Coating defect identification method, device, equipment, medium and program product

Abstract

The present application discloses a coating defect recognition method, device, equipment, medium and program product, wherein the method includes obtaining coating simulation parameters of a simulation model of an electronic device; the simulation model includes a three-dimensional simulation model of the electronic device and a simulated coating on the surface of the three-dimensional simulation model; the coating simulation parameters are used to drive the simulated coating to undergo phase change, and obtain state parameters of the simulated coating during the process of changing from liquid phase to solid phase; display a parameter distribution image of the simulated coating during the process of changing from liquid phase to solid phase; wherein the parameter distribution image is determined based on the state parameters of the simulated coating; based on the parameter distribution image of the simulated coating, output the defect recognition result of the simulated coating. In this way, the accuracy of identifying coating defects can be improved.

Description

Technical Field

[0001] The present application relates to the field of simulation analysis technology, and in particular to a coating defect identification method, device, equipment, medium and program product. Background Art

[0002] In the electronics manufacturing industry, the use of conformal coatings has become an important means of protecting printed circuit boards from harsh environments. However, during the curing process of conformal coatings, bubble defects are prone to occur due to design, environment and other reasons, which will reduce the protective effect and service life of the printed circuit board. Therefore, in the coating process of the printed electronic assembly (PCBA) product production stage, it is necessary to identify and intercept it to improve the product yield. In the related technology, defects are often detected on the PCBA object during the production stage of the PCBA. However, this solution requires that the PCBA product be coated with conformal coating and cured before bubble defects can be effectively identified. This will not only reduce the efficiency of identifying conformal coating defects, but also reduce the accuracy of identifying coating defects due to the difficulty in obtaining information during the curing process of the conformal coating. Summary of the invention

[0003] In view of this, the embodiments of the present application at least provide a coating defect identification method, device, equipment, and storage medium.

[0004] The technical solution of the embodiment of the present application is implemented as follows:

[0005] On the one hand, an embodiment of the present application provides a coating defect identification method, the method comprising:

[0006] Acquire coating simulation parameters of the simulation model of the electronic device; the simulation model includes a three-dimensional simulation model of the electronic device and a simulated coating on the surface of the three-dimensional simulation model; the coating simulation parameters are used to drive the simulated coating to undergo phase change, and obtain state parameters of the simulated coating in the process of changing from a liquid phase to a solid phase;

[0007] Displaying a parameter distribution image of a simulated coating during a process of changing from a liquid phase to a solid phase; wherein the parameter distribution image is determined based on a state parameter of the simulated coating;

[0008] Based on the parameter distribution image of the simulated coating, the defect recognition result of the simulated coating is output.

[0009] In the embodiment of the present application, the coating simulation parameters of the simulation model obtained by coating the three-dimensional simulation model of the electronic device with the simulation coating are first obtained. The coating simulation parameters of the simulation model can drive the simulation coating to undergo phase change, and obtain the state parameters of the simulation coating during the phase change process; then the parameter distribution image determined by the state parameters of the simulation coating during the process of the simulation coating changing from the liquid phase to the solid phase can be displayed; finally, based on the parameter distribution image of the simulation coating, the defect recognition result of the simulation coating is output. In this way, by numerically simulating the three-dimensional simulation model of the electronic device, it is possible to determine whether the electronic device has defects in the coating stage before the actual production of the electronic device, thereby advancing the node for identifying defects, and timely improving the actual production process, thereby improving the yield of the electronic device. At the same time, the numerical simulation can also iterate the results of coating under different production processes for multiple times, thereby saving costs and shortening the development cycle; in addition, because the state parameters of the simulation coating during the phase change process can be obtained, the accuracy of identifying coating defects can be improved.

[0010] In some embodiments, the parameter distribution image of the simulated coating includes a phase field interface image of the simulated coating; based on the parameter distribution image of the simulated coating, a defect identification result of the simulated coating is output, including: determining the property parameters of the bubbles in the simulated coating based on the phase field interface image of the simulated coating; determining the defect identification result based on the component information of the simulation model and the property parameters of the bubbles.

[0011] In the embodiment of the present application, the property parameters of the bubbles in the simulated coating are first determined by the phase field interface image information of the simulated coating; then the defect identification result is jointly determined based on the component information in the simulation model and the property parameters of the bubbles. In this way, the accuracy of identifying the defects of the simulated coating can be effectively improved by combining the component information in the simulation model and the property parameters of the bubbles.

[0012] In some embodiments, the property parameters of the bubbles include at least one of the following: shape information of the bubbles and distribution information of the bubbles; the shape information includes maximum size information of the bubbles; based on the component information of the simulation model and the property parameters of the bubbles, a defect identification result is determined, including at least one of the following: when it is determined based on the component information and the distribution information of the bubbles that there are bubbles around the simulated device in the simulated coating, and the maximum size information of the bubbles is greater than a first value, it is determined that there is a bubble defect in the simulated coating; when it is determined based on the component information and the distribution information of the bubbles that there is no simulated device around the bubbles in the simulated coating, and the maximum size information of the bubbles without the simulated device around is greater than a second value The second value is greater than the first value; based on the bubble distribution information and component information, the number of bubbles in the simulated coating that are located around the same simulated device is determined; when the number of bubbles in the periphery of the same simulated device is greater than the third value, and the maximum size information of each bubble is greater than the fourth value, it is determined that there is a bubble defect in the simulated coating; the fourth value is less than the first value; based on the bubble distribution information and component information, the number of bubbles in the simulated coating that are located on the pin side of the simulated device is determined; when the number of bubbles on the pin side of the simulated device is greater than the fifth value, it is determined that there is a bubble defect in the simulated coating; the fifth value is less than the third value.

[0013] In the embodiment of the present application, it is possible to determine whether the bubble is located around the simulated device or in a device-free area according to the location of different bubbles in the candidate area, the size of the bubble, and the component information in the candidate area, and then determine whether there is a bubble defect in the candidate area based on the size of the bubble according to different situations. In this way, the accuracy of determining whether there is a bubble defect in the candidate area can be improved.

[0014] In some embodiments, the parameter distribution image of the simulated coating includes other parameter distribution images of the simulated coating; in the case where there are bubble defects in the simulated coating, the method further includes: based on the other parameter distribution images, determining the regional parameter distribution image of the area where the bubble defects exist in the simulated coating; the other parameter distribution images include at least one of the following: a temperature field image, a pressure field image, and a flow velocity field image; based on the regional parameter distribution image of the area where the bubble defects exist, determining the cause of the bubble defects in the simulated coating.

[0015] In an embodiment of the present application, by analyzing the temperature field image, pressure field image and flow velocity field image of the area where the bubble defects exist, the cause of the bubble defects in the area can be determined, so that the coating coating process can be adjusted in a targeted manner, thereby reducing the occurrence of bubble defects in the coating.

[0016] In some embodiments, based on the parameter distribution image of the simulated coating, a defect identification result of the simulated coating is output, including: determining a candidate area in the simulated coating; the candidate area is an area in the simulated coating where the probability of a bubble defect occurring is greater than a preset probability; the candidate area is determined based on other parameter distribution images of the simulated coating; the other parameter distribution images include at least one of the following: a temperature field image, a pressure field image, and a flow velocity field image; based on the phase field interface image of the simulated coating, a phase field interface image of the candidate area is obtained; based on the phase field interface image of the candidate area, a defect identification result of determining whether there is a bubble defect in the candidate area is determined.

[0017] In the embodiment of the present application, the candidate area where the probability of bubble defects in the simulated coating is greater than the preset probability is first determined through at least one of the temperature field image, the pressure field image, and the velocity field image; then, the phase field interface image of the candidate area is obtained in the phase field interface image of the simulated coating; finally, based on the phase field interface image of the candidate area, the defect recognition result of whether there is a bubble defect in the candidate area is determined. In this way, the area where the probability of bubble defects in the simulated coating is greater than the preset probability is first screened out, and then the phase field interface image of the candidate area is used to verify whether there is a bubble defect in the candidate area, thereby reducing the amount of calculation for generating the phase field interface image, thereby improving the efficiency of determining whether there is a bubble defect in the simulated coating.

[0018] In some embodiments, determining a candidate area in the simulated coating includes at least one of the following: based on the pressure parameters in the pressure field image of the simulated coating, determining an area including a simulated fluid having a pressure parameter less than a preset pressure parameter as a candidate area; based on the temperature parameters in the temperature field image of the simulated coating, determining the temperature change information of the simulated coating; determining an area having temperature change information greater than a preset parameter as a candidate area; based on the flow velocity parameters in the flow velocity field image of the simulated coating, determining an area where eddies appear in the simulated coating, and determining an area where eddies appear as a candidate area.

