glue, excessive glue, or glue overflow.

[0035] Proceed to step S4 to determine if there are any abnormal results. If the result is yes, that is, the actual coating result is abnormal, return to step S1 and perform parameter adjustment (4), such as adjusting the viscosity coefficient, surface tension coefficient, dispensing speed, glue valve height, moving speed, etc., to obtain different simulated coating results.

[0036] In one embodiment, when reviewing the adhesive application results, the actual adhesive application results can be compared with the simulated adhesive application results as a reference for fine-tuning the model parameters and correcting the gap between the actual adhesive application results and the simulated adhesive application results, but this is not limited to this.

[0037] In step S4, when determining whether there is an abnormal result, if the determination result is no, that is, the actual glue application result is not abnormal, then proceed to step S5.

[0038] In step S5, the glue coating device begins production, for example, by having the glue coating device complete the glue coating operation according to the planned glue coating path and glue coating parameters.

[0039] Figure 2 is a schematic diagram of one of the adhesive coating results described in the embodiments disclosed herein, wherein (a) is a schematic diagram of the actual adhesive width W, (b) is a schematic diagram of the actual adhesive height H, and (c) is a schematic diagram of the adhesive coating result.

[0040] In one embodiment, there may be cases where the colloid used, such as commercially available colloids, does not provide information on its viscosity coefficient and surface tension coefficient. In such cases, the following experimental procedure can be used to determine the viscosity coefficient and surface tension coefficient values ​​that can be used for simulation.

[0041] Specifically, the model can be fine-tuned and corrected by comparing the results of the simulated colloid with the width or height of the actual colloid obtained from the experiment, as shown in Figure 2, so that the model can produce simulation results that are close to those of the actual colloid, and the values ​​of the viscosity coefficient and surface tension coefficient in the simulation can be deduced from these results.

[0042] Figure 3 is a flowchart of a method for finding coating parameters and establishing a colloidal physical model according to an embodiment of the present disclosure.

[0043] First, in the experimental step (a), a glue application experiment was conducted. Different glue dispensing amounts were set, and the actual glue application or dispensing experiment was carried out using the robotic arm of the glue application device or the XY(-Z) positioning slide table carrying the glue valve system.

[0044] Then, the experimental results were captured using an imaging device 406 (e.g., a 2D or 3D camera) to obtain an image of the colloid. The height (using a 3D camera) and width (using a 2D or 3D camera) of the colloid were measured based on the image, as shown in Figure 3(a).

[0045] Proceed to step (b) of the sampling process and perform colloidal simulation. Based on the dispensing settings of the experiment, set parameters such as the moving speed of the dispensing valve, the dispensing speed of the dispensing valve, the dispensing height of the dispensing valve on a surface, and the diameter of the dispensing valve (these variables are referred to as ν). Set different viscosity coefficients μ and surface tension coefficients σ to perform colloidal physical simulation.

[0046] In one embodiment, the simulation process may be based on the Navier-Stokes equations and a surface tension physical model, and solved using numerical methods (such as the finite element method or the finite difference method), but is not limited thereto.

[0047] In one embodiment, the simulated colloid height h and simulated colloid width w of each simulation result are extracted, but this is not the only embodiment.

[0048] Proceed to step (c) to establish a continuous function, that is, to perform interpolation on the above simulation results, using the viscosity coefficient μ, surface tension coefficient σ and other known variables ν as inputs, and the simulated colloid height h or simulated colloid width w as outputs.

[0049] In one embodiment, the interpolation method may use linear interpolation, polynomial interpolation, or spline interpolation, but is not limited thereto. In one embodiment, two continuous functions w(μ,σ,v) and h(μ,σ,v) may be obtained, representing the simulated colloid width w and the simulated colloid height h, respectively, but are not limited thereto.

[0050] Proceed to step (d) Optimization to optimize the simulation parameters, that is, to find the optimal viscosity coefficient using optimization algorithms (such as Nelder-Mead method, L-BFGS-B (Limited-memory Broyden–Fletcher–Goldfarb–Shanno with Bounds) method, Newton-CG (Conjugate Gradient) method, Powell method, and other commonly used optimization algorithms, but not limited to the above methods). and surface tension coefficient In order to reduce the error between the simulation results and the experimental results, in addition, in order to reduce the error between the simulation results and the experimental results, the residual minimization formula disclosed herein (Equation (1)) is used to subtract the simulated colloid width w (simulated colloid height h) obtained by simulation from the experimentally measured colloid width W (colloid height H) for different known variables v, take the square, and sum them to obtain a value. The above optimization algorithm is used to search for the optimal viscosity coefficient and the optimal surface tension coefficient so that this value is as close as possible to 0.