[0019] In the embodiment of the present application, by comparing the size relationship between the pressure parameter of the simulated fluid in the simulated coating and the preset pressure parameter, the low pressure area in the simulated coating can be determined as the candidate area. By using the temperature parameter of the simulated fluid, the area where the temperature suddenly changes can be determined as the candidate area. By using the flow rate parameter of the simulated fluid, the area where eddy currents appear in the simulated coating can be determined as the candidate area. In this way, by using the pressure parameters, temperature parameters and flow rate parameters that have a greater influence on the generation of bubbles in the simulated coating, the area with a high probability of generating bubble defects can be accurately screened out.

[0020] In some embodiments, the method also includes: obtaining an initial three-dimensional simulation model of the electronic device; the initial three-dimensional simulation model includes a first simulation device that is not coated with paint and a second simulation device to be coated with paint; removing the first simulation device and refining the structure of the second simulation device to obtain a three-dimensional simulation model; constructing a simulation coating on the surface of the three-dimensional simulation model to obtain a simulation model.

[0021] In the embodiment of the present application, by removing the first simulation device in the initial three-dimensional simulation model of the electronic device, the interference of the first simulation device on the numerical simulation process can be reduced, thereby improving the accuracy of the numerical simulation. In addition, the structure of the second simulation device in the initial three-dimensional simulation model of the electronic device needs to be refined, so that the accuracy of the simulated paint coating can be improved.

[0022] In some embodiments, the method also includes: configuring the paint simulation parameters of the simulation model; the paint simulation parameters include at least the attribute parameters of the simulation model and the driving parameters of the simulation model during the simulation process; determining the target multiphase flow mathematical model based on the scale information of the actual paint coating during the phase change process; using a grid type that matches the geometric characteristics of the simulation model to mesh the simulation model to obtain a meshed simulation model; the meshed simulation model includes multiple grid nodes; based on the attribute parameters and the driving parameters, performing convergence analysis on the target multiphase flow mathematical model and the basic mathematical model to make the simulated coating undergo phase change, and obtaining the state parameters of multiple grid nodes during the phase change of the simulated coating.

[0023] In the embodiment of the present application, the convergence analysis of the target multiphase flow mathematical model and the basic mathematical model can be performed by configuring the attribute parameters of the simulation model and the driving parameters of the simulation model to drive the simulated coating to undergo phase change, thereby obtaining the state parameters of multiple grid nodes of the simulation model. In this way, by numerically simulating the simulation model, the state parameters of the simulated coating during the phase change process can be accurately obtained, thereby improving the accuracy of identifying defects in the simulated coating. Moreover, the target multiphase flow mathematical model corresponds to the scale information of the actual paint coating during the phase change process, which can also improve the accuracy of numerical simulation of the simulated coating.

[0024] On the other hand, an embodiment of the present application provides a coating defect identification device, the device comprising:

[0025] An acquisition module is used to acquire coating simulation parameters of a simulation model of an electronic device; the simulation model includes a three-dimensional simulation model of the electronic device and a simulated coating on the surface of the three-dimensional simulation model; the coating simulation parameters are used to drive the simulated coating to undergo phase change, and obtain state parameters of the simulated coating in the process of changing from a liquid phase to a solid phase;

[0026] A display module, used to display a parameter distribution image of the simulated coating during the process of changing from a liquid phase to a solid phase; wherein the parameter distribution image is determined based on the state parameters of the simulated coating;

[0027] The defect recognition module is used to output defect recognition results of the simulated coating based on the parameter distribution image of the simulated coating.

[0028] On the other hand, an embodiment of the present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program that can be executed on the processor, and when the processor executes the program, some or all of the steps in the above method are implemented.

[0029] On the other hand, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which implements part or all of the steps in the above method when executed by a processor.

[0030] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and are not intended to limit the technical solutions of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present application and are used together with the specification to illustrate the technical solution of the present application.

[0032] Figure 1 A schematic diagram of the implementation process of a coating defect identification method provided in an embodiment of the present application Figure 1 ;

[0033] Figure 2 A schematic diagram of the implementation process of a coating defect identification method provided in an embodiment of the present application Figure 2 ;

[0034] Figure 3 A schematic diagram of the phase field interface of the candidate region provided in the embodiment of the present application;

[0035] Figure 4 A schematic diagram of the implementation process of a coating defect identification method provided in an embodiment of the present application Figure 3 ;

[0036] Figure 5 A schematic diagram of the implementation process of a coating defect identification method provided in an embodiment of the present application Figure 4 ;

[0037] Figure 6 A schematic diagram of an initial three-dimensional simulation model provided in an embodiment of the present application;

[0038] Figure 7 A schematic diagram of a simulation device with a refined structure provided in an embodiment of the present application;

[0039] Figure 8 A schematic diagram of a simulated coating provided in an embodiment of the present application;

[0040] Fig. 9 A schematic diagram of the implementation process of a coating defect identification method provided in an embodiment of the present application Figure 5 ;

[0041] Fig.10 A schematic diagram of the implementation process of a coating defect identification method provided in an embodiment of the present application Figure 6 ;

[0042] Fig.11 A schematic diagram of the composition structure of a coating defect identification device provided in an embodiment of the present application;

[0043] Fig.12 A hardware entity diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions of the present application are further elaborated in detail below in conjunction with the drawings and embodiments. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0045] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict. The terms "first / second / third" involved are merely used to distinguish similar objects and do not represent a specific order for the objects. It is understood that "first / second / third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing this application and are not intended to limit this application.

[0047] The embodiment of the present application provides a coating defect identification method, which can be executed by a processor of a computer device. The computer device may refer to a server, a laptop, a tablet computer, a desktop computer, a smart TV, a set-top box, a mobile device (such as a mobile phone, a portable video player, a personal digital assistant, a dedicated messaging device, a portable gaming device), or other device with data processing capabilities.

[0048] Figure 1 A schematic diagram of the implementation process of a coating defect identification method provided in an embodiment of the present application Figure 1 ,like Figure 1 As shown, the method includes the following steps S101 to S103:

[0049] Step S101, obtaining the coating simulation parameters of the simulation model of the electronic device; the simulation model includes a three-dimensional simulation model of the electronic device and a simulated coating on the surface of the three-dimensional simulation model; the coating simulation parameters are used to drive the simulated coating to undergo phase change, and obtain the state parameters of the simulated coating in the process of changing from liquid phase to solid phase.

[0050] Here, the electronic device refers to a component including a semiconductor device. In some embodiments, the electronic device may be a PCBA composed of at least one semiconductor device and a printed circuit board (PCB). In other embodiments, the electronic device may also be a semiconductor device, that is, the electronic device is composed of at least one semiconductor device (e.g., electronic components), such as resistors and capacitors. A three-dimensional simulation model refers to a three-dimensional geometric model of an electronic device.

[0051] In the embodiment of the present application, when the electronic device is a PCBA, a three-dimensional geometric model corresponding to the PCBA can be constructed by referring to the size of the actual PCB and the parameters of various electronic components.

[0052] In an embodiment of the present application, after obtaining a three-dimensional simulation model of an electronic device, a simulated coating can be constructed on the surface of the three-dimensional simulation model according to the actual coating area of ​​the electronic device to simulate the coating process of the electronic device in an actual scenario, thereby obtaining a simulation model including the three-dimensional simulation model and the simulated coating.

[0053] It is understandable that in actual scenarios, not all surfaces of electronic devices need to be coated. Therefore, when constructing a simulated coating on the surface of a three-dimensional simulation model, it is necessary to refer to the actual coating area of ​​the electronic device and construct the simulated coating on the corresponding area. For example, when the electronic device is a PCBA, the PCBA includes areas where coating is prohibited, such as mounting holes, safety slots, and barcodes.

[0054] In some embodiments, after obtaining the three-dimensional simulation model of the electronic device, the prohibited coating area may be removed according to the actual coating area of ​​the electronic device, and then a simulated coating may be constructed on the surface of the processed three-dimensional simulation model.

[0055] In an embodiment of the present application, the coating simulation parameters of the simulation model include at least the property parameters of the simulation model and the driving parameters of the simulation model during the simulation process. The property parameters of the simulation model can characterize the physical information of the electronic device and the simulated coating, such as the material properties of the electronic device and the material properties of the simulated coating. The driving parameters can be parameters for starting and controlling the simulation process. Exemplarily, the driving parameters can include boundary conditions, initial conditions, etc. for controlling the simulation.

[0056] In the embodiment of the present application, the simulated coating may be a simulated conformal coating. In some embodiments, the simulated coating may also be any simulated coating that can be applied to an electronic device. Exemplarily, the simulated coating may also be a simulated thermal conductive coating, a simulated flame retardant coating, and a simulated electromagnetic shielding coating.

[0057] In the embodiment of the present application, based on the paint simulation parameters of the simulation model, the simulated coating can be driven to undergo phase change to obtain state parameters of the simulated coating during the phase change process.

[0058] Here, the phase change process of the simulated coating refers to the process in which the simulated coating changes from a liquid state to a solid state. The state parameter of the simulated coating during the phase change process refers to the physical property information related to defect formation of the simulated fluid in the simulated coating during the phase change process. Exemplarily, the physical property information may be the temperature, pressure, flow rate, etc. of the simulated fluid.

[0059] It is understandable that when the state parameters of the simulated fluid itself are abnormal during the phase change process, abnormal defects are likely to occur. For example, when the pressure of the simulated fluid is low, the temperature parameters of the simulated fluid jump, and eddy currents appear due to the abnormal flow rate of the simulated fluid. Therefore, by obtaining the state parameters related to defect formation in the simulated coating during the phase change process, it is possible to accurately determine whether the simulated coating has defects during the phase change process.

[0060] In an embodiment of the present application, the physical meaning of the electronic device and the simulated coating is defined by the property parameters of the simulation model, and the state parameters of the simulated coating during the phase change process can be obtained by driving the simulated coating to undergo phase change based on its own property parameters through driving parameters.

[0061] Step S102, displaying a parameter distribution image of the simulated coating in the process of changing from a liquid phase to a solid phase; wherein the parameter distribution image is determined based on the state parameters of the simulated coating.

[0062] In an embodiment of the present application, after driving the simulated coating to undergo phase change based on its own property parameters through driving parameters and obtaining the state parameters of the simulated coating during the phase change process, the parameter distribution image of the simulated coating during the process of changing from liquid phase to solid phase can be determined through the state parameters, and then the parameter distribution image can be displayed.

[0063] Exemplarily, when the state parameter of the simulated coating is a temperature parameter, the parameter distribution image may be a temperature field image at any time during the process of the simulated coating changing from a liquid phase to a solid phase; when the state parameter of the simulated coating is a pressure parameter, the parameter distribution image may be a pressure field image at any time during the process of the simulated coating changing from a liquid phase to a solid phase; when the state parameter of the simulated coating is a flow rate parameter, the parameter distribution image may be a flow rate field image at any time during the process of the simulated coating changing from a liquid phase to a solid phase; when the state parameter of the simulated coating is a phase field parameter, the parameter distribution image may be a phase field interface image at any time during the process of the simulated coating changing from a liquid phase to a solid phase.

[0064] It can be understood that the parameter distribution image of the simulated coating can intuitively represent the process of the simulated coating changing from a liquid phase to a solid phase, so that it is possible to determine whether defects occur in the simulated coating during the phase change process through the parameter distribution image.

[0065] Step S103: outputting a defect recognition result of the simulated coating based on the parameter distribution image of the simulated coating.

[0066] Here, the defect identification result can characterize whether bubble defects occur in the simulated coating.

[0067] In an embodiment of the present application, the area in the simulated coating where defects are more likely to occur can be determined by first using a parameter distribution image, and then the defect identification result of the simulated coating can be determined based on the area.

[0068] In some embodiments, the defect identification result of the simulated coating may also be directly determined through the parameter distribution image of the simulated coating.

[0069] In the embodiment of the present application, the coating simulation parameters of the simulation model obtained by coating the three-dimensional simulation model of the electronic device with the simulation coating are first obtained. The coating simulation parameters of the simulation model can drive the simulation coating to undergo phase change, and obtain the state parameters of the simulation coating during the phase change process; then the parameter distribution image determined by the state parameters of the simulation coating during the process of the simulation coating changing from the liquid phase to the solid phase can be displayed; finally, based on the parameter distribution image of the simulation coating, the defect recognition result of the simulation coating is output. In this way, by numerically simulating the three-dimensional simulation model of the electronic device, it is possible to determine whether the electronic device has defects in the coating stage before the actual production of the electronic device, thereby advancing the node for identifying defects, and timely improving the actual production process, thereby improving the yield of the electronic device. At the same time, the numerical simulation can also iterate the results of coating under different production processes for multiple times, thereby saving costs and shortening the development cycle; in addition, because the state parameters of the simulation coating during the phase change process can be obtained, the accuracy of identifying coating defects can be improved.

[0070] Figure 2 This is a schematic diagram of the implementation process of a coating defect identification method provided in an embodiment of the present application. Figure 2 The method can be executed by a processor of a computer device. The parameter distribution image of the simulated coating includes a phase field interface image of the simulated coating; Figure 1 , Figure 1 S103 in can be updated to S201 and S202, which will be combined Figure 2 The steps shown are explained.

[0071] Step S201, determining the property parameters of bubbles in the simulated coating based on the phase field interface image of the simulated coating.

[0072] Here, the bubble property parameters are used to characterize the shape of the bubbles and their distribution in the simulated coating.

[0073] In an embodiment of the present application, edge detection can be performed on the phase field interface image to obtain the contour line of the bubble in the phase field interface image, and then the attribute parameters of the bubble in the phase field interface image are determined based on the coordinate information of the contour line of the bubble. In some embodiments, edge detection can include at least one of the following: differential method, Sobel algorithm, cross differential algorithm and Canny algorithm.

[0074] It is understandable that, because the contour line of the bubble is a closed curve, the shape information of the bubble can be determined by the coordinate information of the contour line, such as the maximum size information, roundness information, aspect ratio information, etc. of the bubble. Moreover, because the bubbles are generally distributed separately, the contour lines of different bubbles are not connected, so the number of bubbles in the phase field interface image and the position information of the bubbles can be determined by the coordinate information of the contour line. Exemplarily, when the coordinates of the geometric center point of the bubble are used as the coordinates of the bubble, the average of the horizontal and vertical coordinates of each point on the contour line of the bubble can be used as the horizontal and vertical coordinates of the geometric center point of the bubble.

[0075] For example, Figure 3 A schematic diagram of the phase field interface of the candidate region provided in the embodiment of the present application, such as Figure 3 As shown, there are bubbles 302 in the phase-field interface image 301 , and the number of bubbles 302 is 3.

[0076] Step S202, determining a defect recognition result based on the component information of the simulation model and the property parameters of the bubble.

[0077] Here, the component information of the simulation model may represent the location information of the simulation device in the simulation model and the attribute information of the simulation device, wherein the attribute information of the simulation device may be the type information of the simulation device.

[0078] In the embodiment of the present application, the location area of ​​the bubble in the simulated coating can be determined based on the component information in the simulation model and the property parameters of the bubble, and the defect identification result can be determined based on the location area of ​​the bubble in the simulated coating and the shape information of the bubble. The location area of ​​the bubble in the simulated coating may include a device area and a device-free area.

[0079] It is understandable that bubbles mainly affect the functions of components in electronic devices, so even if the bubble size is small, if the bubble is in the device area, then the simulated coating may also have bubble defects. On the contrary, if the bubble size is large and the bubble is in the device-free area, then the simulated coating may not have bubble defects. Therefore, it is necessary to combine the location area of ​​the bubble in the simulated coating and the shape information of the bubble to jointly determine the defect recognition result of the simulated coating.

[0080] In the embodiment of the present application, the property parameters of the bubbles in the simulated coating are first determined by the phase field interface image information of the simulated coating; then the defect identification result is jointly determined based on the component information in the simulation model and the property parameters of the bubbles. In this way, the accuracy of identifying the defects of the simulated coating can be effectively improved by combining the component information in the simulation model and the property parameters of the bubbles.

[0081] In some embodiments, the attribute parameters of the bubble include at least one of the following: shape information of the bubble and distribution information of the bubble; the shape information includes maximum size information of the bubble; the above step S202 can be implemented by at least one of steps S2021 to S2024:

[0082] Step S2021, when it is determined based on the component information and the distribution information of the bubbles that bubbles exist around the simulated device in the simulated coating, and the maximum size information of the bubbles is greater than a first value, it is determined that there is a bubble defect in the simulated coating.

[0083] In the embodiment of the present application, the component information represents the position information of the simulated device in the simulated coating and the type information of the simulated device in the simulation model, and the bubble distribution information represents the position information of the bubble in the simulated coating. Therefore, through the position information of the two in the simulated coating, it is possible to determine whether the bubble is in the device area, and when it is in the device area, it is possible to determine whether the bubble is around the simulated device.

[0084] In some embodiments, the distance between the bubble and the simulation device can be determined by the position information of the simulation device in the simulation model and the position information of the bubble in the simulation coating. When the distance is less than a preset distance, it is determined that there are bubbles around the simulation device. At this time, when the simulation device is determined to be a resistor-capacitor type simulation device through the type information of the simulation device, it can be determined whether there is a bubble defect in the simulation coating through the maximum size information of the bubble, that is, when the maximum size information of the bubble is greater than a first value, it is determined that there is a bubble defect in the simulation coating. Exemplarily, the first value can be 0.8, and the unit can be millimeters, that is, when the maximum size information of the bubble around the resistor-capacitor type component is greater than 0.8mm, it is determined that there is a bubble defect in the simulation coating.

[0085] In some embodiments, when the shape of the bubble is circular, the maximum size information of the bubble may be the diameter of the bubble.

[0086] Step S2022, when it is determined based on the component information and the distribution information of the bubbles that there are no simulated devices around the bubbles in the simulated coating, and the maximum size information of the bubbles without simulated devices around them is greater than a second value, it is determined that there is a bubble defect in the simulated coating; the second value is greater than the first value.