[0051] [Residual Minimization Formula] …Formula (1)

[0052] Where μ* is the optimal viscosity coefficient (unit: kg / m*s), σ* is the optimal surface tension coefficient (unit: N / m), H(v) represents the experimentally measured colloid height given v, and W(v) represents the experimentally measured colloid width given v.

[0053] In one embodiment, the above process is not limited to optimizing the viscosity coefficient μ and the surface tension coefficient σ. For example, it can also be used to find other optimization parameters such as the dispensing speed (flow rate) (unit m / s), dispensing time (unit s), and dispensing size (unit cm), and is not limited to these.

[0054] Figure 4 is a schematic diagram illustrating the actual optimization results according to the embodiments disclosed herein.

[0055] Figure 4 shows the parameter optimization results in a real-world case. The Y-axis represents the width of the colloid in millimeters (mm); the X-axis represents the moving velocity of the colloid valve; the dots represent the experimentally measured colloid width data, and the dashed lines represent the simulated colloid width results obtained using the optimized simulation parameters.

[0056] As can be seen from Figure 4, the simulation results obtained using the optimized simulation parameters have a good agreement with the experimental results. This indicates that the parameter optimization method proposed in this paper can effectively find appropriate simulation parameters, making the simulation results closer to the real situation.

[0057] Figure 5 is a block diagram of an adhesive application system 400 for applying adhesive according to an embodiment of the present disclosure.

[0058] The adhesive application system 400 disclosed herein mainly includes the following components: a computer device 401, an adhesive application device 402, and an image acquisition device 406.

[0059] The computer device 401 is the core control unit of the adhesive application system 400. It is responsible for setting, simulating, comparing and planning the adhesive application parameters, and adjusting and optimizing the parameters based on the image information returned by the imaging device to achieve the best adhesive application effect.

[0060] In one embodiment, the computer device 401 may include a single processor or a plurality of processors. Furthermore, the computer device 401 may be a single-core processor or a multi-core processor, and may include a general-purpose microprocessor, or a combination of a general-purpose microprocessor and a special-purpose processor and / or related chipsets, such as an instruction-set processor, a special-purpose microprocessor, etc., but is not limited thereto.

[0061] In one embodiment, the computer device 401 may further include memory, which may include random access memory (RAM) or non-volatile memory, such as one or more disk storage devices, flash memory devices or other non-volatile solid-state memory devices, but is not limited thereto.

[0062] In one embodiment, the computer device 401 includes: a path planning module 401A, an adhesive application parameter setting module 401B, a simulated adhesive application module 401C, and a comparison module 401D.

[0063] In one embodiment, the path planning module 401A plans the movement path of the glue valve system according to, for example, the shape and size of the workpiece and the glue application requirements, to ensure that the glue can be uniformly applied to the target area.

[0064] In one embodiment, the adhesive application parameter setting module 401B sets at least one adhesive application parameter for the adhesive valve system. These adhesive application parameters include, but are not limited to, adhesive width W, adhesive height H, spacing D, viscosity coefficient η, surface tension coefficient σ, adhesive valve moving speed, adhesive dispensing speed, and adhesive valve height.

[0065] In one embodiment, the simulated coating module 401C performs a colloidal physical simulation, that is, it simulates the simulated coating result that can be obtained by performing a coating operation under given coating parameters. The simulated coating result may include the colloidal width W, colloidal height, spacing D, presence or absence of defects, etc., but is not limited to these.

[0066] In one embodiment, the comparison module 401D compares the simulated coating result with the actual coating result returned by the imaging device after performing the simulated coating to evaluate the coating quality and optimize parameters. In one embodiment, the coating parameter setting module 401B can also adjust and optimize the coating parameters based on the comparison result from the comparison module 401D, but is not limited thereto.

[0067] In one embodiment, the adhesive application device 402 includes components such as a robotic arm or an XY(-Z) positioning slide, and an adhesive valve system, for performing the actual adhesive application operation. In one embodiment, the robotic arm or XY(-Z) positioning slide is used to support and move the adhesive valve system, enabling it to accurately apply adhesive to the workpiece; the adhesive valve system includes an adhesive valve, a controller, and related drive mechanisms, for controlling parameters such as the amount of adhesive dispensed, the application speed, and the path, but is not limited thereto.