[0087] In the embodiment of the present application, first determine whether there are simulated devices around the bubbles through component information and bubble distribution information. When it is determined that there are no simulated devices around the bubbles, determine whether there are bubble defects in the simulated coating through the maximum size information of the bubbles, that is, when the maximum size information of the bubbles in the device-free area is greater than the second value, it is determined that there are bubble defects in the simulated coating. Exemplarily, the second value can be 1, and the unit can be millimeters, that is, when the maximum size information of the bubbles in the device-free area is greater than 1mm, it is determined that there are bubble defects in the simulated coating.

[0088] Step S2023, based on the bubble distribution information and component information, determine the number of bubbles in the simulated coating that are located around the same simulated device; when the number of bubbles in the periphery of the same simulated device is greater than a third value, and the maximum size information of each bubble is greater than a fourth value, determine that there is a bubble defect in the simulated coating; the fourth value is less than the first value.

[0089] In an embodiment of the present application, the number of bubbles existing around the same simulated device in the simulated coating can be determined based on the distribution information of the bubbles that characterize their own positions and the position information of the simulated device in the simulated coating. When the number is greater than a third value and the maximum size information of each bubble is greater than a fourth value, it is determined that there is a bubble defect in the simulated coating.

[0090] In some embodiments, determining the number of bubbles existing around the same simulated device in the simulated coating may be determining the number of bubbles existing around non-jumping pins of the same simulated device.

[0091] Exemplarily, the third value may be 3 and the fourth value may be 0.5 mm, that is, when the number of bubbles existing around the same simulation device is greater than 3 and the maximum size information of each bubble is greater than 0.5 mm, it is determined that there is a bubble defect in the candidate area.

[0092] Step S2024, based on the bubble distribution information and component information, determine the number of bubbles on the pin side of the simulated device in the simulated coating; when the number of bubbles on the pin side of the simulated device is greater than a fifth value, determine that there is a bubble defect in the simulated coating; the fifth value is less than the third value.

[0093] Exemplarily, the fifth value may be 2, and when the number of bubbles on the pin side (PIN foot) of the simulation device is greater than 2, it is determined that there is a bubble defect in the candidate area.

[0094] In the embodiment of the present application, it is possible to determine whether the bubble is located around the simulated device or in a device-free area according to the location of different bubbles in the candidate area, the size of the bubble, and the component information in the candidate area, and then determine whether there is a bubble defect in the candidate area based on the size of the bubble according to different situations. In this way, the accuracy of determining whether there is a bubble defect in the candidate area can be improved.

[0095] In some embodiments, the parameter distribution image of the simulated coating includes other parameter distribution images of the simulated coating; when there are bubble defects in the simulated coating, the above coating defect identification method can also be implemented through step 1 and step 2:

[0096] Step 1: Determine a regional parameter distribution image of a region where bubble defects exist in a simulated coating based on other parameter distribution images; the other parameter distribution images include at least one of the following: a temperature field image, a pressure field image, and a flow velocity field image.

[0097] In the embodiment of the present application, when it is determined that there is a bubble defect in the simulated coating, the region where the bubble defect exists in the simulated coating can be determined first, and then a regional parameter distribution image corresponding to the region can be obtained from other parameter distribution images of the simulated coating. The regional parameter distribution image includes at least one of the following: a temperature field image, a pressure field image, and a flow velocity field image.

[0098] Step 2: determining the cause of the bubble defect in the simulated coating based on the regional parameter distribution image of the region where the bubble defect exists.

[0099] It is understandable that the pressure, temperature and flow rate of the fluid in the simulation coating have a significant effect on the appearance of bubbles in the simulation coating body. Exemplarily, when the pressure of the fluid in the simulation coating decreases, the solubility of the gas in the liquid will also decrease, which will cause more gas to precipitate from the liquid and form bubbles. When the temperature of the fluid in the simulation coating changes rapidly, thermal stress will be generated inside the fluid, and thermal stress may cause uneven pressure distribution inside the fluid, thereby generating low-pressure areas in local areas, in which the gas dissolved in the fluid will escape due to reduced pressure to form bubbles; when the flow rate of the fluid in the simulation coating is abnormal, and eddy currents appear in the simulation coating, low-pressure areas often appear due to the rotation and acceleration of the fluid. This low-pressure environment is conducive to the escape of gas dissolved in the fluid, thereby forming bubbles. Therefore, by analyzing the temperature field image, pressure field image and flow rate field image of the area where the bubble defect exists, the cause of the bubble defect in the area can be determined, so that the coating coating process can be adjusted in a targeted manner, thereby reducing the occurrence of bubble defects in the coating.

[0100] In some embodiments, Figure 4As shown, the above step S103 can be implemented through steps S401 to S403:

[0101] Step S401, determine the candidate area in the simulated coating; the candidate area is an area in the simulated coating where the probability of bubble defects is greater than a preset probability; the candidate area is determined based on other parameter distribution images of the simulated coating; the other parameter distribution images include at least one of the following: temperature field image, pressure field image and flow velocity field image.

[0102] In the embodiment of the present application, by analyzing other parameter distribution images of the simulated coating, candidate areas in which the probability of bubble defects occurring in the simulated coating is greater than a preset probability can be screened out.

[0103] In other embodiments, the candidate area can also be determined in the simulated coating in response to the user's instruction to determine the candidate area, wherein the user can view the displayed distribution image of other parameters in the process of the simulated coating changing from liquid phase to solid phase, and then select the candidate area where the bubble defect may occur.

[0104] It can be understood that because when the pressure parameters, temperature parameters and flow rate parameters of the simulated fluid are abnormal, the probability of defects occurring in the corresponding area is relatively high, it is necessary to first determine the candidate area in the simulated coating where the probability of bubble defects occurring is greater than the preset probability through at least one of the temperature field image, the pressure field image and the flow rate field image.

[0105] In some embodiments, the above step S401 may be implemented by at least one of the following steps:

[0106] Step S4011, based on the pressure parameters in the pressure field image of the simulated coating, a region including the simulated fluid whose pressure parameter is less than a preset pressure parameter is determined as a candidate region.

[0107] Here, the pressure parameter of the simulated fluid refers to the pressure parameter of the simulated fluid at the grid node in the simulation model. That is to say, before driving the simulated coating to undergo phase change, the simulation model needs to be meshed first, and then the pressure parameters of multiple grid nodes on the simulation model are traversed to determine the grid nodes whose pressure parameters are less than the preset pressure parameters among the multiple grid nodes, and the region including the grid nodes whose pressure parameters are less than the preset pressure parameters is determined as the candidate region.

[0108] In the embodiment of the present application, the pressure field image of the simulated coating includes the pressure parameters of the simulated fluid of multiple grid nodes of the simulated coating. Therefore, through the pressure field image of the simulated coating, a candidate area including the simulated fluid having a pressure parameter less than a preset pressure parameter can be determined.

[0109] It is understandable that the solubility of a gas in a liquid is proportional to the partial pressure of the gas above the liquid. When the pressure decreases, the solubility of the gas will also decrease. In this way, in an environment with lower pressure, the gas originally dissolved in the fluid will more easily reach a supersaturated state, thereby escaping to form bubbles. In addition, as the pressure decreases, the saturation point of the fluid will also change. When the pressure drops to a certain level, the gas originally dissolved under a higher pressure may precipitate out and form bubbles because it exceeds the new saturation point. Therefore, areas with lower pressure can be identified as candidate areas.

[0110] Step S4012, determining temperature change information of the simulated coating based on the temperature parameters in the temperature field image of the simulated coating; and determining a region where the temperature change information is greater than a preset parameter as a candidate region.

[0111] In an embodiment of the present application, the temperature parameters of multiple grid nodes on the simulation model at different times can be obtained, and then the temperature change information of the grid nodes at adjacent times can be determined. When the temperature change information is greater than the preset parameters, it can be determined that the temperature of the adjacent grid nodes has jumped, and the corresponding area is determined as the candidate area.

[0112] In the embodiment of the present application, the temperature field image of the simulated coating includes temperature field images at multiple moments, and each temperature field image includes temperature parameters of the simulated fluid at multiple grid nodes of the simulated coating. Therefore, through the temperature field image of the simulated coating, it is possible to determine the area where the temperature change information is greater than the preset parameter.

[0113] It is understandable that when the temperature of the simulated fluid changes greatly, thermal stress will be generated inside the fluid, and thermal stress may cause uneven pressure distribution inside the fluid, thereby generating low-pressure areas in local areas. In these low-pressure areas, the gas dissolved in the fluid will escape due to the pressure reduction, forming bubbles. In addition, the change in temperature will change the flow state of the fluid, such as generating turbulence or eddies. When the flow state of the fluid changes, it may promote the formation of bubbles, because turbulence and eddies increase the shear force and extrusion force inside the fluid, which is conducive to the generation of bubbles.

[0114] Step S4013, based on the velocity parameters in the velocity field image of the simulated coating, determine the region where the eddy current appears in the simulated coating, and determine the region where the eddy current appears as a candidate region.

[0115] In an embodiment of the present application, the flow velocity parameters of multiple grid nodes on the simulation model can be obtained, and then the flow velocity distribution information of the multiple grid nodes can be determined based on the flow velocity parameters. When the flow velocity distribution information characterizes uneven flow velocity distribution, the area with uneven flow velocity distribution can be determined as a candidate area.

[0116] In the embodiment of the present application, the velocity field image of the simulated coating includes the velocity parameters of the simulated fluid at multiple grid nodes of the simulated coating. Therefore, the region where the eddy current appears in the simulated coating can be determined by the velocity field image of the simulated coating.

[0117] In some embodiments, the flow velocity parameters of multiple grid nodes on the simulation model at different times can also be obtained, and then the flow velocity change information of the grid nodes at adjacent times can be determined. When the flow velocity change information is greater than the preset parameters, it can be determined that the flow velocity of the adjacent grid nodes has jumped, and the corresponding area is determined as the candidate area.

[0118] It is understandable that in the vortex area, the pressure distribution of the fluid is usually uneven. Especially in the central area of ​​the vortex, due to the rotation and acceleration of the fluid, a low-pressure area often appears. This low-pressure environment is conducive to the escape of the gas dissolved in the fluid, thereby forming bubbles. In addition, the fluid in the vortex undergoes a complex shear and extrusion process. The action of these forces will change the ability of the fluid to dissolve gas, so that the gas molecules dissolved in the fluid will separate from the fluid and form tiny bubbles.

[0119] In the embodiment of the present application, by comparing the size relationship between the pressure parameter of the simulated fluid in the simulated coating and the preset pressure parameter, the low pressure area in the simulated coating can be determined as the candidate area. By using the temperature parameter of the simulated fluid, the area where the temperature suddenly changes can be determined as the candidate area. By using the flow rate parameter of the simulated fluid, the area where eddy currents appear in the simulated coating can be determined as the candidate area. In this way, by using the pressure parameters, temperature parameters and flow rate parameters that have a greater influence on the generation of bubbles in the simulated coating, the area with a high probability of generating bubble defects can be accurately screened out.

[0120] Step S402: acquiring a phase field interface image of the candidate region based on the phase field interface image of the simulated coating.

[0121] In the embodiment of the present application, after the candidate region in the simulated coating is determined, a phase field interface image of the candidate region may be acquired from the phase field interface image of the simulated coating.

[0122] In some embodiments, the phase field interface image of the candidate region may not be acquired, and the defect recognition result of whether there is a bubble defect in the candidate region is determined on the phase field interface image of the simulated coating.

[0123] Step S403 , based on the phase field interface image of the candidate area, determining a defect recognition result of whether there is a bubble defect in the candidate area.

[0124] In an embodiment of the present application, when bubbles exist in the candidate area, the property parameters of the bubbles can be determined through the phase field interface image of the candidate area, and then the defect identification result of whether there is a bubble defect in the candidate area is determined through the property parameters of the bubbles.

[0125] It is understandable that due to various reasons, the simulated coating cannot be completely free of bubbles during the phase change process, and the location of some bubbles is not critical, and the size of the bubbles is also small, so the existence of these bubbles will not affect the electronic devices. Therefore, even if there are bubbles in the simulated coating, it will not be considered that the simulated coating has bubble defects. Therefore, it is necessary to determine the defect recognition result of whether there is a bubble defect in the candidate area through the attribute information of the bubbles existing in the candidate area.

[0126] In the embodiment of the present application, the candidate area where the probability of bubble defects in the simulated coating is greater than the preset probability is first determined through at least one of the temperature field image, the pressure field image, and the velocity field image; then, the phase field interface image of the candidate area is obtained in the phase field interface image of the simulated coating; finally, based on the phase field interface image of the candidate area, the defect recognition result of whether there is a bubble defect in the candidate area is determined. In this way, the area where the probability of bubble defects in the simulated coating is greater than the preset probability is first screened out, and then the phase field interface image of the candidate area is used to verify whether there is a bubble defect in the candidate area, thereby reducing the amount of calculation for generating the phase field interface image, thereby improving the efficiency of determining whether there is a bubble defect in the simulated coating.

[0127] Figure 5 This is a schematic diagram of the implementation process of a coating defect identification method provided in an embodiment of the present application. Figure 4 The method can be executed by a processor of a computer device. The coating defect recognition method can also be implemented by steps S501 to S503. Figure 5 The steps shown are explained.

[0128] Step S501, obtaining an initial three-dimensional simulation model of an electronic device; the initial three-dimensional simulation model includes a first simulation device that is not coated with paint and a second simulation device that is to be coated with paint.

[0129] Here, the initial three-dimensional simulation model refers to a simulation model constructed according to the size information of the electronic device. The electronic device refers to a component that is not coated with paint. Exemplarily, when the electronic device is a PCBA, the first simulation device may include at least one of the following: an air pressure sensor, a radio frequency socket, a relay body except for the pins, a ball grid array (BGA) area, a burner socket, and a connector body.

[0130] For example, Figure 6As shown, the electronic device is a PCBA, and the initial three-dimensional simulation model of the PCBA includes a simulated PCB 601 and a simulated device 602, wherein the simulated device 602 includes a first simulated device not coated with paint and a second simulated device to be coated with paint (the first simulated device and the second simulated device not shown in the figure).

[0131] Step S502 , removing the first simulation component and refining the structure of the second simulation component to obtain a three-dimensional simulation model.

[0132] It is understandable that in order to reduce the interference of the first simulation device on the subsequent numerical simulation of the simulation model, the first simulation device that does not need to be coated with paint is first removed. Then, according to the packaging parameters of the electronic device, the structure of the second simulation device is refined. Exemplarily, the device pins of the second simulation device can be structurally refined.

[0133] For example, Figure 7 As shown, by performing structural refinement on the simulation device 602, a simulation device 701 after structural refinement is obtained.

[0134] In some embodiments, areas in the initial three-dimensional simulation model that do not need to be painted may also be removed. Exemplarily, the areas that do not need to be painted include at least one of the following: mounting holes, safety slots, and barcodes.

[0135] Step S503: constructing a simulated coating on the surface of the three-dimensional simulation model to obtain a simulation model.

[0136] In the embodiment of the present application, a simulated coating can be constructed on the surface of the three-dimensional simulation model with reference to the coating process standard of the electronic device, wherein the coating process standard of the electronic device includes the actual coating area and coating thickness of the electronic device coating.

[0137] like Figure 8 As shown, a simulated coating 702 is constructed on the surface of the simulated device 701 after the structure is refined.

[0138] In the embodiment of the present application, by removing the first simulation device in the initial three-dimensional simulation model of the electronic device, the interference of the first simulation device on the numerical simulation process can be reduced, thereby improving the accuracy of the numerical simulation. In addition, the structure of the second simulation device in the initial three-dimensional simulation model of the electronic device needs to be refined, so that the accuracy of the simulated paint coating can be improved.

[0139] Fig. 9 This is a schematic diagram of the implementation process of a coating defect identification method provided in an embodiment of the present application. Figure 5The method can be executed by a processor of a computer device. The coating defect recognition method can be implemented by steps S801 to S804. Fig. 9 The steps shown are explained.

[0140] Step S801, configuring the paint simulation parameters of the simulation model; the paint simulation parameters at least include the property parameters of the simulation model and the driving parameters of the simulation model during the simulation process.

[0141] Here, the property parameters of the simulation model are used to characterize the inherent characteristics of the simulation model. In an embodiment of the present application, the property parameters of the simulation model can be used as the initial conditions for the phase change of the simulation model. Wherein, in the case where the electronic device is a PCBA and the coating is a conformal coating, the property parameters of the simulation model at least include the material property parameters of the simulated device in the simulation model, the material property parameters of the printed circuit board model in the simulation model, and the material property parameters of the conformal coating simulation coating. Exemplarily, the material property parameters may include at least one of the following: density, specific heat capacity, axial thermal conductivity, and dynamic viscosity.

[0142] The driving parameters of the simulation model during the simulation process may be boundary conditions of the simulation model. When the coating is a conformal coating, the boundary conditions include at least: a heat dissipation method of the conformal coating simulation coating, an initial temperature of the conformal coating simulation coating, a pressure boundary condition of the conformal coating simulation coating, a velocity boundary condition of the conformal coating simulation coating, and a phase interface parameter of the conformal coating simulation coating.