[0068] In one embodiment, the image capturing device 406 is, for example, a 2D or 3D camera, used to capture images of the coated colloid, obtain information such as the shape, size, position, and presence of any abnormal defects of the colloid, and transmit the image information back to the computer device 401 for analysis and parameter optimization.

[0069] In one embodiment, the method for establishing a colloidal physical model by a computer device 401 in the adhesive coating system disclosed herein includes: an experimental step, comprising: setting at least one dispensing amount to form a colloidal morphology on a surface, capturing an image of the colloidal morphology through an imaging device, and using the image to measure the height or width of the colloidal morphology; and a sampling step, comprising setting at least one coating parameter, performing a colloidal physical simulation based on the coating parameter, wherein the coating parameter is selected from a group consisting of a valve diameter, a valve movement speed, a valve dispensing speed, a coating height between the valve and the surface, a first group of multiple viscosity coefficients, and a first group of multiple surface tension coefficients. At least one of the following steps: A compensation step, performing colloidal physics simulation based on the coating parameters to obtain a plurality of simulation results, extracting a first group of majority adhesive heights and majority adhesive widths from the simulation results, outputting the first group of viscosity coefficients and surface tension coefficients corresponding to the first group of adhesive heights and adhesive widths, and performing interpolation to obtain a continuous function for adhesive width and a continuous function for adhesive height; and an optimization step, calculating a second group of majority adhesive widths and majority adhesive heights by inputting a second group of majority viscosity coefficients and a second group of majority surface tension coefficients, and obtaining an optimized viscosity coefficient and an optimized surface tension coefficient through an optimization algorithm.

[0070] In one embodiment, the optimization algorithm is selected from at least one of the group consisting of Nelder-Mead, L-BFGS-B, Newton-CG, and Powell.

[0071] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operations may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of embodiments of this disclosure includes the entire scope of the claims and all available equivalents of the claims. The terminology used in this application is for describing embodiments only and is not intended to limit the scope of the claims. Additionally, when used in this application, the terms "comprising" and / or "including" mean the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase "comprising a..." does not exclude the presence of additional identical elements in the process, method, or apparatus that includes that element. Throughout this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments may be referred to mutually. For methods, products, etc. disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to in the description of the method section.

[0072] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented using electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the working process of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0073] The methods and products disclosed in the embodiments herein (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of modules may only be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the couplings, connections, or communication lines between the devices or modules shown or discussed may be through some interfaces, and the couplings or communication lines between devices or modules may be electrical, mechanical, or other forms. Modules described as separate components may or may not be physically separate. Components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to implement this embodiment according to actual needs. Furthermore, the functional modules in the embodiments of this disclosure may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0074] In one embodiment, the adhesive applicator includes: a robotic arm or an XY(-Z) positioning slide for carrying the adhesive valve system and precisely moving the adhesive valve according to a preset adhesive application path; and the adhesive valve system, including the adhesive valve, a controller, and related drive mechanisms, for controlling the amount of adhesive dispensed, the dispensing speed, and the adhesive application trajectory, but not limited thereto.

[0075] In one embodiment, an imaging device, such as a 2D or 3D camera, is used to capture an image of the coated colloid and obtain information such as the shape, size, and position of the colloid.

[0076] In one embodiment, the computer device 400 stores an adhesive application control program and a colloidal physics simulation program for controlling the adhesive application process, performing colloidal simulations, analyzing simulation results, and optimizing adhesive application parameters based on the simulation results.

[0077] The digital twin-based coating system and method disclosed herein can quickly find the optimal coating parameters through colloidal physics simulation, reducing manual trial and error and adjustment time, and improving coating efficiency. In addition, the accurate simulation results can effectively predict the coating shape and size of the colloid, avoid defects, and improve coating quality. Furthermore, through the prediction of the simulated coating module, manual operation and material waste can be reduced, thereby lowering production costs.