[0143] Exemplarily, the liquid density of the conformal coating simulation coating is 880 kg / m³; the specific heat capacity of the conformal coating simulation coating is 1500 J / kg•K; the axial thermal conductivity of the conformal coating simulation coating is 0.25 W / m•K; the density of the printed circuit board model is 2145 kg / m³; the specific heat capacity of the printed circuit board model is 1136 J / kg•K; the axial thermal conductivity of the printed circuit board model is 0.25 W / m•K; the radial thermal conductivity of the printed circuit board model is 16.5 W / m•K; the density of the simulated device (taking a typical plastic package as an example) is 2000 kg / m³; the specific heat capacity of the simulated device (taking a typical plastic package as an example) is 395 J / kg•K; the thermal conductivity of the simulated device (taking a typical plastic package as an example) is 5 W / m•K.

[0144] For example, the heat dissipation mode of the three-conformal paint simulation coating is natural heat dissipation; the initial temperature of the three-conformal paint simulation coating is 25°C; the pressure boundary condition of the three-conformal paint simulation coating is 101KPa; the velocity boundary condition of the anti-paint simulation coating is 0.001m / s in the -Z direction; the phase interface parameter (such as heat transfer coefficient) of the three-conformal paint simulation coating is 10m / m 2 •K.

[0145] Step S802, determining a target multiphase flow mathematical model based on the phase change scene information of the actual paint coating.

[0146] Here, the multiphase flow mathematical model refers to a mathematical model used to describe a fluid system in which different phases are mixed or flowed, wherein the different phases may refer to liquid and solid.

[0147] In the embodiment of the present application, the target multiphase flow mathematical model can be determined from a plurality of different types of multiphase flow mathematical models according to the phase change scenario information of the actual paint coating. Among them, different multiphase flow mathematical models correspond to different phase change scenario information. The target multiphase flow mathematical model in the embodiment of the present application corresponds to a multiphase flow mathematical model of a fluid including relatively few bubbles, droplets or suspended particles.

[0148] Exemplarily, different multiphase flow mathematical models may include: a bubbly flow model, a mixing model, an Euler-Euler model, and an Euler-Lagrangian model; the multiphase flow mathematical model that meets the current scenario may be the Euler-Lagrangian model.

[0149] Step S803, meshing the simulation model using a mesh type that matches the geometric characteristics of the simulation model to obtain a meshed simulation model; the meshed simulation model includes a plurality of mesh nodes.

[0150] Here, the mesh type that matches the geometric characteristics of the simulation model may be a hexahedral mesh or a locally encrypted mesh.

[0151] In some embodiments, when selecting a grid type, factors such as the characteristics of the physical phenomenon, the requirements of the solution method, computing resource limitations, and software support need to be considered.

[0152] Step S804, based on the attribute parameters and the driving parameters, a convergence analysis is performed on the target multiphase flow mathematical model and the basic mathematical model to make the simulated coating undergo phase change, and in the process of the simulated coating undergoing phase change, state parameters of multiple grid nodes are obtained.

[0153] Here, the basic mathematical model refers to the continuity equation, momentum equation and energy equation.

[0154] In an embodiment of the present application, the convergence analysis of the parameters of the target multiphase flow mathematical model and the basic mathematical model can be performed through attribute parameters and driving parameters. When the parameters converge, the state parameters of multiple grid nodes in the process of phase change of the simulated coating can be obtained.

[0155] In the embodiment of the present application, when obtaining the state parameters of multiple grid nodes in the process of simulating the phase change of the coating, a parameter distribution image corresponding to the state parameters can be generated, and the generated parameter distribution image can be displayed.

[0156] In the embodiment of the present application, the convergence analysis of the target multiphase flow mathematical model and the basic mathematical model can be performed by configuring the attribute parameters of the simulation model and the driving parameters of the simulation model to drive the simulated coating to undergo phase change, thereby obtaining the state parameters of multiple grid nodes of the simulation model. In this way, by numerically simulating the simulation model, the state parameters of the simulated coating during the phase change process can be accurately obtained, thereby improving the accuracy of identifying defects in the simulated coating. Moreover, the target multiphase flow mathematical model corresponds to the scale information of the actual paint coating during the phase change process, which can also improve the accuracy of numerical simulation of the simulated coating.

[0157] The following describes the application of the coating defect recognition method provided by the embodiment of the present application in a practical scenario. Fig.10 As shown, the above coating defect identification method can be implemented by the following steps:

[0158] Step S901, obtaining a three-dimensional geometric model of a PCBA product.

[0159] Step S902: according to the conformal coating standard, remove the electronic components prohibited from coating in the three-dimensional geometric model.

[0160] In the embodiment of the present application, the SCDM (Space Claim Direct Modeler) software can be used to eliminate the electronic components that are prohibited from coating in the three-dimensional geometric model. Exemplarily, the electronic components that are prohibited from coating include at least one of the following: an air pressure sensor, a radio frequency socket, a relay body except for the pins, a BGA area, a burning socket (including the TP position), and a connector body.

[0161] Step S903: construct a simulation model based on the eliminated three-dimensional geometric model.

[0162] In the embodiments of the present application, according to the packaging parameters of the electronic components, the structural design of the corresponding components is refined through CAD software, such as the key parts of the device pins; in addition, the areas where the three-proof paint is prohibited from being coated, such as mounting holes, safety slots, and barcodes, are eliminated.

[0163] Step S904: According to the actual coating area and control parameters of the actual PCBA product, a conformal coating model is established on the simulation model to obtain a PCBA product simulation model including the conformal coating model.

[0164] Here, the control parameter may refer to the coating thickness. Exemplarily, the coating thickness may be 100±20 μm.

[0165] In the embodiment of the present application, a conformal coating model can be established on the surface of the electronic component model and the printed circuit board (PCB) in the simulation model, that is, the process of coating the conformal coating on the PCBA is simulated, so that the electronic component model and the PCB model in the simulation model have the conformal coating model on their surfaces.

[0166] Step S905, performing numerical simulation calculation on the PCBA product simulation model to obtain conformal coating bubble defect data.

[0167] In the embodiment of the present application, numerical simulation calculation of the PCBA product simulation model can be achieved through Computer Aided Engineering (CAE) software.

[0168] In the embodiment of the present application, the above step S905 can be implemented by the following steps:

[0169] Step S9051, configuring the material properties corresponding to the PCB model, the electronic component model, and the conformal coating model in the PCBA product simulation model.

[0170] In the embodiment of the present application, the material properties may include at least one of the following: density, specific heat capacity, thermal conductivity, specific heat, and dynamic viscosity.

[0171] Step S9052, determining a target multiphase flow mathematical model suitable for the liquid-solid phase change simulation calculation in the current scenario, and other mathematical models for solving the problem.

[0172] In the embodiment of the present application, a target multiphase flow mathematical model suitable for a fluid containing relatively few bubbles, droplets or suspended particles can be determined as a target multiphase flow mathematical model suitable for liquid-solid phase change simulation calculation in the current scenario.

[0173] In the embodiments of the present application, other mathematical models to be solved include the continuity equation, the momentum equation and the energy equation.

[0174] Step S9053, configuring the boundary conditions of the PCBA product simulation model.

[0175] In the embodiment of the present application, the boundary conditions include: heat dissipation method, initial temperature, pressure, speed, and phase interface parameters.

[0176] Step S9054: determine the mesh type according to the geometric characteristics of the PCBA product simulation model, and mesh the PCBA product simulation model based on the mesh type.

[0177] Step S9055, performing convergence analysis on the target multiphase flow mathematical model and other solution mathematical models to obtain conformal coating bubble defect data.

[0178] In the embodiment of the present application, the bubble defect data of the conformal coating includes temperature, pressure, flow rate, and phase interface.

[0179] Step S906, identifying the conformal coating bubble defects according to the conformal coating bubble defect data calculated by numerical simulation.

[0180] In the embodiment of the present application, the above step S906 can be implemented by the following steps:

[0181] Step S9061, based on the conformal coating bubble defect data, construct a data cloud map of the conformal coating model and a data cloud map of the electronic component model.

[0182] In the embodiment of the present application, the data cloud map includes a pressure field cloud map, a temperature field cloud map, a velocity field cloud map and a phase field interface map.

[0183] Step S9062: According to the pressure field cloud map, the temperature field cloud map and the velocity field cloud map, the low pressure area, the area with poor temperature transition and the area where the flow generates eddy current of the conformal coating model and the electronic component model are identified to obtain the area to be analyzed.

[0184] Step S9063, identifying whether there are bubbles on the conformal coating interface, and the shape and position of the bubbles based on the phase field interface image.

[0185] Step S9064, extracting the bubble region in the area to be analyzed, and performing bubble parameter quantitative analysis on the bubble region.

[0186] In the embodiment of the present application, the bubble parameters of the bubble area can be quantitatively analyzed by CAD software. For example, in terms of the number of bubbles generated, the number of bubbles is calculated; in terms of the shape factor of the bubbles, the shape of the bubbles, such as roundness, aspect ratio, etc., is analyzed; in terms of the position information of the bubbles, the relative position and distribution of the bubbles in the conformal coating model are determined.

[0187] Step S9065: Identify conformal coating bubble defects based on the quantitative analysis results.

[0188] In the embodiment of the present application, when the property parameters of the bubble meet at least one of the following conditions, it is determined that the conformal coating model has a bubble defect:

[0189] The size of the bubbles on the side of the resistor and capacitor components is greater than 0.8mm;

[0190] The size of bubbles in the device-free area is greater than 1 mm;

[0191] The bubbles at the non-jump pin position on the same component are larger than 0.5mm, and the number of bubbles exceeds 9;

[0192] There are more than 2 bubbles on the same side of the component PIN.

[0193] In the embodiment of the present application, an assessment of the severity of bubble defects can also be carried out to eliminate bubbles that have no impact on the reliability of the product before delivery, and the risky bubble defect areas can be matched and marked with the PCBA three-dimensional model to provide designers with guidance for optimization and improvement.

[0194] Based on the foregoing embodiments, an embodiment of the present application provides a coating defect identification device, which includes the various units included and the various modules included in the units, which can be implemented by a processor in a computer device; of course, it can also be implemented by a specific logic circuit; in the implementation process, the processor can be a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP) or a field programmable gate array (FPGA), etc.

[0195] Fig.11 A schematic diagram of the composition structure of a coating defect identification device provided in an embodiment of the present application is shown in FIG. Fig.11 As shown, the coating defect recognition device 1000 includes: an acquisition module 1010, a display module 1020, and a defect recognition module 1030, wherein:

[0196] The acquisition module 1010 is used to acquire the coating simulation parameters of the simulation model of the electronic device; the simulation model includes a three-dimensional simulation model of the electronic device and a simulated coating on the surface of the three-dimensional simulation model; the coating simulation parameters are used to drive the simulated coating to undergo phase change, and obtain the state parameters of the simulated coating in the process of changing from a liquid phase to a solid phase;

[0197] The display module 1020 is used to display a parameter distribution image of the simulated coating during the process of changing from a liquid phase to a solid phase; wherein the parameter distribution image is determined based on the state parameters of the simulated coating;

[0198] The defect recognition module 1030 is used to output a defect recognition result of the simulated coating based on the parameter distribution image of the simulated coating.

[0199] In some embodiments, the parameter distribution image of the simulated coating includes a phase field interface image of the simulated coating; the defect identification module 1030 is used to determine the property parameters of the bubbles in the simulated coating based on the phase field interface image of the simulated coating; and determine the defect identification result based on the component information of the simulation model and the property parameters of the bubbles.

[0200] In some embodiments, the attribute parameters of the bubble include at least one of the following: shape information of the bubble and distribution information of the bubble; the shape information includes maximum size information of the bubble; a defect identification module 1030 is used for at least one of the following: determining that there are bubbles around the simulated device in the simulated coating based on the component information and the distribution information of the bubble, and the maximum size information of the bubble is greater than a first value, determining that there is a bubble defect in the simulated coating; determining that there is no simulated device around the bubble in the simulated coating based on the component information and the distribution information of the bubble, and the maximum size information of the bubble without the simulated device around the bubble is greater than a second value, determining that there is a bubble defect in the simulated coating; the second value is greater than the first value; determining the number of bubbles around the same simulated device in the simulated coating based on the distribution information of the bubble and the component information; determining that there is a bubble defect in the simulated coating when the number of bubbles around the same simulated device is greater than a third value and the maximum size information of each bubble is greater than a fourth value; the fourth value is less than the first value; determining the number of bubbles on the pin side of the simulated device in the simulated coating based on the distribution information of the bubble and the component information; determining that there is a bubble defect in the simulated coating when the number of bubbles on the pin side of the simulated device is greater than a fifth value; the fifth value is less than the third value.

[0201] In some embodiments, the parameter distribution image of the simulated coating includes other parameter distribution images of the simulated coating; the coating defect identification device 1000 includes a defect cause determination module, which is used to determine the regional parameter distribution image of the area where bubble defects exist in the simulated coating based on the other parameter distribution images; the other parameter distribution images include at least one of the following: a temperature field image, a pressure field image, and a flow velocity field image; based on the regional parameter distribution image of the area where the bubble defects exist, the cause of the bubble defects in the simulated coating is determined.

[0202] In some embodiments, the defect identification module 1030 is used to determine candidate areas in the simulated coating; the candidate areas are areas in the simulated coating where the probability of bubble defects occurring is greater than a preset probability; the candidate areas are determined based on other parameter distribution images of the simulated coating; the other parameter distribution images include at least one of the following: temperature field image, pressure field image, and flow velocity field image; based on the phase field interface image of the simulated coating, a phase field interface image of the candidate area is obtained; based on the phase field interface image of the candidate area, a defect identification result of whether there is a bubble defect in the candidate area is determined.

[0203] In some embodiments, the defect identification module 1030 is used for at least one of the following: based on the pressure parameters in the pressure field image of the simulated coating, determining the area including the simulated fluid with a pressure parameter less than the preset pressure parameter as a candidate area; based on the temperature parameters in the temperature field image of the simulated coating, determining the temperature change information of the simulated coating; determining the area with temperature change information greater than the preset parameter as a candidate area; based on the flow velocity parameters in the flow velocity field image of the simulated coating, determining the area where eddies appear in the simulated coating, and determining the area where eddies appear as a candidate area.

[0204] In some embodiments, the coating defect identification device 1000 also includes a model building module; the model building module is used to obtain an initial three-dimensional simulation model of the electronic device; the initial three-dimensional simulation model includes a first simulation device that is not coated with paint and a second simulation device to be coated with paint; the first simulation device is removed and the structure of the second simulation device is refined to obtain a three-dimensional simulation model; a simulated coating is constructed on the surface of the three-dimensional simulation model to obtain a simulation model.

[0205] In some embodiments, the coating defect identification device 1000 also includes a parameter configuration module; the parameter configuration module is used to configure the coating simulation parameters of the simulation model; the coating simulation parameters at least include the attribute parameters of the simulation model and the driving parameters of the simulation model during the simulation process; based on the phase change scene information of the actual coating, the target multiphase flow mathematical model is determined; the simulation model is meshed using a grid type that matches the geometric characteristics of the simulation model to obtain a meshed simulation model; the meshed simulation model includes multiple grid nodes; based on the attribute parameters and the driving parameters, the target multiphase flow mathematical model and the basic mathematical model are subjected to convergence analysis to make the simulated coating undergo phase change, and in the process of the phase change of the simulated coating, the state parameters of multiple grid nodes are obtained.

[0206] In some embodiments, the coating is a conformal coating, and the simulation coating is a conformal coating simulation coating;

[0207] The attribute parameters of the simulation model include at least: material attribute parameters of the simulated device in the simulation model, material attribute parameters of the printed circuit board model in the simulation model, and material attribute parameters of the three-conformal paint simulation coating; the driving parameters of the simulation model during the simulation process include at least: the heat dissipation method of the three-conformal paint simulation coating, the initial temperature of the three-conformal paint simulation coating, the pressure boundary condition of the three-conformal paint simulation coating, the velocity boundary condition of the three-conformal paint simulation coating, and the phase interface parameters of the three-conformal paint simulation coating.

[0208] The description of the above device embodiment is similar to the description of the above method embodiment, and has similar beneficial effects as the method embodiment. In some embodiments, the functions or modules included in the device provided in the embodiment of the present application can be used to execute the method described in the above method embodiment. For technical details not disclosed in the device embodiment of the present application, please refer to the description of the method embodiment of the present application for understanding.

[0209] It should be noted that in the embodiment of the present application, if the above-mentioned coating defect identification method is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application can be essentially or partly reflected in the form of a software product that contributes to the relevant technology. The software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM), a disk or an optical disk. In this way, the embodiment of the present application is not limited to any specific hardware, software or firmware, or any combination of hardware, software, and firmware.

[0210] An embodiment of the present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the program, some or all of the steps in the above method are implemented.

[0211] The embodiment of the present application provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, some or all of the steps in the above method are implemented. The computer-readable storage medium can be transient or non-transient.

[0212] An embodiment of the present application provides a computer program, including a computer-readable code. When the computer-readable code is run in a computer device, a processor in the computer device executes some or all of the steps for implementing the above method.

[0213] The embodiment of the present application provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and when the computer program is read and executed by a computer, some or all of the steps in the above method are implemented. The computer program product can be implemented in hardware, software, or a combination thereof. In some embodiments, the computer program product is specifically embodied as a computer storage medium, and in other embodiments, the computer program product is specifically embodied as a software product, such as a software development kit (SDK), etc.

[0214] It should be noted here that the description of the various embodiments above tends to emphasize the differences between the various embodiments, and the same or similar aspects can be referenced to each other. The description of the above device, storage medium, computer program and computer program product embodiments is similar to the description of the above method embodiment, and has similar beneficial effects as the method embodiment. For technical details not disclosed in the embodiments of the device, storage medium, computer program and computer program product of this application, please refer to the description of the method embodiment of this application for understanding.