[0078] S0-S5: Steps H: Colloidal height W: Colloidal width h: Simulated colloid height w: Simulated colloid width 400: Glue application system 401: Computer Device 401A: Path Planning Module 401B: Adhesive Coating Parameter Measurement Module 401C: Simulated Glue Application Module 401D: Comparison Module 402: Glue application device 404: Coating Result (Colloid) 406: Image capturing device D: Spacing η: viscosity coefficient σ: Surface tension coefficient

Claims

1. An adhesive application system, comprising: A computer device includes: a coating parameter setting module for setting at least one coating parameter; and a simulated coating module for performing a colloidal physics simulation based on the coating parameters to generate simulated coating results; the colloidal physics simulation is based on at least one of the Navier-Stokes equations and a surface tension physical model; the computer device further includes: a comparison module for comparing the measurement results of an experimental colloid with the simulated coating results and detecting any abnormal results; if abnormal results are detected, the coating parameter setting module adjusts the coating parameters.

2. The adhesive application system as claimed in claim 1, wherein the adhesive application parameters include at least one of: viscosity coefficient, surface tension coefficient, moving speed, dispensing speed, adhesive valve height, adhesive height, and adhesive width.

3. The adhesive application system as described in claim 1 further includes: The adhesive application device is used to apply adhesive experimentally according to these adhesive application parameters to produce the experimental colloid.

4. The adhesive application system as described in claim 3 further includes: The imaging device is used to capture an image of the experimental colloid and transmit the image to the computer device.

5. The coating system as described in claim 4, wherein the comparison module compares the acquired image of the experimental colloid with the simulated coating result, detects the difference between the image of the experimental colloid and the simulated coating result, and corrects the model of the colloid physical simulation based on the difference, so that the simulated coating result generated by the colloid physical simulation is close to the measurement result of the experimental colloid.

6. The adhesive application system as claimed in claim 1, wherein the computer device further includes a path planning module for planning an adhesive application path based on the adhesive application parameters; the planning of the adhesive application path is based on at least one of the adhesive width, adhesive height, and adhesive radius in the simulated adhesive application results.

7. The adhesive application system as claimed in claim 3, wherein the adhesive application device includes a robotic arm or positioning slide for carrying an adhesive valve system.

8. The coating system as described in claim 4, wherein the imaging device is a 2D camera or a 3D camera for capturing images of the experimental colloid.

9. A method for applying adhesive, comprising: The glue application parameter setting step involves setting at least one glue application parameter. The method includes a simulated coating step, which performs a colloidal physics simulation based on the coating parameters to generate a digital twin of the simulated coating result; the colloidal physics simulation is based on at least one of the Navier-Stokes equations and a surface tension physical model; the coating method further includes a comparison and correction step, which compares the measurement results of an experimental colloid with the simulated coating result and detects whether there are abnormal results. If abnormal results are determined, the coating parameter setting step adjusts the coating parameters.

10. A method for establishing a colloidal physical model, comprising: The experimental procedure includes: setting at least one dispensing amount to form a colloidal morphology on a surface, capturing an image of the colloidal morphology using an imaging device, and measuring the height or width of the colloidal morphology based on the image; the sampling procedure involves setting at least one dispensing parameter, performing colloidal physics simulation based on the dispensing parameter, wherein the dispensing parameter is selected from at least one of the following groups: the diameter of a dispensing valve, the moving speed of a dispensing valve, the dispensing speed of a dispensing valve, the dispensing height of the dispensing valve on the surface, a first group of multiple viscosity coefficients, and a first group of multiple surface tension coefficients. The first step involves a compensation step, which performs colloidal physics simulations based on the coating parameters to obtain multiple simulation results. A first group of majority adhesive heights and widths is extracted from these simulation results. The simulation results are then output as the first group of viscosity coefficients and surface tension coefficients, mapped to the first group of adhesive heights and widths, and interpolated to obtain a continuous function for adhesive width and a continuous function for adhesive height. The second step involves an optimization step, which calculates a second group of majority adhesive widths and heights by inputting a second group of majority viscosity coefficients and a second group of majority surface tension coefficients. An optimization algorithm is used to obtain an optimized viscosity coefficient and an optimized surface tension coefficient. Specifically, for different known coating parameters, the majority adhesive heights and widths of the simulation results that may be generated are subtracted from the adhesive height or width measured in the experimental step, the squares are taken, and the sums are obtained. The optimization algorithm is then used to search for the optimized viscosity coefficient and the optimized surface tension coefficient, making the value approach 0.

11. The method for establishing a colloidal physical model as described in claim 10, wherein the optimization algorithm is selected from at least one of the group consisting of the Nelder-Mead method, the L-BFGS-B method, the Newton-CG method, and the Powell method.

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