[0215] Fig.12 A hardware entity diagram of a computer device provided in an embodiment of the present application is shown in FIG. Fig.12 As shown, the hardware entity of the computer device 1100 includes: a processor 1101, a communication interface 1102 and a memory 1103, wherein:

[0216] The processor 1101 generally controls the overall operation of the computer device 1100 , and the overall operation may be to implement the method provided in the embodiment of the present application.

[0217] The communication interface 1102 enables the computer device to communicate with other terminals or servers through a network.

[0218] The memory 1103 is configured to store instructions and applications executable by the processor 1101, and can also cache data to be processed or processed by the processor 1101 and each module in the computer device 1100 (for example, image data, audio data, voice communication data, and video communication data), which can be implemented by flash memory (FLASH) or random access memory (Random Access Memory, RAM). Data can be transmitted between the processor 1101, the communication interface 1102, and the memory 1103 through the bus 1104.

[0219] An embodiment of the present application provides a computer storage medium, which stores one or more programs. The one or more programs can be executed by one or more processors to implement the steps of the coating defect identification method of any of the above embodiments.

[0220] It should be noted here that the description of the above storage medium and device embodiments is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0221] The processor may be at least one of an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a Central Processing Unit (CPU), a controller, a microcontroller, and a microprocessor. It is understandable that the electronic device that implements the functions of the processor may also be other, and the embodiments of the present application do not specifically limit this.

[0222] The above-mentioned computer storage medium / memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory (Flash Memory), a magnetic surface memory, an optical disk, or a compact disc read-only memory (CD-ROM) and the like; it can also be various terminals including one or any combination of the above-mentioned memories, such as a mobile phone, a computer, a tablet device, a personal digital assistant, etc.

[0223] The above is only an implementation method of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A coating defect identification method, characterized in that: The coating defect identification method comprises: Acquire an initial three-dimensional simulation model of the electronic device; the initial three-dimensional simulation model includes a first simulation device that is not coated with paint and a second simulation device that is to be coated with paint; Performing a removal process on the first simulation device and performing a refinement process on the structure of the second simulation device to obtain a three-dimensional simulation model; constructing a simulation coating on the surface of the three-dimensional simulation model to obtain a simulation model of the electronic device; Acquire coating simulation parameters of the simulation model of the electronic device; the simulation model includes a three-dimensional simulation model of the electronic device and a simulated coating on the surface of the three-dimensional simulation model; the coating simulation parameters are used to drive the simulated coating to undergo phase change, and obtain state parameters of the simulated coating in the process of changing from a liquid phase to a solid phase; Displaying a parameter distribution image of the simulated coating during the process of changing from a liquid phase to a solid phase; wherein the parameter distribution image is determined based on the state parameters of the simulated coating; Based on the parameter distribution image of the simulated coating, a defect recognition result of the simulated coating is output.

2. The coating defect identification method according to claim 1, characterized in that: The parameter distribution image of the simulated coating includes a phase field interface image of the simulated coating; The step of outputting a defect recognition result of the simulated coating based on the parameter distribution image of the simulated coating comprises: Determining property parameters of bubbles in the simulated coating based on the phase field interface image of the simulated coating; The defect identification result is determined based on the component information of the simulation model and the property parameters of the bubble.

3. The coating defect identification method according to claim 2, characterized in that: The attribute parameters of the bubbles include at least one of the following: shape information of the bubbles and distribution information of the bubbles; the shape information includes maximum size information of the bubbles; the defect recognition result is determined based on the component information of the simulation model and the attribute parameters of the bubbles, including at least one of the following: In the case where it is determined based on the component information and the distribution information of the bubbles that bubbles exist around the simulated device in the simulated coating, and the maximum size information of the bubbles is greater than a first value, it is determined that there is a bubble defect in the simulated coating; In the case where it is determined based on the component information and the distribution information of the bubbles that there is no simulated device around the bubbles in the simulated coating, and the maximum size information of the bubbles without simulated devices around them is greater than a second value, it is determined that there is a bubble defect in the simulated coating; The second value is greater than the first value; Based on the distribution information of the bubbles and the component information, determining the number of bubbles in the simulated coating that are located around the same simulated component; When the number of bubbles around the same simulation device is greater than a third value, and the maximum size information of each bubble is greater than a fourth value, it is determined that there is a bubble defect in the simulation coating; The fourth value is smaller than the first value; Based on the distribution information of the bubbles and the component information, determining the number of bubbles in the simulation coating on the pin side of the simulation component; When the number of bubbles on the pin side of the simulated device is greater than a fifth value, it is determined that bubble defects exist in the simulated coating; and the fifth value is smaller than the third value.

4. The coating defect identification method according to claim 3, characterized in that: The parameter distribution image of the simulated coating includes other parameter distribution images of the simulated coating; In the case where there are bubble defects in the simulated coating, the method further comprises: Based on the other parameter distribution images, determining a regional parameter distribution image of a region where bubble defects exist in the simulated coating; The other parameter distribution images include at least one of the following: a temperature field image, a pressure field image, and a flow velocity field image; Based on the regional parameter distribution image of the region where the bubble defect exists, the cause of the bubble defect existing in the simulated coating is determined.

5. The coating defect identification method according to claim 1, characterized in that: The step of outputting a defect recognition result of the simulated coating based on the parameter distribution image of the simulated coating comprises: Determine a candidate region in the simulated coating; the candidate region is a region in which the probability of bubble defects occurring in the simulated coating is greater than a preset probability; the candidate region is determined based on other parameter distribution images of the simulated coating; the other parameter distribution images include at least one of the following: a temperature field image, a pressure field image, and a flow velocity field image; Based on the phase-field interface image of the simulated coating, acquiring a phase-field interface image of the candidate area; A defect recognition result of whether a bubble defect exists in the candidate region is determined based on the phase field interface image of the candidate region.

6. The coating defect identification method according to claim 5, characterized in that: The determining of the candidate area in the simulated coating comprises at least one of the following: Based on the pressure parameter in the pressure field image of the simulated coating, determining a region including the simulated fluid whose pressure parameter is less than a preset pressure parameter as the candidate region; Determining temperature change information of the simulated coating based on temperature parameters in the temperature field image of the simulated coating; Determine the area where the temperature change information is greater than a preset parameter as the candidate area; Based on the velocity parameters in the velocity field image of the simulated coating, a region where eddy currents appear in the simulated coating is determined, and the region where eddy currents appear is determined as the candidate region.

7. The method according to any one of claims 1 to 6, characterized in that: The coating defect identification method further comprises: Configuring the paint simulation parameters of the simulation model; the paint simulation parameters at least include the attribute parameters of the simulation model and the driving parameters of the simulation model during the simulation process; Determine the target multiphase flow mathematical model based on the phase change scenario information of the actual coating; Using a grid type that matches the geometric characteristics of the simulation model, the simulation model is meshed to obtain a meshed simulation model; the meshed simulation model includes a plurality of grid nodes; Based on the attribute parameters and the driving parameters, a convergence analysis is performed on the target multiphase flow mathematical model and the basic mathematical model to make the simulated coating undergo a phase change, and during the phase change of the simulated coating, the state parameters of the multiple grid nodes are obtained.

8. The method according to claim 7, characterized in that The coating is a conformal coating, and the simulation coating is a conformal coating simulation coating; The property parameters of the simulation model at least include: material property parameters of the simulation device in the simulation model, material property parameters of the printed circuit board model in the simulation model, and material property parameters of the conformal coating simulation coating; The driving parameters of the simulation model during the simulation process include at least: the heat dissipation method of the three-conformal paint simulation coating, the initial temperature of the three-conformal paint simulation coating, the pressure boundary condition of the three-conformal paint simulation coating, the velocity boundary condition of the three-conformal paint simulation coating and the phase interface parameters of the three-conformal paint simulation coating.

9. A coating defect identification device, characterized in that: The coating defect recognition device comprises: A model building module is used to obtain an initial three-dimensional simulation model of an electronic device; the initial three-dimensional simulation model includes a first simulation device that is not coated with paint and a second simulation device that is to be coated with paint; the first simulation device is removed and the structure of the second simulation device is refined to obtain a three-dimensional simulation model; a simulation coating is constructed on the surface of the three-dimensional simulation model to obtain a simulation model of the electronic device; An acquisition module is used to acquire coating simulation parameters of a simulation model of an electronic device; the simulation model includes a three-dimensional simulation model of the electronic device and a simulated coating on the surface of the three-dimensional simulation model; the coating simulation parameters are used to drive the simulated coating to undergo phase change, and obtain state parameters of the simulated coating in the process of changing from a liquid phase to a solid phase; A display module, used to display a parameter distribution image of the simulated coating during the process of changing from a liquid phase to a solid phase; wherein the parameter distribution image is determined based on the state parameters of the simulated coating; The defect recognition module is used to output a defect recognition result of the simulated coating based on the parameter distribution image of the simulated coating.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the program, the steps in the method according to any one of claims 1 to 8 are implemented.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 8 are implemented.

12. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps in the method according to any one of claims 1 to 8 are implemented.

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